Air-gapped source code examiner / I want to start a new chat session (in this same project) to continue discussing and working on CodeExam's --claim-analyze and --claim-search commands, focusing especially on extracting multisect terms from patent-claim text. We've been using Claude and local model Qwen Coder for that, but have started discussing intriguing possibility of using CodeExam's own --discover-vocabulary feature as part of that. Maybe given the LLM not just the claim text, but also a list of the top 1000 or 2000 vocabulary terms (and names of functions they principally reside in), and having it use that as a sort-of "concordance" to bridge the gap between claim language on the one hand and the target source code's likely quite different nomenclature. Claude asked that I add: Assessed continuity requirements for transitioning between chat sessions Your summary is good. A few things to add for seamless pickup: Current state of the code: Tarball is code-exam-phase8b.tar, 240 tests passing Local model term extraction works: --analyze-model falls through to term extraction (no separate --claim-model needed) Simplified local prompt (~260 tokens) in _CLAIM_EXTRACTION_PROMPT_LOCAL in claim.js Claude prompt (~1466 tokens) in _CLAIM_EXTRACTION_PROMPT in claim.js — much more detailed with examples Response parser _parseResponse handles both, expects TIGHT: and BROAD: lines Key files for the new session: src/commands/claim.js — term extraction (both Claude API and local model), claim-search pipeline src/commands/analyze.js — claim-analyze pipeline (steps 1-4), calls claim.js for extraction src/core/CodeSearchIndex.js — getTopVocabulary() returns scored vocabulary, ensureVocabulary() builds it, _buildVocabularyFromFiles() is the implementation (now includes comments/strings) src/commands/multisect.js — parseMultisectTerms() parses the semicolon-separated term syntax TODO_master_022226.md — item #203 describes the vocabulary-guided extraction idea What we learned about local model term extraction: TIGHT terms are usable but include too many generic words ("presence", "substantially", "all", "within") BROAD terms are garbage from 7B — just appends "search" to everything Template/example leakage is a persistent problem — the model copies prompt examples as output The sanitizer functions sanitizeLlmTerms() and sanitizeBroadTerms() in claim.js already filter some degenerate terms but could be strengthened The vocabulary-guided design question to carry forward: getTopVocabulary(2000) gives you scored domain terms with representative files Each vocabulary entry has: token, doc_freq, total_count, score, top_files The function index has function names which are themselves vocabulary Open question: do you give the LLM just the terms, or terms + their top files/functions? More context helps mapping but eats tokens Claude at the very end of the previous session suggested: --discover-vocabulary 200 on the .CodeExam2 index (code-exam's own source) would show the LLM terms like multisect, funcstring, hotspot, callee, inverted, dedup — exactly the domain bridge words. I just ran that test, and "multisect" appears but within the context of larger CamelCase or snake_case naming: "multisect_search", "doMultisectAnalyze", etc. Similarly, there's "doShowFuncstring", "show_funcstring"; "doClassHotspots", "getDomainHotspots"; "findCallees"; "loadInvertedIndex"; "dedup" in the filename dedup.js shown for vocabulary like "show_dupes". So Claude was correct, but we might need to split CamelCase or snake_case? Anyway, I want to explore this possibility more. Show more 5:23 PM Synthesized project continuity details for vocabulary-guided term extraction exploration Synthesized project continuity details for vocabulary-guided term extraction exploration Let me review the current project files to get oriented before we dive in. Synthesized vocabulary-guided extraction strategy and continuity requirements Synthesized vocabulary-guided extraction strategy and continuity requirements Now let me look at the key parts of ce_multisect.py — the claim extraction prompt and the relevant functions: Synthesized vocabulary-guided extraction strategy and identified key implementation files Synthesized vocabulary-guided extraction strategy and identified key implementation files Good — I'm now fully caught up on the project state. I've read the TODO, the source files, and searched through your past conversations. Here's my synthesis and thoughts on the vocabulary-guided extraction idea. What I understand the situation to be: The fundamental problem is vocabulary mismatch between patent claims and source code. A patent says "determining the presence of audio data"; the code says detectAudioStream() or hasMediaPayload(). Currently you have two approaches working: TIGHT extraction — pull keywords directly from the claim text. Works great for literal matches but misses implementation vocabulary. BROAD extraction — ask the LLM to generate code-side synonyms. Works well with Claude (~1466 token prompt with examples), but the local 7B model (Qwen Coder) produces garbage BROAD terms — appending "search" to everything, template leakage, generic filler words. The insight is: the index already knows what vocabulary exists in the target codebase. getTopVocabulary(2000) gives you scored domain terms with {token, doc_freq, total_count, score, top_files}. Instead of asking the LLM to blindly guess what code synonyms might exist, you can hand it the actual vocabulary of the target as a concordance, and ask it to map claim concepts to terms from that list. Your test confirmed the concept works but surfaced a preprocessing question: When you ran --discover-vocabulary 200 on CodeExam's own index, the terms like "multisect" appeared embedded in CamelCase/snake_case compound names — multisect_search, doMultisectAnalyze, doShowFuncstring, findCallees, loadInvertedIndex. The raw vocabulary token is the compound, not the constituent parts. On the CamelCase/snake_case splitting question: You're right that splitting is needed, and it should happen at vocabulary build time (or as a post-processing step on getTopVocabulary() output), not in the LLM prompt. Three levels to consider: snake_case is trivial: split on _. multisect_search → multisect, search. show_funcstring → show, funcstring. CamelCase is well-understood: split before uppercase transitions. doMultisectAnalyze → do, Multisect, Analyze. findCallees → find, Callees. CodeSearchIndex → Code, Search, Index. Lowercase the results for matching. The interesting edge cases are things like getDomainHotspots → get, Domain, Hotspots where get is noise but Domain and Hotspots are both valuable. And abbreviations: IPC, TLS, SHA1, URL — you'd want these kept whole, not split letter-by-letter. The practical approach: split, but then apply the same sweet-spot frequency band filtering to the sub-tokens. do, get, set, is will appear in hundreds of files (very high frequency → filtered out as noise). multisect, hotspot, funcstring, callee will be in the sweet spot. So the splitting + existing TF-IDF scoring handles this naturally. For the LLM prompt, you'd want to present the split, filtered vocabulary — not raw compound names. Something like: CODEBASE VOCABULARY (top domain terms with representative locations): multisect (8 files: multisect.js, interactive.js) funcstring (5 files: dedup.js, browse.js) hotspot (7 files: metrics.js, interactive.js) callee (4 files: callers.js, graph.js) inverted (3 files: CodeSearchIndex.js) dedup (6 files: dedup.js, metrics.js) selectivity (2 files: multisect.js) ... Design thoughts for the new session — three big questions: Q1: How much vocabulary to send, and in what format? Token budget matters especially for 7B local models. Options: Minimal (200-400 tokens): Just the terms, one per line, no file context. multisect, funcstring, hotspot... Fits comfortably in a 260-token local prompt alongside the claim text. Medium (500-1000 tokens): Terms + top 1-2 files each. Gives the LLM location hints for mapping. "The code has selectivity in multisect.js" helps it understand that selectivity relates to search result scoring. Rich (1500+ tokens): Terms + files + function names from those files. Maximum context but may overwhelm 7B attention. My instinct: start minimal for local models, rich for Claude. You could have _CLAIM_EXTRACTION_PROMPT_LOCAL_VOCAB that just lists terms, and _CLAIM_EXTRACTION_PROMPT_VOCAB that includes the richer context. Q2: Should vocabulary guide TIGHT, BROAD, or both? TIGHT terms come from the claim text itself — the vocabulary concordance shouldn't change those. The vocabulary's value is in BROAD generation: instead of "guess what synonyms a developer would use", it becomes "look at these actual terms from the codebase and identify which ones relate to the claim concepts." This is exactly where 7B models fail (generating garbage BROAD terms) and where the concordance could rescue them — they're now selecting from a list rather than generating from nothing. Q3: Should function names be a separate vocabulary tier? You mentioned "the function index has function names which are themselves vocabulary." This is interesting because function names encode intent at a higher level than raw tokens. findCallees is more informative than the individual tokens find + callees. For Claude, you might want both: the token vocabulary for literal search term generation, plus a separate list of "key function names in this codebase" that helps the LLM understand what the code does. For 7B, probably just the tokens to stay within budget. Concrete next steps I'd suggest for the new session: Implement a splitCompoundToken(token) utility that handles CamelCase, snake_case, and preserves abbreviations. Add a getVocabularyForPrompt(n, {split: true, minScore: ...}) method that returns pre-processed, split, filtered vocabulary suitable for LLM consumption. Design two prompt variants: _CLAIM_EXTRACTION_PROMPT_VOCAB (Claude, ~2000 tokens, terms + files + brief instructions on using the concordance) and _CLAIM_EXTRACTION_PROMPT_LOCAL_VOCAB (7B, ~400 tokens, just the term list with a one-line instruction). Test with the CodeExam self-index and a sample claim to see if the vocabulary actually improves BROAD term quality from Qwen Coder. The --show-prompt flag is already there — perfect for iterating on prompt design without burning model inference time. Shall I draft any of this as a more formal session-handoff document, or is this discussion enough to carry forward? Before I answer your questions in detail, an important note: I am almost positive that you have not consulted the most recent version of the code. Claude will not allow me to upload .tar files into the project. Whatever .py or .js files are in the project now are very likely out-of-date. It may likely be that your general assessment will be unchanged even after you've seen the most recent code, but just in case I would like to attach the most recent tarball to the chat. Okay if I do that, and can you after review it, and let me know if anything in there changes or adds to your very thorough-looking summary? 5:28 PM Acknowledged readiness to review updated codebase from tarball Acknowledged readiness to review updated codebase from tarball Absolutely — please go ahead and attach the tarball. You're right to flag this. The project files I've been reading (the ce_*.py files and code_exam.py) are the Python originals, but the active development is the Node.js port, and the most recent state is code-exam-phase8b.tar with 240 tests passing. I haven't seen the actual Node.js source for claim.js, analyze.js, CodeSearchIndex.js, or the vocabulary implementation — everything I said about those was inferred from conversation history and the Python code. I'll review the tarball thoroughly once you attach it, focusing especially on: src/commands/claim.js — the actual extraction prompts (both Claude and local), _parseResponse, sanitizers src/core/CodeSearchIndex.js — getTopVocabulary(), ensureVocabulary(), _buildVocabularyFromFiles() src/commands/analyze.js — the claim-analyze pipeline src/commands/multisect.js — parseMultisectTerms() The test suite to understand what's already covered I'll let you know what changes or additions to make to the summary. code-exam-phase8b_11.tar tar See attached. Don't feel obligated to redo the entire analysis you generated before seeing this .tar file. Just tell me what's different. When and if you want, I can also upload output I just generated with this command: .CodeExam code-exam> /vocabulary 200 > codeexam_vocab_200.txt 5:30 PM Reconciled codebase findings with previous analysis Reconciled codebase findings with previous analysis Good — I've now thoroughly reviewed the actual tarball. Here's what's different or new compared to my earlier analysis: My earlier analysis was mostly accurate. The architecture, prompts, sanitizers, vocabulary system, and claim-analyze pipeline all match what the conversation history described. The code is well-organized and further along than I expected in some areas. Key things I can now confirm or correct: CamelCase splitting already exists in scoring, but not in output. Line 3352 of CodeSearchIndex.js has: js const parts = token .split(/(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|_/) .filter(p => p.length > 0).length; This splits for the compoundBonus calculation only — the vocabulary entries are still the raw compound tokens (doMultisectAnalyze, not multisect + analyze). So the split regex is right there already; it just needs to be used to also emit sub-tokens. This is exactly the gap your --discover-vocabulary 200 test revealed. The vocabulary entry structure is richer than I assumed. Each entry has { doc_freq, total_count, score, top_files: [{path, count, concentration}] }. The concentration field (count/fileLines) is a nice touch — it tells you "this token is 5% of the content of this file" which is a