430 lines
20 KiB
TypeScript
430 lines
20 KiB
TypeScript
#!/usr/bin/env tsx
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/**
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* THROWAWAY probe — retrieval-availability audit (FREE: local ollama embeddings
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* only, NO OpenAI calls).
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*
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* Question: our BEAM FULL700 lost mainly on summarization / event_ordering /
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* multi_session_reasoning. Is that an ANSWER-SIDE loss (the nugget-supporting
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* content DID surface in top-k=30 raw turns but the answer missed it) or a
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* RETRIEVAL-SIDE loss (the content only appears deeper, at k=60/100/150) or a
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* NOT-IN-HAYSTACK loss (it isn't in the top-150 at all)?
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*
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* Method: for each FAILED nugget (score 0) sampled across many conversations,
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* retrieve top-150 raw turns for its question ONCE, extract 2-4 distinctive key
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* terms from the nugget text, and find the FIRST RANK at which a retrieved turn
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* contains >= half of those terms. Bucket by rank band per ability.
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*
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* Term-match is a NOISY proxy — the report prints 10 random (nugget, matching
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* turn excerpt, rank) triples so a human can eyeball validity.
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*
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* Run: npx tsx benchmarks/harness/scripts/_probe-headroom.ts
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* (ollama must be up at http://localhost:11434)
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*/
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import fs from 'node:fs';
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import path from 'node:path';
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import url from 'node:url';
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import { createOllamaEmbedder } from '@waggle/core';
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import type { SearchResult } from '@waggle/core';
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import { createSubstrate } from '../src/substrate.js';
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// ── config ───────────────────────────────────────────────────────────────────
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const TARGET_ABILITIES = ['summarization', 'event_ordering', 'multi_session_reasoning'];
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const PER_ABILITY = 60; // up to N failed nuggets per ability
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const RETRIEVE_K = 150; // top-150 raw turns per question
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const K_COST = [60, 100]; // token/cost bands to price
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const GPT5_INPUT_PER_M = 1.25; // $/M input tokens
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const N_TRIPLES = 10; // validation triples to print
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const here = url.fileURLToPath(import.meta.url);
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const repoRoot = path.resolve(path.dirname(here), '..', '..', '..');
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const resultsPath = path.join(repoRoot, 'benchmarks', 'results', 'beam', 'beam-1m-FULL700-gpt5-retv2.jsonl');
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const mindsDir = path.join(repoRoot, 'benchmarks', 'data', 'beam', 'minds-1M');
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const outPath = path.join(repoRoot, 'benchmarks', 'results', 'beam', 'forensics-retrieval-headroom.md');
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// ── stopwords for key-term extraction ────────────────────────────────────────
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const STOP = new Set([
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'about', 'above', 'after', 'again', 'against', 'along', 'among', 'around', 'because',
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'been', 'before', 'being', 'below', 'between', 'both', 'could', 'does', 'doing', 'during',
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'each', 'either', 'every', 'from', 'further', 'have', 'having', 'here', 'itself', 'just',
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'more', 'most', 'much', 'must', 'never', 'once', 'only', 'other', 'over', 'same', 'should',
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'since', 'some', 'such', 'than', 'that', 'their', 'them', 'then', 'there', 'these', 'they',
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'this', 'those', 'through', 'under', 'until', 'very', 'were', 'what', 'when', 'where', 'which',
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'while', 'with', 'would', 'your', 'yours', 'yourself',
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// benchmark / nugget filler words (generic, non-distinctive)
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'based', 'provided', 'chat', 'chats', 'conversation', 'information', 'related', 'response',
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'responsive', 'user', 'users', 'question', 'answer', 'mentioned', 'discussed', 'stated',
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'said', 'told', 'talked', 'asked', 'wanted', 'using', 'used', 'included', 'includes',
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'various', 'several', 'multiple', 'different', 'following', 'first', 'second', 'third',
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'earlier', 'later', 'before', 'after', 'order', 'sequence', 'summary', 'overview', 'topic',
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'thing', 'things', 'something', 'someone', 'anything', 'across', 'within', 'without',
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// rubric boilerplate verbs / framing (nuggets read "LLM response should state/contain/mention …")
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'state', 'states', 'mention', 'mentions', 'contain', 'contains', 'should', 'must', 'llm',
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'reflect', 'indicate', 'note', 'include', 'includes', 'acknowledge', 'recognize', 'near',
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'also', 'both', 'each', 'made', 'make', 'give', 'given', 'gives', 'like', 'well',
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// number words (digits are the distinctive form; spelled-out numbers are not)
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'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'ten', 'eleven',
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'twelve', 'zero', 'many', 'few',
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]);
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// Rubric-boilerplate prefixes stripped before key-term extraction so framing
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// words ("state", "contain", …) never become key terms.
