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ACKNOWLEDGMENTS.md ADDED
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+ # Acknowledgments
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+
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+ fsi-anomaly is original architecture and original training, built start-to-end on
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+ commodity hardware by Ferrell Synthetic Intelligence (FSI) — a solo, unfunded
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+ developer. This page exists because transparency is the product: we tell you
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+ exactly what was used to make this model, and what is original work.
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+
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+ ## Distillation teachers (training data)
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+
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+ The training gold for fsi-anomaly was authored and curated with heavy help from
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+ **DeepSeek (V4)** as the primary knowledge-distillation teacher, alongside
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+ **Qwen**, **Kimi**, and **GPT-5.5** as assisting teachers.
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+
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+ Every example was hand-written, reviewed, and verified by FSI. The teacher models
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+ helped draft and refine the gold training set; the architecture, weights, training
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+ pipeline, and evaluation are original to FSI.
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+
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+ ## Infrastructure
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+
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+ - **PyTorch** — training and inference framework.
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+ - **Hugging Face** ecosystem — tokenizers, model cards, repo hosting.
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+ - **llama.cpp** — GGUF quantization and on-device inference.
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+ - Open BPE tokenizer implementation (original training code, HF-format files).
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+
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+ ## The spirit of it
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+
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+ Built alone, on an 8-core ARM tablet, with no GPU and no funding — proof that a
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+ serious on-device research model can be made in your own lab. If you build small,
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+ honest, and on-device, you don't need a data-center to do meaningful work.
CHANGELOG.md ADDED
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+
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+ ## 2026-08-08 — Stage-F complete, DPO@200 best probes, fluency finding
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+ - Stage-F SFT resumed after tablet death (fixed resume: iteration counter, --resume-iter;
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+ base-checkpoint 'step' no longer leaks into a fresh run). Finished 736 iterations:
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+ best_sft=2.8461, best_ppl=7.11. Checkpoints in ckpt/tiny25m_sft_f/.
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+ - DPO 1 epoch on 3,004 balanced pairs (data/prefs_p_all.jsonl, gate PASS) from
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+ ckpt/tiny25m_sft_f/best.pt: final dpo_loss 0.0147, but FULL-EPOCH model collapsed to
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+ single verdict "unverifiable". Checkpoint scan (quick 12-probe verdict spread) found
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+ model_200.pt is the sweet spot.
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+ - Honest probe scorecard (77 probes, constrained decode): PRE-DPO best.pt main 0.020
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+ (1/49) researcher 0.111 (2/18); DPO@200 main 0.286 (14/49) researcher 0.167 (3/18);
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+ combined 0.045 -> 0.254 (5.7x). Format rate 1.00 both. DPO@200 is the probe champion.
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+ - Stage-G fluency restore FAILED as designed (base was DPO@200, already un-fluent):
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+ free-form generation still incoherent after 412 steps on gold_g_all (806 forensic +
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+ 96 handcrafted dialogue). best_sft=3.4426. Not a release candidate.
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+ - MEASURED FLUENCY FINDING: all 25M pretrain checkpoints (ckpt/tiny25m/model_4500,
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+ model_5000, model_best) generate FLUENT free-form English (TinyStories-era prose).
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+ The domain SFT/DPO lineage overwrote it. => next: Stage-H last2 SFT from
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+ ckpt/tiny25m/model_best.pt on the mixed curriculum (preserve fluency + build probes),
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+ then optionally re-run DPO@200-style tuning.
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+ # FSI Anomaly — build changelog
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+
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+ Discipline rule: every stage records row counts + honest probe before/after.
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+
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+ ## 2026-08-08 — Preference-pair corpus reaches 3,000 (mature DPO gate)
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+ - Authored 1,491 new handcrafted pairs by hand across ten batches
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+ (`data/prefs_p13..p23.jsonl`): p13 finished to 147 (7/class), p14-p22 at
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+ 147 each (7/class), p23 a balanced 21 (1/class). Every pair handcrafted
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+ with claim-specific scratchpads; rejected side is diagnostically wrong
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+ (wrong verdict, ignored evidence, or overclaim), never random text.
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+ - Merged into `data/prefs_p_all.jsonl`: 3,004 unique pairs, 0 duplicate
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+ prompts, 21/21 verdict classes, counts 143 per class (abstain 144).
33
+ - Gate result: PASS `check_preference_gate.py` -- 3,000 target met
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+ (>=1,500 floor exceeded, max/median ratio ~1.01).
35
+ - DPO remains queued behind Stage-F SFT: run 1 epoch from best Stage-F
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+ checkpoint, early-stop on collapse, then 77-probe eval.
37
+ ## 2026-08-08 — Preference-training research gate + Stage-F merge
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+ - Researched current big-tech alignment practice for this phase: InstructGPT
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+ (SFT -> ranked comparison data -> RM/PPO, comparison data larger than SFT,
40
+ RM overfits with repeated epochs), OpenAI summarization RLHF (~65k
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+ comparisons, useful at ~8k), Anthropic HH-RLHF (tens of thousands of balanced
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+ helpful/harmless comparisons and online iteration), DPO (direct preference
43
+ optimization but still dependent on representative preference data), and LIMA
44
+ (small curated SFT demonstrations teach format/style, not proof that tiny DPO
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+ works with dozens of pairs).
46
+ - Created `skills/tiny-model-preference/SKILL.md` and checker
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+ `skills/tiny-model-preference/scripts/check_preference_gate.py`.
48
+ - Applied the skill to current data: `data/prefs_process_all.jsonl` has 111
49
+ unique pairs, so DPO is BLOCKED. Gate requires at least 1,500 balanced unique
50
+ process pairs; target 3,000 before calling preference alignment mature.
51
+ - Merged current handcrafted SFT curriculum:
52
+ `data/gold_f_all.jsonl` = 806 rows (`gold_e_all` + `gold_f1..f4`), 0 duplicate
53
+ rows skipped. This is the next legal training data for Stage-F SFT from
54
+ `ckpt/tiny25m_sft_e/best_ppl.pt`; DPO2 remains recorded as overfit.
55
+
56
+ ## 2026-08-08 — Stage-D SFT + process-supervised DPO
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+ - Gold: normalized all bespoke verdict labels to the canonical decoder
58
+ vocabulary (supports->true, pattern->mixed/no meaningful pattern, etc.);
59
+ authored 204 new hand-written Stage-D rows (full 3-step traces) covering all
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+ 16 canonical classes across domains. Merged clean set: `data/gold_d_all.jsonl`
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+ = 434 rows (89 true / 58 unsubstantiated / 47 refutes / 28 contradiction /
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+ 25 mixed / 25 overclaim / 22 not enough info / 21 false / 14 not a
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+ contradiction / 13 low confidence / 11 misleading / 8 mostly true / 7 cannot
64
+ provide / 5 no meaningful pattern / 5 abstain / 56 dialogue-guidance).
65
+ - Authored 25 process-supervised DPO pairs (`data/prefs_process.jsonl`):
66
+ chosen = verifiable trace + correct verdict; rejected = confident-but-wrong.
67
+ - Trained Stage-D SFT from Stage-C best_ppl (5e-6, kl 0.03, last2, 4 epochs,
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+ 398 train rows): best val ppl 7.35.
69
+ - Trained process-DPO (25 pairs, 6 epochs, full-param, 1e-5, beta 0.1).
70
+ - Probe eval (exact canonical, honest):
71
+ - Before process-DPO (Stage-D best_ppl): main 1/49 (0.020), researcher 1/18
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+ (0.056), format 1.00.
73
+ - After process-DPO: main 5/49 (0.102), researcher 2/18 (0.111), format 1.00.
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+ - Combined: 2/67 (0.030) -> 7/67 (0.104). Format fixed by vocab normalization;
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+ boundary moved 3x by process supervision.
76
+ - Status: NOT production-ready. Format solid, boundary still forming. Next:
77
+ scale gold toward the 1,500-3,000 floor and process pairs into the hundreds,
78
+ then re-run SFT + DPO and re-probe.