strong signal for the LLM concordance. The local model prompt is already quite lean (~260 tokens as described). _CLAIM_EXTRACTION_PROMPT_LOCAL is 338 words, well within a tight budget. There's room to append a vocabulary concordance section without blowing the context. analyze.js already handles the LLM singleton pattern. The AnalysisLLM class with ensureLocalModel() means vocabulary-guided prompts could reuse the same loaded model instance — no double-loading needed. One thing I got slightly wrong: I said the prompts export from claim.js for analyze.js to use. They do — CLAIM_EXTRACTION_PROMPT, CLAIM_EXTRACTION_PROMPT_LOCAL, and parseTermResponse are all exported. But the analyze.js claim-analyze pipeline constructs its own combined prompt (line 962) rather than calling doClaimSearch directly. This means the vocabulary augmentation needs to happen in two places: the standalone --claim-search path in claim.js, and the doClaimAnalyze term-extraction step in analyze.js. (Or better: factor the vocabulary-augmented prompt construction into a shared helper in claim.js.) Nothing in the tarball changes my design recommendations — the three questions (how much vocab, guide TIGHT or BROAD, function names as separate tier) and the four concrete next steps all still apply. The existing split regex at line 3352 makes step 1 (splitCompoundToken) nearly trivial. Yes, please upload that codeexam_vocab_200.txt — seeing the actual output will ground the discussion in real data. codeexam_vocab_200.txt txt Attached the vocab 200 output. One thing that may be relevant to your "One thing I got slightly wrong": claim-analyze needs to provide the LLM with a different prompt than multisect-analyze, because with claim-analyze the LLM needs to discuss the selected code in the context of the actual patent-claim text that the user originally passed in, as well as in the context of the terms that were selected. I can also upload (if not already in the project; they might be) examples of good working output for claim-analyze, I think from the .py version, and almost definitely with --use-claude rather than with --claim-model gguf. There were a few small examples that were victories that we should aim for, as we move to more real-world conditions (including the local LLM that will be needed in the eventual target system, must needs to operate air-gapped on computers without internet, under Court Protective Orders in IP litigation). Show more 5:38 PM Examined vocabulary output to validate extraction methodology Examined vocabulary output to validate extraction methodology This is very revealing. Let me look at the truncated middle section too: Synthesized vocabulary output and extraction methodology insights Synthesized vocabulary output and extraction methodology insights This is excellent data. Here's what it tells us about the vocabulary-guided design: What the raw output looks like — confirming your observation: The top 200 entries are overwhelmingly compound tokens: getCallCountsWithDefinitions, _findContainingFunctionFromIndex, findFunctionMatches, parseMultisectTerms, buildMultisectAnalyzePrompt, doListFunctionsAlpha, sanitizeBroadTerms, etc. If this were fed to an LLM as a "concordance," the LLM would see the names of things but would struggle to map claim language to them, because the constituent concepts are packed inside CamelCase compounds. A patent claim says "intersection search" — the vocabulary says multisectSearch. The LLM needs to see multisect and search as separate matchable tokens to make that connection. But there's also very useful signal in the un-split form. The function names themselves tell the LLM what the code does: doClaimAnalyze, findCallees, extractClaimTerms, getTopVocabulary, sanitizeLlmTerms. If you split too aggressively, you lose "this codebase has a function that analyzes claims." The compound name is the intent; the sub-tokens are the searchable vocabulary. This suggests a two-tier concordance for the LLM prompt: Tier 1 — Domain sub-tokens (for BROAD term generation). Split the top vocabulary compounds, filter by sweet-spot frequency, deduplicate. From this list, you'd get: multisect, funcstring, hotspot, callee, selectivity, dedup, vocab, inverted, sanitize, masker, mermaid, canonical, intersection, concentration, etc. These are the words the LLM should consider when generating regex alternations for BROAD terms. Compact, one per line, maybe 100-200 of them in ~300-500 tokens. Tier 2 — Key function names (for mapping claim concepts to code locations). A curated shorter list: doClaimAnalyze, findCallees, multisectSearch, getTopVocabulary, extractClaimTerms, buildFunctionIndex, getEntryPoints, etc. These tell the LLM "the codebase implements these capabilities." Maybe top 30-50 function names with their file, in ~200-400 tokens. For a 7B local model, you might only send Tier 1 (the sub-tokens). For Claude, you send both. On your important architectural point about claim-analyze vs. multisect-analyze prompts: You're right, and I want to make sure I'm not conflating two different things. There are two places vocabulary could help, and they have different prompt needs: Term extraction (claim.js, extractClaimTerms): "Given this patent claim text [and optionally this codebase vocabulary], produce TIGHT and BROAD search terms." This is where vocabulary-guided extraction lives. The claim text is the primary input; the vocabulary is supplementary context to help the LLM generate better BROAD terms. Claim analysis (analyze.js, buildClaimAnalyzePrompt): "Given this extracted function source code AND this original patent claim text, analyze whether the code implements the claim elements." This is a completely different task — the vocabulary concordance isn't needed here because the LLM already has the actual code. What matters here is the claim text plus the function source. The vocabulary concordance should augment step 1 (term extraction) only, not step 2 (code analysis). My earlier mention of "two places" was about the fact that step 1 can be invoked from two entry points — doClaimSearch in claim.js and doClaimAnalyze in analyze.js — but it's the same extraction logic either way. Yes, please upload the good working claim-analyze output examples. Those victories are important as design targets — they show what "good" looks like from the LLM, which is exactly what we need to be aiming for when we try to get a 7B local model to produce something useful with vocabulary guidance. Even if the examples are from Claude, they define the quality bar, and having them in the project means we can reference them when tuning prompts. You're definitely on the right track here. I think you might be slightly wrong saying that "the vocabulary concordance isn't needed here [in claim analysis] because the LLM already has the actual code". Definitely different from the situation with term extraction, but wouldn't being given the target source-code's own nomenclature help provide a bridge between the claim language and the source-code language? (Assuming of course such a bridge can be built. Sometimes there just isn't a match-up, despite what was asserted in initial legal contentions. Part of the job of this CodeExam system is to help code examiners see when there isn't a match-up, as well as to see when there is.) Okay, I'm switching away to the Project to see what's there. 5:44 PM Reconsidered vocabulary concordance's dual utility bridging claim and code language Reconsidered vocabulary concordance's dual utility bridging claim and code language You're making a really good point, and I think you're right that I was too hasty in dismissing vocabulary's role in claim analysis. Let me reconsider. When the LLM is doing claim analysis, it has the function source and the claim text, but it's doing a translation task between two very different registers of language. The claim says "determining the presence of audio data in a received bitstream"; the code says if (payload.hasAudioTrack()). A capable model like Claude usually handles that mapping, but a 7B model might not see the connection — it's staring at hasAudioTrack and the word "determining" and doesn't confidently bridge them. The vocabulary concordance could help in a different way here than in term extraction. In extraction, it's "here are words that exist in the codebase, use them to generate search terms." In analysis, it's more like: "this codebase uses hasAudioTrack to mean 'check for audio presence', payload to mean 'received data', bitstream doesn't appear but stream and packet do" — contextualizing the code's naming conventions so the LLM can make a more informed judgment about whether there's a real correspondence or just a superficial one. And your point about helping identify non-matches is critical. Without the broader vocabulary context, a 7B model might over-claim coverage — "yes, this function handles audio!" — because it sees the word stream and the claim mentions bitstream. With the vocabulary concordance showing that stream in this codebase is about I/O streams (appearing in fileStream, outputStream, logStream) rather than media streams, the LLM has evidence to say "no, this isn't the same concept." So maybe three use cases for the concordance, not two: Term extraction — "what vocabulary exists to search for?" Claim analysis — "what does this codebase's nomenclature actually mean?" Non-match detection — "here's evidence that the apparent overlap is superficial" Take your time with the project files. I'll be here when you're ready. I've seen in this project that LLMs, including Claude, really really want to be helpful, so if asked to find a match they will stretch for it. Something stretch too much. I don't remember if current prompts in the .js or .py code say anything about this, and/or if it's the kind of request that carries much weight during the thrill of the chase when the LLM has 15 checkboxes to tick off or not. Meanwhile, speaking of stretching, now that I look at the top files I was thinking of, already uploaded to the project, I don't think either one resulted in a "slam-dunk," all elements found. (Again, of course, for half of the parties in litigation, ABSENCE of one or more elements will be the slam-dunk: evidence of non-infringement, or (with asserted prior art) of non-anticipation.) The two files are moltbook_pseudo_claim_search_test.txt and code_exam_demo_claim_analyze.txt . Or maybe I'm not focusing well, and one of these has an all-elements example. In any case, you'll see what the output looks like when trying to match up claim text with code, going through our pipeline. If I find a file with one of the "slam dunk" examples I know I've seen, I will upload it and tell you. Show more 5:55 PM Examined user's concern about LLM confirmation bias in matching tasks Examined user's concern about LLM confirmation bias in matching tasks You're identifying a real and well-documented LLM behavior — the "helpfulness bias" or confirmation bias. When you frame a task as "analyze whether this code implements these claim elements," the LLM reads that as "find the implementation" and stretches to connect things that shouldn't be connected. It's the same phenomenon that makes LLMs bad at saying "I don't know" — they'd rather generate a plausible-sounding but wrong connection than admit there's no match. This is especially dangerous in the litigation context, where a false positive ("yes, element 4 is implemented by this line!") could waste hours of expert time investigating a dead end, or worse, end up in a report that gets challenged on cross-examination. Let me read those two sample files now. Assembled sample files to address LLM matching bias concerns Assembled sample files to address LLM matching bias concerns These are really valuable examples. Let me respond to your points and then discuss what the samples show. On the helpfulness bias in current prompts: Looking at the actual buildClaimAnalyzePrompt in analyze.js, the current instructions say: "For each element/limitation in the patent claim, state whether this function contains code that could implement it. Quote the relevant code lines." "If the function only partially implements the claim (some elements present, others absent), say so explicitly and list which elements are missing." The prompt does ask for partial/absent reporting, which is good. But it frames the task as "state whether this function contains code that could implement it" — and that word "could" is an invitation to stretch. There's nothing that explicitly says "do not speculate about possible matches — if the connection requires inference beyond what the code plainly shows, mark it absent" or "a configuration parameter that would be used by an implementation is not the same as the implementation itself." And yet, look how well Claude actually performed in the demo. On load_tls_config, it correctly identified that the function only loads configuration, doesn't actually perform the operations, and gave it 2/5. That's honest, precise analysis — Claude drew the crucial distinction between "configures TLS settings" and "performs a handshake." On establishConnection, it gave 3.5/5 and correctly noted that certificate validation detail is hidden inside a called function, and data transmission is absent entirely. It even identified operations beyond the claim (connection pooling, retry logic), which is useful for the "no, this code does more/different things" side of the analysis. So Claude, at least with the current prompt, is doing a reasonable job of not over-claiming. The "3.5 of 5" is an honest assessment, not a stretch-to-match. But here's the worry for the 7B local model: The restraint Claude shows — drawing that subtle distinction between "configures" vs. "performs," noting that validation detail is in a called function — requires judgment that a 7B model may not have. A smaller model with the same prompt might see config.getMinTlsVersion() and the