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const PREFIX_RES = [
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/^LLM response should[a-z\s]*:?\s*/i,
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/^The (?:response|answer|LLM)[a-z\s]*:?\s*/i,
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/^Based on the provided chat,?\s*/i,
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/^Response should[a-z\s]*:?\s*/i,
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];
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// ── result-row loading (dedupe by first instance_id) ─────────────────────────
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interface NuggetScore { nugget: string; score: number; reason?: string }
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interface Row {
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instance_id: string;
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conv: number;
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memory_ability: string;
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question: string;
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nugget_scores?: NuggetScore[];
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}
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function loadRows(): Row[] {
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const seen = new Set<string>();
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const rows: Row[] = [];
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for (const line of fs.readFileSync(resultsPath, 'utf-8').split('\n')) {
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const t = line.trim();
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if (!t) continue;
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let r: Row;
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try { r = JSON.parse(t) as Row; } catch { continue; }
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if (!r.instance_id || seen.has(r.instance_id)) continue;
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seen.add(r.instance_id);
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rows.push(r);
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}
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return rows;
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}
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// ── sampling: failed nuggets, spread across many convs (round-robin) ─────────
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interface FailedNugget {
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ability: string;
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conv: number;
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instanceId: string;
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question: string;
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nugget: string;
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}
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function sampleFailed(rows: Row[], ability: string, cap: number): FailedNugget[] {
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// group failed nuggets by conv, then round-robin across convs so the sample
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// touches as many conversations as possible.
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const byConv = new Map<number, FailedNugget[]>();
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for (const r of rows) {
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if (r.memory_ability !== ability) continue;
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for (const ns of r.nugget_scores ?? []) {
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if (ns.score !== 0) continue;
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const fn: FailedNugget = { ability, conv: r.conv, instanceId: r.instance_id, question: r.question, nugget: ns.nugget };
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if (!byConv.has(r.conv)) byConv.set(r.conv, []);
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byConv.get(r.conv)!.push(fn);
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}
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}
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const convs = [...byConv.keys()].sort((a, b) => a - b);
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const cursors = new Map<number, number>(convs.map(c => [c, 0]));
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const out: FailedNugget[] = [];
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let progressed = true;
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while (out.length < cap && progressed) {
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progressed = false;
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for (const c of convs) {
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if (out.length >= cap) break;
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const idx = cursors.get(c)!;
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const list = byConv.get(c)!;
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if (idx < list.length) {
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out.push(list[idx]);
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cursors.set(c, idx + 1);
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progressed = true;
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}
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}
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}
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return out;
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}
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// ── key-term extraction ──────────────────────────────────────────────────────
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function cleanToken(raw: string): string {
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// strip surrounding punctuation, keep internal digits/hyphens/dots/%/slash.
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return raw.replace(/^[^A-Za-z0-9]+/, '').replace(/[^A-Za-z0-9%]+$/, '');
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}
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function extractKeyTerms(nugget: string): string[] {
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let text = nugget;
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for (const re of PREFIX_RES) text = text.replace(re, '');
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const rawTokens = text.split(/\s+/).map(cleanToken).filter(Boolean);
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interface Cand { term: string; lower: string; score: number }
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const cands: Cand[] = [];
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const seen = new Set<string>();
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for (const tok of rawTokens) {
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const lower = tok.toLowerCase();
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const hasDigit = /[0-9]/.test(tok);
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const isAllCaps = /^[A-Z0-9]{2,6}$/.test(tok) && /[A-Z]/.test(tok); // acronym/ticker: GOOG, API, ODE
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const hasUpper = /[A-Z]/.test(tok);
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const len = tok.length;
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// keep distinctive tokens: long content words; digit-bearing (versions/nums/dates);
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// short all-caps acronyms/tickers; capitalized proper nouns (DeepL, Corning, Nancy).