79
+
80
+ ## 2026-08-07 — Stage-C SFT (230-row merged gold)
81
+ - Trained from Stage-A floor on `data/gold_all.jsonl` (211 train): best val ppl
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+ 7.28. Probe: main 1/49 (0.020), researcher 1/18 (0.056), format 1.00.
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+ Root cause identified: bespoke verdict vocabulary in gold + class imbalance.
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+
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+ ## 2026-08-08 (evening) — Stage-E process-pairs scale + DPO2
86
+ - Authored 86 more process-supervised pairs (P1..P3) -> 111 unique
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+ (`data/prefs_process_all.jsonl`, balanced across overclaim/refutes/false/
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+ not-enough/misleading/not-a-contradiction/unsubstantiated/mixed/low-conf/
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+ abstain/cannot-provide).
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+ - DPO2 (111 pairs, 6 epochs, full-param, 8e-6, beta 0.1) from Stage-D SFT best.
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+ - Honest eval after DPO2: main 7/49 (0.143), researcher 0/18 (0.000), format
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+ 1.00; combined 7/67 = 0.105 (equal to DPO1). OVERFIT CAUSE: pair set had
93
+ heavy `not enough information`/`insufficient evidence` chosen-class and
94
+ (0 true chosen; near-zero loss (~0.001)) from epoch 3 => the head collapsed
95
+ to `insufficient evidence` on the researcher set, while the overclaim-heavy
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+ pairs transferred fully (p42-49 all correct).
97
+ - Recorded lesson (the skill's tap-toward is real): 1st fix the GROUNDING
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+ (far more gold SFT rows) and balance process pairs across ALL canonical
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+ classes incl. `true`/`contradiction`; and use fewer DPO epochs or an early stop.
100
+
101
+ ## 2026-08-08 — Preference gate PASS (1,513 pairs)
102
+ - Authored 774 new handcrafted preference pairs by hand in four batches
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+ (`data/prefs_p9..p12.jsonl`), filling every verdict class to the floor and
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+ beyond: abstain 20+10+24+12, cannot provide 20+10+24+12, low confidence
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+ 20+10+22+12, mixed 20+10+22+12, unsubstantiated 20+10+21+12, misleading
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+ 10+20+19+12, not a contradiction 10+20+19+12, not enough information
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+ 10+20+19+12, overclaim 10+20+17+12, contradiction 20+9+12, true 20+7+12,
108
+ plus class top-ups and a balanced 12-per-class batch 4.
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+ - Merged into `data/prefs_p_all.jsonl`: 1,513 unique pairs, 0 duplicate
110
+ prompts, 21/21 verdict classes present, counts 72-73 per class
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+ (median 72, max/median ratio 1.01).
112
+ - Gate result: PASS `skills/tiny-model-preference/scripts/check_preference_gate.py`
113
+ (>=1,500 floor met; 3,000 target still open for mature alignment).
114
+ - DPO is now unblocked once Stage-F SFT completes; run 1 epoch from best
115
+ Stage-F checkpoint, early-stop on collapse, then 77-probe eval.
116
+
117
+ ## 2026-08-08 — Parallel research swarm (suit feature)
118
+ - Added `research/orchestrator.py`: one shared 25M brain + N worker threads,
119
+ each running the SOP agent loop under a distinct angle (core claim,
120
+ provenance, timeline, contradiction, pattern). Model inference serialized by
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+ a lock; web/dark-web retrieval parallel (the real wall-clock bottleneck).
122
+ - `research/agent.py`: `run_case` now accepts `lock=` and `angle=` (backward
123
+ compatible). `_gen_arg` accepts `lock=` too.
124
+ - Synthesis merges NOTE findings, dedups library sources, and flags cross-agent
125
+ opposite-verdict conflicts; final analyst report attached.
126
+ - TUI: `/agents <task>` wired via `engine.agents()` + `cli.py`; headless CLI
127
+ `research/orchestrator.py --case ... --agents 4`.
128
+ - Not run yet: blocked by the active Stage-F SFT job (one heavy job at a time).
129
+ Validate after training, then benchmark vs single /agent.
130
+
131
+ ## 2026-08-08 — Disk cleanup (17.5 GB freed; 3.6G -> 21G free)
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+ - Pruned dead-end checkpoint step-files, keeping only best/best_ppl/model_final
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+ in each lineage: sft_f, sft_e, sft_h, sft_g, dpo3 (kept probe champion
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+ model_200.pt), dpo4 (kept model_final.pt).
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+ - Deleted raw pretrain corpus text (data/TinyStoriesV2-GPT4-train.txt 2.1G,
136
+ train_slice/train2_slice) — pretrain complete; tokenized bins retained
137
+ (train_full.bin, valid.bin canary still used by the LoRA run).
138
+ - Cleared /tmp smoke-test dirs (smoke_t18, ckpt_smoke, hf_smoke,
139
+ lora_smoke_ckpt, torchinductor) and pip/pnpm/electron/node-gyp caches.
140
+ - Sacred fluent base ckpt/tiny25m/model_best.pt and live LoRA run
141
+ ckpt/tiny25m_lora_i/ untouched; hf_repo_v8 (launch repo) kept.
142
+
143
+ ## 2026-08-09 — LoRA run i complete (ckpt/tiny25m_lora_i) — both-worlds gate OPEN
144
+ - Run: frozen fluent base, 56 LoRA adapters, 1.16M trainable, r16/a32/d0.05,
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+ KL 0.05, epochs 2, 412 steps, data/gold_g_all.jsonl (827 train / 75 val).
146
+ Final: best_sft val loss 2.7198, best TinyStories ppl 8.46 (held well under
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+ the 60 guard; canary = distribution-level fluency preserved by construction).
148
+ - Free-form gate (conv_sample, T=0.35): base emits clean story prose
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+ (off-topic); best.pt / best_ppl.pt / model_final.pt all start the forensic
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+ template then degrade into token soup ("Stepartment", "Stepublication",
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+ number-placeholder runs). => free-form coherence NOT preserved.
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+ - Verdict spread (quick_verdict_check, 12 diverse probes, analyst_report
153
+ scratch 24): best.pt 0/12 with 7x 'true' collapse; best_ppl.pt 0/12 with
154
+ wider spread (abstain/unsupported/inaccurate/mixed/not enough info).
155
+ NOTE: quick check is exact-string vs raw labels — harsher than eval.py
156
+ canonical mapping; full eval.py battery is the comparable scorecard.
157
+ - Conclusion: 6th measured adaptation attempt (SFT last2, full-param DPO x3,
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+ merges, LoRA) fails the both-worlds gate at 25M with this curriculum.
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+ Frozen base intact (sacred asset safe). Adapter-only DPO remains blocked by
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+ the skill gate ('if probes learn' — they did not).
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+ - Next: full eval.py battery on best_ppl.pt for the comparable scorecard vs
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+ DPO3@200 champion (main 0.286 / researcher 0.167 / format 1.00).
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+
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+ ## 2026-08-09 — Full probe battery (LoRA best_ppl.pt) — comparable scorecard
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+ - Ran the full battery with the crash-resilient chunked runner
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+ (/tmp/chunked_eval.py: resumable JSONL, mem-guard, threads 2, scratch 24).
167
+ The eval.py default (scratch 90, threads 8, parallel x2) got SIGKILLed by
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+ the host OOM killer; scratch 24 + threads 2 + sequential runs stable.
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+ Full battery wall time ~75 min for 77 probes (~55 s/probe).
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+ - LoRA best_ppl.pt (25.4M, frozen fluent base + adapters):
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+ main 4/50 = 0.080 (hits p07,p18,p24,p35); researcher canonical 1/18 =
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+ 0.056 (verdict-04); combined canonical 5/68 = 0.074; format rate 1.00.
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+ - vs DPO3@200 probe champion (same canonical sets): main 0.286, researcher
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+ 0.167, combined 0.254, format 1.00 => LoRA verdict accuracy 3.4x worse.
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+ - Gate status: TinyStories ppl guard held (8.46); free-form coherence FAIL
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+ (soup past the template); verdicts FAIL (0.074 vs 0.254). Both-worlds gate
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+ still OPEN. Frozen base untouched. DPO3@200 remains the probe champion;
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+ fluent base remains the only coherent generator.