claim words "minimum protocol version" and just check the box. That's where the helpfulness bias will hit hardest, and where vocabulary context might actually help or hurt: if the concordance gives the model more ammunition to draw connections, it might draw wrong connections more confidently. This suggests the claim-analysis prompt for local models might need to be more explicitly adversarial — something like "for each element, first describe what the code actually does in plain language, THEN state whether that matches the claim element. If the match requires assumptions about what happens in called functions, say ASSUMED not PRESENT." Force the model to describe before judging, so it can't skip straight to checkbox-ticking. No slam dunk in either file, as you said — but the establishConnection analysis is actually a really good demonstration of what the tool should produce: honest partial coverage with specific citations. That's exactly what a code examiner needs. Yes, I agree with almost all this. You wanting something like " if the connection requires inference beyond what the code plainly shows, mark it absent" I think goes too far the other direction. While too many human code examiners will jump to conclusions, and act as people-pleasers, on the other hand the code examiner can't just take on the entirety of what may end up being a lay jury's role, and with the lawyers wanting to lay uncertainty before the jury to have decide based on how the case "smells". So, when you later want to tell the LLM "If the match requires assumptions about what happens in called functions, say ASSUMED not PRESENT," that I think is exactly right. This relates to another big concern of mine with limited local LLMs and us feeding them one function at a time. Many times the function text we pass in will have callees. Those callees will have useful-sounding suggestive names. Without the text of the callee as well as that of the caller, the LLM is going to be making assumptions. Such assumptions are why we have this --mask-all option, currently only partly implemented in JS but close-to-fully implemented in PY. We found that, if a lot of the code were masked, and comments and "helpful" names removed, the LLM was forced to actually read the darn code. Sorry I'm rambling. How do you think we should proceed, given this good discussion we've had, and the code and sample output you've looked at? Show more 6:05 PM Synthesized interconnected technical concerns into actionable implementation strategy Synthesized interconnected technical concerns into actionable implementation strategy You're not rambling — you're connecting pieces that need to be connected. The callee problem is real and it directly ties the vocabulary discussion to the masking discussion to the prompt design discussion. A function calls validateCertificateChain(session, hostname) — even Claude gave that a half-point because the name is suggestive, but the actual validation logic is elsewhere. A 7B model with unmasked code will give it a full point every time. Here's how I think we should proceed, in priority order: First: Vocabulary-guided term extraction. This is the most self-contained, testable improvement, and it's where the 7B model fails hardest right now (garbage BROAD terms). The implementation path is clear from our discussion: splitCompoundToken() utility using the regex already at line 3352 of CodeSearchIndex.js getVocabularyForPrompt(n, opts) method that returns split, filtered, deduplicated sub-tokens Two new prompt variants: _CLAIM_EXTRACTION_PROMPT_LOCAL_VOCAB (compact, sub-tokens only) and _CLAIM_EXTRACTION_PROMPT_VOCAB (richer, for Claude) Wire it into both entry points (claim.js doClaimSearch and analyze.js doClaimAnalyze step 2) Testable with --show-prompt against CodeExam's own index without burning model inference Second: The ASSUMED/PRESENT distinction in claim-analyze prompts. This is a prompt change — small code diff, big behavioral impact. Adjust buildClaimAnalyzePrompt to require the LLM to categorize each element as PRESENT (code plainly implements it), ASSUMED (suggestive names or calls to functions whose internals aren't shown), PARTIAL (configuration but not execution, or only part of the logic), or ABSENT. This helps regardless of whether you're running Claude or a local model, and it gives the code examiner exactly the honest uncertainty information they need without overstepping into the jury's role. Third: Callee-aware analysis. This is harder but it's the real problem behind ASSUMED. When we send establishConnection to the LLM and it sees certValidator.validateCertificateChain(session, hostname), we could also extract and include that callee's source. The caller graph already exists in the index (findCallees). A --deep or --follow-calls flag on claim-analyze that includes callee source (up to some budget, say 500 additional lines across top N callees) would turn many ASSUMED verdicts into PRESENT or ABSENT. This is a bigger feature but it builds on existing infrastructure. The vocabulary concordance slots in at different levels for each of these. For term extraction, it's the primary input. For claim analysis, it's supplementary context — "here's what this codebase's naming conventions mean." For callee-aware analysis, you might not need the concordance at all because you have the actual code. Want me to start implementing the vocabulary-guided term extraction? I'd begin with splitCompoundToken() and getVocabularyForPrompt(), write tests, and then build the prompt variants — all testable against the vocab output you just showed me. This all sounds great, and in the right order to proceed. But right now I doubt that local models will be able to support your Third stage Callee-aware analysis. --deep and --follow-calls are probably things that should be done, if only for the human code examiner. But how will local LLMs handle all this? One way that was suggested about a week ago, maybe in a different context, is that local LLMs be asked to separately summarize different small pieces, and then be asked to summarize the summaries. But that is for the future. Right now, yes, do the first one. If that works, then the second thing of modifying prompt is easy enough. I want to get this in a state where we know where it's headed (is this going to work?), and then move onto other tasks. I've got a somewhat re-organized TODO list I'll share as we near knowing how vocabulary-guided term extraction is working or not. Show more 6:18 PM Charted implementation roadmap for vocabulary-guided term extraction Charted implementation roadmap for vocabulary-guided term extraction Let me get started. I'll build incrementally — splitCompoundToken() first, then getVocabularyForPrompt(), then the prompt variants, with tests at each step. Architecting vocabulary-guided extraction implementation sequentially Architecting vocabulary-guided extraction implementation sequentially Now let me build this. Starting with the utility function, then the CodeSearchIndex method, then prompts. Charted implementation sequence for vocabulary-guided extraction feature Charted implementation sequence for vocabulary-guided extraction feature Now I'll implement step by step. First, splitCompoundToken in utils.js: Devised testing strategy for token-splitting functionality Devised testing strategy for token-splitting functionality Now let me write a quick test to validate the splitter: Diagnosed test failures and architected noise-word filtering solution Diagnosed test failures and architected noise-word filtering solution Right — get is 3 chars so it passes the filter, but it's noise as a vocabulary sub-token. I need a noise word set for common verb prefixes that provide zero domain discrimination: Refined filtered vocabulary extraction logic and prepared tests Refined filtered vocabulary extraction logic and prepared tests Now fix the tests to match the noise filtering: Architected vocabulary extraction method with structured token scoring and formatting Architected vocabulary extraction method with structured token scoring and formatting 11/11 pass. Now let me implement getVocabularyForPrompt() in CodeSearchIndex.js: Charted next method implementation in development sequence Charted next method implementation in development sequence Now add the method: Examined existing test patterns for vocabulary function validation Examined existing test patterns for vocabulary function validation Now let me add tests for the vocabulary-for-prompt methods. Let me check how existing tests create test indices: Devised test strategy for vocabulary indexing functionality Devised test strategy for vocabulary indexing functionality Now let me add vocabulary-for-prompt tests to the test file: Diagnosed vocabulary filtering issue and recalibrated test expectations Diagnosed vocabulary filtering issue and recalibrated test expectations The test index only has 3 files, so most tokens only appear in 1 file (below minDocFreq=2). Only 5 vocabulary tokens survived. Let me fix the test to be realistic about what a tiny index produces: Architected dynamic prompt builders for vocabulary-augmented extraction Architected dynamic prompt builders for vocabulary-augmented extraction 20/20 passing. Now the prompt variants. The prompts need to be dynamic since vocabulary depends on the loaded index. Let me create builder functions in claim.js: Architected vocabulary integration across extraction pipeline functions Architected vocabulary integration across extraction pipeline functions Now wire vocabulary into the extraction flow. First, update extractClaimTerms to accept and use vocabulary: Pinpointed function updates needed for vocabulary integration Pinpointed function updates needed for vocabulary integration Now wire vocabulary into doClaimSearch. Let me find where the index is available and LLM calls happen: Strategized vocabulary injection across three implementation sites Strategized vocabulary injection across three implementation sites Now inject vocabulary fetching and usage. I'll add it after the variable declarations and before the --show-prompt check: Pivoted toward refining local model prompt construction methodology Pivoted toward refining local model prompt construction methodology Now update the local model prompt to use vocabulary: Prepared to integrate vocabulary parameters into API implementation Prepared to integrate vocabulary parameters into API implementation Now pass vocabulary to the Claude API call: Investigated function call patterns in analysis module Investigated function call patterns in analysis module Now wire vocabulary into analyze.js's doClaimAnalyze. Update the imports first: Orchestrated test construction and validation procedures Orchestrated test construction and validation procedures Now add tests for the prompt builders and run the full suite: Verified test completion and safeguarded against regressions Verified test completion and safeguarded against regressions 26/26 pass. Now let me verify the existing 240 tests still pass: Verified test results and devised demonstration of vocabulary-augmented prompts Verified test results and devised demonstration of vocabulary-augmented prompts 240 + 26 = 266 tests, all green. Let me generate a quick demonstration of what the vocabulary-augmented prompts look like, then package everything: Evaluated vocabulary filtering and verified implementation readiness Evaluated vocabulary filtering and verified implementation readiness Let me do a final full count and then package: Verified test suite completion and prepared deliverable packaging Verified test suite completion and prepared deliverable packaging 266/266 pass, 0 failures. Let me create the tarball: Prepared to summarize the discussion clearly Prepared to summarize the discussion clearly Here's what's in the tarball — 266 tests passing (240 existing + 26 new): What was built: 1. splitCompoundToken(token) in utils.js — Splits CamelCase/snake_case identifiers into sub-tokens. Uses the same regex already in CodeSearchIndex.js for scoring, plus a _SUB_TOKEN_NOISE set that filters get, set, has, with, from, etc. — common verb prefixes that provide zero domain discrimination. 2. getVocabularyForPrompt(opts) and formatVocabularyForPrompt(format, opts) in CodeSearchIndex.js — Two-tier concordance builder. Tier 1: split, deduplicated, scored sub-tokens (cipher, certificate, multisect, sanitize). Tier 2: key function names with files. Two output formats: compact (comma-separated terms, ~300-500 tokens, for 7B) and rich (terms with parent context + function names, for Claude). 3. buildExtractionPromptWithVocab() and buildLocalExtractionPromptWithVocab() in claim.js — Prompt builders that append vocabulary concordance to the extraction prompts. Claude version has detailed instructions about preferring vocab for BROAD and ignoring for TIGHT. Local version is deliberately brief. 