diff --git a/scripts/parity_runner.js b/scripts/parity_runner.js index 168c906..5dda02c 100644 --- a/scripts/parity_runner.js +++ b/scripts/parity_runner.js @@ -9,6 +9,7 @@ const fs = require("fs"); const MSO = require("../web/mso-core.js"); const MSOEstimate = require("../web/mso-estimate.js"); +const MSO_Priors = require("../web/mso-priors.js"); const inPath = process.argv[2]; const outPath = process.argv[3]; @@ -59,4 +60,18 @@ out.risk = cases.risk.map((p) => MSO.rankNodesByRisk(p).map((r) => ({ fi: r.fan_in_degree, fo: r.fan_out_degree, bott: r.is_bottleneck, }))); +out.seed_node = (cases.seed_node || []).map((c) => { + const s = MSO_Priors.seedNode(c.model, c.task_type); + const p = s.provenance; + return { + seeds: s.seeds, + sigma_skill: s.sigma_skill != null ? s.sigma_skill : null, + catch_rate: s.catch_rate, + confidence: p.confidence, + band_low: p.band.low, + band_mid: p.band.mid, + band_high: p.band.high, + }; +}); + fs.writeFileSync(outPath, JSON.stringify(out)); diff --git a/src/minimal_oversight/data/priors.yaml b/src/minimal_oversight/data/priors.yaml new file mode 100644 index 0000000..25d4bb2 --- /dev/null +++ b/src/minimal_oversight/data/priors.yaml @@ -0,0 +1,1045 @@ +# priors.yaml — cold-start task-competence priors for MSO. +# +# Each entry estimates a (model x task_type) cell. For generator task-types the +# payload is `sigma_raw` (raw success probability BEFORE any review); for the +# `review` task-type the payload is `catch_rate` (how well the model catches an +# upstream node's errors). Both are LOW/MID/HIGH bands with provenance, never +# point estimates. +# +# THESE ARE PRIORS: starting hypotheses to seed a cockpit/notebook node when the +# user has no traces yet. They are meant to be refined by the user's own logged +# outcomes (see minimal_oversight.estimation). A wide band == weak evidence. +# +# Normalization rules baked into the curated values: +# - ABSOLUTE metrics (pass@1, accuracy, F1) ~ success probability -> sigma_raw +# directly, with a modest domain-shift discount (recorded in the note). +# - RELATIVE metrics (Arena Elo, win-rates) are RANKINGS, never inverted into +# sigma. They only rank/interpolate models whose scale is pinned by an +# absolute benchmark. metric_kind=relative cells always carry a wider band. +# - review/catch_rate is the SOFTEST column (little public evidence) -> widest +# bands; treat as a placeholder until the user logs corrected outcomes. +# +# Sources: +# LiveBench Coding: https://livebench.ai/livebench.pdf (June-2024, arXiv 2406.19314) +# JSONSchemaBench: https://arxiv.org/abs/2501.10868 (EPFL-DLab, Feb 2025) +# Vectara HHEM: https://github.com/vectara/hallucination-leaderboard +# FaithBench/RAGTruth: https://arxiv.org/abs/2505.04847 (EMNLP 2025) +# AlpacaEval LC: https://arxiv.org/abs/2404.04475 +# MT-Bench: https://arxiv.org/abs/2306.05685 +# RewardBench-2: https://arxiv.org/abs/2506.01937 +# CriticBench: https://arxiv.org/pdf/2402.14809 +# FaithJudge: https://arxiv.org/abs/2505.04847 + +meta: + schema_version: 1 + gamma: 0.8333333333333334 # return-operator fixed-point gain eta/(eta+delta)=10/12 + status: curated # placeholder -> curated after research pass (2026-06-17) + observed_date: "2026-06-17" # date this table was compiled; check for staleness + generator_default_catch_rate: 0.0 # a generator does not correct its parent + +task_types: + - code_generation + - classification + - extraction + - drafting + - retrieval + - review # catch-rate proxy + +# ---- CODE GENERATION ---------------------------------------------------------- +# Anchor: LiveBench Coding (contamination-limited, ground-truth scored, no LLM +# judge, LCB-anchored pass-style). Scores in [0,100]; mapped to sigma_raw with +# ~10% domain-shift discount and widened bands. Frontier 2026 models (Opus/ +# Sonnet 4.x, o-series, Gemini 2.x) lack clean 2026 absolute numbers and are +# interpolated up from June-2024 snapshots via relative leaderboards — flagged. +# HumanEval cross-check: confirms small-model HumanEval inflates (~87% → SWE- +# bench-Verified ~23%), so bands are wide to capture both saturation cases. +# Source: https://livebench.ai/livebench.pdf (Table 1, June 2024) +priors: + - model: claude-opus-4 + task_type: code_generation + sigma_raw: { low: 0.62, mid: 0.74, high: 0.86 } + primary_benchmark: LiveBench-Coding + metric: pass@1 (LCB-style, ground-truth) + metric_kind: absolute + reported_value: "63.2 (claude-3.5-sonnet anchor) + upward interpolation" + sample_size: "~500 problems (LiveCodeBench)" + observed_date: "2024-06" + source_url: https://livebench.ai/livebench.pdf + normalization_note: interpolated above claude-3.5-sonnet 63.2 via relative leaderboard; 10% domain-shift discount; wide band + + - model: claude-sonnet-4 + task_type: code_generation + sigma_raw: { low: 0.58, mid: 0.70, high: 0.83 } + primary_benchmark: LiveBench-Coding + metric: pass@1 (LCB-style, ground-truth) + metric_kind: absolute + reported_value: "63.2 (claude-3.5-sonnet best snapshot)" + sample_size: "~500 problems (LiveCodeBench)" + observed_date: "2024-06" + source_url: https://livebench.ai/livebench.pdf + normalization_note: anchored to claude-3.5-sonnet 63.2; 10% domain-shift discount; Sonnet-4 interpolated near that anchor + + - model: claude-haiku-4 + task_type: code_generation + sigma_raw: { low: 0.30, mid: 0.45, high: 0.62 } + primary_benchmark: LiveBench-Coding + metric: pass@1 (LCB-style, ground-truth) + metric_kind: absolute + reported_value: "24.5 (claude-3-haiku LiveBench); HumanEval 75.9 (saturated)" + sample_size: "~500 problems" + observed_date: "2024-06" + source_url: https://livebench.ai/livebench.pdf + normalization_note: anchored claude-3-haiku 24.5; HumanEval 75.9 inflates vs SWE-bench ~23%; wide band captures both + + - model: gpt-4o + task_type: code_generation + sigma_raw: { low: 0.40, mid: 0.50, high: 0.62 } + primary_benchmark: LiveBench-Coding + metric: pass@1 (LCB-style, ground-truth) + metric_kind: absolute + reported_value: "46.4 (gpt-4o Jun-2024)" + sample_size: "~500 problems" + observed_date: "2024-06" + source_url: https://livebench.ai/livebench.pdf + normalization_note: direct read from Table 1; 10% domain-shift discount; model may have since improved + + - model: gpt-4o-mini + task_type: code_generation + sigma_raw: { low: 0.32, mid: 0.46, high: 0.62 } + primary_benchmark: LiveBench-Coding + HumanEval cross-check + metric: pass@1 + metric_kind: absolute + reported_value: "HumanEval 87.2% (vendor); SWE-bench-Verified ~23% (harder); LiveBench interpolated" + sample_size: null + observed_date: "2024-07" + source_url: https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/ + normalization_note: HumanEval 87.2 inflates vs SWE-bench-Verified ~23%; mid splits the difference; wide band + + - model: openai-o3 + task_type: code_generation + sigma_raw: { low: 0.55, mid: 0.68, high: 0.84 } + primary_benchmark: LiveBench-Coding (interpolated) + metric: pass@1 (interpolated via relative leaderboard) + metric_kind: relative + reported_value: "no clean 2026 absolute snapshot; interpolated as reasoning-leader" + sample_size: null + observed_date: "2025-12" + source_url: https://livebench.ai/livebench.pdf + normalization_note: relative-only interpolation; reasoning models excel on coding; wide band mandatory + + - model: gemini-2-pro + task_type: code_generation + sigma_raw: { low: 0.45, mid: 0.58, high: 0.74 } + primary_benchmark: LiveBench-Coding + metric: pass@1 (LCB-style, ground-truth) + metric_kind: absolute + reported_value: "32.8 (gemini-1.5-pro anchor) + upward interpolation" + sample_size: "~500 problems" + observed_date: "2024-06" + source_url: https://livebench.ai/livebench.pdf + normalization_note: interpolated above gemini-1.5-pro 32.8; Gemini 2.x generation boost assumed modest + + - model: gemini-2-flash + task_type: code_generation + sigma_raw: { low: 0.35, mid: 0.48, high: 0.62 } + primary_benchmark: LiveBench-Coding + metric: pass@1 (LCB-style, ground-truth) + metric_kind: absolute + reported_value: "39.1 (gemini-1.5-flash Jun-2024)" + sample_size: "~500 problems" + observed_date: "2024-06" + source_url: https://livebench.ai/livebench.pdf + normalization_note: anchored to gemini-1.5-flash 39.1; 2.0/2.5 Flash improvements likely push mid up; widen + + - model: llama-3.3-70b + task_type: code_generation + sigma_raw: { low: 0.35, mid: 0.48, high: 0.62 } + primary_benchmark: LiveBench-Coding (interpolated) + metric: pass@1 (interpolated) + metric_kind: relative + reported_value: "interpolated; no clean LiveBench row for llama-3.3-70b" + sample_size: null + observed_date: "2024-12" + source_url: https://livebench.ai/livebench.pdf + normalization_note: relative-only interpolation from model-class ranking; wide band + + - model: deepseek-v3 + task_type: code_generation + sigma_raw: { low: 0.42, mid: 0.55, high: 0.70 } + primary_benchmark: LiveBench-Coding + metric: pass@1 (LCB-style) + metric_kind: absolute + reported_value: "41.1 (deepseek-coder-v2) + upward interpolation for V3" + sample_size: "~500 problems" + observed_date: "2024-06" + source_url: https://livebench.ai/livebench.pdf + normalization_note: anchored deepseek-coder-v2 41.1; V3 and R1 interpolated up; coding-focused model widens high end + + - model: deepseek-r1 + task_type: code_generation + sigma_raw: { low: 0.48, mid: 0.62, high: 0.78 } + primary_benchmark: LiveBench-Coding (interpolated) + metric: pass@1 (interpolated via reasoning premium) + metric_kind: relative + reported_value: "deepseek-coder-v2 41.1 + reasoning-model premium interpolation" + sample_size: null + observed_date: "2025-01" + source_url: https://livebench.ai/livebench.pdf + normalization_note: R1 reasoning premium likely lifts coding above V3 base; no clean 2025 absolute snapshot + + - model: qwen2.5-72b + task_type: code_generation + sigma_raw: { low: 0.32, mid: 0.45, high: 0.60 } + primary_benchmark: LiveBench-Coding + metric: pass@1 (LCB-style) + metric_kind: absolute + reported_value: "31.8 (qwen2-72b Jun-2024)" + sample_size: "~500 problems" + observed_date: "2024-06" + source_url: https://livebench.ai/livebench.pdf + normalization_note: anchored qwen2-72b 31.8; Qwen2.5 improvements likely push mid up modestly + + - model: mistral-large + task_type: code_generation + sigma_raw: { low: 0.32, mid: 0.45, high: 0.60 } + primary_benchmark: LiveBench-Coding (interpolated) + metric: pass@1 (interpolated) + metric_kind: relative + reported_value: "no clean LiveBench row; interpolated from class ranking" + sample_size: null + observed_date: "2024-12" + source_url: https://livebench.ai/livebench.pdf + normalization_note: relative-only interpolation; no direct LiveBench measurement found + +# ---- CLASSIFICATION / ROUTING / INTENT ---------------------------------------- +# CAUTION: NO surviving absolute benchmark in the verified set. The MMLU vendor +# proxy (GPT-4o-mini 82.0%) was REFUTED 2-1 (vendor-optimism + contamination). +# Cells below are conservative interpolations from coding/faithfulness model +# orderings. This is the second-softest task-type after review/critique. +# Refine from traces immediately — do not trust these numbers past 5 observations. +# Source: derived conservatively; see research caveats + + - model: claude-opus-4 + task_type: classification + sigma_raw: { low: 0.85, mid: 0.92, high: 0.97 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; frontier tier well-specified task" + sample_size: null + observed_date: null + source_url: null + normalization_note: NO absolute benchmark survived verification; frontier tier conservative interpolation; refine immediately + + - model: claude-sonnet-4 + task_type: classification + sigma_raw: { low: 0.78, mid: 0.87, high: 0.94 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; mid tier well-specified task" + sample_size: null + observed_date: null + source_url: null + normalization_note: NO absolute benchmark survived verification; mid tier conservative interpolation; refine immediately + + - model: claude-haiku-4 + task_type: classification + sigma_raw: { low: 0.70, mid: 0.82, high: 0.92 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; small tier well-specified task" + sample_size: null + observed_date: null + source_url: null + normalization_note: NO absolute benchmark survived verification; small tier conservative interpolation; refine immediately + + - model: gpt-4o + task_type: classification + sigma_raw: { low: 0.78, mid: 0.87, high: 0.94 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; mid tier well-specified task" + sample_size: null + observed_date: null + source_url: null + normalization_note: MMLU vendor proxy refuted; mid tier conservative interpolation; wide band + + - model: gpt-4o-mini + task_type: classification + sigma_raw: { low: 0.70, mid: 0.82, high: 0.92 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; small tier" + sample_size: null + observed_date: null + source_url: null + normalization_note: MMLU vendor proxy refuted; small tier conservative interpolation + + - model: openai-o3 + task_type: classification + sigma_raw: { low: 0.85, mid: 0.92, high: 0.97 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; frontier tier" + sample_size: null + observed_date: null + source_url: null + normalization_note: reasoning premium assumed; frontier tier conservative interpolation; refine immediately + + - model: gemini-2-pro + task_type: classification + sigma_raw: { low: 0.78, mid: 0.87, high: 0.94 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; mid-to-frontier tier" + sample_size: null + observed_date: null + source_url: null + normalization_note: NO absolute benchmark survived verification; mid tier conservative interpolation + + - model: gemini-2-flash + task_type: classification + sigma_raw: { low: 0.75, mid: 0.85, high: 0.93 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; mid tier Flash" + sample_size: null + observed_date: null + source_url: null + normalization_note: NO absolute benchmark survived verification; mid tier conservative interpolation + + - model: llama-3.3-70b + task_type: classification + sigma_raw: { low: 0.75, mid: 0.85, high: 0.93 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; mid tier" + sample_size: null + observed_date: null + source_url: null + normalization_note: NO absolute benchmark survived verification; mid tier conservative interpolation + + - model: deepseek-v3 + task_type: classification + sigma_raw: { low: 0.78, mid: 0.87, high: 0.94 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; mid tier" + sample_size: null + observed_date: null + source_url: null + normalization_note: NO absolute benchmark survived verification; mid tier conservative interpolation + + - model: deepseek-r1 + task_type: classification + sigma_raw: { low: 0.78, mid: 0.87, high: 0.94 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; reasoning premium uncertain for routing" + sample_size: null + observed_date: null + source_url: null + normalization_note: NO absolute benchmark survived verification; mid tier; reasoning premium unclear for classification + + - model: qwen2.5-72b + task_type: classification + sigma_raw: { low: 0.75, mid: 0.85, high: 0.93 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; mid tier" + sample_size: null + observed_date: null + source_url: null + normalization_note: NO absolute benchmark survived verification; mid tier conservative interpolation + + - model: mistral-large + task_type: classification + sigma_raw: { low: 0.75, mid: 0.85, high: 0.93 } + primary_benchmark: NONE (conservative interpolation) + metric: accuracy (estimated) + metric_kind: relative + reported_value: "no reliable public absolute benchmark; mid tier" + sample_size: null + observed_date: null + source_url: null + normalization_note: NO absolute benchmark survived verification; mid tier conservative interpolation + +# ---- EXTRACTION / STRUCTURED OUTPUT ------------------------------------------- +# Anchor: JSONSchemaBench (EPFL-DLab, Feb 2025, arXiv 2501.10868). +# CRITICAL: dominated by schema-difficulty x constrained-decoding, NOT model +# family. Rows below assume MODERATE schema (most common real-world case) with +# PLAIN prompting (no constrained decoding). For constrained-decoding APIs, +# raise mid by +0.10 to +0.20. For hard/deeply-nested schemas, lower low by -0.30. +# Source: https://arxiv.org/abs/2501.10868 (Table 4, Feb 2025) + + - model: claude-opus-4 + task_type: extraction + sigma_raw: { low: 0.82, mid: 0.91, high: 0.98 } + primary_benchmark: JSONSchemaBench + metric: schema-compliance rate (plain prompting, moderate schema) + metric_kind: absolute + reported_value: "frontier tier: GlaiveAI ~0.97-1.00 (constrained); plain moderate ~0.85-0.95" + sample_size: "1707-1943 schemas per dataset" + observed_date: "2025-02" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: moderate schema, plain prompting; frontier tier; schema difficulty dominates; wide band + + - model: claude-sonnet-4 + task_type: extraction + sigma_raw: { low: 0.75, mid: 0.87, high: 0.96 } + primary_benchmark: JSONSchemaBench + metric: schema-compliance rate (plain prompting, moderate schema) + metric_kind: absolute + reported_value: "mid tier: moderate schema compliance ~0.80-0.93" + sample_size: "1707-1943 schemas per dataset" + observed_date: "2025-02" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: moderate schema, plain prompting; mid tier; wide band to capture difficulty variance + + - model: claude-haiku-4 + task_type: extraction + sigma_raw: { low: 0.50, mid: 0.70, high: 0.88 } + primary_benchmark: JSONSchemaBench + metric: schema-compliance rate (plain prompting, moderate schema) + metric_kind: absolute + reported_value: "small tier: 1B-class floor ~0.13 hard, ~0.65 easy (Llama-3.2-1B)" + sample_size: "1707-1943 schemas per dataset" + observed_date: "2025-02" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: small/efficient tier; Llama-1B floor; Haiku expected above floor but degrades on hard schemas + + - model: gpt-4o + task_type: extraction + sigma_raw: { low: 0.80, mid: 0.91, high: 0.98 } + primary_benchmark: JSONSchemaBench + metric: schema-compliance rate (constrained-decoding, moderate schema) + metric_kind: absolute + reported_value: "OpenAI constrained GlaiveAI 1.00, GitHub-Easy 0.97, GitHub-Hard 1.00 (but low coverage on hard)" + sample_size: "1707-1943 schemas per dataset" + observed_date: "2025-02" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: OpenAI JSON-mode raises compliance on accepted schemas; coverage drops on hard schemas; moderate schema default + + - model: gpt-4o-mini + task_type: extraction + sigma_raw: { low: 0.65, mid: 0.80, high: 0.93 } + primary_benchmark: JSONSchemaBench + metric: schema-compliance rate (moderate schema) + metric_kind: absolute + reported_value: "mid tier small model; JSON-mode helps on accepted schemas" + sample_size: "1707-1943 schemas per dataset" + observed_date: "2025-02" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: smaller model; JSON-mode available but coverage limited; wide band + + - model: openai-o3 + task_type: extraction + sigma_raw: { low: 0.82, mid: 0.92, high: 0.99 } + primary_benchmark: JSONSchemaBench (interpolated) + metric: schema-compliance rate (frontier reasoning) + metric_kind: absolute + reported_value: "interpolated; structured-output APIs + reasoning likely near-ceiling on moderate schemas" + sample_size: null + observed_date: "2025-12" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: frontier reasoning model; constrained API expected; interpolated upward; wide band + + - model: gemini-2-pro + task_type: extraction + sigma_raw: { low: 0.80, mid: 0.91, high: 0.98 } + primary_benchmark: JSONSchemaBench + metric: schema-compliance rate (constrained decoding, moderate schema) + metric_kind: absolute + reported_value: "Gemini constrained GlaiveAI 1.00, GitHub-Hard 1.00 (but low coverage on hard)" + sample_size: "1707-1943 schemas per dataset" + observed_date: "2025-02" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: Gemini constrained-mode matches OpenAI on accepted schemas; coverage limited on hard schemas + + - model: gemini-2-flash + task_type: extraction + sigma_raw: { low: 0.72, mid: 0.85, high: 0.95 } + primary_benchmark: JSONSchemaBench + metric: schema-compliance rate (moderate schema) + metric_kind: absolute + reported_value: "mid tier Flash; constrained-mode available but less than Pro" + sample_size: "1707-1943 schemas per dataset" + observed_date: "2025-02" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: mid tier; constrained output may vary; moderate schema assumed + + - model: llama-3.3-70b + task_type: extraction + sigma_raw: { low: 0.65, mid: 0.80, high: 0.93 } + primary_benchmark: JSONSchemaBench + metric: schema-compliance rate (plain prompting, moderate schema) + metric_kind: absolute + reported_value: "mid tier open model; no constrained-decode API by default" + sample_size: "1707-1943 schemas per dataset" + observed_date: "2025-02" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: mid tier open model; plain prompting assumed; degrades on hard schemas + + - model: deepseek-v3 + task_type: extraction + sigma_raw: { low: 0.72, mid: 0.85, high: 0.95 } + primary_benchmark: JSONSchemaBench (interpolated) + metric: schema-compliance rate (moderate schema) + metric_kind: absolute + reported_value: "interpolated; strong instruction-following model" + sample_size: null + observed_date: "2025-01" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: interpolated mid-to-frontier tier; V3 strong at structured output + + - model: deepseek-r1 + task_type: extraction + sigma_raw: { low: 0.65, mid: 0.80, high: 0.93 } + primary_benchmark: JSONSchemaBench (interpolated) + metric: schema-compliance rate (moderate schema) + metric_kind: relative + reported_value: "interpolated; reasoning models sometimes over-think simple extraction" + sample_size: null + observed_date: "2025-01" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: reasoning overhead may reduce extraction accuracy; conservative mid-tier interpolation + + - model: qwen2.5-72b + task_type: extraction + sigma_raw: { low: 0.65, mid: 0.80, high: 0.93 } + primary_benchmark: JSONSchemaBench (interpolated) + metric: schema-compliance rate (moderate schema) + metric_kind: absolute + reported_value: "interpolated; mid tier" + sample_size: null + observed_date: "2024-12" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: mid tier; Qwen2.5 strong at structured output but no direct JSONSchemaBench row + + - model: mistral-large + task_type: extraction + sigma_raw: { low: 0.65, mid: 0.80, high: 0.93 } + primary_benchmark: JSONSchemaBench (interpolated) + metric: schema-compliance rate (moderate schema) + metric_kind: relative + reported_value: "interpolated; no direct JSONSchemaBench row found" + sample_size: null + observed_date: "2024-12" + source_url: https://arxiv.org/abs/2501.10868 + normalization_note: relative-only interpolation; mid tier; wide band + +# ---- DRAFTING / OPEN GENERATION / SUMMARIZATION -------------------------------- +# NO clean absolute per-step success benchmark for open generation. +# Faithfulness sub-dimension: seeded from Vectara HHEM (absolute faithfulness rate +# for grounded summarization). Style/instruction-following quality is RELATIVE-ONLY +# (AlpacaEval, Arena Elo) and is NOT inverted to sigma — bands below reflect +# faithfulness-subdimension only. Flag to user that IQ/style quality is unquantified. +# Source: Vectara HHEM (https://github.com/vectara/hallucination-leaderboard) + +# FaithBench/RAGTruth (https://arxiv.org/abs/2505.04847) + + - model: claude-opus-4 + task_type: drafting + sigma_raw: { low: 0.83, mid: 0.89, high: 0.95 } + primary_benchmark: Vectara-HHEM + FaithBench/RAGTruth (faithfulness subdim) + metric: factual-consistency rate (faithfulness proxy for drafting) + metric_kind: absolute + reported_value: "claude-3.7-sonnet 83.95% faithfulness; Opus interpolated slightly above" + sample_size: "511 human-annotated items (FaithBench); 7700+ docs (HHEM)" + observed_date: "2025-05" + source_url: https://github.com/vectara/hallucination-leaderboard + normalization_note: faithfulness subdim ONLY; style/IF quality is relative-only (AlpacaEval/Arena) and NOT captured here + + - model: claude-sonnet-4 + task_type: drafting + sigma_raw: { low: 0.80, mid: 0.87, high: 0.93 } + primary_benchmark: Vectara-HHEM + FaithBench/RAGTruth + metric: factual-consistency rate (faithfulness proxy) + metric_kind: absolute + reported_value: "claude-3.7-sonnet 83.95% (~16% hallu per FaithBench); Sonnet-4.5 >10% hallu per Vectara next-gen" + sample_size: "511 human-annotated items" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: Sonnet-4.5 >10% hallucination rate per Vectara next-gen; faithfulness subdim only + + - model: claude-haiku-4 + task_type: drafting + sigma_raw: { low: 0.75, mid: 0.83, high: 0.91 } + primary_benchmark: Vectara-HHEM (faithfulness proxy, interpolated) + metric: factual-consistency rate (faithfulness proxy) + metric_kind: relative + reported_value: "interpolated from smaller model position; no direct Haiku-4 row" + sample_size: null + observed_date: "2025-05" + source_url: https://github.com/vectara/hallucination-leaderboard + normalization_note: interpolated below Sonnet faithfulness; faithfulness subdim only; style quality relative-only + + - model: gpt-4o + task_type: drafting + sigma_raw: { low: 0.78, mid: 0.85, high: 0.91 } + primary_benchmark: FaithBench/RAGTruth + metric: factual-consistency rate (faithfulness proxy) + metric_kind: absolute + reported_value: "gpt-4o 15.85% hallu rate per FaithBench → faithfulness ~84.15%" + sample_size: "511 human-annotated items" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: direct FaithBench measurement; faithfulness subdim only; style relative-only + + - model: gpt-4o-mini + task_type: drafting + sigma_raw: { low: 0.72, mid: 0.81, high: 0.90 } + primary_benchmark: Vectara-HHEM (interpolated) + metric: factual-consistency rate (faithfulness proxy) + metric_kind: relative + reported_value: "interpolated below GPT-4o; small-model faithfulness penalty" + sample_size: null + observed_date: "2025-05" + source_url: https://github.com/vectara/hallucination-leaderboard + normalization_note: interpolated small-tier; faithfulness subdim only; refine quickly + + - model: openai-o3 + task_type: drafting + sigma_raw: { low: 0.80, mid: 0.88, high: 0.94 } + primary_benchmark: Vectara next-gen (interpolated) + metric: factual-consistency rate (faithfulness proxy) + metric_kind: relative + reported_value: "reasoning flagships >10% hallu per Vectara next-gen; o3 likely near frontier" + sample_size: null + observed_date: "2026-01" + source_url: https://www.vectara.com/blog/introducing-the-next-generation-of-vectaras-hallucination-leaderboard + normalization_note: reasoning flagships WORSE than Flash on faithfulness; faithfulness subdim only + + - model: gemini-2-pro + task_type: drafting + sigma_raw: { low: 0.87, mid: 0.93, high: 0.97 } + primary_benchmark: FaithBench/RAGTruth + metric: factual-consistency rate (faithfulness proxy) + metric_kind: absolute + reported_value: "Gemini-2.5-Pro 6.65% hallu rate → faithfulness 93.35% (best in set)" + sample_size: "511 human-annotated items" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: best-in-class faithfulness per FaithBench; faithfulness subdim only; style quality relative-only + + - model: gemini-2-flash + task_type: drafting + sigma_raw: { low: 0.88, mid: 0.93, high: 0.97 } + primary_benchmark: Vectara-HHEM + metric: factual-consistency rate (faithfulness proxy) + metric_kind: absolute + reported_value: "gemini-2.5-flash-lite 96.7% faithfulness (Vectara next-gen, best overall)" + sample_size: "7700+ documents" + observed_date: "2026-05" + source_url: https://github.com/vectara/hallucination-leaderboard + normalization_note: Flash-class tops HHEM faithfulness leaderboard; faithfulness subdim only; note HHEM is Vectara's own judge + + - model: llama-3.3-70b + task_type: drafting + sigma_raw: { low: 0.78, mid: 0.84, high: 0.90 } + primary_benchmark: FaithBench/RAGTruth + metric: factual-consistency rate (faithfulness proxy) + metric_kind: absolute + reported_value: "llama-3.3-70b 16.44% hallu rate → faithfulness ~83.56%" + sample_size: "511 human-annotated items" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: direct FaithBench measurement; faithfulness subdim only; style relative-only + + - model: deepseek-v3 + task_type: drafting + sigma_raw: { low: 0.88, mid: 0.93, high: 0.97 } + primary_benchmark: Vectara-HHEM + DeepSeek blog + metric: factual-consistency rate (faithfulness proxy) + metric_kind: absolute + reported_value: "V3 3.9-6.1% hallu rate (HHEM-2.1 / HHEM-2.3) → faithfulness ~94-96%" + sample_size: "7700+ documents" + observed_date: "2026-01" + source_url: https://www.vectara.com/blog/deepseek-r1-hallucinates-more-than-deepseek-v3 + normalization_note: V3 is notably less hallucinatory than R1; one of the best base-model faithfulness rates + + - model: deepseek-r1 + task_type: drafting + sigma_raw: { low: 0.80, mid: 0.87, high: 0.91 } + primary_benchmark: Vectara-HHEM + DeepSeek blog + metric: factual-consistency rate (faithfulness proxy) + metric_kind: absolute + reported_value: "R1 14.3/11.3% hallu rate (HHEM-2.1/2.3) vs V3 3.9/6.1% — reasoning PENALTY" + sample_size: "7700+ documents" + observed_date: "2026-01" + source_url: https://www.vectara.com/blog/deepseek-r1-hallucinates-more-than-deepseek-v3 + normalization_note: REASONING PENALTY confirmed; R1 substantially worse than V3 on faithfulness; R1-0528 narrowed gap ~45-50% — widen + + - model: qwen2.5-72b + task_type: drafting + sigma_raw: { low: 0.78, mid: 0.85, high: 0.92 } + primary_benchmark: Vectara-HHEM (interpolated) + metric: factual-consistency rate (faithfulness proxy) + metric_kind: relative + reported_value: "interpolated mid tier; no direct Qwen2.5 row in verified sources" + sample_size: null + observed_date: "2024-12" + source_url: https://github.com/vectara/hallucination-leaderboard + normalization_note: interpolated; mid tier faithfulness; wide band + + - model: mistral-large + task_type: drafting + sigma_raw: { low: 0.78, mid: 0.85, high: 0.92 } + primary_benchmark: Vectara-HHEM (interpolated) + metric: factual-consistency rate (faithfulness proxy) + metric_kind: relative + reported_value: "interpolated mid tier; no direct Mistral-Large row in verified sources" + sample_size: null + observed_date: "2024-12" + source_url: https://github.com/vectara/hallucination-leaderboard + normalization_note: interpolated; mid tier faithfulness; wide band + +# ---- RETRIEVAL / RAG-ANSWER CORRECTNESS ---------------------------------------- +# Proxy: Vectara HHEM (grounded-summarization faithfulness) + FaithBench/RAGTruth. +# NOTE: this measures FAITHFULNESS (groundedness), not full end-to-end RAG answer- +# correctness. The two can diverge — DeepSeek-R1 scores low on faithfulness but +# hits 86% clinical accuracy in RAG pipelines. Flag this proxy gap to users. +# HHEM is Vectara's own commercial judge (~82% accuracy); rate conditioned on +# answer-rate (high-refusal models look artificially good). +# Source: https://github.com/vectara/hallucination-leaderboard (updated 2026-05-11) +# https://arxiv.org/abs/2505.04847 (FaithBench Table 7) + + - model: claude-opus-4 + task_type: retrieval + sigma_raw: { low: 0.83, mid: 0.89, high: 0.94 } + primary_benchmark: Vectara-HHEM + FaithBench/RAGTruth + metric: factual-consistency / faithfulness rate (PROXY for RAG correctness) + metric_kind: absolute + reported_value: "claude-3.7-sonnet ~84%; Opus-4 interpolated slightly above" + sample_size: "511 items (FaithBench); 7700+ docs (HHEM)" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: faithfulness PROXY — not full RAG answer-correctness; interpolated above Sonnet anchor + + - model: claude-sonnet-4 + task_type: retrieval + sigma_raw: { low: 0.80, mid: 0.87, high: 0.93 } + primary_benchmark: FaithBench/RAGTruth + metric: factual-consistency rate (faithfulness proxy for RAG) + metric_kind: absolute + reported_value: "claude-3.7-sonnet 83.95% (~16.05% hallu); Sonnet-4.5 confirmed >10% hallu" + sample_size: "511 human-annotated items" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: direct FaithBench measurement; faithfulness PROXY only; Sonnet-4.5 confirmed >10% hallu — use low end of band + + - model: claude-haiku-4 + task_type: retrieval + sigma_raw: { low: 0.75, mid: 0.83, high: 0.90 } + primary_benchmark: Vectara-HHEM (interpolated) + metric: factual-consistency rate (faithfulness proxy) + metric_kind: relative + reported_value: "interpolated below Sonnet; small-tier faithfulness penalty" + sample_size: null + observed_date: "2025-05" + source_url: https://github.com/vectara/hallucination-leaderboard + normalization_note: interpolated; small tier; faithfulness proxy only; refine from traces + + - model: gpt-4o + task_type: retrieval + sigma_raw: { low: 0.80, mid: 0.84, high: 0.90 } + primary_benchmark: FaithBench/RAGTruth + metric: factual-consistency rate (faithfulness proxy for RAG) + metric_kind: absolute + reported_value: "gpt-4o 15.85% hallu → faithfulness ~84.15%" + sample_size: "511 human-annotated items" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: direct FaithBench measurement; faithfulness PROXY only + + - model: gpt-4o-mini + task_type: retrieval + sigma_raw: { low: 0.72, mid: 0.80, high: 0.88 } + primary_benchmark: Vectara-HHEM (interpolated) + metric: factual-consistency rate (faithfulness proxy) + metric_kind: relative + reported_value: "interpolated below GPT-4o; small-tier penalty" + sample_size: null + observed_date: "2025-05" + source_url: https://github.com/vectara/hallucination-leaderboard + normalization_note: interpolated; small tier; faithfulness proxy only + + - model: openai-o3 + task_type: retrieval + sigma_raw: { low: 0.80, mid: 0.87, high: 0.93 } + primary_benchmark: Vectara next-gen (interpolated) + metric: factual-consistency rate (faithfulness proxy) + metric_kind: relative + reported_value: "reasoning flagship >10% hallu per Vectara next-gen; o3 likely mid-frontier band" + sample_size: null + observed_date: "2026-01" + source_url: https://www.vectara.com/blog/introducing-the-next-generation-of-vectaras-hallucination-leaderboard + normalization_note: reasoning models WORSE on faithfulness than Flash; wide band; faithfulness proxy only + + - model: gemini-2-pro + task_type: retrieval + sigma_raw: { low: 0.88, mid: 0.93, high: 0.97 } + primary_benchmark: FaithBench/RAGTruth + metric: factual-consistency rate (faithfulness proxy for RAG) + metric_kind: absolute + reported_value: "Gemini-2.5-Pro 6.65% hallu → faithfulness 93.35% (best in FaithBench)" + sample_size: "511 human-annotated items" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: best faithfulness in FaithBench set; faithfulness PROXY only; note HHEM conflict-of-interest + + - model: gemini-2-flash + task_type: retrieval + sigma_raw: { low: 0.88, mid: 0.93, high: 0.97 } + primary_benchmark: Vectara-HHEM + FaithBench + metric: factual-consistency rate (faithfulness proxy for RAG) + metric_kind: absolute + reported_value: "gemini-2.5-flash-lite 96.7% (HHEM); gemini-2.0-flash 89.82% (FaithBench)" + sample_size: "7700+ docs (HHEM); 511 items (FaithBench)" + observed_date: "2026-05" + source_url: https://github.com/vectara/hallucination-leaderboard + normalization_note: Flash-class tops HHEM; FaithBench confirms strong faithfulness; PROXY only + + - model: llama-3.3-70b + task_type: retrieval + sigma_raw: { low: 0.78, mid: 0.84, high: 0.90 } + primary_benchmark: FaithBench/RAGTruth + metric: factual-consistency rate (faithfulness proxy for RAG) + metric_kind: absolute + reported_value: "llama-3.3-70b 16.44% hallu → faithfulness ~83.56%" + sample_size: "511 human-annotated items" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: direct FaithBench measurement; faithfulness PROXY only; 70B tier + + - model: deepseek-v3 + task_type: retrieval + sigma_raw: { low: 0.88, mid: 0.93, high: 0.96 } + primary_benchmark: Vectara-HHEM + DeepSeek blog + metric: factual-consistency rate (faithfulness proxy for RAG) + metric_kind: absolute + reported_value: "V3 3.9-6.1% hallu (HHEM-2.1/2.3) → faithfulness ~94-96%" + sample_size: "7700+ documents" + observed_date: "2026-01" + source_url: https://www.vectara.com/blog/deepseek-r1-hallucinates-more-than-deepseek-v3 + normalization_note: V3 outstanding faithfulness; faithfulness PROXY only; does not predict end-to-end RAG correctness + + - model: deepseek-r1 + task_type: retrieval + sigma_raw: { low: 0.78, mid: 0.86, high: 0.91 } + primary_benchmark: Vectara-HHEM + DeepSeek blog + metric: factual-consistency rate (faithfulness proxy for RAG) + metric_kind: absolute + reported_value: "R1 14.3/11.3% hallu vs V3 3.9/6.1% — reasoning PENALTY; but RAG accuracy can diverge" + sample_size: "7700+ documents" + observed_date: "2026-01" + source_url: https://www.vectara.com/blog/deepseek-r1-hallucinates-more-than-deepseek-v3 + normalization_note: REASONING PENALTY on faithfulness; R1+RAG can hit 86% clinical accuracy despite poor faithfulness — proxy gap CRITICAL + + - model: qwen2.5-72b + task_type: retrieval + sigma_raw: { low: 0.75, mid: 0.85, high: 0.92 } + primary_benchmark: Vectara-HHEM (interpolated) + metric: factual-consistency rate (faithfulness proxy) + metric_kind: relative + reported_value: "interpolated mid tier; no direct Qwen2.5 row found" + sample_size: null + observed_date: "2024-12" + source_url: https://github.com/vectara/hallucination-leaderboard + normalization_note: interpolated; mid tier faithfulness proxy; wide band + + - model: mistral-large + task_type: retrieval + sigma_raw: { low: 0.75, mid: 0.85, high: 0.92 } + primary_benchmark: Vectara-HHEM (interpolated) + metric: factual-consistency rate (faithfulness proxy) + metric_kind: relative + reported_value: "interpolated mid tier; no direct Mistral-Large row found" + sample_size: null + observed_date: "2024-12" + source_url: https://github.com/vectara/hallucination-leaderboard + normalization_note: interpolated; mid tier faithfulness proxy; wide band + +# ---- REVIEW / CRITIQUE / VERIFICATION (catch_rate) ---------------------------- +# SOFTEST COLUMN. Widest bands. Best public evidence: FaithJudge zero-shot on +# FaithBench (arXiv 2505.04847) + MT-Bench human agreement (arXiv 2306.05685) + +# RewardBench-2 (arXiv 2506.01937) + CriticBench (arXiv 2402.14809). +# +# Key constraints: +# - 84% FaithJudge headline is FEW-SHOT (9 peer examples in-context) — an +# optimistic ceiling a real pipeline won't have. +# - Zero-shot best: o3-mini-high 68.8% balanced accuracy on FaithBench. +# - RewardBench-2 shows ~20pp drop vs v1 on harder/realistic distributions. +# - Same-family self-critique is PARTIALLY RELATIVE (self-enhancement bias) — +# subtract ~0.05-0.10 from mid and widen. +# - CriticBench: stronger critic vs weaker target = better catch; weaker critic +# can surpass stronger on self-critique (pairing matters more than identity). +# Bands below are ZERO-SHOT estimates for catching ANOTHER model's errors. +# Source: https://arxiv.org/abs/2505.04847 (FaithJudge), +# https://arxiv.org/abs/2306.05685 (MT-Bench) + + - model: claude-opus-4 + task_type: review + catch_rate: { low: 0.62, mid: 0.72, high: 0.84 } + primary_benchmark: FaithJudge / MT-Bench (zero-shot LLM judge) + metric: balanced accuracy catching upstream errors (zero-shot judge) + metric_kind: absolute + reported_value: "strong frontier tier; o3-mini-high zero-shot 68.8% bal-acc ceiling; GPT-4 judge ~80-85% pairwise-agreement" + sample_size: "511 items (FaithBench zero-shot); ~80 questions (MT-Bench)" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: SOFTEST COLUMN; strong frontier judge tier; zero-shot only; few-shot raises to ~84% (not assumed here); wide band mandatory + + - model: claude-sonnet-4 + task_type: review + catch_rate: { low: 0.55, mid: 0.65, high: 0.78 } + primary_benchmark: FaithJudge / MT-Bench (zero-shot LLM judge) + metric: balanced accuracy catching upstream errors (zero-shot judge) + metric_kind: absolute + reported_value: "mid judge tier; GPT-4o-class ~0.65 estimated zero-shot" + sample_size: "511 items (FaithBench zero-shot)" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: SOFTEST COLUMN; mid judge tier; zero-shot only; RewardBench-2 shows ~20pp drop on harder distributions; wide band + + - model: claude-haiku-4 + task_type: review + catch_rate: { low: 0.48, mid: 0.58, high: 0.70 } + primary_benchmark: FaithJudge / MT-Bench (zero-shot LLM judge) + metric: balanced accuracy catching upstream errors (zero-shot judge) + metric_kind: absolute + reported_value: "small/cheap judge tier; below mid-judge zero-shot" + sample_size: "511 items (FaithBench zero-shot)" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: SOFTEST COLUMN; small judge tier; near-random on hard distributions; wide band; do not use as sole quality gate + + - model: gpt-4o + task_type: review + catch_rate: { low: 0.55, mid: 0.65, high: 0.78 } + primary_benchmark: MT-Bench + FaithJudge + metric: balanced accuracy catching upstream errors (zero-shot judge) + metric_kind: absolute + reported_value: "GPT-4 judge ~80-85% pairwise agreement (MT-Bench); pairwise easier than error-catch → widen down" + sample_size: "~80 questions (MT-Bench); 511 items (FaithBench)" + observed_date: "2023-06" + source_url: https://arxiv.org/abs/2306.05685 + normalization_note: pairwise preference easier than absolute error-catch; MT-Bench bias documented (position/verbosity/self-enhancement); mid judge tier + + - model: gpt-4o-mini + task_type: review + catch_rate: { low: 0.48, mid: 0.58, high: 0.70 } + primary_benchmark: FaithJudge (zero-shot) + metric: balanced accuracy catching upstream errors (zero-shot judge) + metric_kind: absolute + reported_value: "small judge tier; below GPT-4o zero-shot" + sample_size: "511 items (FaithBench zero-shot)" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: SOFTEST COLUMN; small judge tier; wide band; do not use as sole quality gate + + - model: openai-o3 + task_type: review + catch_rate: { low: 0.62, mid: 0.72, high: 0.84 } + primary_benchmark: FaithJudge (o3-mini-high zero-shot anchor) + metric: balanced accuracy catching upstream errors (zero-shot judge) + metric_kind: absolute + reported_value: "o3-mini-high zero-shot best: 68.8% balanced accuracy on FaithBench" + sample_size: "511 items (FaithBench zero-shot)" + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: SOFTEST COLUMN; best public zero-shot judge evidence; o3-mini-high is the anchor; strong frontier judge tier + + - model: gemini-2-pro + task_type: review + catch_rate: { low: 0.55, mid: 0.65, high: 0.78 } + primary_benchmark: FaithJudge (interpolated) + metric: balanced accuracy catching upstream errors (zero-shot judge) + metric_kind: relative + reported_value: "interpolated mid-to-frontier judge tier; Gemini Pro strong at faithfulness detection" + sample_size: null + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: SOFTEST COLUMN; interpolated; no direct zero-shot catch-rate row for Gemini-2-Pro; mid-to-frontier tier + + - model: gemini-2-flash + task_type: review + catch_rate: { low: 0.52, mid: 0.62, high: 0.75 } + primary_benchmark: FaithJudge (interpolated) + metric: balanced accuracy catching upstream errors (zero-shot judge) + metric_kind: relative + reported_value: "interpolated mid judge tier; Flash cheaper but weaker than Pro at critique" + sample_size: null + observed_date: "2025-05" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: SOFTEST COLUMN; interpolated; mid judge tier; wide band + + - model: llama-3.3-70b + task_type: review + catch_rate: { low: 0.50, mid: 0.62, high: 0.75 } + primary_benchmark: FaithJudge / CriticBench (interpolated) + metric: balanced accuracy catching upstream errors (zero-shot judge) + metric_kind: relative + reported_value: "no reliable public absolute evidence for Llama-3.3 as zero-shot critic" + sample_size: null + observed_date: null + source_url: https://arxiv.org/pdf/2402.14809 + normalization_note: SOFTEST COLUMN; conservative wide band fallback; no reliable evidence; refine from traces immediately + + - model: deepseek-v3 + task_type: review + catch_rate: { low: 0.52, mid: 0.63, high: 0.76 } + primary_benchmark: FaithJudge (interpolated) + metric: balanced accuracy catching upstream errors (zero-shot judge) + metric_kind: relative + reported_value: "interpolated mid tier; V3 strong at instruction-following; limited critic-specific evidence" + sample_size: null + observed_date: "2025-01" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: SOFTEST COLUMN; interpolated; mid tier; CriticBench pairing asymmetry applies + + - model: deepseek-r1 + task_type: review + catch_rate: { low: 0.50, mid: 0.62, high: 0.76 } + primary_benchmark: FaithJudge (interpolated) + metric: balanced accuracy catching upstream errors (zero-shot judge) + metric_kind: relative + reported_value: "reasoning models can over-think simple review; no R1-specific zero-shot critic evidence" + sample_size: null + observed_date: "2025-01" + source_url: https://arxiv.org/abs/2505.04847 + normalization_note: SOFTEST COLUMN; reasoning overhead uncertain for critique; conservative mid tier; wide band + + - model: qwen2.5-72b + task_type: review + catch_rate: { low: 0.50, mid: 0.60, high: 0.74 } + primary_benchmark: FaithJudge / CriticBench (no reliable evidence) + metric: balanced accuracy catching upstream errors + metric_kind: relative + reported_value: "no reliable public absolute evidence" + sample_size: null + observed_date: null + source_url: null + normalization_note: SOFTEST COLUMN; conservative wide-band fallback per research guidance; no reliable evidence + + - model: mistral-large + task_type: review + catch_rate: { low: 0.50, mid: 0.60, high: 0.74 } + primary_benchmark: FaithJudge / CriticBench (no reliable evidence) + metric: balanced accuracy catching upstream errors + metric_kind: relative + reported_value: "no reliable public absolute evidence" + sample_size: null + observed_date: null + source_url: null + normalization_note: SOFTEST COLUMN; conservative wide-band fallback per research guidance; no reliable evidence diff --git a/src/minimal_oversight/priors.py b/src/minimal_oversight/priors.py new file mode 100644 index 0000000..a059b53 --- /dev/null +++ b/src/minimal_oversight/priors.py @@ -0,0 +1,181 @@ +"""Cold-start task-competence priors for MSO. + +When a practitioner has no traces yet, they still need *starting* numbers to seed +a pipeline node. This module loads a curated, provenance-bound prior table +(`data/priors.yaml`) and maps a ``(model, task_type)`` cell onto the quantities a +:class:`~minimal_oversight.models.Node` needs: + +- generator task-types -> ``sigma_skill`` (via ``sigma_raw_mid / gamma``), so the + model reproduces the prior's ``sigma_raw`` at the return-operator fixed point; +- the ``review`` task-type -> ``catch_rate`` (how well the model catches an + upstream node's errors). + +Priors are *hypotheses*, not ground truth. Each carries a low/mid/high band and a +source; a wide band means weak evidence. They are meant to be refined by the +user's own logged outcomes — see :mod:`minimal_oversight.estimation`. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from functools import lru_cache +from importlib import resources +from typing import Any + +import yaml + +REVIEW_TASK = "review" + + +@dataclass(frozen=True) +class Band: + """A low/mid/high estimate. ``width`` is a crude evidence-strength proxy.""" + + low: float + mid: float + high: float + + @property + def width(self) -> float: + return self.high - self.low + + @classmethod + def from_mapping(cls, m: dict[str, float]) -> Band: + return cls(low=float(m["low"]), mid=float(m["mid"]), high=float(m["high"])) + + +@dataclass(frozen=True) +class PriorCell: + """One ``(model, task_type)`` prior with provenance.""" + + model: str + task_type: str + sigma_raw: Band | None # generator task-types + catch_rate: Band | None # the `review` task-type + primary_benchmark: str | None + metric: str | None + metric_kind: str | None # "absolute" | "relative" + reported_value: Any + sample_size: Any + observed_date: str | None + source_url: str | None + normalization_note: str | None + + +def _clamp(x: float, lo: float, hi: float) -> float: + return max(lo, min(hi, x)) + + +@lru_cache(maxsize=4) +def load_priors(path: str | None = None) -> dict[str, Any]: + """Load and lightly validate the prior table. + + Returns a dict with ``meta``, ``task_types`` and a ``cells`` map keyed by + ``(model, task_type)``. Result is cached; pass an explicit ``path`` to bypass + the packaged file (e.g. in tests). + """ + if path is None: + text = resources.files("minimal_oversight.data").joinpath("priors.yaml").read_text() + else: + with open(path, encoding="utf-8") as fh: + text = fh.read() + raw = yaml.safe_load(text) + + meta = raw.get("meta", {}) + task_types = list(raw.get("task_types", [])) + gamma = float(meta.get("gamma", 10 / 12)) + if not (0.0 < gamma <= 1.0): + raise ValueError(f"priors meta.gamma out of range: {gamma}") + + cells: dict[tuple[str, str], PriorCell] = {} + for row in raw.get("priors", []): + model = str(row["model"]) + task = str(row["task_type"]) + if task not in task_types: + raise ValueError(f"unknown task_type {task!r} for model {model!r}") + sigma = Band.from_mapping(row["sigma_raw"]) if "sigma_raw" in row else None + catch = Band.from_mapping(row["catch_rate"]) if "catch_rate" in row else None + if task == REVIEW_TASK and catch is None: + raise ValueError(f"review cell {model!r} must carry a catch_rate band") + if task != REVIEW_TASK and sigma is None: + raise ValueError(f"generator cell {model!r}/{task!r} must carry a sigma_raw band") + for b in (sigma, catch): + if b is not None and not (0.0 <= b.low <= b.mid <= b.high <= 1.0): + raise ValueError(f"non-monotone or out-of-range band for {model!r}/{task!r}") + key = (model, task) + if key in cells: + raise ValueError(f"duplicate prior cell {key}") + cells[key] = PriorCell( + model=model, + task_type=task, + sigma_raw=sigma, + catch_rate=catch, + primary_benchmark=row.get("primary_benchmark"), + metric=row.get("metric"), + metric_kind=row.get("metric_kind"), + reported_value=row.get("reported_value"), + sample_size=row.get("sample_size"), + observed_date=row.get("observed_date"), + source_url=row.get("source_url"), + normalization_note=row.get("normalization_note"), + ) + return {"meta": meta, "gamma": gamma, "task_types": task_types, "cells": cells} + + +def list_models(path: str | None = None) -> list[str]: + """Sorted distinct model names in the table.""" + return sorted({m for (m, _t) in load_priors(path)["cells"]}) + + +def list_task_types(path: str | None = None) -> list[str]: + return list(load_priors(path)["task_types"]) + + +def get_cell(model: str, task_type: str, path: str | None = None) -> PriorCell: + cells = load_priors(path)["cells"] + try: + return cells[(model, task_type)] + except KeyError: + raise KeyError(f"no prior for model={model!r} task_type={task_type!r}") from None + + +def seed_node(model: str, task_type: str, path: str | None = None) -> dict[str, Any]: + """Map a prior cell onto ``Node`` keyword arguments + provenance. + + For generator task-types returns ``sigma_skill`` (so the model reproduces the + prior's ``sigma_raw`` at the fixed point) and a zero ``catch_rate``. For the + ``review`` task-type returns the ``catch_rate`` (and no ``sigma_skill``; the + caller pairs the reviewer with the node it corrects). + + The returned ``provenance`` block carries the band, source, and a + ``confidence`` proxy (``1 - band_width``) so the UI can render priors as + hypotheses, never as ground truth. + """ + data = load_priors(path) + gamma = data["gamma"] + cell = get_cell(model, task_type, path) + + out: dict[str, Any] = {"model": model, "task_type": task_type, "is_prior": True} + if task_type == REVIEW_TASK: + band = cell.catch_rate + out["catch_rate"] = _clamp(band.mid, 0.0, 1.0) + out["seeds"] = "catch_rate" + else: + band = cell.sigma_raw + meta = data["meta"] + default_catch = float(meta.get("generator_default_catch_rate", 0.0)) + out["sigma_skill"] = _clamp(band.mid / gamma, 0.05, 0.98) + out["catch_rate"] = default_catch + out["seeds"] = "sigma_skill" + + out["provenance"] = { + "band": {"low": band.low, "mid": band.mid, "high": band.high}, + "confidence": round(1.0 - band.width, 3), + "primary_benchmark": cell.primary_benchmark, + "metric": cell.metric, + "metric_kind": cell.metric_kind, + "observed_date": cell.observed_date, + "source_url": cell.source_url, + "normalization_note": cell.normalization_note, + } + return out diff --git a/tests/test_parity.py b/tests/test_parity.py index fabe7c3..0f19b1b 100644 --- a/tests/test_parity.py +++ b/tests/test_parity.py @@ -20,6 +20,7 @@ from minimal_oversight import _formulae as F from minimal_oversight import analyze_pipeline from minimal_oversight import estimation as E +from minimal_oversight import priors as P from minimal_oversight.allocation import select_scope from minimal_oversight.capacity import check_feasibility from minimal_oversight.models import AggregationType, Node, PipelineGraph @@ -122,6 +123,14 @@ def _nd(node_id, skill, catch, parents, agg="product"): {"sigma": [0.667, 0.517, 0.417, 0.375, 0.708, 0.542], "p_min": 0.5, "coverage": 0.0}, {"sigma": [0.8, 0.4, 0.6, 0.3, 0.7], "p_min": 0.6, "coverage": 0.5}, ], + # mso-priors.js seeds a node from (model × task) benchmark priors. + # Covers generator branch (sigma_skill) and review branch (catch_rate). + "seed_node": [ + {"model": "claude-opus-4", "task_type": "code_generation"}, + {"model": "gpt-4o", "task_type": "drafting"}, + {"model": "gemini-2-flash", "task_type": "review"}, + {"model": "deepseek-r1", "task_type": "extraction"}, + ], # web/mso-estimate.js turns a practitioner's real outcomes into per-node # sigma_raw / sigma_corr / catch / masking. Pin those to estimation.py. "estimate": [ @@ -250,6 +259,19 @@ def _python_expected() -> dict: } for r in rep.node_risks ]) + exp["seed_node"] = [] + for c in CASES["seed_node"]: + s = P.seed_node(c["model"], c["task_type"]) + prov = s["provenance"] + exp["seed_node"].append({ + "seeds": s["seeds"], + "sigma_skill": s.get("sigma_skill"), + "catch_rate": s["catch_rate"], + "confidence": prov["confidence"], + "band_low": prov["band"]["low"], + "band_mid": prov["band"]["mid"], + "band_high": prov["band"]["high"], + }) return exp @@ -318,3 +340,13 @@ def test_browser_port_matches_python_reference(): assert gn["fi"] == en["fi"] and gn["fo"] == en["fo"] and gn["bott"] == en["bott"] for k in ["sota", "dc", "masking"]: assert _close(gn[k], en[k]), f"risk {en['name']} {k} mismatch" + + for i, (g, e) in enumerate(zip(got["seed_node"], exp["seed_node"])): + c = CASES["seed_node"][i] + label = f"{c['model']}|{c['task_type']}" + assert g["seeds"] == e["seeds"], f"seed_node seeds mismatch for {label}" + for k in ["band_low", "band_mid", "band_high", "confidence"]: + assert _close(g[k], e[k]), f"seed_node {k} mismatch for {label}" + if e["sigma_skill"] is not None: + assert _close(g["sigma_skill"], e["sigma_skill"]), f"seed_node sigma_skill mismatch for {label}" + assert _close(g["catch_rate"], e["catch_rate"]), f"seed_node catch_rate mismatch for {label}" diff --git a/tests/test_priors.py b/tests/test_priors.py new file mode 100644 index 0000000..348d86e --- /dev/null +++ b/tests/test_priors.py @@ -0,0 +1,103 @@ +"""Tests for the cold-start priors loader and the (model, task) -> Node seeding.""" + +from __future__ import annotations + +import textwrap + +import pytest + +from minimal_oversight import priors as P + + +def test_table_loads_and_lists(): + models = P.list_models() + tasks = P.list_task_types() + assert models, "expected at least one model in the prior table" + assert "review" in tasks + assert "code_generation" in tasks + + +def test_generator_seed_reproduces_sigma_raw_at_fixed_point(): + """sigma_skill is set so gamma * sigma_skill == the prior's sigma_raw mid.""" + data = P.load_priors() + gamma = data["gamma"] + for (model, task), cell in data["cells"].items(): + if task == "review": + continue + seed = P.seed_node(model, task) + assert seed["seeds"] == "sigma_skill" + assert 0.05 <= seed["sigma_skill"] <= 0.98 + assert seed["catch_rate"] == 0.0 # a generator does not correct its parent + # round-trips unless the mid was clamped at the [0.05, 0.98] rails + unclamped = cell.sigma_raw.mid / gamma + if 0.05 <= unclamped <= 0.98: + assert gamma * seed["sigma_skill"] == pytest.approx(cell.sigma_raw.mid, abs=1e-9) + + +def test_review_seed_yields_catch_rate(): + review_cells = [(m, t) for (m, t) in P.load_priors()["cells"] if t == "review"] + assert review_cells, "expected at least one review cell" + for model, _ in review_cells: + seed = P.seed_node(model, "review") + assert seed["seeds"] == "catch_rate" + assert 0.0 <= seed["catch_rate"] <= 1.0 + assert "sigma_skill" not in seed + + +def test_provenance_block_is_well_formed(): + for (model, task) in P.load_priors()["cells"]: + prov = P.seed_node(model, task)["provenance"] + assert 0.0 <= prov["confidence"] <= 1.0 + b = prov["band"] + assert 0.0 <= b["low"] <= b["mid"] <= b["high"] <= 1.0 + assert prov["metric_kind"] in {"absolute", "relative", None} + + +def test_unknown_cell_raises(): + with pytest.raises(KeyError): + P.seed_node("no_such_model", "code_generation") + + +def _write(tmp_path, body: str) -> str: + p = tmp_path / "priors.yaml" + p.write_text(textwrap.dedent(body)) + return str(p) + + +def test_loader_rejects_nonmonotone_band(tmp_path): + path = _write(tmp_path, """ + meta: {schema_version: 1, gamma: 0.8333333333333334, status: test} + task_types: [code_generation, review] + priors: + - model: m + task_type: code_generation + sigma_raw: {low: 0.8, mid: 0.5, high: 0.9} + """) + with pytest.raises(ValueError): + P.load_priors(path) + + +def test_loader_rejects_review_without_catch_rate(tmp_path): + path = _write(tmp_path, """ + meta: {schema_version: 1, gamma: 0.8333333333333334, status: test} + task_types: [code_generation, review] + priors: + - model: m + task_type: review + sigma_raw: {low: 0.4, mid: 0.6, high: 0.8} + """) + with pytest.raises(ValueError): + P.load_priors(path) + + +def test_loader_rejects_unknown_task_type(tmp_path): + path = _write(tmp_path, """ + meta: {schema_version: 1, gamma: 0.8333333333333334, status: test} + task_types: [code_generation] + priors: + - model: m + task_type: telepathy + sigma_raw: {low: 0.4, mid: 0.6, high: 0.8} + """) + with pytest.raises(ValueError): + P.load_priors(path) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..c715882 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4661 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.15'", + "python_full_version == '3.14.*'", + "python_full_version >= '3.11' and 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sigma_skill == band.mid at the fixed point. + * - review task-type → catch_rate = clamp(band.mid, 0, 1) + * - provenance.confidence = 1 − band_width (a crude evidence-strength proxy) + * + * Data sourced from a cited deep-research pass (2026-06-17) against: + * - LiveBench Coding (code_generation, absolute) + * - JSONSchemaBench arXiv:2501.10868 (extraction, absolute) + * - Vectara HHEM-2.3 + FaithBench/RAGTruth (drafting + retrieval, absolute) + * - FaithJudge / MT-Bench / RewardBench-2 / CriticBench (review, absolute/relative) + * - Conservative tier-interpolation (classification — no surviving absolute benchmark) + * + * Parity: tests/test_parity.py pins JS ↔ Python to within 1e-9 on seedNode outputs. + */ +(function (root, factory) { + if (typeof module === "object" && module.exports) module.exports = factory(); + else root.MSO_Priors = factory(); +})(typeof self !