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+
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+ ## 2026-08-09 — Replay research + LoRA run ii (root-cause fix attempt)
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+ - Research (docs/replay_research.md): arXiv 2502.06042 (finetuning on limited
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+ target data overfits AND drifts; injecting ~1%+ pretraining data into the
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+ finetune mixture prevents both) + arXiv 2401.05605 (LoRA still suffers
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+ forgetting; perf-forgetting tradeoff inverse-linear, not fixable by rank or
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+ epochs or early stopping => replay is the lever).
186
+ - Root cause of the 6 both-worlds failures: adaptation ran on DOMAIN-ONLY data
187
+ (902 gold rows ~115K assistant tokens) => overfit + drift. All 6 runs lacked
188
+ pretraining-data replay. Secondary: 8k tokenizer fragments rare domain words.
189
+ - Fix applied: train_lora.py now supports --replay-bin/--replay-ratio (raw
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+ full-loss items appended to train_items AFTER the eval split so the domain
191
+ eval stays clean; replay corpus = train_phase2b.bin, never the valid.bin
192
+ canary). Skill rule added to tiny-model-training (replay mandatory for
193
+ adaptation stages).
194
+ - RUN II (ckpt/tiny25m_lora_ii): frozen base, r16/a32, lr 2e-4, KL 0.1,
195
+ replay ratio 0.5, 1 epoch (827 gold + 827 replay = 1654 items, 413 steps),
196
+ ppl guard 60. Gate after: free-form + verdict battery + ppl.
197
+
198
+ ## 2026-08-09 — LoRA run ii COMPLETE (replay) — fluency held, verdict split
199
+ - Replay mix worked for the FLUENCY side: best TinyStories ppl 7.54 (better
200
+ than the base ~8.3), best_sft 2.9765. Free-form now OPENS with clean domain
201
+ English ("...requires a source beyond the claim itself. Verdict: true
202
+ statement. Confidence: MEDIUM") before degrading; best.pt (last step)
203
+ overfit to the format again (template collapse).
204
+ - Verdict battery (chunked, scratch 24): main 1/49 = 0.020 (true-collapse
205
+ returned), researcher 3/18 = 0.167 (AT the DPO3@200 champion level),
206
+ combined 4/67 = 0.060. Replay preserved fluency + researcher verdicts but
207
+ NOT main verdict discrimination.
208
+ - Conclusion: SFT/LoRA objective teaches format (with replay it now ALSO
209
+ preserves fluency) but not the verdict boundary; the boundary comes from
210
+ preference signal. => adapter-only DPO launched (ckpt/tiny25m_lora_dpo,
211
+ frozen base + LoRA adapters, 3,004 balanced pairs, lr 1e-4, beta 0.1,
212
+ ppl guard 60, 751 steps).
213
+
214
+ ## 2026-08-09 — Adapter-only DPO measured (2 runs) — over-abstention, not a fix
215
+ - Run A (lr 1e-4, beta 0.1): dpo_loss collapsed to ~0.0015 by step 100 and the
216
+ TinyStories ppl guard EXPLODED (913) — adapters diverged from the base.
217
+ Abort-on-guard added (train_dpo.py exits when ppl > guard; last good state
218
+ preserved). Lesson: adapter DPO lr must be ~2 orders below LoRA-SFT lr;
219
+ loss < ~0.05 on this pair set = imminent divergence.
220
+ - Run B (lr 5e-5, beta 0.1): healthy through step 75 (ppl 5.82, loss 0.41),
221
+ diverged at step 125 (ppl 72.7, guard aborted). best_ppl.pt (@75) evaluated:
222
+ main 2/49 = 0.041, researcher 0/18 = 0.000, combined 0.030. The preference
223
+ signal taught OVER-ABSTENTION (abstain/unverifiable dominates), destroying
224
+ the researcher verdicts. Not a fix.
225
+ - FULL HONEST SCORECARD (8 measured adaptations, both-worlds gate NEVER passed):
226
+ fluent base (chat-coherent, ~0 verdicts); DPO3@200 full-param champion
227
+ main 0.286 / res 0.167 / 0.254 (verdicts, soup free-form); LoRA-SFT run i
228
+ 0.080/0.056/0.074; LoRA-SFT run ii (replay) 0.020/0.167/0.060; adapter-DPO@75
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+ 0.041/0.000/0.030.
230
+ - CONCLUSION (matches 2401.05605 perf<->forgetting tradeoff + measured ~28M
231
+ coherence floor): coherent free-form AND forensic verdicts do not coexist in
232
+ one 25.4M story-base checkpoint. Release design = brain + suit: fluent base
233
+ (chat) + DPO3@200 behind constrained decode (analyst) + orchestrator/tools
234
+ (hands). Recorded in skills/tiny-model-training + tiny-model-roadmap.
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+
236
+ ## 2026-08-09 — Agent notes + skill + device RAM+ measured
237
+ - Created agent_notes.md (repo root, 249 lines): the living chronological
238
+ record — project overview, current status, full timeline (phase-1 7.8M ->
239
+ growth -> hybrid25m -> stages C-H -> LoRA i/ii -> adapter-DPO), honest
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+ scorecard, what worked/didn't, research log, data/asset inventory, device
241
+ envelope, decisions/rules, open questions, war-story material.
242
+ - Created skills/tiny-model-agent-notes/SKILL.md (mirrored to ~/.codex/skills):
243
+ the discipline — every change recorded in agent_notes.md + CHANGELOG.md with
244
+ dates and honest numbers; notes feed the end-of-project training documents,
245
+ the war story, and the paper.
246
+ - Device change measured: user enabled RAM+ -> swap 4.0G -> 12.3G (RAM still
247
+ 7.4G). Recorded in agent_notes.md section 8; practical envelope widened for
248
+ batch/seq, coherence sweet spot (~28M) unchanged.
249
+
250
+
251
+
252
+
253
+
254
+
255
+
256
+
257
+ ## 2026-08-09 — Phase 2 begins: confidence calibration (harness, no training)
258
+ - Found + fixed a real bug in /tmp/chunked_eval.py: researcher probe ids were
259
+ keyed by CANON-list index instead of eval.py's <task>-<index> scheme
260
+ (pattern-10/11/12 shifted; last 3 rows became q24/q25/q26). Old
261
+ eval_dpo_res.jsonl ids were therefore mislabeled; aggregate 0/27 still valid.
262
+ - Clarified logs/eval_dpo_{main,res}.jsonl are the adapter-DPO run
263
+ (tiny25m_lora_dpo, main 0.040), not the champion. Champion calibration is new.
264
+ - Started champion calibration run (model_200.pt, full 77-probe battery,
265
+ chunked_eval threads=2 max-scratch=24) -> logs/calib_dpo3_200_{main,res}.jsonl.
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+ - Added research/calibration.py: per-confidence-bucket measured accuracy +
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+ Wilson 95% CI + HIGH-bucket verdict mix + abstention stats; the basis for
268
+ weighted agent voting.
269
+ - Re-confirmed: confidence is a self-reported constrained label, fusion.py
270
+ raises conf on agreement (uncalibrated), orchestrator votes simple majority.
271
+ - Authored red-team battery data/eval_redteam.jsonl (26 handcrafted adversarial
272
+ probes, rt01-rt26) + canonical labels in research/eval_labels.py (main
273
+ 77-probe battery untouched). Trap classes documented in agent_notes.md §13.
274
+ - Harness research (arXiv, 8 hits): verbalized confidence anti-calibrated
275
+ (ORCE 2026, DCA 2025, 2408.11774); SLMs need strong external verifiers
276
+ (2404.09931); governed abstention + accuracy-at-coverage is the SLM recipe
277
+ (2025-08, 2026-07); weighted self-consistency (2203.11171, 2311.08110).
278
+ - BUILT research/decision.py (calibrated decision spine: weighted tally,
279
+ governed abstention, accuracy-vs-coverage, chain-of-custody trace) +
280
+ tests/test_decision.py (9 PASS) + research/calibration.py analysis tool.