4. Wired into both entry points — doClaimSearch (claim.js) and doClaimAnalyze (analyze.js) both now fetch vocabulary from the index and pass it through to the LLM call. Falls back gracefully if vocabulary isn't available. --show-prompt displays the vocabulary-augmented prompt so you can iterate without burning inference. What you can test immediately: --claim-search @patent.txt --show-prompt against any indexed codebase. It will print the full vocabulary-augmented prompt to stderr so you can see exactly what the LLM would receive. No API call needed. Compare with and without vocabulary to see the difference. Code exam phase8b vocab TAR C:\work\code_exam\Nodejs_port\code-exam>node src\index.js --index-path .CodeExam2 --claim-search @multisect_pseudo_claim.txt --show-prompt Loaded existing index: 26 files Loaded 1557 cached vocabulary tokens Vocabulary concordance: 6449 chars (rich format) ======================================================================== CLAIM EXTRACTION PROMPT (sent as system message to LLM) (vocabulary-augmented: 6449 chars) ======================================================================== You are a patent-claim-to-source-code keyword extractor with expertise in both patent law terminology and software engineering implementation patterns. ....... [... Whew, a lot of text.... Isn't this going to blow out local LLM context window? ...] ....... TARGET CODEBASE VOCABULARY — use this to improve BROAD term generation. The following vocabulary was extracted from the codebase being searched. For BROAD terms, PREFER using words from this vocabulary over guessing synonyms. These are actual identifiers, variable names, and function names that exist in the code. Map patent claim concepts to these terms when there is a reasonable semantic connection. For TIGHT terms: ignore the vocabulary — use only words from the claim text. CODEBASE VOCABULARY (150 domain terms from 1557 indexed tokens): file (in: writeFileSync, buildFileAnalyzePrompt) function (in: _findContainingFunctionFromIndex, findFunctionMatches) index (in: _findContainingFunctionFromIndex, CodeSearchIndex) list (in: doListFunctionsAlpha, doListFunctionsSize) analyze (in: buildMultisectAnalyzePrompt, doMultisectAnalyze) search (in: CodeSearchIndex, code_search_index) functions (in: doListFunctionsAlpha, doListFunctionsSize) multisect (in: parseMultisectTerms, buildMultisectAnalyzePrompt) name (in: extractFunctionByName, _getBareNameCounts) dupes (in: doStructDupes, getStructDupes) matches (in: findFunctionMatches, findPathMatches) path (in: findPathMatches, include_path) claim (in: buildClaimAnalyzePrompt, extractClaimTerms) terms (in: parseMultisectTerms, sanitizeBroadTerms) files (in: setupTestFiles, files_indexed) struct (in: doStructDiffAll, struct_diff_all) ...... KEY FUNCTIONS (40 top functions in this codebase): getCallCountsWithDefinitions [test_phase2.js] findFunctionMatches [CodeSearchIndex.js] parseMultisectTerms [test_phase5.js] doListFunctionsAlpha [interactive.js] buildMultisectAnalyzePrompt [test_phase8b.js] doListFunctionsSize [interactive.js] getFunctionSource [test_basic.js] displayMultisectResults [claim.js] getStructuralNormalized [test_phase4.js] printSelectivityReport [claim.js] sanitizeBroadTerms [test_phase8a.js] doMultisectAnalyze [interactive.js] .... doStructDupes [test_phase4.js] listFunctions [CodeSearchIndex.js] multisectSearch [test_phase5.js] ... IMPORTANT: Do NOT include vocabulary terms that have no plausible connection to the patent claim. Only select terms that a developer might use to implement the concepts described in the claim. ... USER MESSAGE (the patent claim text) ======================================================================== A method of searching multiple search terms in code, in which the intersection of substantially all the terms is to be found in the smallest location possible: all within a single function, or among multiple functions in a single file or class, or among multiple files in a single folder or subdirectory. Okay, we got multisect in here a bunch of times. There's so much it might get "lost in the sauce", but somehow this approach does feel promising to me. I want to try it with a much-less relevant pseudo-claim and see how that effects the CODEBASE VOCABULARY and KEY FUNCTIONS that are shown: C:\work\code_exam\Nodejs_port\code-exam>node src\index.js --index-path .CodeExam2 --claim-search @\work\code_exam\7472398_claim1.txt --show-prompt where the claim reads: A computer system comprising: a CPU; a memory unit coupled to the CPU; an application stored in the memory unit and executable by the CPU; a facade server stored in the memory unit and executable by the CPU; and a program stored in the memory unit and executable by the CPU, wherein the program creates an interface between the facade server and a web-browser for exchanging data associated with the application, wherein the facade server hosts the application without utilizing network protocols and without opening network ports. I have to say, the two CODEBASE VOCABULARY and KEY FUNCTIONS lists shown seem the same, regardless of the claim. Shouldn't they differ, depending at least roughly on what the claim says? Or we'll always just put out key vocabulary and key functions, and see if the LLM can figure it out? Show more 6:49 PM Recognized vocabulary should filter by claim relevance, proposed middle-ground approach Recognized vocabulary should filter by claim relevance, proposed middle-ground approach You've identified a real flaw. The vocabulary is completely claim-blind right now — it just dumps the top 150 terms by TF-IDF score regardless of what the patent is about. For the facade server claim against CodeExam's index, you're sending 150 terms about multisect, sanitize, funcstring, dedup — none of which have any connection to facades, servers, browsers, or network protocols. That's wasted context tokens and, worse, it might actively confuse a 7B model into grabbing one of those terms. The vocabulary should be claim-aware. Here's what I'm thinking: Extract simple keywords from the claim text (tokenize, strip patent boilerplate like "comprising", "wherein", "method"). Then score each vocabulary sub-token by whether it has any overlap with claim keywords — exact match, substring, or shared stem. For the multisect claim, search, terms, intersection, function, file, folder would match vocabulary entries containing those sub-tokens. For the facade claim, server, network, browser, interface, protocol would match — and against CodeExam's vocabulary, almost nothing would match, so the vocabulary section would be very short or absent entirely. This filtering happens before the LLM sees anything — it's just string matching, no inference needed. The LLM still does the creative part (mapping "facade" to "proxy" or "wrapper"), but we're only showing it vocabulary terms that have at least a plausible surface-level connection to the claim's domain. If very few terms match (say <5), we could either omit the vocabulary section with a note, or include a small general set with a qualifier like "No strong vocabulary overlap found — these are general domain terms from the codebase." Shall I implement this claim-aware filtering? It's a targeted change to getVocabularyForPrompt — add an optional claimKeywords parameter that re-ranks and filters the sub-tokens. pat_topwords.txt 179 lines txt rand707_words_top.txt 262 lines txt Yes, I think it has to be this way. For what it's worth, I have lists of words commonly appearing in patent claims, and specifically in software-related patent claims, derived some years ago from a huge number of actual claims. They're small, so I'll attach here; if seem generally useful, I can also add to project. You'll see each line is count of patents, followed by the word; sorted with most-prevalent words at the top. Probably works better with genuine claims than with the bogus pseudo ones I type in. 6:56 PM Analyzed patent word lists to implement claim-aware vocabulary filtering Analyzed patent word lists to implement claim-aware vocabulary filtering These are excellent — exactly the right data for this. The generic pat_topwords.txt is the patent boilerplate stoplist: "comprising", "wherein", "plurality", "substantially", "thereof" — words that appear in every patent and carry zero discrimination for code search. The class 707 list is fascinating because the ** markers show which words are domain-specific to software patents — "database", "server", "query", "search", "network" — and those are exactly the words that should survive filtering and drive vocabulary matching. Let me implement claim-aware vocabulary filtering now. Architected claim keyword extraction and vocabulary matching strategy Architected claim keyword extraction and vocabulary matching strategy Now let me build the claim-aware filtering. I'll embed the patent boilerplate list directly in the code — it's a stable, empirical dataset that should ship with the tool: Devised filtering strategy for vocabulary matching against patent claim keywords Devised filtering strategy for vocabulary matching against patent claim keywords Now update getVocabularyForPrompt to accept claim keywords and filter: Architected filtering strategy to integrate patent keyword lists into vocabulary matching Architected filtering strategy to integrate patent keyword lists into vocabulary matching The ** markers in the 707 list are gold — they distinguish software-domain words from generic patent boilerplate. Words like database, query, server, file, interface, search, index appear commonly in software claims but NOT in general patents. They're bridge words between patent language and code vocabulary. I already embedded the generic stoplist. Now let me add claim-aware filtering and also incorporate the 707 software domain words as a secondary signal. The key insight: a vocabulary sub-token is relevant if it has surface overlap with a claim keyword that survived stopword filtering. Architected keyword-matching system with relevance filtering logic Architected keyword-matching system with relevance filtering logic Now I'll replace the entire getVocabularyForPrompt and formatVocabularyForPrompt with claim-aware versions: Deliberated optimal placement for standalone token relevance function Deliberated optimal placement for standalone token relevance function Now add the _computeTokenRelevance helper function. I'll put it as a module-level function after the class: Plotted integration of keyword extraction across multiple function callers Plotted integration of keyword extraction across multiple function callers Now wire extractClaimKeywords into the vocabulary fetch in both doClaimSearch and doClaimAnalyze: Prepared to implement keyword extraction in analyze.js file Prepared to implement keyword extraction in analyze.js file Now update analyze.js — import extractClaimKeywords and use it: Orchestrated test expansion for keyword extraction functionality Orchestrated test expansion for keyword extraction functionality Now update the tests. Add extractClaimKeywords tests and claim-aware filtering tests: Verified test suite completion and validation status Verified test suite completion and validation status 36/36 pass. Full regression check: Verified tests passed and prepared demonstration simulation Verified tests passed and prepared demonstration simulation 276/276. Let me demonstrate what the claim-aware filtering actually does with the two claims you tested: Deliberated vocabulary filtering approach and opted for immediate implementation Deliberated vocabulary filtering approach and opted for immediate implementation Zero overlap — exactly right. The multisect claim will match search, intersection, function, file, folder, code, terms from CodeExam's vocabulary. The facade claim will match almost nothing — maybe server or interface if they exist — and the vocabulary section will be tiny or absent. That's the correct behavior. Prepared to synthesize patent terminology data Prepared to synthesize patent terminology data What changed from v1: Claim-aware vocabulary filtering. The vocabulary concordance is now filtered by relevance to the specific patent claim being searched. Three matching levels: 1.0 exact: vocab search ↔ claim search 0.8 substring: vocab cert ↔ claim certificate (either direction) 0.6 shared stem: vocab encrypt ↔ claim encrypted (4+ char prefix) 0.0 no match: vocab multisect ↔ claim facade → filtered out Patent stopword list embedded in claim.js — 175 words from your pat_topwords.txt cross-validated corpus. extractClaimKeywords() strips these before filtering vocabulary, so "comprising", "wherein", "plurality", "substantially" etc. never drive vocabulary selection. What you should see when you test: Multisect claim → vocabulary shows search, multisect, intersection, function, file, terms, code, folder and relevant functions like multisectSearch, findFunctionMatches, findPathMatches. Much shorter, much more focused than v1's 150-term dump. Facade server claim → vocabulary section should be nearly empty or absent against CodeExam's index (no facade/browser/port vocabulary exists). That's the correct answer — the codebase genuinely doesn't implement anything related to that claim. Both the 707 software-domain list and the generic patent list are valuable reference data. I'd suggest adding them to the project — they're stable empirical datasets and the ** markers in the 707 list could inform future refinements (e.g., treating database, query, server, interface as bridge words that get a relevance boost when they appear in both claim keywords AND code vocabulary). Code exam phase8b vocab v2 TAR Those two files now in project. Yes, when trying to read the facade-server claim onto CodeExam source code, very little vocabulary/function lists now: CODEBASE VOCABULARY (10 domain terms from 1557 indexed — filtered to claim-relevant terms): application (in: Application) compute (in: compute_score) int (in: parseInt) create (in: createContext) interactive (in: interactive, doInteractive) progress (in: showProgress) compare (in: localeCompare) intersection (in: intersection) comprising (in: comprising) compatibility (in: compatibility) KEY FUNCTIONS (5 relevant functions in this codebase): parseInt [interactive.js] createContext [test_phase8b.js] showProgress [CodeSearchIndex.js] localeCompare [CodeSearchIndex.js] doInteractive [index.js] whereas with the multisect pseudo-claim: C:\work\code_exam\Nodejs_port\code-exam>findstr /i multi vocab_multisect.txt terms (in: parseMultisectTerms, sanitizeBroadTerms) multiple (in: multiple) multisect (in: parseMultisectTerms, buildMultisectAnalyzePrompt) parseMultisectTerms [test_phase5.js] multisectSearch [test_phase5.js] Possibly a shame that the only filenames associated with multisect here are test files, but OTOH I've seen cases where it was unclear if a party was actually *practicing* an invention (as opposed to building dead code or latent code), and evidence of