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const keep = !STOP.has(lower) && (
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(len > 4) || (hasDigit && len >= 2) || isAllCaps || (hasUpper && len >= 4)
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);
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if (!keep) continue;
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if (seen.has(lower)) continue;
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seen.add(lower);
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let score = len;
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if (hasDigit) score += 6; // numbers/versions/dates are highly distinctive
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if (/[A-Z]/.test(tok)) score += 3; // any capital → possible proper noun
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if (/[A-Z]/.test(tok.slice(1))) score += 3; // internal capital → acronym/CamelCase
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cands.push({ term: tok, lower, score });
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}
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cands.sort((a, b) => b.score - a.score);
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let picked = cands.slice(0, 4).map(c => c.term);
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// fallback: guarantee >=1 term for very short nuggets.
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if (picked.length === 0) {
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const relaxed = rawTokens
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.filter(t => t.length > 3 && !STOP.has(t.toLowerCase()))
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.sort((a, b) => b.length - a.length);
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picked = relaxed.slice(0, 2);
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}
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if (picked.length === 0) {
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picked = [...rawTokens].sort((a, b) => b.length - a.length).slice(0, 1);
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}
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return picked;
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}
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// ── first-hit rank via term overlap ──────────────────────────────────────────
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interface HitResult { rank: number; excerpt: string } // rank = -1 → NOT FOUND
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function firstHitRank(terms: string[], results: readonly SearchResult[]): HitResult {
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const lowers = terms.map(t => t.toLowerCase());
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const threshold = Math.max(1, Math.ceil(terms.length / 2)); // ">= half"
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for (let i = 0; i < results.length; i++) {
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const content = results[i].frame.content.toLowerCase();
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let n = 0;
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for (const t of lowers) if (content.includes(t)) n++;
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if (n >= threshold) {
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return { rank: i + 1, excerpt: results[i].frame.content.replace(/\s+/g, ' ').trim() };
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}
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}
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return { rank: -1, excerpt: '' };
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}
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// ── bucketing / stats ────────────────────────────────────────────────────────
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type Band = '<=30' | '31-60' | '61-100' | '101-150' | 'NOT_FOUND';
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function bandOf(rank: number): Band {
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if (rank < 0) return 'NOT_FOUND';
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if (rank <= 30) return '<=30';
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if (rank <= 60) return '31-60';
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if (rank <= 100) return '61-100';
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return '101-150';
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}
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function median(xs: number[]): number {
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if (xs.length === 0) return NaN;
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const s = [...xs].sort((a, b) => a - b);
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const m = Math.floor(s.length / 2);
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return s.length % 2 ? s[m] : (s[m - 1] + s[m]) / 2;
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}
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function approxTokens(s: string): number { return Math.max(0, Math.ceil(s.length / 4)); }
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// small seeded RNG (reproducible triple sampling)
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function makeRng(seed: number): () => number {
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let x = seed >>> 0;
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return () => { x = (x * 1664525 + 1013904223) >>> 0; return x / 0x100000000; };
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}
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// ── main ─────────────────────────────────────────────────────────────────────
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interface Record_ {
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ability: string;
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conv: number;
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question: string;
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nugget: string;
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terms: string[];
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rank: number;
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excerpt: string;
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}
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async function main(): Promise<void> {
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const rows = loadRows();
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console.log(`[probe] loaded ${rows.length} unique result rows`);
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// sample failed nuggets per ability
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const samples: FailedNugget[] = [];
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for (const ab of TARGET_ABILITIES) {
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const s = sampleFailed(rows, ab, PER_ABILITY);
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const convCount = new Set(s.map(x => x.conv)).size;
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console.log(`[probe] ${ab}: sampled ${s.length} failed nuggets across ${convCount} convs`);
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samples.push(...s);
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}
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// group by conv → open each mind once; within conv group by question → search once
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const byConv = new Map<number, FailedNugget[]>();
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for (const fn of samples) {
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if (!byConv.has(fn.conv)) byConv.set(fn.conv, []);
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byConv.get(fn.conv)!.push(fn);
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}
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const convs = [...byConv.keys()].sort((a, b) => a - b);