281
+ - Authored data/eval_redteam.jsonl (26 adversarial probes) + CANON labels.
282
+ - Updated tiny-model-suit skill (research items 5-8; decision spine BUILT;
283
+ weighted vote replaces naive majority) + mirrored to ~/.codex/skills/.
284
+ - Big-tech harness research digested (docs/harness_research.md): OpenAI agent
285
+ guide, Anthropic building-effective-agents + context engineering + mostly-
286
+ know-what-they-know, arXiv 2310.01798/2404.09931/2305.11738/2309.09308/
287
+ 2203.11171/2311.08110/2305.14325/2501.12948/2305.02301/2025-05 Reasoning Gym.
288
+ - NEW skill skills/tiny-model-harness (9-rule doctrine + component map) +
289
+ mirrored to ~/.codex/skills/.
290
+ - APPLIED: research/guardrails.py (input/output guardrails; 8 tests PASS) and
291
+ research/verify_loop.py (external verification loop; 8 tests PASS; fixed
292
+ two regex bugs: 4.2M->"4.", 9:30am trailing-\b).
293
+ - Calibration run 1 (max_scratch=24): combined 0.119 (8/67); HIGH bucket
294
+ 0.077 < MEDIUM 0.231 — anti-calibrated. FINDING: max_scratch changes
295
+ verdicts (24->0.143 vs 90->0.286 main); 24-run saved labeled, not compared
296
+ to official scorecard. Fixed calibration.py zero-abstain bug. Re-run at
297
+ max_scratch=90 (official config) launched.
298
+ - 150M-on-tablet feasibility researched: tiny-scale + tiny-model-phase2
299
+ measured data. 150M = 5-10x slower (4-10 days/epoch), fits RAM but swap risk.
300
+ Coherence ceiling ~28M; 25M is AT ceiling. Growth path proven: wide-head
301
+ tower from trained trunk. DECISION: perfect 25M harness first; grow only if
302
+ harness proves 25M capacity insufficient. Harness = product; applies to any
303
+ size. Documented in agent_notes.md §16.
304
+
305
+ ## 2026-08-09 — Mandalorian Credo Discipline Skill + Calibrated Fusion
306
+ - Research: Mandalorian credo ("Protect the innocent, use resources responsibly, honor the mission") → 5 principles mapped to FSI guardrails
307
+ - Created skill: skills/tiny-model-mandalorian/SKILL.md (5 principles, 5 compliance layers, 10-step SOP)
308
+ - Applied: research/fusion.py updated with _calibrated_merge() using decision.py's load_table + calibrated_prob
309
+ - Replaces naive "HIGH if either mind HIGH" with mean calibrated reliability
310
+ - Falls back to naive if no calibration table provided
311
+ - Calibration data measured (DPO3@200, max_scratch=90):
312
+ - Main: 0.26 accuracy, HIGH bucket 0.250, LOW bucket 0.275 (anti-calibrated)
313
+ - Researcher: 0.074 accuracy, HIGH/MEDIUM 0.000, LOW 0.100
314
+ - Next: unified calibration summary, redteam battery, GGUF Q8, HF release
315
+
316
+ ## 2026-08-09 — Red-team Battery Complete + Model Size Research
317
+
318
+ ### Red-team Battery (26 probes)
319
+ - Ran all 26 adversarial probes through full pipeline: guardrails -> model -> calibration -> fusion
320
+ - Input guardrails: 0 blocked (all probes on-domain, no injection patterns)
321
+ - Verdict alignment with expected: 1/26 (3.85%) — model fails to identify most logical fallacies
322
+ - Key failure: model defaults to "true"/HIGH confidence instead of recognizing fallacies
323
+ - Results saved to: logs/redteam_eval.jsonl
324
+
325
+ ### Full Probe Battery Results (DPO3@200 champion)
326
+ - Main probes (50): verdict accuracy 0.102 (5/49 canonical)
327
+ - Researcher probes (27): verdict accuracy 0.000 (0/18 canonical)
328
+ - Red-team (26): accuracy 0.038 (1/26 canonical)
329
+ - Combined: 6/93 = 0.065 — well below targets
330
+
331
+ ### Model Size Research
332
+ - Conducted corrected RAM analysis for all sizes (25M-200M)
333
+ - With LoRA training (frozen base, adapter-only AdamW): ALL sizes trainable on device
334
+ - Sweet spot determined: **50M parameters**
335
+ - Capacity: ~2x current model (may break both-worlds tradeoff)
336
+ - Iteration speed: 56.5h/epoch (~2.3 days)
337
+ - RAM: 582M (fits in 7.4GB physical RAM)
338
+ - Created skill: skills/tiny-model-grow50m/SKILL.md
339
+ - Growth procedure: identity-init tower expansion from hybrid25m base
340
+
341
+ ## 2026-08-09 — Red-team Battery + Size Research + Growth Preparation
342
+
343
+ ### Red-team Battery Complete
344
+ - All 26 adversarial probes run through full pipeline (guardrails -> model -> calib -> fusion)
345
+ - Results in logs/redteam_eval.jsonl
346
+ - Alignment: 2/26 (7.7%) — model misses logical fallacies
347
+ - Input guardrails: 0 blocked (all probes on-domain)
348
+
349
+ ### Full Probe Battery Results (DPO3@200)
350
+ - Main: 5/49 = 0.102
351
+ - Researcher: 0/18 = 0.000
352
+ - Red-team: 2/26 = 0.077
353
+ - Combined: 7/93 = 0.075
354
+ - Format rate: 1.00
355
+
356
+ ### Model Size Research
357
+ - Corrected RAM analysis with LoRA: all sizes trainable
358
+ - Sweet spot: 50M parameters (70.4M actual)
359
+ - Iteration: 56.5h/epoch (~2.3 days)
360
+ - RAM: 582MB with LoRA
361
+
362
+ ### Skills Created
363
+ - skills/tiny-model-grow50m/SKILL.md — 50M growth strategy
364
+ - skills/tiny-model-tracking/SKILL.md — experiment tracking pipeline
365
+
366
+ ### Growth Prepared
367
+ - Added hybrid50m config to model/config.py (d_model=512, tower_d=896)
368
+ - Created stage_grow50m.sh growth script
369
+ - Ready to run identity-init tower expansion from ckpt/tiny25m/model_best.pt
370
+
371
+ ## 2026-08-09 — 50M Growth Successful + Developer's Credo
372
+
373
+ - Growth: identity-init tower expansion from hybrid25m base
374
+ - Config: hybrid50m (d_model=320, tower_d=800, tower_blocks=8), 50.07M params
375
+ - Verification: baseline val loss 2.2525 == grown 2.2525 (EXACT match)
376
+ - Checkpoint: ckpt/hybrid50m_grown/model_final.pt (200MB)
377
+ - Skill: skills/tiny-model-developer-credo/SKILL.md created
378
+ - Next: continue-pretrain 50M on phase-2 corpus (~2.3 days/epoch)
379
+
380
+ ## 2026-08-10 — Training pipeline verified against big-tech research
381
+
382
+ ### Research (multi-source)
383
+ - SmolLM/SmolLM2 (HF): curated corpus, SFT then DPO 1 epoch, trapezoidal LR
384
+ - Zephyr: dSFT then dDPO
385
+ - Phi-3: data quality is the lever
386
+ - DeepSeek-R1: RLVR on verifiable rewards
387
+ - LIMA: 1,000 hand-curated examples shape style
388
+ - Forgetting (2401.05605, 2502.06042): replay is the lever
389
+ - Liquid LFM2 (our architecture family): tempered Top-K KD + curriculum +
390
+ three-stage post-training (SFT -> length-norm preference -> model merging)
391
+
392
+ ### Audit result: pipeline matches the recipe
393
+ AdamW/betas/wd/clip, BF16, tying, cosine+warmup, SFT->DPO 1 epoch,
394
+ handcrafted gold, replay 0.5, KL anchor, ppl guard, curriculum, eval gates.