in-house testing made the difference. One concern I have: we're surfacing key vocabulary and possibly also related files, based on what appears to be important across the entire codebase. Yet cases often turn, not on the core functionality of a product, but on some aspect of it that seems peripheral but that just happens to correspond (at least assertedly) to someone's patent claim. Is it possibly a delusion that what our discover-vocabulary method surfaces as representing the most important part of the code, is going to just happen to correspond to what someone's patent says? Just asking. Not trying to shoot down this idea. I think we have to try this, and it might work often enough to be worthwhile. But it sort of rests on an assumption (what discover-vocabulary thinks is important in the code is going to usefully overlap with the sort of thing a patent owner wants to assert against the product) that may not hold. Looks like you're on the right track. I should do a lot more testing, but I'm calling it a day. I'll get back into this in the morning. Show more 7:25 PM Acknowledged vocabulary approach's limitation with peripheral features, proposed mitigation Acknowledged vocabulary approach's limitation with peripheral features, proposed mitigation That's a sharp observation and it's worth sitting with rather than dismissing. You're right that discover-vocabulary is biased toward the core of a codebase — tokens that appear across many files, score high on TF-IDF, and represent the main architectural concepts. But a patent assertion might target a session caching mechanism that lives in two files out of 500, uses ttl, expiry, sessionStore — terms that rank #900 in vocabulary and never make the top 300 we process. There's a partial mitigation already available: we can increase topN significantly. We process 300 entries now, but the vocabulary has 1,557 tokens in your CodeExam index. A real production codebase might have 10,000+. Bumping to topN=1000 or even topN=2000 before claim-aware filtering costs almost nothing computationally — the expensive part is building the vocabulary, which is already cached. The claim filter then selects the 10-30 terms that actually matter. So even if sessionCache ranks #900, its sub-tokens session and cache would still get extracted and tested against claim keywords. But your deeper point stands: this approach fundamentally assumes the target vocabulary exists in the index in some recognizable form. If a developer implemented session expiration using _checkStaleness() and _purgeOldEntries(), and the claim says "expiration of cached session data," the vocabulary bridge needs stale→expiration or purge→expire — which is exactly the kind of leap a 7B model can't make and Claude sometimes can. So vocabulary guidance is a useful tool, probably sufficient for the common case, but not a complete solution. It's strongest when claim language and code language share at least some lexical surface. When they don't, you're back to relying on the LLM's reasoning ability — which is exactly where Claude shines and where local models struggle. Good stopping point. The pieces to test in the morning: run both pseudo-claims with --show-prompt to verify the filtering looks right, then try an actual --claim-search (not just --show-prompt) to see if the vocabulary-augmented BROAD terms are better than the old ones. The multisect claim against CodeExam's own index is the ideal first test since you know what "good" looks like. stale->expiration or purge->expire was precisely why this project in the beginning used as some kind of ChromaDB default 'all-MiniLM-L6-v2'. It was slow and didn't work, at least as we were using in the PY code. Things have gone so much better after we stopped using it. But isn't it what should work for, umm, putting code and patent claims in the same embedding space, and then being able to measure distance among them? 7:33 PM Examined embedding model paradox and semantic bridging potential Examined embedding model paradox and semantic bridging potential Yes, that's exactly what embeddings should do in theory. "Purge" and "expire" would be close in a general-purpose embedding space. "Stale" and "expired" too. The whole promise of semantic search is that you stop needing lexical overlap. But there are several reasons it didn't work well for this use case, and they're instructive: all-MiniLM-L6-v2 was trained on natural language, not code. It knows that "purge" and "expire" are related in English prose. But in source code, purge appears in a context like _purgeOldEntries(cache, maxAge) surrounded by variable names, braces, and syntax. The embedding of that code chunk is pulled in a dozen directions by all the non-semantic tokens. The signal-to-noise ratio for semantic matching in raw code is terrible compared to, say, matching two English paragraphs. Patent claims aren't normal English either. "Wherein the expiration of cached session data is determined by comparing a timestamp value associated with said data to a predetermined threshold" — that's a 25-word sentence that means if (Date.now() - entry.ts > TTL). An embedding model trained on Wikipedia and StackOverflow has never seen language structured like this. The patent register and the code register are both far from the training distribution. Granularity mismatch. You were embedding chunks of code and chunks of claim text, then measuring distance. But the semantic correspondence isn't chunk-to-chunk — it's concept-to-concept. One claim element maps to three lines buried inside a 50-line function. The function-level embedding averages over all 50 lines, drowning the signal. What might actually work — and this is a future direction, not something to build now — is embedding at the term level rather than the chunk level. Take the claim keywords that survive patent stopword filtering (expiration, cached, session). Take the vocabulary sub-tokens from the index (purge, stale, cache, entry, timeout). Compute pairwise similarity between those short terms in embedding space. That's a much cleaner signal — you're comparing individual concepts, not noisy code chunks against noisy legal prose. It's essentially what we're doing now with _computeTokenRelevance using string matching (exact, substring, shared stem), but an embedding model could catch purge↔expire where string matching can't. The trick is that this would be fast and small — you're embedding maybe 30 claim keywords against 200 vocabulary sub-tokens, so 6,000 pairwise comparisons of short strings. Not embedding the entire codebase. And a model like all-MiniLM-L6-v2 might actually do fine at that scale because you're giving it clean single-word or short-phrase inputs, not raw code blocks. But that's a later enhancement. What you have now — vocabulary sub-tokens filtered by claim keywords with string matching — covers the common case where there's some lexical surface shared between claim and code. Embeddings would extend it to the semantic-but-not-lexical cases. Worth keeping in mind for when the string-matching approach hits its limits. code_exam_claim_search_demo_results_022226_745pm.txt 258 lines txt I did one more test: C:\work\code_exam\Nodejs_port\code-exam>node src\index.js --index-path \work\ai_code_exam\.demo --claim-search @\work\ai_code_exam\sample_patent_claim.txt --show-prompt Looks like there is a good match between the codebase vocabulary and the key functions on the one hand, and the pseudo claim text on the other hand. Of course, Claude itself wrote the pseudo claim text to match some of what Claude itself also wrote in the demo source code. Still, it did align. See the attached text file with full output. 7:49 PM Validated filtering effectiveness despite synthetic test conditions Validated filtering effectiveness despite synthetic test conditions That's a really satisfying result. Compare the three tests side by side: Claim Index Sub-tokens Functions Signal quality TLS secure connection Demo TLS codebase 105 14 Excellent — cipher, certificate, handshake, hostname, validateCertificateChain Facade server CodeExam 10 5 Correctly sparse — parseInt, createContext are noise, telling the examiner "this codebase doesn't implement this" Multisect search CodeExam ~30-40 ~10 Good — multisect, search, intersection, terms, multisectSearch The claim-aware filtering is doing its job: high signal for genuine overlap, near-silence for irrelevant claims, and proportional output for partial overlap. You're right that the TLS test is somewhat circular — Claude wrote both sides. The real proof will come when you run this against a real codebase with a real patent claim, where the vocabulary gap between claim language and code language is genuine and unpredictable. The multisect pseudo-claim against CodeExam is actually the more honest test because the code wasn't written to match that claim text. Where things stand for morning: Vocabulary-guided term extraction — implemented, claim-filtered, tested (276/276). Ready for real --claim-search runs (not just --show-prompt). Next up when ready — the ASSUMED/PRESENT prompt change for buildClaimAnalyzePrompt. Small diff, big behavioral impact. Patent stopwords — embedded in code from your empirical corpus. The 707 software-domain list with ** markers is now in the project for future use (potential bridge-word boosting). Your concern about peripheral code — valid and worth revisiting. Increasing topN from 300 to 1000+ before claim filtering is a cheap mitigation. The deeper answer is term-level embeddings, but that's future work. Have a good night. When you're back with the updated TODO list, we can figure out what to tackle next. code_exam_claim_search_demo_results_MORE_022226_755pm.txt txt I removed --show-prompt, set the API key, and got further. But I can't quite tell what I'm looking at: [1] NetworkManager::establishConnection (java\net\NetworkManager.java, 87 lines) [17/20] [1,2,4,8] L73 public SSLSocket establishConnection(String hostname, int port, [7,11,20] L74 ConnectionConfig config) { [5] L93 /* Step 2: Initialize SSL context */ [6,19] L106 /* Step 4: Configure cipher suites */ [18] L107 String[] ciphers = cipherNegotiator.selectCipherSuites( [12] L109 config.getMinKeyBits()); [9,13] L112 /* Step 5: Configure TLS versions */ [14,15,16] L122 /* Step 7: Validate certificate chain */ [3] -- not found -- [10] -- not found -- [17] -- not found -- [2] tls_connect (c\tls_handler.c, 103 lines) [17/20] [1,2,8] L176 tls_connection_t *tls_connect(const char *hostname, int port, [7,11,20] L177 const tls_config_t *config) [5] L189 log_info("Initiating secure connection to %s:%d", hostname, port); [6,19] L191 /* Step 1: Initialize cryptographic context */ [4] L198 /* Step 2: Create underlying TCP socket */ [18] L219 /* Step 5: Negotiate cipher parameters */ [14,15,16] L247 /* Step 7: Verify server certificate chain */ [10] L253 /* Build connection object */ [9,13] L268 hostname, port, SSL_get_version(ssl), [3] -- not found -- [12] -- not found -- [17] -- not found -- I think this is before LLM analysis, happening inside CodeExam? The functions chosen looks very appropriate, but not sure what -- not found -- means here, and who is saying "not found". See output file attached. Show more 7:58 PM Elucidated CodeExam's lexical search term matching and absence indicators Elucidated CodeExam's lexical search term matching and absence indicators This is all CodeExam's own multisect search engine — no LLM involved yet at this stage. Here's what you're looking at: The numbered brackets are term indices from the TIGHT search term list. Looking at line 52: [1] /establish|establishing/ [2] secure [3] communication [4] connection [5] /initializ|creating/ ... [17] credentials [18] /negotiat|negotiating/ [19] cipher [20] parameters So when the function-level match for establishConnection shows: [1,2,4,8] L73 public SSLSocket establishConnection(String hostname, int port, [3] -- not found -- [10] -- not found -- [17] -- not found -- That's saying: terms 1, 2, 4, and 8 were all found on line 73. But term [3] (communication), term [10] (object), and term [17] (credentials) don't appear anywhere in that function's body. That's why it's 17/20 — seventeen terms were found, three weren't. This is the "scavenger hunt" working exactly as designed. The code examiner sees at a glance: "this function covers 17 of 20 claim-derived terms. The word 'communication' never appears, 'object' never appears, 'credentials' never appears. Are those gaps meaningful?" Maybe communication is implied by the TLS context. Maybe credentials is handled in a caller. Maybe object was just a patent-boilerplate word that shouldn't have survived extraction (and arguably the sanitizer should have caught it). This happens before any LLM analysis. If you had used --claim-analyze instead of --claim-search, the next step would be to send those top functions to the LLM for element-by-element analysis — which is where the PRESENT/ASSUMED/PARTIAL/ABSENT categorization we discussed would kick in. What you're seeing is a clean pipeline: vocabulary-guided extraction produced good terms → multisect found the right functions → the gap report tells the examiner exactly what's present and what's missing. The LLM claim-analysis step would then interpret whether those gaps are real or just terminology differences. In this scenario, with this small proportion of --not found--, would establishConnection and tls_connect be sent to the LLM for analysis, or not? 8:04 PM Verified top matches would reach LLM for analysis Verified top matches would reach LLM for analysis Yes, both would be sent. The doClaimAnalyze pipeline (in analyze.js) picks the top 2 function-level matches, filtering out (global) scope and zero-line entries. With 17/20 terms each, establishConnection and tls_connect are the top 2 function matches — they'd both go to the LLM for element-by-element claim analysis. That's actually what happened in the code_exam_demo_claim_analyze.txt file already in the project. That output shows this same TLS claim analyzed against this same demo index. The pipeline found these same two functions, sent them to Claude, and got back the analysis where load_tls_config got 2/5 elements (it's config, not implementation) and establishConnection got 3.5/5 (real implementation but certificate validation detail is in a callee and data transmission is absent). The threshold for "good enough to send to the LLM" is essentially "did multisect find any function matches at all?" If TIGHT finds functions, it sends the top 2. If TIGHT finds nothing, it falls back to BROAD with a lower min_terms threshold (60% instead of 80%). If BROAD also finds nothing at the function level, it falls back to file-level matches. The 3/20 not-found terms don't disqualify the match — they become part of what the LLM evaluates. The LLM sees the function source plus the full claim text and judges whether term [3] communication being absent is a real gap or just a terminology difference. Okay, great. I was worried that --not found-- here meant these would get kicked out. And these were selected using your new codebase vocabulary method? 