== "undefined" ? self : this, function () { + "use strict"; + + var GAMMA = 10 / 12; // η/(η+δ) return-operator fixed-point gain + + function clamp(x, a, b) { return Math.max(a, Math.min(b, x)); } + + // ---- bundled prior table (generated from data/priors.yaml) ----------------- + var CELLS = { + "claude-opus-4|code_generation": {kind:"generator",band:{low:0.62,mid:0.74,high:0.86},benchmark:"LiveBench-Coding",metric_kind:"absolute",note:"interpolated above claude-3.5-sonnet 63.2 via relative leaderboard; 10% domain-shift discount; wide band"}, + "claude-sonnet-4|code_generation": {kind:"generator",band:{low:0.58,mid:0.7,high:0.83},benchmark:"LiveBench-Coding",metric_kind:"absolute",note:"anchored to claude-3.5-sonnet 63.2; 10% domain-shift discount; Sonnet-4 interpolated near that anchor"}, + "claude-haiku-4|code_generation": {kind:"generator",band:{low:0.3,mid:0.45,high:0.62},benchmark:"LiveBench-Coding",metric_kind:"absolute",note:"anchored claude-3-haiku 24.5; HumanEval 75.9 inflates vs SWE-bench ~23%; wide band captures both"}, + "gpt-4o|code_generation": {kind:"generator",band:{low:0.4,mid:0.5,high:0.62},benchmark:"LiveBench-Coding",metric_kind:"absolute",note:"direct read from Table 1; 10% domain-shift discount; model may have since improved"}, + "gpt-4o-mini|code_generation": {kind:"generator",band:{low:0.32,mid:0.46,high:0.62},benchmark:"LiveBench-Coding + HumanEval cross-check",metric_kind:"absolute",note:"HumanEval 87.2 inflates vs SWE-bench-Verified ~23%; mid splits the difference; wide band"}, + "openai-o3|code_generation": {kind:"generator",band:{low:0.55,mid:0.68,high:0.84},benchmark:"LiveBench-Coding (interpolated)",metric_kind:"relative",note:"relative-only interpolation; reasoning models excel on coding; wide band mandatory"}, + "gemini-2-pro|code_generation": {kind:"generator",band:{low:0.45,mid:0.58,high:0.74},benchmark:"LiveBench-Coding",metric_kind:"absolute",note:"interpolated above gemini-1.5-pro 32.8; Gemini 2.x generation boost assumed modest"}, + "gemini-2-flash|code_generation": {kind:"generator",band:{low:0.35,mid:0.48,high:0.62},benchmark:"LiveBench-Coding",metric_kind:"absolute",note:"anchored to gemini-1.5-flash 39.1; 2.0/2.5 Flash improvements likely push mid up; widen"}, + "llama-3.3-70b|code_generation": {kind:"generator",band:{low:0.35,mid:0.48,high:0.62},benchmark:"LiveBench-Coding (interpolated)",metric_kind:"relative",note:"relative-only interpolation from model-class ranking; wide band"}, + "deepseek-v3|code_generation": {kind:"generator",band:{low:0.42,mid:0.55,high:0.7},benchmark:"LiveBench-Coding",metric_kind:"absolute",note:"anchored deepseek-coder-v2 41.1; V3 and R1 interpolated up; coding-focused model widens high end"}, + "deepseek-r1|code_generation": {kind:"generator",band:{low:0.48,mid:0.62,high:0.78},benchmark:"LiveBench-Coding (interpolated)",metric_kind:"relative",note:"R1 reasoning premium likely lifts coding above V3 base; no clean 2025 absolute snapshot"}, + "qwen2.5-72b|code_generation": {kind:"generator",band:{low:0.32,mid:0.45,high:0.6},benchmark:"LiveBench-Coding",metric_kind:"absolute",note:"anchored qwen2-72b 31.8; Qwen2.5 improvements likely push mid up modestly"}, + "mistral-large|code_generation": {kind:"generator",band:{low:0.32,mid:0.45,high:0.6},benchmark:"LiveBench-Coding (interpolated)",metric_kind:"relative",note:"relative-only interpolation; no direct LiveBench measurement found"}, + "claude-opus-4|classification": {kind:"generator",band:{low:0.85,mid:0.92,high:0.97},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"NO absolute benchmark survived verification; frontier tier conservative interpolation; refine immediately"}, + "claude-sonnet-4|classification": {kind:"generator",band:{low:0.78,mid:0.87,high:0.94},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"NO absolute benchmark survived verification; mid tier conservative interpolation; refine immediately"}, + "claude-haiku-4|classification": {kind:"generator",band:{low:0.7,mid:0.82,high:0.92},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"NO absolute benchmark survived verification; small tier conservative interpolation; refine immediately"}, + "gpt-4o|classification": {kind:"generator",band:{low:0.78,mid:0.87,high:0.94},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"MMLU vendor proxy refuted; mid tier conservative interpolation; wide band"}, + "gpt-4o-mini|classification": {kind:"generator",band:{low:0.7,mid:0.82,high:0.92},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"MMLU vendor proxy refuted; small tier conservative interpolation"}, + "openai-o3|classification": {kind:"generator",band:{low:0.85,mid:0.92,high:0.97},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"reasoning premium assumed; frontier tier conservative interpolation; refine immediately"}, + "gemini-2-pro|classification": {kind:"generator",band:{low:0.78,mid:0.87,high:0.94},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"NO absolute benchmark survived verification; mid tier conservative interpolation"}, + "gemini-2-flash|classification": {kind:"generator",band:{low:0.75,mid:0.85,high:0.93},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"NO absolute benchmark survived verification; mid tier conservative interpolation"}, + "llama-3.3-70b|classification": {kind:"generator",band:{low:0.75,mid:0.85,high:0.93},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"NO absolute benchmark survived verification; mid tier conservative interpolation"}, + "deepseek-v3|classification": {kind:"generator",band:{low:0.78,mid:0.87,high:0.94},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"NO absolute benchmark survived verification; mid tier conservative interpolation"}, + "deepseek-r1|classification": {kind:"generator",band:{low:0.78,mid:0.87,high:0.94},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"NO absolute benchmark survived verification; mid tier; reasoning premium unclear for classification"}, + "qwen2.5-72b|classification": {kind:"generator",band:{low:0.75,mid:0.85,high:0.93},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"NO absolute benchmark survived verification; mid tier conservative interpolation"}, + "mistral-large|classification": {kind:"generator",band:{low:0.75,mid:0.85,high:0.93},benchmark:"NONE (conservative interpolation)",metric_kind:"relative",note:"NO absolute benchmark survived verification; mid tier conservative interpolation"}, + "claude-opus-4|extraction": {kind:"generator",band:{low:0.82,mid:0.91,high:0.98},benchmark:"JSONSchemaBench",metric_kind:"absolute",note:"moderate schema, plain prompting; frontier tier; schema difficulty dominates; wide band"}, + "claude-sonnet-4|extraction": {kind:"generator",band:{low:0.75,mid:0.87,high:0.96},benchmark:"JSONSchemaBench",metric_kind:"absolute",note:"moderate schema, plain prompting; mid tier; wide band to capture difficulty variance"}, + "claude-haiku-4|extraction": {kind:"generator",band:{low:0.5,mid:0.7,high:0.88},benchmark:"JSONSchemaBench",metric_kind:"absolute",note:"small/efficient tier; Llama-1B floor; Haiku expected above floor but degrades on hard schemas"}, + "gpt-4o|extraction": {kind:"generator",band:{low:0.8,mid:0.91,high:0.98},benchmark:"JSONSchemaBench",metric_kind:"absolute",note:"OpenAI JSON-mode raises compliance on accepted schemas; coverage drops on hard schemas; moderate schema"}, + "gpt-4o-mini|extraction": {kind:"generator",band:{low:0.65,mid:0.8,high:0.93},benchmark:"JSONSchemaBench",metric_kind:"absolute",note:"smaller model; JSON-mode available but coverage limited; wide band"}, + "openai-o3|extraction": {kind:"generator",band:{low:0.82,mid:0.92,high:0.99},benchmark:"JSONSchemaBench (interpolated)",metric_kind:"absolute",note:"frontier reasoning model; constrained API expected; interpolated upward; wide band"}, + "gemini-2-pro|extraction": {kind:"generator",band:{low:0.8,mid:0.91,high:0.98},benchmark:"JSONSchemaBench",metric_kind:"absolute",note:"Gemini constrained-mode matches OpenAI on accepted schemas; coverage limited on hard schemas"}, + "gemini-2-flash|extraction": {kind:"generator",band:{low:0.72,mid:0.85,high:0.95},benchmark:"JSONSchemaBench",metric_kind:"absolute",note:"mid tier; constrained output may vary; moderate schema assumed"}, + "llama-3.3-70b|extraction": {kind:"generator",band:{low:0.65,mid:0.8,high:0.93},benchmark:"JSONSchemaBench",metric_kind:"absolute",note:"mid tier open model; plain prompting assumed; degrades on hard schemas"}, + "deepseek-v3|extraction": {kind:"generator",band:{low:0.72,mid:0.85,high:0.95},benchmark:"JSONSchemaBench (interpolated)",metric_kind:"absolute",note:"interpolated mid-to-frontier tier; V3 strong at structured output"}, + "deepseek-r1|extraction": {kind:"generator",band:{low:0.65,mid:0.8,high:0.93},benchmark:"JSONSchemaBench (interpolated)",metric_kind:"relative",note:"reasoning overhead may reduce extraction accuracy; conservative mid-tier interpolation"}, + "qwen2.5-72b|extraction": {kind:"generator",band:{low:0.65,mid:0.8,high:0.93},benchmark:"JSONSchemaBench (interpolated)",metric_kind:"absolute",note:"mid tier; Qwen2.5 strong at structured output but no direct JSONSchemaBench row"}, + "mistral-large|extraction": {kind:"generator",band:{low:0.65,mid:0.8,high:0.93},benchmark:"JSONSchemaBench (interpolated)",metric_kind:"relative",note:"relative-only interpolation; mid tier; wide band"}, + "claude-opus-4|drafting": {kind:"generator",band:{low:0.83,mid:0.89,high:0.95},benchmark:"Vectara-HHEM + FaithBench/RAGTruth (faithfulness subdim)",metric_kind:"absolute",note:"faithfulness subdim ONLY; style/IF quality is relative-only (AlpacaEval/Arena) and NOT captured here"}, + "claude-sonnet-4|drafting": {kind:"generator",band:{low:0.8,mid:0.87,high:0.93},benchmark:"Vectara-HHEM + FaithBench/RAGTruth",metric_kind:"absolute",note:"Sonnet-4.5 >10% hallucination rate per Vectara next-gen; faithfulness subdim only"}, + "claude-haiku-4|drafting": {kind:"generator",band:{low:0.75,mid:0.83,high:0.91},benchmark:"Vectara-HHEM (faithfulness proxy, interpolated)",metric_kind:"relative",note:"interpolated below Sonnet faithfulness; faithfulness subdim only; style quality relative-only"}, + "gpt-4o|drafting": {kind:"generator",band:{low:0.78,mid:0.85,high:0.91},benchmark:"FaithBench/RAGTruth",metric_kind:"absolute",note:"direct FaithBench measurement; faithfulness subdim only; style relative-only"}, + "gpt-4o-mini|drafting": {kind:"generator",band:{low:0.72,mid:0.81,high:0.9},benchmark:"Vectara-HHEM (interpolated)",metric_kind:"relative",note:"interpolated small-tier; faithfulness subdim only; refine quickly"}, + "openai-o3|drafting": {kind:"generator",band:{low:0.8,mid:0.88,high:0.94},benchmark:"Vectara next-gen (interpolated)",metric_kind:"relative",note:"reasoning flagships WORSE than Flash on faithfulness; faithfulness subdim only"}, + "gemini-2-pro|drafting": {kind:"generator",band:{low:0.87,mid:0.93,high:0.97},benchmark:"FaithBench/RAGTruth",metric_kind:"absolute",note:"best-in-class faithfulness per FaithBench; faithfulness subdim only; style quality relative-only"}, + "gemini-2-flash|drafting": {kind:"generator",band:{low:0.88,mid:0.93,high:0.97},benchmark:"Vectara-HHEM",metric_kind:"absolute",note:"Flash-class tops HHEM faithfulness leaderboard; faithfulness subdim only; note HHEM is Vectara's own judge"}, + "llama-3.3-70b|drafting": {kind:"generator",band:{low:0.78,mid:0.84,high:0.9},benchmark:"FaithBench/RAGTruth",metric_kind:"absolute",note:"direct FaithBench measurement; faithfulness subdim only; style relative-only"}, + "deepseek-v3|drafting": {kind:"generator",band:{low:0.88,mid:0.93,high:0.97},benchmark:"Vectara-HHEM + DeepSeek blog",metric_kind:"absolute",note:"V3 is notably less hallucinatory than R1; one of the best base-model faithfulness rates"}, + "deepseek-r1|drafting": {kind:"generator",band:{low:0.8,mid:0.87,high:0.91},benchmark:"Vectara-HHEM + DeepSeek blog",metric_kind:"absolute",note:"REASONING PENALTY confirmed; R1 substantially worse than V3 on faithfulness; R1-0528 narrowed gap ~45-50%"}, + "qwen2.5-72b|drafting": {kind:"generator",band:{low:0.78,mid:0.85,high:0.92},benchmark:"Vectara-HHEM (interpolated)",metric_kind:"relative",note:"interpolated; mid tier faithfulness; wide band"}, + "mistral-large|drafting": {kind:"generator",band:{low:0.78,mid:0.85,high:0.92},benchmark:"Vectara-HHEM (interpolated)",metric_kind:"relative",note:"interpolated; mid tier faithfulness; wide band"}, + "claude-opus-4|retrieval": {kind:"generator",band:{low:0.83,mid:0.89,high:0.94},benchmark:"Vectara-HHEM + FaithBench/RAGTruth",metric_kind:"absolute",note:"faithfulness PROXY — not full RAG answer-correctness; interpolated above Sonnet anchor"}, + "claude-sonnet-4|retrieval": {kind:"generator",band:{low:0.8,mid:0.87,high:0.93},benchmark:"FaithBench/RAGTruth",metric_kind:"absolute",note:"direct FaithBench measurement; faithfulness PROXY only; Sonnet-4.5 confirmed >10% hallu — use low end"}, + "claude-haiku-4|retrieval": {kind:"generator",band:{low:0.75,mid:0.83,high:0.9},benchmark:"Vectara-HHEM (interpolated)",metric_kind:"relative",note:"interpolated; small tier; faithfulness proxy only; refine from traces"}, + "gpt-4o|retrieval": {kind:"generator",band:{low:0.8,mid:0.84,high:0.9},benchmark:"FaithBench/RAGTruth",metric_kind:"absolute",note:"direct FaithBench measurement; faithfulness PROXY only"}, + "gpt-4o-mini|retrieval": {kind:"generator",band:{low:0.72,mid:0.8,high:0.88},benchmark:"Vectara-HHEM (interpolated)",metric_kind:"relative",note:"interpolated; small tier; faithfulness proxy only"}, + "openai-o3|retrieval": {kind:"generator",band:{low:0.8,mid:0.87,high:0.93},benchmark:"Vectara next-gen (interpolated)",metric_kind:"relative",note:"reasoning models WORSE on faithfulness than Flash; wide band; faithfulness proxy only"}, + "gemini-2-pro|retrieval": {kind:"generator",band:{low:0.88,mid:0.93,high:0.97},benchmark:"FaithBench/RAGTruth",metric_kind:"absolute",note:"best faithfulness in FaithBench set; faithfulness PROXY only; note HHEM conflict-of-interest"}, + "gemini-2-flash|retrieval": {kind:"generator",band:{low:0.88,mid:0.93,high:0.97},benchmark:"Vectara-HHEM + FaithBench",metric_kind:"absolute",note:"Flash-class tops HHEM; FaithBench confirms strong faithfulness; PROXY only"}, + "llama-3.3-70b|retrieval": {kind:"generator",band:{low:0.78,mid:0.84,high:0.9},benchmark:"FaithBench/RAGTruth",metric_kind:"absolute",note:"direct FaithBench measurement; faithfulness PROXY only; 70B tier"}, + "deepseek-v3|retrieval": {kind:"generator",band:{low:0.88,mid:0.93,high:0.96},benchmark:"Vectara-HHEM + DeepSeek blog",metric_kind:"absolute",note:"V3 outstanding faithfulness; faithfulness PROXY only; does not predict end-to-end RAG correctness"}, + "deepseek-r1|retrieval": {kind:"generator",band:{low:0.78,mid:0.86,high:0.91},benchmark:"Vectara-HHEM + DeepSeek blog",metric_kind:"absolute",note:"REASONING PENALTY on faithfulness; R1+RAG can hit 86% clinical accuracy despite poor faithfulness — proxy gap CRITICAL"}, + "qwen2.5-72b|retrieval": {kind:"generator",band:{low:0.75,mid:0.85,high:0.92},benchmark:"Vectara-HHEM (interpolated)",metric_kind:"relative",note:"interpolated; mid tier faithfulness proxy; wide band"}, + "mistral-large|retrieval": {kind:"generator",band:{low:0.75,mid:0.85,high:0.92},benchmark:"Vectara-HHEM (interpolated)",metric_kind:"relative",note:"interpolated; mid tier faithfulness proxy; wide band"}, + "claude-opus-4|review": {kind:"review",band:{low:0.62,mid:0.72,high:0.84},benchmark:"FaithJudge / MT-Bench (zero-shot LLM judge)",metric_kind:"absolute",note:"SOFTEST COLUMN; strong frontier judge tier; zero-shot only; few-shot raises to ~84% (not assumed here); wide band"}, + "claude-sonnet-4|review": {kind:"review",band:{low:0.55,mid:0.65,high:0.78},benchmark:"FaithJudge / MT-Bench (zero-shot LLM judge)",metric_kind:"absolute",note:"SOFTEST COLUMN; mid judge tier; zero-shot only; RewardBench-2 shows ~20pp drop on harder distributions; wide band"}, + "claude-haiku-4|review": {kind:"review",band:{low:0.48,mid:0.58,high:0.7},benchmark:"FaithJudge / MT-Bench (zero-shot LLM judge)",metric_kind:"absolute",note:"SOFTEST COLUMN; small judge tier; near-random on hard distributions; wide band; do not use as sole quality gate"}, + "gpt-4o|review": {kind:"review",band:{low:0.55,mid:0.65,high:0.78},benchmark:"MT-Bench + FaithJudge",metric_kind:"absolute",note:"pairwise preference easier than absolute error-catch; MT-Bench bias documented (position/verbosity/self-enhancement); mid judge tier"}, + "gpt-4o-mini|review": {kind:"review",band:{low:0.48,mid:0.58,high:0.7},benchmark:"FaithJudge (zero-shot)",metric_kind:"absolute",note:"SOFTEST COLUMN; small judge tier; wide band; do not use as sole quality gate"}, + "openai-o3|review": {kind:"review",band:{low:0.62,mid:0.72,high:0.84},benchmark:"FaithJudge (o3-mini-high zero-shot anchor)",metric_kind:"absolute",note:"SOFTEST COLUMN; best public zero-shot judge evidence; o3-mini-high is the anchor; strong frontier judge tier"}, + "gemini-2-pro|review": {kind:"review",band:{low:0.55,mid:0.65,high:0.78},benchmark:"FaithJudge (interpolated)",metric_kind:"relative",note:"SOFTEST COLUMN; interpolated; no direct zero-shot catch-rate row for Gemini-2-Pro; mid-to-frontier tier"}, + "gemini-2-flash|review": {kind:"review",band:{low:0.52,mid:0.62,high:0.75},benchmark:"FaithJudge (interpolated)",metric_kind:"relative",note:"SOFTEST COLUMN; interpolated; mid judge tier; wide band"}, + "llama-3.3-70b|review": {kind:"review",band:{low:0.5,mid:0.62,high:0.75},benchmark:"FaithJudge / CriticBench (interpolated)",metric_kind:"relative",note:"SOFTEST COLUMN; conservative wide band fallback; no reliable evidence; refine from traces immediately"}, + "deepseek-v3|review": {kind:"review",band:{low:0.52,mid:0.63,high:0.76},benchmark:"FaithJudge (interpolated)",metric_kind:"relative",note:"SOFTEST COLUMN; interpolated; mid tier; CriticBench pairing asymmetry applies"}, + "deepseek-r1|review": {kind:"review",band:{low:0.5,mid:0.62,high:0.76},benchmark:"FaithJudge (interpolated)",metric_kind:"relative",note:"SOFTEST COLUMN; reasoning overhead uncertain for critique; conservative mid tier; wide band"}, + "qwen2.5-72b|review": {kind:"review",band:{low:0.5,mid:0.6,high:0.74},benchmark:"FaithJudge / CriticBench (no reliable evidence)",metric_kind:"relative",note:"SOFTEST COLUMN; conservative wide-band fallback per research guidance; no reliable evidence"}, + "mistral-large|review": {kind:"review",band:{low:0.5,mid:0.6,high:0.74},benchmark:"FaithJudge / CriticBench (no reliable evidence)",metric_kind:"relative",note:"SOFTEST COLUMN; conservative wide-band fallback per research guidance; no reliable evidence"} + }; + + var MODELS = ["claude-haiku-4","claude-opus-4","claude-sonnet-4","deepseek-r1","deepseek-v3","gemini-2-flash","gemini-2-pro","gpt-4o","gpt-4o-mini","llama-3.3-70b","mistral-large","openai-o3","qwen2.5-72b"]; + var TASK_TYPES = ["code_generation","classification","extraction","drafting","retrieval","review"]; + + // ---- public API ------------------------------------------------------------ + + /** List all model names in the table. */ + function listModels() { return MODELS.slice(); } + + /** List all task-type names in the table. */ + function listTaskTypes() { return TASK_TYPES.slice(); } + + /** Return true if a prior exists for the (model, taskType) pair. */ + function hasCell(model, taskType) { return !!(CELLS[model + "|" + taskType]); } + + /** + * Seed a cockpit Node from a (model, taskType) prior. + * + * Returns an object ready to be merged onto a node: + * { model, task_type, is_prior: true, seeds, sigma_skill?, catch_rate, provenance } + * + * Mirrors Python priors.seed_node exactly. Throws if no cell exists. + */ + function seedNode(model, taskType) { + var cell = CELLS[model + "|" + taskType]; + if (!cell) throw new Error("no prior for model=" + model + " task_type=" + taskType); + + var b = cell.band; + var out = { model: model, task_type: taskType, is_prior: true }; + + if (taskType === "review") { + out.catch_rate = clamp(b.mid, 0, 1); + out.seeds = "catch_rate"; + } else { + out.sigma_skill = clamp(b.mid / GAMMA, 0.05, 0.98); + out.catch_rate = 0.0; + out.seeds = "sigma_skill"; + } + + out.provenance = { + band: { low: b.low, mid: b.mid, high: b.high }, + confidence: Math.round((1 - (b.high - b.low)) * 1000) / 1000, + benchmark: cell.benchmark, + metric_kind: cell.metric_kind, + note: cell.note + }; + return out; + } + + return { listModels: listModels, listTaskTypes: listTaskTypes, hasCell: hasCell, seedNode: seedNode, GAMMA: GAMMA }; +}); diff --git a/web/widgets/cockpit.html b/web/widgets/cockpit.html index 37a782c..7de6698 100644 --- a/web/widgets/cockpit.html +++ b/web/widgets/cockpit.html @@ -67,6 +67,22 @@

MSO governance cockpit

+ +
@@ -101,6 +117,7 @@

Selected node

+