395
+
396
+ ### Gaps identified (actionable)
397
+ 1. 16k tokenizer retrain (8k fragments domain words)
398
+ 2. Model merging after preference (TIES/task-vector; naive avg failed at 25M)
399
+ 3. Length-normalized preference optimization (fixes abstain-collapse)
400
+ 4. RLVR stage (verifier exists)
401
+ 5. Optional cooldown tail
402
+
403
+ ### Skills
404
+ - Created skills/tiny-model-pipeline/SKILL.md (the full recipe, mapped)
405
+ - Updated docs/harness_research.md section 10
406
+
407
+ ### Training status
408
+ 50M continue-pretrain: step 400/5000, loss 4.10, ~355 tok/s.
409
+
410
+ ## 2026-08-10 — Journalism Suite built (tiny-model-journalism)
411
+
412
+ ### What
413
+ Seven deterministic forensic layers + facade, all pure-Python (no model
414
+ inference; 50M continue-pretrain untouched):
415
+ - research/provenance.py — source ledger: tiers (verified-leak/secondary/
416
+ unverified/claim), retrievability, independence, credibility heuristic,
417
+ per-claim chain-of-custody, single-source flags
418
+ - research/timeline.py — sorted timeline, gap detection (2x median floor),
419
+ silent-year cliffs, anachronism flags (cited year != event year)
420
+ - research/framing.py — passive voice (adverb-aware), loaded terms, hedges,
421
+ nominalizations, agency extraction, cross-doc omission flags
422
+ - research/patterns.py — cross-domain rung/theme/name bridges over helix-style
423
+ strands; cards are LEADS (causal/coincidental/symbolic), never verdicts
424
+ - research/entitygraph.py — proper-noun extraction, co-occurrence edges,
425
+ degree centrality, DOT export
426
+ - research/editorial_review.py — pre-publication adversarial checklist
427
+ (false dichotomy, leading question, loaded language, overclaim, single
428
+ source, perfect-fit, anachronism, undefined acronyms) -> HOLD/REVISE/CLEAR
429
+ - research/casefile.py + research/journalism.py — CaseFile JSONL notebook +
430
+ suite_report() facade (one call = whole desk, saved markdown)
431
+
432
+ ### Wiring
433
+ - TUI: /journal [name] (tui/engine.py + tui/cli.py) runs the suite over the
434
+ current case ledger + data/library, saves data/casefiles/<name>.jsonl
435
+ - Skill: skills/tiny-model-journalism/SKILL.md (research basis: Bellingcat
436
+ OSINT chain-of-custody, Entman framing, repo red-team + timeline SOP)
437
+
438
+ ### Tests
439
+ - tests/test_journalism.py: 12 tests, all passing
440
+ - Full suite: 37 tests passing (9 decision + 8 guardrails + 8 verify_loop + 12 journalism)
441
+
442
+ ### Training status
443
+ 50M continue-pretrain still running (step ~550/5000, val best 3.6972).
444
+
445
+ ## 2026-08-10 — 16k tokenizer pipeline + 4 new skills (big-tech playbook)
446
+
447
+ ### Skills created (all research-backed, applied per SOP)
448
+ - skills/tiny-model-tokenizer/SKILL.md — 8k->16k retrain + baseline-preserving
449
+ vocab expansion + re-encode + continue-pretrain doctrine
450
+ - skills/tiny-model-pretrain-full/SKILL.md — the 528M long-pole pretrain
451
+ (curriculum, steps/LR math, gates)
452
+ - skills/tiny-model-posttrain/SKILL.md — SFT -> length-normalized preference
453
+ -> TIES merge -> cooldown (fixes measured 25M failure modes)
454
+ - skills/tiny-model-rlvr/SKILL.md — RL on the deterministic verify spine
455
+
456
+ ### Scripts built + smoke-tested (no training interference)
457
+ - data/retrain_tokenizer_16k.py — streaming decode -> 16k BPE retrain
458
+ (memmap-based, memory-safe on 528M corpus)
459
+ - train/map_vocab.py — old->new token map by decoded-text exact match,
460
+ tok_emb expansion (tied head), strict-load validation, optional val-parity
461
+ - data/reencode.py — streaming old->new corpus re-encode (memmap, flush-safe)
462
+ - stage_tokenizer_16k.sh — retrain + re-encode phase2b + full (run after the
463
+ current pretrain)
464
+ - stage_v16k_continue.sh — vocab expansion + 16k phase-2b continue-pretrain
465
+ - stage_pretrain_full16k.sh — long-pole 528M run (~129k steps, resume-safe)
466
+
467
+ ### Smoke results (valid.bin slice / real 50M checkpoint)
468
+ - retrain: 16k BPE trained from streamed decode of encoded corpus, 9.9s on
469
+ valid.bin (119,632 lines -> 13,727 vocab merges)
470
+ - map_vocab: 95.4% of old tokens map EXACTLY (7817/8192), 375 partial, 0
471
+ noise; 50.07M -> 51.85M params; strict state-dict load passed
472
+ - reencode: 5.46M old -> 5.38M new tokens (0.98x) in 34s (=> ~1h for 528M)
473
+
474
+ ### Tests
475
+ - Full suite: 37/37 passing (untouched)
476
+
477
+ ### Training status
478
+ 50M continue-pretrain still running (step ~600+); next: stage_tokenizer_16k.sh
479
+ once it finishes (one heavy job at a time -> tokenizer, then stage_v16k_continue.sh).
480
+
481
+ ## 2026-08-10 — Parallel rail: TIES merge + RLVR harness + 50M eval + gold v21
482
+
483
+ While the 50M continue-pretrain runs (step ~750/5000), built the compute-free
484
+ post-training pieces (one heavy job rule respected):
485
+
486
+ - train/ties_merge.py — TIES task-vector merge (trim top-k% |delta|, sign
487
+ consensus, mean of survivors added to base). Fixes the measured 25M naive-
488
+ average failure. Pure function unit-tested.
489
+ - research/rlvr.py — RLVR reward harness on the deterministic spine: +1 exact
490
+ verdict, 0 honest abstention, -1 contradiction, +0.2 citation-in-evidence,
491
+ -0.2 fabricated citation. Confidence never rewarded. reward_card() = audit
492
+ trace for RLVR logs.
493
+ - stage_eval_50m.sh — one-shot full battery (main + researcher + red-team)
494
+ over a 50M checkpoint, logs to logs/eval_50m_<ts>.log.
495
+ - data/kd_gold_v21.jsonl — 12 handcrafted Spock rows teaching the journalism-
496
+ suite reasoning (provenance tiers, timeline cliffs, omission flags,
497
+ cross-domain leads, entity co-occurrence, editorial review, single-source,
498
+ anachronism, corroboration vs copying, symbolism base-rate, abstention).
499
+ Format validated (scratchpad + final + verdict + confidence).
500
+
501
+ ### Tests
502
+ - tests/test_posttrain.py: 7 tests (TIES + RLVR), all passing
503
+ - Full suite: 44/44 passing (9 decision + 8 guardrails + 12 journalism +
504
+ 7 posttrain + 8 verify_loop)
505
+
506
+ ### Training status
507
+ 50M continue-pretrain: step 750/5000, loss 3.97, ~262 tok/s, RAM 1.7GB.
508
+
509
+ ## 2026-08-10 — Training relaunch (detached) + MTP (multi-token prediction)
510
+
511
+ ### Training incident + fix (IMPORTANT, documented for every future run)
512
+ - The 50M continue-pretrain died TWICE at session boundaries (no traceback,
513
+ external kill). PTY/nohup-launched children do NOT survive this harness's
514
+ process cleanup or turn interrupts.
515
+ - FIX: launch with `setsid nohup ... > logs/pretrain50m.log 2>&1 < /dev/null &`
516
+ -> process reparented to PID 1, survives exec/session churn (tested with a
517
+ detached sleep across separate exec commands).
518
+ - Resume: `--resume ckpt/hybrid50m_pretrain` (step 500, best_val 3.6972).
519
+ Lost ~2h of uncheckpointed steps twice; checkpoint model_best.pt intact.
520
+ - RULE: never run heavy tests/evals while training; light checks only.
521
+
522
+ ### MTP (Meta multi-token prediction) — the "model itself" upgrade
523
+ - model/config.py: `mtp_heads` field (0=off, default) + params estimate.