8:07 PM Examined vocabulary's influence on term extraction and search outcomes Examined vocabulary's influence on term extraction and search outcomes Partly. The vocabulary concordance was loaded — you can see it on line 4: Vocabulary concordance: 4539 chars (rich format, claim-filtered) So it was included in the prompt sent to Claude for term extraction. But look at what actually happened: the TIGHT search succeeded (lines 54-57, found 2 survivor files with min_terms=16/20), so the pipeline never needed to fall back to BROAD. The TIGHT terms come from claim language only — the prompt explicitly says "ignore the vocabulary for TIGHT terms." So in this run, the vocabulary was present in the prompt but the TIGHT terms alone were sufficient to find the right functions. The BROAD terms (which would have been vocabulary-influenced) were generated but never used for searching. The vocabulary guidance will make the real difference in the harder cases — when the claim language doesn't literally appear in the code, TIGHT finds nothing, and the pipeline falls back to BROAD. That's when having tls, ssl, config, cert, handshake from the actual codebase vocabulary should produce better BROAD terms than a 7B model guessing blindly. This demo codebase is almost too perfect a match — Claude's TLS claim uses words like "cipher", "certificate", "handshake" that appear verbatim in the code. A real-world scenario where someone's patent says "credential verification apparatus" and the code says checkAuthToken() is where vocabulary guidance earns its keep. Got it. Chat more tomorrow. 8:08 PM Synthesized session progress and prepared continuity Synthesized session progress and prepared continuity Sounds good. Quick summary of where things stand: Done and tested (276/276 passing): splitCompoundToken() — CamelCase/snake_case splitting with noise filtering getVocabularyForPrompt() — claim-aware relevance filtering using patent stopword corpus formatVocabularyForPrompt() — compact (7B) and rich (Claude) formats extractClaimKeywords() — patent boilerplate stripping from your empirical corpus Wired into both doClaimSearch (claim.js) and doClaimAnalyze (analyze.js) --show-prompt displays the vocabulary-augmented prompt Ready to do next: ASSUMED/PRESENT/PARTIAL/ABSENT prompt change for buildClaimAnalyzePrompt Your updated TODO list More testing with real claims against real codebases Have a good night. Make the ASSUMED/PRESENT/PARTIAL/ABSENT prompt change. Can you also try NOT ignoring vocabulary for TIGHT terms? Maybe add as an --option. I need to test with local model Qwen before we get too much further out on this. The prompts I saw last night were very large, and working well for Claude may not translate into even working okay for local models. How hard or easy would it be for you to go ahead with --deep or --follow-calls (just synonyms for same thing?) initially as an extra tool for the human code examiner? There is already --callees. In the demo for example: tls_connect (c\tls_handler.c, 103 lines) calls 7 functions: tls_connect [recursive] c\tls_handler.c (103 lines) log_error c\utils\logging.c (7 lines) log_info c\utils\logging.c (7 lines) initialize_crypto_context c\tls_handler.c (53 lines) create_tcp_socket c\tls_handler.c (36 lines) negotiate_cipher_params c\tls_handler.c (41 lines) verify_certificate_chain c\cert_verify.c (72 lines) A --follow-calls option to function extract would I think dump out tls_connection itself, followed immediately by the two log functions (yawn), followed by initialize_crypto_context, create_tcp_socket, etc. Would note the recursive call, but of course not dump tls_connect again. A --deep would do the same but down N levels of hell. I'd like to see what this looks like. Especially if combined with my earlier TODO for an --extract-comments function (just show the entire-line comments from the code), might be a useful tool right away, even without thinking what an LLM would do with all of this. Of course, might tempt the viewer (either human or LLM) into assuming comments are accurate. But add a Tip at the end: "Remember, comments can lie!". I want one or max two more turns of the crank for vocabulary-enhanced term extraction, and for --follow-calls/--deep if you choose to implement those now rather than put on TODO, and then we need to turn to other TODO items. I'll put my new TODO list at the top of a new chat, since this one by then will be getting too long. Show more 10:27 AM Prioritized prompt modifications and planned efficient implementation strategy Prioritized prompt modifications and planned efficient implementation strategy Let me tackle all of these. Starting with the prompt change, then the vocabulary-for-TIGHT option, then --follow-calls. Prioritized prompt refinement before implementing additional feature options Prioritized prompt refinement before implementing additional feature options Now let me also check the --callees implementation and function extraction for --follow-calls: Orchestrated implementation of prompt refinements and new extraction features Orchestrated implementation of prompt refinements and new extraction features Good, I have the full picture. Let me implement all four changes. Starting with the prompt, then options, then --follow-calls. 1. ASSUMED/PRESENT/PARTIAL/ABSENT prompt change: Prepared to revise file-level claim prompt specifications Prepared to revise file-level claim prompt specifications Now update buildClaimFilePrompt similarly: Prepared to revise test verification for prompt content Prepared to revise test verification for prompt content 2. --vocab-tight option — don't ignore vocabulary for TIGHT terms: Identified need to conditionally handle vocabulary settings for tight terms Identified need to conditionally handle vocabulary settings for tight terms 3. Wire --vocab-tight into the prompt builders: Resumed interrupted implementation of prompt refinements and feature additions Resumed interrupted implementation of prompt refinements and feature additions I was partway through implementing these. Let me continue from where I left off — the prompt change and argparse entries are done. Now I need to wire --vocab-tight, implement --follow-calls, and --extract-comments. Charted integration pathway for vocabulary enhancement through prompt builders Charted integration pathway for vocabulary enhancement through prompt builders Now wire vocabTight through doClaimSearch: Prepared to examine vocabulary implementations in analyze.js file Prepared to examine vocabulary implementations in analyze.js file Now wire vocabTight through analyze.js: Prepared to implement follow-calls and extract-comments features Prepared to implement follow-calls and extract-comments features 4. Implement --follow-calls and --extract-comments in browse.js: Orchestrated testing strategy and feature implementation sequencing Orchestrated testing strategy and feature implementation sequencing Now let me run the full test suite to check for regressions: Identified and resolved test failures from structural modifications Identified and resolved test failures from structural modifications Two tests need updating for the new prompt language: Orchestrated test implementation for follow-calls and extract-comments features Orchestrated test implementation for follow-calls and extract-comments features 276/276. Now add tests for --follow-calls and --extract-comments: Verified test completion and prepared for comprehensive integration testing Verified test completion and prepared for comprehensive integration testing 8/8 new tests passing. Full suite: Prepared to demonstrate features before packaging implementation Prepared to demonstrate features before packaging implementation 284/284. Let me demo the output so you can see what --follow-calls and --extract-comments look like: Synthesized feature requests and packaged implementation strategy Synthesized feature requests and packaged implementation strategy That's a nice, scannable view. A code examiner can quickly see the claimed intent of an entire call tree without wading through implementation. Now package: Prepared to synthesize tarball contents comprehensively Prepared to synthesize tarball contents comprehensively 284/284 tests, 0 failures. Here's what's in this build: 1. PRESENT/ASSUMED/PARTIAL/ABSENT prompt — buildClaimAnalyzePrompt and buildClaimFilePrompt now require the LLM to categorize each claim element with one of four labels. The key distinction: if the LLM can only see validateCertChain(session) but not the validation logic, it must say ASSUMED, not PRESENT. Configuration that would be used by an implementation is PARTIAL. Gives the code examiner honest uncertainty information. 2. --vocab-tight — When specified, the vocabulary concordance also influences TIGHT term generation (normally TIGHT uses only claim words). Both prompt builders (buildExtractionPromptWithVocab, buildLocalExtractionPromptWithVocab) accept the flag. Usage: --claim-search @patent.txt --vocab-tight 3. --follow-calls / --deep — Works with --extract. Finds all callees of the extracted function and dumps their source immediately after. Skips recursive calls (noted but not re-dumped). Skips already-shown functions (avoids cycles). Respects --depth N for multi-level descent (default 1). Example: --extract tls_connect --follow-calls 4. --extract-comments — Works with --extract, optionally combined with --follow-calls. Strips everything except full-line comments (//, #, /* */, """docstrings"""). Shows the narrative of a call tree without the implementation noise. Ends with "Tip: Comments can lie! Verify against actual code logic." Example: --extract tls_connect --follow-calls --extract-comments For your Qwen testing: The prompts are definitely large for a 7B model. The compact vocabulary format is shorter, but the base extraction prompt itself is substantial. You may want to try with and without --vocab-tight to see if Qwen handles the vocabulary section or gets confused by it. Code exam phase9 vocab deep TAR For testing, I created a new index of our code: C:\work\code_exam\Nodejs_port\code-exam>ce --index-path .CodeExam4 --build-index . --exclude-extensions .txt,.json,.ts,.d.ts --skip-semantic --use-tree-sitter I wanted to exclude *.ts from this because it's not our code. A real-world code base will usually contain lots of things unrelated to the code examiner's task, and it's important it work in an codebase with a lot of "clutter," but I didn't want it now. --exclude-extensions worked to keep out .txt and .json, but not to keep out .ts (which I also tried with .d.ts) The .CodeExam4 index has lots of infrastructure like: node_modules/universal-user-agent/index.d.ts /extract doMultisect --follow-calls didn't work in interactive (ditto --deep): Function 'doMultisect --follow-calls' not found in index. I tried /extract doMultisect /follow-calls interactive also, and didn't work either. Did work on command line. Looks good. --extract doMultisect --extract-comments --follow-calls on cmd line works great! Good thing there's the "Tip" at the end, because it's going to be very tempting to rely on this. Idle thought: would local LLMs do better with just the quasi-English that appears in --follow-calls? Exact opposite of what we've been doing with --mask-all, but shrinking the 287 lines of multisectSearch down to 35 lines (which --follows-calls nicely labels as "[called by doMultisect]") might be something to try. My 35 lines count is ignoring blank lines and lines of --------- and =====. Is --deep just a synonym for --follow-calls, or does it take an [N] level of depth? If --extract-comments is simply an option to --extract (it doesn't behave as a standalone cmd; maybe it should be), then it ought to be called --comments-only or something? Now I'll test PRESENT/ASSUMED and --vocab-tight, especially with local model. Show more 11:20 AM Catalogued multiple feature bugs and parameter naming inconsistencies for fixes Catalogued multiple feature bugs and parameter naming inconsistencies for fixes Good feedback on all counts. Let me fix the issues and address each point. 