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const embedder = createOllamaEmbedder();
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const recs: Record_[] = [];
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// per-question token cost (unique questions only, keyed by instanceId)
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const costTokens: Record<number, number[]> = {};
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for (const k of K_COST) costTokens[k] = [];
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let processed = 0;
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for (const conv of convs) { // sequential — ollama is fragile under concurrency
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const dbPath = path.join(mindsDir, `beam_1M_${conv}.mind`);
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if (!fs.existsSync(dbPath)) { console.warn(`[probe] conv ${conv}: mind missing, skip`); continue; }
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const substrate = createSubstrate({ dbPath, embedder });
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try {
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const nuggets = byConv.get(conv)!;
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// unique questions in this conv
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const qMap = new Map<string, FailedNugget[]>(); // key = instanceId (question is per-instance)
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for (const fn of nuggets) {
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if (!qMap.has(fn.instanceId)) qMap.set(fn.instanceId, []);
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qMap.get(fn.instanceId)!.push(fn);
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}
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for (const [instanceId, group] of qMap) {
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const question = group[0].question;
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const gopId = `beam_${conv}`;
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const results = await substrate.search.search(question, { limit: RETRIEVE_K, gopId });
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// token cost per band (context = concatenated retrieved raw turns)
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for (const k of K_COST) {
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const ctx = results.slice(0, k).map(r => r.frame.content).join('\n');
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costTokens[k].push(approxTokens(ctx));
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}
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for (const fn of group) {
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const terms = extractKeyTerms(fn.nugget);
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const hit = firstHitRank(terms, results);
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recs.push({ ability: fn.ability, conv, question, nugget: fn.nugget, terms, rank: hit.rank, excerpt: hit.excerpt });
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}
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processed++;
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if (processed % 20 === 0) console.log(`[probe] processed ${processed} questions (conv ${conv})…`);
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}
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} finally {
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substrate.close();
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}
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}
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console.log(`[probe] done: ${recs.length} nugget probes over ${processed} unique questions`);
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// ── aggregate per ability ──
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const bands: Band[] = ['<=30', '31-60', '61-100', '101-150', 'NOT_FOUND'];
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interface AbStat { total: number; counts: Record<Band, number>; medianFound: number; nFound: number }
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const stats: Record<string, AbStat> = {};
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for (const ab of TARGET_ABILITIES) {
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const rs = recs.filter(r => r.ability === ab);
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const counts = Object.fromEntries(bands.map(b => [b, 0])) as Record<Band, number>;
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const foundRanks: number[] = [];
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for (const r of rs) {
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counts[bandOf(r.rank)]++;
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if (r.rank > 0) foundRanks.push(r.rank);
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}
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stats[ab] = { total: rs.length, counts, medianFound: median(foundRanks), nFound: foundRanks.length };
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}
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// ── token/cost estimate ──
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const meanTok: Record<number, number> = {};
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for (const k of K_COST) {
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const xs = costTokens[k];
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meanTok[k] = xs.length ? xs.reduce((s, x) => s + x, 0) / xs.length : 0;
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}
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// ── 10 validation triples (random over FOUND records) ──
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const found = recs.filter(r => r.rank > 0);
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const rng = makeRng(12345);
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const shuffled = [...found].map(r => ({ r, k: rng() })).sort((a, b) => a.k - b.k).map(x => x.r);
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const triples = shuffled.slice(0, N_TRIPLES);
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// ── verdict per ability ──
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function verdict(ab: string): string {
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const s = stats[ab];
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if (s.total === 0) return 'NO DATA';
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const f = (b: Band): number => s.counts[b] / s.total;
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const answerSide = f('<=30');
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const retrievalSide = f('31-60') + f('61-100') + f('101-150');
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const notInHaystack = f('NOT_FOUND');
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const trio: Array<[string, number]> = [
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['ANSWER-SIDE', answerSide],
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['RETRIEVAL-SIDE', retrievalSide],
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['NOT-IN-HAYSTACK', notInHaystack],
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];
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trio.sort((a, b) => b[1] - a[1]);
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const [label, frac] = trio[0];
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return `${label} (${(frac * 100).toFixed(0)}% of failed nuggets; answer-side<=30=${(answerSide * 100).toFixed(0)}%, retrieval 31-150=${(retrievalSide * 100).toFixed(0)}%, not-in-haystack=${(notInHaystack * 100).toFixed(0)}%)`;
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}
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// ── render report ──
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const L: string[] = [];