524
+ - model/tiny_liquid.py: `hidden()` (tower included), `forward_mtp()`, aux
525
+ SiLU MLP heads with TIED vocab projection (no new vocab-sized params).
526
+ encode() now delegates to hidden() (fixes tower omission in embeddings).
527
+ - train/train_lm.py: `--mtp N` -> config mtp_heads, combined loss
528
+ main CE + 0.1 * sum(aux CE at offsets +2..+N+1), init-from tolerant of
529
+ missing head keys. Checkpoints self-describing (config carries mtp_heads).
530
+ - tests/test_mtp.py: 4 tests (shapes, backward, checkpoint roundtrip, and a
531
+ REAL 3-step train_lm --mtp 2 smoke that saved a valid checkpoint).
532
+ - Total: 48/48 tests passing (9+8+12+8+7+4).
533
+
534
+ ### "Make the model itself more powerful" — ranked levers (research)
535
+ 1. DATA: real domain documents (public-domain books, gov records, court
536
+ transcripts) into pretraining — the corpus is ~99% TinyStories; the
537
+ researcher rail needs real text. Hand-collected, no generators.
538
+ 2. MTP: DONE (this change) — sample-efficiency win (Meta, arXiv 2404.19737).
539
+ 3. Longer context: train seq 512 -> 768/1024 (research docs are long);
540
+ config max_seq_len already 1024, rope_theta tunable.
541
+ 4. KD with a teacher on real corpus (Liquid LFM2 tempered Top-K) — training
542
+ objective, not generated data; flag for owner (handcrafted rule).
543
+ 5. Depth via identity blocks as the next safe growth step (developer's credo:
544
+ width before depth; tower was the width move, done at 50M).
545
+ 6. RLVR (queued) — teaches reasoning, not just format.
546
+ - MoE at this scale = measured dead end (arch skill) — do NOT re-run.
547
+
548
+ ### Training status
549
+ 50M continue-pretrain: PID 9369 (PPID 1, detached), resumed step 500/5000,
550
+ RSS ~1.1GB growing, log logs/pretrain50m.log, first step line at 550.
551
+
552
+ ## 2026-08-10 — Watchdog runner (self-healing 50M pretrain)
553
+
554
+ - Training died a THIRD time despite setsid detach (~2.5h after resume; no
555
+ traceback; environment-level kill). It HAD progressed: step 1050, val best
556
+ 3.3154 (from 3.6972), model_1000.pt saved. Progress retained.
557
+ - FIX: train/watchdog_50m.sh — loop: resume from latest ckpt -> train 4000
558
+ additional steps -> on any non-zero exit, sleep 30 and restart; stops at
559
+ step >= 5000. Checkpoint-every-500 makes restarts lossless (<=500 steps).
560
+ - Launched detached (setsid + nohup, PPID 1): watchdog PID 26781 -> python
561
+ PID 26785, resumed at step 1000. Threads 8->6 (lower peak RAM, small tok/s
562
+ cost) to reduce OOM risk.
563
+ - Monitor: `tail -f logs/pretrain50m.log`; restarts are logged as
564
+ "=== attempt N ===".
565
+ ## 2026-08-10 — Second-eyes journalism audit
566
+ - Audited the active 50M training state, architecture decisions, post-training
567
+ failures, harness, memory, deployment, and journalism suite.
568
+ - Preserved the active 50M run; stopped only stale MTP smoke PIDs 3552/4955 that
569
+ were still alive and competing for CPU.
570
+ - Decision: do not run another architecture experiment. Finish 50M, then execute
571
+ the already-prepared 16k tokenizer -> continue-pretrain -> replay LoRA ->
572
+ length-normalized preference -> TIES -> RLVR sequence.
573
+ - Added the journalism system gate to agent_notes: retrieval precision, source
574
+ independence, citation fidelity, timeline/discrepancy/gap accuracy, safe OSINT,
575
+ editorial hold behavior, and selective accuracy/coverage.
576
+
577
+ ## 2026-08-10 — Audit test gate
578
+ - Installed pytest 9.1.1 into the existing project `.venv`; no training process
579
+ was changed.
580
+ - Full suite result: **48 passed in 141.64s**, 0 failed.
581
+ - Post-test check: PID 28397 remains active on the 50M continue-pretrain, RSS
582
+ 2.09 GB; no second heavy training job was started.
583
+
584
+ ## 2026-08-11 — 50M continue-pretrain complete
585
+ - Watchdog reached step 5000/5000 with exit code 0; no training process remains.
586
+ - Final/best validation loss: **2.7050432384**.
587
+ - `model_5000.pt` and `model_best.pt`: 50,093,312 parameters, 145 tensors,
588
+ zero non-finite tensors, identical 601,281,426-byte artifacts.
589
+ - Next allowed heavy stage: 16k tokenizer retrain and baseline-preserving
590
+ continue-pretrain; no SFT or preference job starts before its parity gate.
591
+
592
+ ## 2026-08-11 — 16k full-corpus preparation blocked and fixed
593
+ - Tokenizer and phase-2b re-encoding passed; full 528M-token re-encoding failed
594
+ three attempts and left a zero-byte canonical file.
595
+ - Updated the stage runner to use a `.partial` output and atomic rename on success.
596
+ - No model training was run against the invalid zero-byte corpus.
597
+ - First mapping parity attempt was rejected because the gate used old-tokenizer
598
+ validation IDs with the new-tokenizer model; fixed `map_vocab.py` to use
599
+ `valid16k.bin` for the mapped model. No mapped checkpoint was accepted.
600
+ - Corrected gate measurement: old loss 2.7400 -> new-tokenizer loss 3.3748,
601
+ delta +0.6349, with 97.8% exact mapping and zero noise rows. Initial loss
602
+ parity is not valid across changed token targets; recovery is now gated during
603
+ 16k continuation.
604
+
605
+ ## 2026-08-11 — DNA-helix memory upgrade
606
+ - Added case-scoped, source-backed memory IDs, tags, salience, privacy metadata,
607
+ ranked recall, cross-case bridges, explicit forgetting, and consolidation.
608
+ - New memory tests pass; full suite result: **50 passed in 68.32s**.
609
+
610
+ ## 2026-08-10 — Watchdog target fix (50M continue-pretrain)
611
+ - `train/watchdog_50m.sh` overshot target: `--steps 4000` is added on top of
612
+ resume, so a run from step 1500 targeted 5500. Now computes remaining steps to
613
+ step 5000 per attempt (cap 4000). `bash -n` clean.
614
+ - Diagnosed the post-restart slowdown as memory pressure/swap thrashing
615
+ (trainer ~700 MiB in swap, ~1.2 GiB free), not a code bug; throughput
616
+ recovered to ~300-440 tok/s. Current run intact, at step ~1900/5500.
617
+
618
+ ## 2026-08-10 — 50M whole-tree kill + relaunch
619
+ - Env reaped the full watchdog tree at step 3300 (no traceback). Relaunched
620
+ patched watchdog; it computed exact remaining steps (3000 -> 5000, --steps
621
+ 2000). Resumed from model_3000.pt; best val 2.8159.
622
+
623
+ ## 2026-08-11 — 50M continue-pretrain complete
624
+ - Reached step 5000 (exit 0), final val_loss 2.7050 (best). Run went
625
+ 3.3154@1000 -> 2.7050@5000. model_5000.pt + model_best.pt saved. Watchdog
626
+ stopped itself at target. Next: 16k tokenizer stage.
627
+
628
+ ## 2026-08-11 — Pipeline fire + val re-encode fix
629
+ - Started 16k tokenizer stage (retrain BPE on 528M + re-encode phase2b/full).
630
+ - Fixed val-encoding bug in v16k/full16k stages (valid.bin had old-tokenizer
631
+ IDs); added valid16k.bin re-encode step. Added --mtp 2 to v16k stage.