1. .ts exclusion bug — Let me check why --exclude-extensions doesn't catch .ts: Identified multiple bugs: node_modules indexing, extension filtering, interactive flag parsing, and naming inconsistencies Identified multiple bugs: node_modules indexing, extension filtering, interactive flag parsing, and naming inconsistencies Two bugs and several good naming/UX points. Let me fix them all. Identified duplicate message and resumed fixing multiple bugs systematically Identified duplicate message and resumed fixing multiple bugs systematically I see this is a re-send of your previous message — I was partway through fixes when the turn ended. Let me continue from where I left off and address everything. Catalogued multiple bugs and feature refinements to address systematically Catalogued multiple bugs and feature refinements to address systematically Good — my _SKIP_DIRS reference is already in the code but the constant isn't defined yet. Let me address all the issues systematically. Issues to fix: .d.ts exclusion — path.extname('index.d.ts') returns .ts, not .d.ts. Need compound extension handling. node_modules indexing — need _SKIP_DIRS constant --follow-calls in interactive — REPL treats whole string as function name Rename --extract-comments → --comments-only --deep should accept optional [N] depth Investigated path extension matching logic for TypeScript file filtering Investigated path extension matching logic for TypeScript file filtering Now fix the .d.ts / .ts exclusion bug. The real issue might be that .ts isn't in DEFAULT_EXTENSIONS to begin with: Identified extension handling bugs and catalogued feature refinement priorities Identified extension handling bugs and catalogued feature refinement priorities OK, so .ts IS in DEFAULT_EXTENSIONS. The main problem is node_modules not being skipped — my _SKIP_DIRS fix handles that. But there's also a compound extension issue: path.extname('index.d.ts') returns .ts not .d.ts. Let me handle compound extensions in the extension matching: Architected compound extension filtering mechanism for improved exclusion handling Architected compound extension filtering mechanism for improved exclusion handling Now add the compound exclusion infrastructure to the constructor and a helper method: Architected compound extension handling for exclusion logic Architected compound extension handling for exclusion logic Now wire compound extension handling in index.js: Investigated interactive mode compatibility with extract command flags Investigated interactive mode compatibility with extract command flags Now fix interactive mode. The problem is that /extract doMultisect --follow-calls is parsed as function name doMultisect --follow-calls: Identified flag renaming and parameter enhancement opportunities Identified flag renaming and parameter enhancement opportunities Now rename --extract-comments → --comments-only throughout: Refactored `--deep` flag to accept optional numeric depth parameter Refactored `--deep` flag to accept optional numeric depth parameter Now make --deep accept an optional depth value. Currently both --deep and --follow-calls are just flags: Contemplated updating browse.js to integrate deep option functionality Contemplated updating browse.js to integrate deep option functionality Now update browse.js to handle --deep [N]: Pivoted test implementation toward renamed function parameter Pivoted test implementation toward renamed function parameter Now update the tests — extract_comments was renamed to comments_only: Verified fixes and consolidated feature improvements for delivery Verified fixes and consolidated feature improvements for delivery 284/284. Let me also verify the --deep optional value works on the command line: Examined directory exclusion mechanisms and verified implementation consistency Examined directory exclusion mechanisms and verified implementation consistency Let me also check if there's a second file-walk path that needs the same fix: Synthesized user feedback on indexing, extraction, and command-line functionality Synthesized user feedback on indexing, extraction, and command-line functionality 284/284, all fixes in. Here's what changed: Bug fixes: node_modules and infrastructure dirs now auto-skipped during index builds. _SKIP_DIRS set includes node_modules, __pycache__, .git, dist, build, vendor, venv, .idea, .vscode, etc. Also skips dot-prefixed dirs (.CodeExam4, etc.). Applies to both buildIndex and scanExtensions. Compound extension exclusion (.d.ts): --exclude-extensions .d.ts now works. path.extname('index.d.ts') only returns .ts, so compound extensions get special filename-suffix matching via _isCompoundExcluded(). So --exclude-extensions .ts excludes all .ts AND .d.ts, while --exclude-extensions .d.ts excludes only .d.ts. Interactive mode /extract doMultisect --follow-calls now works. The REPL parses flags out of the /extract argument string. Also supports /extract doMultisect --deep=3 and /extract doMultisect --comments-only. Naming/UX changes: --extract-comments → --comments-only. Still only works with --extract (needs a function target). Making it standalone for whole-file comment extraction is a good TODO. --deep [N] is now separate from --follow-calls. --deep alone = follow calls depth 1. --deep 3 = follow 3 levels. --follow-calls = follow depth 1 (or whatever --depth N says). On the command line: --extract tls_connect --deep 2. In interactive: /extract tls_connect --deep=2. Your idle thought about comments-only for local LLMs — that's a genuinely interesting idea. The 287 lines of multisectSearch compressed to 35 lines of comments gives the LLM a high-density narrative of what the code does at a fraction of the token cost. For a 7B model with 8K context, that's the difference between fitting the function or not. It's the exact opposite of --mask-all (strip comments, keep code) — instead: strip code, keep comments. Both are valid strategies depending on what you're asking the LLM to do. For "does this code implement the claim?" the comments might actually be enough for a first-pass triage, especially when combined with --follow-calls showing the full call tree narrative. Worth experimenting with. The "Comments can lie!" caveat becomes critical there. Code exam phase9b fixes TAR This works now: .CodeExam4 code-exam> /extract doMultisect --follow-calls --comments-only > doMultisect_follow_calls_comments_only.txt While you are sometimes too eager to agree with me, looking at this output I think this is something that should be tried giving to the LLM. Should be a simple change: given a --comments-only modifier to an analyzer command, just internally run extract/comments-only instead of plain extract, and give that to LLM along with the other stuff (and for once, we would be cutting down size of prompt for LLM); given a --follow-calls modifier to analyze cmd..., etc. When giving --comments-only output to an LLM (and maybe when giving to a human too, or maybe not), remove all the //===== and //-------- and blank lines. Unless somehow an LLM would understand them as important separators?? If continue showing to humans, just plain - and = characters that will look ok in redirected file. Looks like "ΓöÇΓöÇΓöÇΓöÇΓö" right now (I forget word for this). The --exclude-extensions worked now, since I end up with only 28 files in the index, instead of about 2000: C:\work\code_exam\Nodejs_port\code-exam>ce --index-path .CodeExam5 --build-index . --exclude-extensions .txt,.json,.ts,.d.ts --skip-semantic --use-tree-sitter C:\work\code_exam\Nodejs_port\code-exam>node src\index.js --index-path .CodeExam5 --build-index . --exclude-extensions .txt .json .ts .d.ts Indexing 28 files from: C:\work\code_exam\Nodejs_port\code-exam Minor point: add output line listing what's excluded. C:\work\code_exam\Nodejs_port\code-exam>ce --index-path .CodeExam5 --extract doMultisect --deep 3 --comments-only > doMultisect_deep3_comments_only.txt I tried it with "main" and --deep 3, but no function named "main". It's the stuff in src\index.js . Closest I could come was: C:\work\code_exam\Nodejs_port\code-exam>ce --index-path .CodeExam5 --extract doInteractive --deep 4 --comments-only > doInteractive_deep4_comments_only.txt Produced an enormous file (168k), towards the bottom dominated by "[already shown above ΓÇö skipped]". This might be one very good way of producing a map of a source-code tree. Again, it directly contradicts the "screw the naming and the comments, let's see what the code itself does" approach. But having multiple different ways of looking at code will be useful. The 168k "map" of everything from doInteractive on down (I assume everything; maybe --deep 5 would get even more?) could be one good pane in the GUI (where usually it would start from main or __main__ or WinMain...). Yet another thought: A variant to --list-functions that works level by level. In many systems, "main" would be at level 1. Then level 2 is what main calls, etc. We currently have --file-map, but this is on a function-level. We have --call-tree and --callees, but that requires knowing the function name, and those (correctly I think) only go down to direct callees. Claude and I have discussed a codebase map that clearly shows sources/sinks. This would be the same. Listing functions by their level is also consistent with the tool's approach of telling the user about key names in code (like --discover-vocabulary). Okay, I keep diverting myself from testing results, especially on local model Qwen Coder, of your PRESENT/ASSUMED and --vocab-tight. Don't yet write any code to address above thoughts, but I bet I'm going to want you to implement some. Show more 12:03 PM Identified three immediate fixes: Unicode replacement, separator stripping, exclusion messaging Identified three immediate fixes: Unicode replacement, separator stripping, exclusion messaging The mojibake is Windows cmd.exe not having UTF-8 codepage — the Unicode box-drawing characters ═ ─ — render as ΓöÇ ΓÇö etc. Easy fix. Let me address the immediate issues without going into the bigger ideas. Identified encoding issues requiring character sanitization across codebase Identified encoding issues requiring character sanitization across codebase Now let me check all source files for Unicode that will mojibake on Windows: Identified which Unicode characters display to users versus internal systems Identified which Unicode characters display to users versus internal systems Most of those are in code comments or JSDoc — they're fine since they never display. Let me focus on what the user actually sees in terminal output: Prepared to implement multiple feature enhancements for code analysis tool Prepared to implement multiple feature enhancements for code analysis tool Now add the exclusion message during build: Noted need to verify test file references for Unicode character updates Noted need to verify test file references for Unicode character updates Let me also verify the test for --follow-calls output still matches (it referenced the Unicode separator): Verified output formatting and contemplated feature implementation priorities Verified output formatting and contemplated feature implementation priorities All in comments. Let me also check the multisect output tests (they reference ━━━): Verified text content preservation despite decorative character modifications Verified text content preservation despite decorative character modifications Good — tests match on text content, not decorative characters. Let me package: Acknowledged feature requests and deferred implementation planning Acknowledged feature requests and deferred implementation planning 284/284, pure ASCII terminal output. Changes: Unicode purge: All ═ ─ ━ — → × ✓ ✗ ⚠ 🤖 replaced with ASCII equivalents = - = - -> x OK: ERROR: WARNING: [AI] across all source files. Comments left alone (they never display). LLM prompts also cleaned — visible with --show-prompt. Excluded extensions message: --exclude-extensions .txt,.json,.ts now prints Excluding extensions: .txt, .json, .ts during build. Good ideas noted but not implemented yet (as requested): comments-only as LLM input (strip //==== separator lines for LLM, keep for human); --comments-only as standalone command; function-level call-depth map (level 1 = main, level 2 = what main calls, etc.) as a codebase overview tool. All good TODO candidates. Code exam phase9c ascii TAR Testing term extraction with latest version (phase 9c ascii): C:\work\code_exam\Nodejs_port\code-exam>ce --index-path .CodeExam5 --claim-search @multisect_pseudo_claim.txt Loaded existing index: 28 files Vocabulary concordance: 2953 chars (rich format, claim-filtered) Extracting search terms from patent claim... ... shows WARNING: EXTERNAL NETWORK REQUEST ... OK: claude-sonnet-4-20250514 (2819 in / 115 out tokens, est. $0.0102) LLM response: TIGHT: search;/term|terms/;intersection;function;file;class;folder;subdirectory;location;multiple BROAD: search;/term|terms/;intersection;/function|func|method/;/file|source/;class;/folder|directory|dir/;/subdirectory|subdir/;location;multiple;/multisect|multi.sect/;/smallest|minimum|min/;/contain|within|inside/;/find|locate|match/ --- TIGHT search --- Phase 1: 4 survivor files (from 28 candidates) in 0.0s Phase 2: details for 4 files in 0.0s --- BROAD search --- Phase 1: 21 survivor files (from 28 candidates) in 0.0s Phase 2: details for 21 files in 0.0s I think this makes sense, given the following of the pseudo claim: Claim text (304 chars): A method of searching multiple search terms in code, in which the intersection of substantially all the terms is to be found in the smallest location possible: all within a single function, or among multiple functions in a single file or class, or among multiple files in a single folder or subdirectory. TIGHT: Term selectivity: [1] search 23 files [2] /term|terms/ 16 files [3] intersection 8 files [4] function 28 files [5] file 28 files [6] class 18 files [7] folder 14 files [8] subdirectory 1 files [9] location 7 files [10] multiple 14 files === FUNCTION-level matches (2 functions) === [1] checkCertExpiration (test/test_vocab_extract.js, 366 lines) [10/10] [4] L187 function checkCertExpiration(cert) { [5] L201 setupVocabTestFiles(); [1] L202 index = new CodeSearchIndex({ indexPath: INDEX_DIR }); [2,10] L360 const claim = A method of searching multiple search terms in code, [3] L361 in which the intersection of substantially all the terms is to be [9] L362 found in the smallest location possible: all within a single function, [6] L363 or among multiple functions in a single file or class, or among [7,8] L364 multiple files in a