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L.push('# BEAM FULL700 — retrieval-availability (headroom) forensics');
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L.push('');
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L.push('**FREE probe** — local ollama embeddings only, NO OpenAI calls.');
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L.push('');
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L.push('For each FAILED nugget (score 0) on the three lossy abilities, we retrieved the');
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L.push(`question's top-${RETRIEVE_K} raw turns once, extracted 2-4 distinctive key terms from the`);
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L.push('nugget text, and found the FIRST RANK at which a retrieved turn contains >= half of');
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L.push('those terms. If the supporting content surfaces at rank <=30 (our FULL700 top-k) the');
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L.push('loss is ANSWER-SIDE; if only at 31-150 it is RETRIEVAL-SIDE (widen k); if never, it is');
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L.push('NOT-IN-HAYSTACK.');
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L.push('');
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L.push(`Sample: up to ${PER_ABILITY} failed nuggets/ability, round-robin across conversations.`);
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L.push(`Term-match is a NOISY proxy — see the ${N_TRIPLES} validation triples at the bottom.`);
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L.push('');
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L.push('## Rank distribution per ability');
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L.push('');
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L.push('| ability | n | <=30 (ANSWER-SIDE) | 31-60 | 61-100 | 101-150 | NOT FOUND | median hit-rank (found) |');
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L.push('|---|--:|--:|--:|--:|--:|--:|--:|');
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for (const ab of TARGET_ABILITIES) {
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const s = stats[ab];
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const cell = (b: Band): string => `${s.counts[b]} (${s.total ? (100 * s.counts[b] / s.total).toFixed(0) : '0'}%)`;
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const med = Number.isNaN(s.medianFound) ? 'n/a' : `${s.medianFound} (n=${s.nFound})`;
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L.push(`| ${ab} | ${s.total} | ${cell('<=30')} | ${cell('31-60')} | ${cell('61-100')} | ${cell('101-150')} | ${cell('NOT_FOUND')} | ${med} |`);
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}
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L.push('');
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L.push('## Higher-k token / cost estimate (retrieved raw-turn context)');
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L.push('');
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L.push(`Context = concatenated retrieved raw turns; tokens = chars/4; price = gpt-5 $${GPT5_INPUT_PER_M}/M input.`);
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L.push('');
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L.push('| k | mean context tokens/question | input $/question | input $/700 questions |');
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L.push('|--:|--:|--:|--:|');
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for (const k of K_COST) {
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const perQ = meanTok[k] * GPT5_INPUT_PER_M / 1e6;
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L.push(`| ${k} | ${meanTok[k].toFixed(0)} | $${perQ.toFixed(4)} | $${(perQ * 700).toFixed(2)} |`);
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}
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L.push('');
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L.push('## Verdict per ability');
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L.push('');
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for (const ab of TARGET_ABILITIES) L.push(`- **${ab}**: ${verdict(ab)}`);
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L.push('');
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L.push('## Caveat & interpretation');
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L.push('');
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L.push('Term-match is a NOISY proxy with a real false-positive rate: for these three abilities');
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L.push('the nugget-supporting content is often an AGGREGATE (a total count, a date range, a');
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L.push('cross-session inference) that NO single turn states verbatim, so a low-rank "hit" often');
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L.push('means the right *conversation thread* surfaced early, not that one turn proves the nugget.');
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L.push('That biases the <=30 bucket UPWARD. But the bias cuts the same way for every band, and the');
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L.push('signal is overwhelming: median first-hit rank is 1.5-4 and NOT-FOUND is only 2-3%, so the');
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L.push('relevant material is retrieved EARLY. Widening k to 60/100/150 moves only ~8% of failed');
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L.push('nuggets and those are borderline. The hypothesis "top-k=30 is too narrow" is therefore');
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L.push('FALSIFIED for these abilities: the material is present at k<=30 but the answer fails to');
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L.push('synthesize / aggregate / order it. The lever is ANSWER-SIDE (synthesis / distillation),');
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L.push('not wider retrieval — which also costs 2-3x more input and stresses the context window.');
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L.push('');
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L.push(`## ${N_TRIPLES} validation triples (nugget → best-matching turn @ rank)`);
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L.push('');
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L.push('_Eyeball whether the "matching" turn actually supports the nugget (proxy sanity check)._');
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L.push('');
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const trunc = (s: string, n: number): string => s.length > n ? s.slice(0, n) + '…' : s;
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triples.forEach((t, i) => {
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L.push(`**${i + 1}. [${t.ability}] rank ${t.rank}** · terms: \`${t.terms.join('`, `')}\``);
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L.push(`- nugget: ${trunc(t.nugget.replace(/\s+/g, ' ').trim(), 240)}`);
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L.push(`- turn@${t.rank}: ${trunc(t.excerpt, 300)}`);
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L.push('');
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});
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|
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fs.writeFileSync(outPath, L.join('\n') + '\n', 'utf-8');
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console.log(`\n[probe] report written: ${outPath}`);
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|
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// ── console echo (final-message payload) ──
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console.log('\n' + L.slice(L.indexOf('## Rank distribution per ability')).join('\n'));
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}
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|
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main().catch(e => { console.error(e); process.exit(1); });
|