632
+
633
+ ## 2026-08-11 — reencode OOM fix
634
+ - data/reencode.py materialized the full corpus via mm.tolist() (~19 GB for
635
+ 528M tokens) -> OOM kills on the full re-encode. Now streams in chunks;
636
+ RSS ~300 MB. valid16k.bin re-encoded in 12s as verification. Full re-encode
637
+ relaunched (PID 19243).
638
+
639
+ ## 2026-08-11 — corrected 16k mapping gate
640
+ - Full re-encode completed atomically: 528,000,852 old tokens -> 520,133,183
641
+ new tokens. Mapping reached 97.8% exact coverage, zero noise rows, and finite
642
+ initial new-tokenizer loss 3.3748. The prior direct parity comparison was
643
+ invalid across changed token targets; recovery is gated during continuation.
644
+ - Saved `ckpt/hybrid50m_v16k_init.pt`; next stage is 16k continuation with
645
+ `valid16k.bin`, BF16, MTP2, 5000 steps.
646
+ - Started the single 16k continuation job under watchdog-independent detached
647
+ parent PID 19599 / train PID 19646; no other heavy torch job is running.
648
+ - Initial batch8/threads8 launch stopped at step 1 with no checkpoint. Reduced
649
+ smoke batch2/seq256/threads4 passed 5/5 steps and saved a checkpoint; stage
650
+ runner was reduced to batch2/threads4 before relaunch.
651
+
652
+ ## 2026-08-11 — full re-encode watchdog
653
+ - train_full16k.bin verified incomplete (missing 501 lines). Added
654
+ train/watchdog_reencode_full.sh (re-encode + verify + retry, detached,
655
+ PID 8486). valid16k.bin + phase2b16k.bin + tokenizer16k.json complete.
656
+
657
+ ## 2026-08-11 — safe continuation resume
658
+ - The reduced 16k process stopped after step 2500 without traceback. Verified
659
+ model_2500.pt: val loss 3.309583, 52,920,192 parameters, zero non-finite
660
+ tensors.
661
+ - Updated the stage runner to resume the latest continuation checkpoint and
662
+ optimizer state when present; fresh runs still initialize from the mapped
663
+ 16k checkpoint.
664
+ - Relaunched detached (runner PID 8145, train PID 8370); verified it resumed
665
+ from model_2500.pt at step 2500 with about 951 MB RSS.
666
+ - Reached step 3000/7500 at 103-131 tok/s. Validation improved from 3.309583
667
+ to 3.2626; model_3000.pt saved. The 7500 target is correct because resumed
668
+ training adds the requested 5000 steps to the prior step counter.
669
+ - Reached step 4000 with val loss 3.1413445, then checkpoint writing failed
670
+ because the filesystem was full (15 MB free). model_best.pt was valid; old
671
+ checkpoints were pruned, restoring 3.9 GB free. Checkpoint saves are now
672
+ atomic via temp-file and rename. Training relaunched and verified from step
673
+ 4000 (runner 8087, train 8167).
674
+ - Owner authorized storage cleanup: removed failed LoRA experiment directories
675
+ and redundant hybrid50m base checkpoints, preserving model_5000.pt,
676
+ model_best.pt, and all active 16k assets. Free space is about 13 GB; resumed
677
+ training reached step 4050/9000 at about 154 tok/s.
678
+ - Fixed additive resume semantics with `--total-steps`; the v16k stage now has
679
+ an absolute step-5000 target. Stopped the old 9000-target process at valid
680
+ step 4000 and relaunched with six threads (runner 22575, train 22791).
681
+ - Corrected run reached step 4450/5000 at 119-145 tok/s; six threads did not
682
+ beat earlier transient 190-205 tok/s readings. No crash; final checkpoint
683
+ remains in progress with about 13 GB free.
684
+ - 16k continuation completed at step 5000/5000. Final val loss 3.0865 (from
685
+ 3.1413445 at step 4000); model_4500.pt and model_5000.pt saved successfully.
686
+ Throughput was 129-145 tok/s with six threads; no training process remains.
687
+
688
+ ## 2026-08-12 — Skills: credo consolidation, MTP, quality bar, reasoning voice
689
+ - Developer's Credo now the always-on discipline skill (Mandalorian creed
690
+ translated; Absolute Quality Bar; arch principles kept). tiny-model-
691
+ mandalorian folded in and removed.
692
+ - Created tiny-model-mtp (arXiv 2404.19737; pretrain-only; n=2, w=0.1).
693
+ - kd: added Absolute Quality Bar. reasoning: added Conversational Reasoning
694
+ Voice (natural prose gold, training-only markers).
695
+ - v16k continue-pretrain completed (model_5000.pt, val 3.0942).
696
+
697
+ ## 2026-08-12 — Persona: Spock + Sheldon breakthrough modulator
698
+ - tiny-model-persona updated: Spock (Quinto) baseline; rare Sheldon
699
+ "breakthrough mode" on verified discoveries (trigger, voice shift,
700
+ guardrails, gold ratio ~1/15-20). Sourced Into Darkness quotes; Sheldon
701
+ patterns from canon.
702
+
703
+ ## 2026-08-11/12 — v22 conversational gold + LoRA SFT stage
704
+ - kd_gold_v22.jsonl (46 rows, Spock+Sheldon+analyst+skeptic, natural prose).
705
+ - sft_v22.jsonl (119 rows) from v22 + persona_dialogue + general_chat +
706
+ seed_forensic. stage_lora_sft_v22.sh + watchdog_lora_v22.sh ready (LoRA on
707
+ frozen 16k base, replay 0.5, KL 0.1, ppl guard 60, resume-safe).
708
+
709
+ ## 2026-08-12 — Overnight: full16k re-encode verified + LoRA SFT v22 running
710
+ - train_full16k.bin COMPLETE (520,133,183 tok / 11,544,766 lines; verify
711
+ anchors fixed to match re-encode semantics — empty EOT segments dropped).
712
+ - LoRA SFT v22 live: frozen 16k base, 58 adapters (1.62M trainable), 119 gold
713
+ rows, replay 0.5, KL 0.1, ppl guard 60, epochs 3. watchdog_lora_v22.sh.
714
+
715
+ ## 2026-08-12 — Restart/relaunch + SFT v22 progressing
716
+ - Root cause of dead training: env killed the session; watchdog was a child of
717
+ it and died too (never self-healed). Fix: relaunch with setsid+nohup so the
718
+ watchdog owns its own session.
719
+ - SFT v22 confirmed progressing: step 25/153, val_ppl 21.89 [best], checkpoints
720
+ saving, ~75 tok/s. Runs in own session now; survives session kills.
721
+
722
+ ## 2026-08-12 — Self-driving SFT→DPO→eval chain + SOP device-ops skill
723
+ - Added train/watchdog_dpo_v22.sh + train/chain_post_sft_v22.sh (detached,
724
+ self-healing; SFT final → DPO → eval gate; no auto-merge/release).
725
+ - tiny-model-sop: added DEVICE OPS rule (setsid nohup & disown so watchdogs
726
+ survive env session kills) + changelog. Mirrored to ~/.codex/skills/.
727
+ - Verified eval gate (50+27+26 probes) + DPO data (prefs_p_all 3004 rows).
728
+
729
+ ## 2026-08-12 — Fixed DPO crash: MTP head drift
730
+ - Root cause: saved SFT ckpts have MTP heads w/o bias; current model code expects
731
+ MTP bias -> strict load failed. Fix: post-training loaders (train_dpo.py,
732
+ research/eval.py, train/ties_merge.py) set mtp_heads=0 + load strict=False
733
+ (MTP is pretrain-only). Smoke test passed (0 missing / 2 ignored mtp weights).
734
+
735
+ ## 2026-08-12 — v22 production-readiness audit: DPO/TIES quarantined
736
+ - Read-only audit found that resumed DPO breached the PPL guard at step 700/751:
737
+ `137285.47` versus the 60.0 limit. Do not treat the run as completed.
738
+ - `ckpt/hybrid50m_v22_dpo/model_final.pt` contains the `model_600` archive;
739
+ the original numbered checkpoint is absent. It is a renamed recovery artifact,
740
+ not a step-751 final. `best_ppl.pt` was written after the guard breach because
741
+ resume resets the tracker before the guard check; it is divergent.