single folder or subdirectory.; Oh, got our own test code! Well that makes sense. [2] restoreRedirect (src/commands/interactive.js, 142 lines) [8/10] [4] L222 if (result && typeof result.then === 'function') { [1] L244 SEARCH: [2,5] L249 /files-search Files containing term, sorted by hit count [alias: /... [7] L250 /folders-search Folders containing term, sorted by hit count [alias: ... [6] L258 /extract Extract function source (Class.method or Class::method) [10] L259 /extract [N] Select from last multiple-match list [3] L284 MULTI-TERM INTERSECTION SEARCH: [8] -- not found -- [9] -- not found -- Yawn. By the way, probably don't bother with folder-level searches when doing an analyze cmd, since never going to pass an entire folder to LLM for analysis. (Okay, not for 1-2 years when LLMs will regularly be able to ingest this much.) And only do file-level searches for --analyze-file ? BROAD: Same top 2 functions, then printUsage, formatVocabularyForPrompt, displatch, _httpPost (!), finally as BROAD file [11] we get finally to: [11] displayMultisectResults (src/commands/multisect.js, 257 lines) [9/14] [4,5,11] L177 export function displayMultisectResults(results, args, totalFiles) { [2] L178 const terms = results.terms; [12] L182 const minT = results.min_terms; [1,3] L199 let label = Multi-term intersection search: ${posLabel}${notLabel}; [14] L210 note = fc ? ${fc} files : '** 0 files -- no matches **'; [7] L218 let folderMatches = results.folder_matches; [6] -- not found -- [8] -- not found -- [9] -- not found -- [10] -- not found -- [13] -- not found -- [14] doMultisectAnalyze (src/commands/analyze.js, 136 lines) [8/14] [4,11] L1313 export async function doMultisectAnalyze(index, args) { [2] L1314 const termsStr = args.multisect_analyze; [1] L1322 console.log('Need at least 2 positive (non-NOT) semicolon-separated search te... [14] L1323 console.log('Example: --multisect-analyze "allocate;free;buffer"'); [12] L1341 const minTermsRaw = args.min_terms || '0'; [5] L1376 // File-level fallback [13] L1384 console.log(`\nNo single function contains all terms, but file '${topFile.fil... [3] -- not found -- [6] -- not found -- [7] -- not found -- [8] -- not found -- [9] -- not found -- [10] -- not found -- [18] doClaimAnalyze (src/commands/analyze.js, 265 lines) [8/14] [4] L925 export async function doClaimAnalyze(index, args) { [2] L947 // Term extraction: --claim-model > --analyze-model > Claude API [5] L955 : (analyzeModel ? local: ${analyzeModel} : '(none ΓÇö will extract source onl... [1] L960 // --- Step 2: Extract search terms from claim --- [11] L1054 // --- Step 3: Run multisect search --- [12] L1067 // Min terms: user override or 80% threshold [14] L1086 if ((!results || !(results.function_matches || []).length) && broadStr) { [7] L1117 const folderMatches = results.folder_matches || []; [3] -- not found -- [6] -- not found -- [8] -- not found -- [9] -- not found -- [10] -- not found -- [13] -- not found -- So definitely finding correct code this way, but too far down for us to pass to analyze. Finds the right file, for sure: === FILE-level matches (13 files (8 suppressed - covered by function matches)) === [1] src/core/CodeSearchIndex.js (4107 lines) [13/14] [1] L2 in (global) * CodeSearchIndex.js - Core data structure for Code Exam. [4,5] L4 in (global) * Holds indexed source code and provides methods for: [6] L7 in (global) * - Function/class parsing (regex-based) [9] L16 in (global) import { forEachEntry, openJSONFile, closeSource, parseValue, countLocations,... [7] L24 in (global) * Directories to always skip during directory walks. [13] L25 in (global) * These contain third-party, build, or infrastructure files [12] L123 in _loadLiteralIndex // Use streaming parser for files > 400MB to avoid Node string limit [14] L172 in _loadLiteralIndexStreaming // First pass: find byte ranges of each top-level key [2] L644 in _parseFunctionsRegex // patterns, the outer function gets prematurely terminated. Detect this [10] L1298 in getFunctionSource console.log(Multiple versions of '${functionName}' found:); [11] L3539 in getTopVocabulary * 150 irrelevant terms about 'multisect' when the claim is about [3] L3743 in formatVocabularyForPrompt // Multi-term intersection search (#146 "scavenger hunt") [8] -- not found -- But given huge size of that file, hits less significant, and we're not going to be passing entire files to analyze, unless user asks for --analyze-file. Okay, how does local LLM do, when asked to extract terms? C:\work\code_exam\Nodejs_port\code-exam>ce --index-path .CodeExam5 --claim-search @multisect_pseudo_claim.txt --claim-model \work\ai_code_exam\qwen2.5-coder-7b-instruct-q4_k_m.gguf > claim_search_term_extraction_Qwen.txt Loaded existing index: 28 files Vocabulary concordance: 330 chars (compact format, claim-filtered) Extracting search terms from patent claim... Loading local model: \work\ai_code_exam\qwen2.5-coder-7b-instruct-q4_k_m.gguf... [node-llama-cpp] load: control-looking token: 128247 '' was not control-type; this is probably a bug in the model. its type will be overridden OK: Local model loaded (context: 4096 tokens). Sending to local model for term extraction... LLM response: TIGHT: search;terms;intersection;function;file;class;folder;subdirectory;smallest location;single function;multiple functions;single file;single class;multiple files;single folder;subdirectory;code;sub;locale;funcstring;locale;funcs;interactive BROAD: search;terms;intersection;function;file;class;folder;subdirectory;smallest location;single function;multiple functions;single file;single class;multiple files;single folder;subdirectory;code;sub;locale;funcstring;locale;funcs;interactive;call;calls;caller;callers;callees;classes;folders;dupefiles;int;multisect;local;funcstring;locale;funcs;interactive [sanitize-TIGHT] Dropped 3 degenerate term(s), trimmed 0 term(s), kept 20 [sanitize-BROAD] Dropped 18 degenerate term(s), trimmed 0 term(s), kept 20 --- TIGHT search --- Phase 1: 1 survivor files (from 28 candidates) in 0.0s Phase 2: details for 1 files in 0.0s --- BROAD search --- Phase 1: 2 survivor files (from 28 candidates) in 0.0s Phase 2: details for 2 files in 0.0s Okay, using terms extracted by Qwen, it found that same test file: === FUNCTION-level matches (1 functions) === [1] checkCertExpiration (test/test_vocab_extract.js, 366 lines) [18/20] [4] L187 function checkCertExpiration(cert) { [5] L201 setupVocabTestFiles(); [1,17] L202 index = new CodeSearchIndex({ indexPath: INDEX_DIR }); [18] L206 it('returns subTokens and functionNames', () => { [19] L314 buildLocalExtractionPromptWithVocab, [2] L360 const claim = `A method of searching multiple search terms in code, Actually, a very good sign. But I am going to remove test files from index. BROAD: same thing. One more time, this time with an index without those (very relevant to the search, but distracting) test files. C:\work\code_exam\Nodejs_port\code-exam>dir /s/b /a-d *.js | findstr /v node_modules | findstr /v test_ > CodeExam_our_js.lst C:\work\code_exam\Nodejs_port\code-exam>node src\index.js --index-path .CodeExam6 --build-index @CodeExam_our_js.lst C:\work\code_exam\Nodejs_port\code-exam>ce --index-path .CodeExam6 --claim-search @multisect_pseudo_claim.txt --claim-model \work\ai_code_exam\qwen2.5-coder-7b-instruct-q4_k_m.gguf > claim_search_term_extraction_Qwen_2.txt Loaded existing index: 16 files Vocabulary concordance: 326 chars (compact format, claim-filtered) Extracting search terms from patent claim... Loading local model: \work\ai_code_exam\qwen2.5-coder-7b-instruct-q4_k_m.gguf... ... LLM response: TIGHT: search;terms;function;file;class;subdirectory;intersection;location;smallest;single;multiple;folder;sub BROAD: search;terms;function;file;class;subdirectory;intersection;location;smallest;single;multiple;folder;sub;code;terms;files;classes;directories;subdirs;call;calls;caller;callers;func;funcs;funcstring;locale;interactive TIGHT: === FUNCTION-level matches (1 functions) === [1] restoreRedirect (commands/interactive.js, 142 lines) [10/13] [3] L222 if (result && typeof result.then === 'function') { [1] L244 SEARCH: [4] L249 /files-search Files containing term, sorted by hit count [alias: /... [12] L250 /folders-search Folders containing term, sorted by hit count [alias: ... [13] L251 /max Set max results for subsequent searches (default: 10) [5] L258 /extract Extract function source (Class.method or Class::method) [11] L259 /extract [N] Select from last multiple-match list [7] L284 MULTI-TERM INTERSECTION SEARCH: [2,9] L285 /multisect t1;t2;t3 Find smallest scope containing all terms (aliases: /ms... [6] -- not found -- [8] -- not found -- [10] -- not found -- BROAD: restoreRedirect, printUsage, formatVocabularyForPrompt I **think** we were doing better with term extraction before the recent vocabulary-use changes. Would it be easy to have a --use-vocabulary option to gate that ON and OFF, for testing? I want to move on from here now, but don't want to abandon use-vocabulary, I still think it's too good an idea, but leave it aside for now. Show more 12:33 PM Proposed vocabulary flag feature and identified encoding bug Proposed vocabulary flag feature and identified encoding bug Quick and clean. I'll add --no-vocabulary to suppress it (vocabulary ON by default, flag turns it OFF for A/B testing): Orchestrated vocabulary flag integration across claim and analyze modules Orchestrated vocabulary flag integration across claim and analyze modules Now wire it into claim.js: Noted identical approach needed for analyze.js file Noted identical approach needed for analyze.js file Same for analyze.js: Hunted down remaining Unicode encoding artifact in terminal output Hunted down remaining Unicode encoding artifact in terminal output Now fix that remaining mojibake the user spotted in doClaimAnalyze output. I see it was in line 955: Hunted for remaining non-ASCII encoding artifacts in output Hunted for remaining non-ASCII encoding artifacts in output That line looks clean already. Let me do a comprehensive final check: Verified code integrity and prepared testing procedures Verified code integrity and prepared testing procedures All clean. Run tests: Analyzed test results comparing LLM term extraction performance Analyzed test results comparing LLM term extraction performance 284/284. Added --no-vocabulary / --no-vocab flag. Now you can A/B test: ce --claim-search @claim.txt --claim-model qwen.gguf # with vocabulary ce --claim-search @claim.txt --claim-model qwen.gguf --no-vocab # without vocabulary Looking at your Qwen results, I see what happened. With vocabulary, Qwen produced terms like funcstring, locale, interactive — those are vocabulary words it sucked in from the concordance and dumped into BOTH tight and broad, even though they have zero connection to the multisect claim. That's the 7B model failing to follow the instruction "only select terms that connect to the patent claim concepts." It saw the vocabulary list and treated it as "bonus terms to include" rather than "a menu to pick from selectively." Claude handled this correctly — it only pulled multisect and multi.sect from the vocabulary into BROAD, which was exactly the right pickup. The 7B model doesn't have the reasoning capacity to distinguish "this vocabulary term relates to my claim" from "this vocabulary term exists." So for Qwen specifically, --no-vocab might actually produce better results than the vocabulary-augmented version. The vocabulary feature is designed for models smart enough to use it as a selective dictionary, not as a dumping ground. Worth testing both ways to confirm. Your test results are also a good argument for the claim-aware filtering we built — without it, Qwen would have gotten 150 vocabulary terms instead of ~20, and the pollution would have been even worse. Code exam phase9d novocab TAR We've learned a lot about term extraction, especially with local model. Despite problems, Qwen Coder is working so much better than DeepSeek Coder or CodeLlama did, so real progress even with the issues we're discussing. And there are some good new commands: --follow-calls, --deep, and --comments-only. I'm moving over to a new chat now. The first task will be implementing --build-index of zip/7z/tar/gz/etc. files. I don't think there's anything the new session needs to be told about this one. I will need to give it the latest code-exam-phase9d-novocab.tar I think. 12:46 PM Synthesized session progress and prepared transition framework Synthesized session progress and prepared transition framework Good stopping point. Solid session — here's a quick summary of everything that shipped: Vocabulary-guided extraction (Phase 8b) splitCompoundToken, getVocabularyForPrompt, formatVocabularyForPrompt Claim-aware filtering with extractClaimKeywords and patent stopword corpus _computeTokenRelevance (exact/substring/stem matching) --vocab-tight, --no-vocabulary for A/B testing PRESENT/ASSUMED/PARTIAL/ABSENT prompt buildClaimAnalyzePrompt and buildClaimFilePrompt updated New commands --follow-calls / --deep [N] — dump callee source recursively --comments-only — strip code, show only comments ("Comments can lie!") Both work in interactive mode and combine together Bug fixes / UX node_modules and infrastructure dirs auto-skipped (_SKIP_DIRS) Compound extension exclusion (.d.ts) All Unicode → ASCII for Windows cmd.exe --exclude-extensions reports what it excludes Interactive /extract parses flags properly Key learning: Vocabulary guidance helps Claude but hurts Qwen — 7B models treat the vocabulary list as a dumping ground rather than a selective menu. --no-vocab is the right default for local models until they're smarter. 284 tests, 0 failures. The phase9d tarball has everything. Good luck with archive indexing — that's a clean standalone feature, no context from this session needed. Claude is AI and can make mistakes. Please double-check responses. Vocabulary-guided patent claim term extraction - Claude