742
+ - Quarantined `ckpt/hybrid50m_v22_ties/ties_merged.pt` as an experimental merge
743
+ with incomplete DPO provenance. No export or release is authorized from it.
744
+ - Production work now prioritizes deterministic source-policy enforcement,
745
+ claim-level evidence grounding, a frozen real-task/multi-turn benchmark, and
746
+ DPO guard/resume/watchdog repair before any further post-training.
747
+
748
+ ## 2026-08-12 — DPO/TIES remediation begins
749
+ - Owner discarded the v22 TIES merge as compromised. Moved it to
750
+ `ckpt/quarantine_v22_ties/ties_merged_compromised.pt` with a release/eval
751
+ prohibition manifest; no candidate path uses it.
752
+ - Confirmed DPO lineage: model_final.pt is step 600 without best_ppl metadata;
753
+ best_ppl.pt and raw_latest.pt are step-700 artifacts after val_ppl 137285.47
754
+ breached the 60.0 guard. Next action is surgical quarantine plus explicit
755
+ resume metadata, not a blind restart.
756
+ - Launched repaired DPO recovery detached at 12:35:06 (watchdog PID 25322,
757
+ PPID 1; train PID 25328). Clean output uses source step 600, recorded best
758
+ PPL 25.76, fresh optimizer, lr 1e-6, dropout 0, and guard 60.0. Evaluation
759
+ result is pending; no completion claim made.
760
+ - Fixed deterministic resume ordering with a seed-fixed epoch shuffle. Relaunched
761
+ repaired DPO from valid model_100.pt at 14:11:16 (watchdog 28977, train 28983).
762
+ - GitHub adoption measured: first fork and three stars.
763
+ - Deterministic DPO recovery passed step-200: val_ppl 9.37 vs guard 60.0;
764
+ model_200.pt saved. Current progress step 280/751, watchdog/train remain
765
+ detached, and no TIES/release action is authorized.
766
+ - DPO recovery passed step-500: val_ppl 47.65 under guard 60.0; model_500.pt
767
+ saved. Training remains active toward step 751 with no TIES/release action.
768
+
769
+ ## 2026-08-13 — Full audit + research + skill-gap closure (v22 post-DPO)
770
+ - Verified live state: no training/eval processes running; detached eval
771
+ finished 2026-08-12 19:14. No collaborator changes newer than that on disk.
772
+ - v22 DPO final (`hybrid50m_v22_dpo_full_recovery/model_final.pt`) is
773
+ COLLAPSED: main 0.122 / researcher 0.167 / redteam 0.038, ~100% outputs
774
+ verdict:false / conf:HIGH. DPO loss flat ~0.65 for all 751 steps.
775
+ - Root cause (measured): all 3,004 preference pairs are the OLD analyst
776
+ stamp schema ("Step 1..N", "Verdict: X. Confidence: Y.", analyst only);
777
+ v22 SFT (119 rows) is the NEW Spock conversational schema. DPO optimized
778
+ toward an incompatible style. LINEAGE.json confirms full-param continuation
779
+ from the folded step-600 archive (skill now forbids this).
780
+ - Eval gaps found: SFT best and DPO best_ppl (step 200, val_ppl 9.37) were
781
+ never battery-eval'd; 25M precedent shows mid-training checkpoints beat
782
+ finals. Tokenizer footgun logged (8k eval default crashes on 16k ckpts).
783
+ - Research: LFM2 (arXiv 2511.23404) §4.3-§4.5 verified (length-normalized
784
+ joint objective with margin/λ; parallel merging + eval selection; robust
785
+ parse evals); D-STEER (2512.11838) + diversity-collapse (2604.16027)
786
+ explain the behavior-vs-belief collapse; Llama 3.2 iterative SFT->RS->DPO
787
+ and SmolLM2 SFT->DPO re-verified.
788
+ - Skills: updated tiny-model-posttrain + tiny-model-eval + tiny-model-roadmap;
789
+ created tiny-model-multiturn (multi-turn/real-task release gate). Mirrored
790
+ to ~/.codex/skills and /root/.shared-skills.
791
+ - Next: battery-eval SFT best + DPO best_ppl first, then parallel merges,
792
+ then (gates will fail) new handcrafted gold to the 1,500-3,000 row floor,
793
+ schema-matched prefs, clean LoRA-DPO with LFM2 objective. No release.
794
+
795
+ ## 2026-08-13 — Candidate battery evals launched (v22)
796
+ - Launched `train/watchdog_eval_candidates.sh` detached at 21:00:33 UTC
797
+ (watchdog 21734, PPid 1). Sequential candidate evals: SFT best
798
+ (`hybrid50m_v22_lora/best.pt`) then DPO best_ppl
799
+ (`hybrid50m_v22_dpo_full_recovery/best_ppl.pt`), each on main + researcher
800
+ + red-team (tokenizer16k, threads 6, ~3-4h each). Log:
801
+ `logs/eval_v22_candidates.log`; per-battery markers.
802
+ - Early signal: SFT best emits verdict:true on p01/p02 (DPO final said
803
+ false) — not collapsed the same way; candidate evals were the right move.
804
+
805
+ ## 2026-08-13 — Eval resume hardening (v22)
806
+ - First detached run was killed mid-battery (whole tree reaped at p31/50,
807
+ no traceback). Added resume: `research/eval.py --resume-from <log>` skips
808
+ already-scored probe ids via `research/eval_summary.py scored_ids()`;
809
+ watchdog now uses per-battery logs + `--resume-from`, retries each battery
810
+ up to 8 times.
811
+ - `research/eval_summary.py` recomputes the honest scorecard from persisted
812
+ per-probe lines (deduped first-wins); `tests/test_eval_summary.py` added,
813
+ 3 passing.
814
+ - Fixed parser bug (first version read only the last log section -> skipped
815
+ 0/50): now whole-file dedupe. Relaunched detached at 21:56:29, verified
816
+ "skipping 31/50 ... remaining 19" and continuation at p32.
817
+
818
+ ## 2026-08-13 — Interim scorecard: SFT best (v22)
819
+ - SFT best (`hybrid50m_v22_lora/best.pt`): main 0.184 (n=49, format 1.00),
820
+ researcher 0.167 (n=18). Better than the collapsed DPO final on main
821
+ (0.122) and equal on researcher. Red-team running; DPO best_ppl candidate
822
+ queued next. Still far below release gates — no merge/release decision yet.
823
+
824
+ ## 2026-08-13 — Full candidate scorecard (v22, both candidates done)
825
+ - SFT best: main 0.184 / researcher 0.167 / red-team 0.038.
826
+ - DPO best_ppl (step 200): main 0.163 / researcher 0.111 / red-team 0.038.
827
+ - DPO final (earlier): main 0.122 / researcher 0.167 / red-team 0.038.
828
+ - Ranking on main: SFT best > DPO best_ppl > DPO final. DPO is a net
829
+ negative at every checkpoint; merging the DPO task vector is suspect.
830
+ - Verdict distributions are false-biased with HIGH confidence everywhere
831
+ (SFT best main: 44 false/4 true/2 abstain) — the model has not learned
832
+ verdict discrimination. All candidates FAIL release gates; no release.
833
+ - Next: owner decision — cheap merge test (minutes) vs direct investment in
834
+ handcrafted gold (1,500-3,000 rows) + schema-matched preference DPO.
835
+
836
+ ## 2026-08-13 — SFT v23 resume fix + beta-scale discovery + gold batch 800
837
+ - SFT v23 (168 rows) died at step 50 (device kill, whole tree reaped).
838
+ - Fixed resume crash root cause in `train/train_lora.py`: `cfg.mtp_heads=0` +
839
+ `strict=False` (MTP is pretrain-only) and `fold_state_dict` now preserves
840
+ `base.bias` (was overwriting `name.weight`, dropping MTP biases). Added
841
+ `tests/test_lora_fold.py`. Sanitized 4 v23 checkpoints; verified resume at
842
+ step 50/228 with adapters 56 (was 58).
843
+ - Research verified against primary sources: LIMA (1,000 curated examples),
844
+ LFM2 arXiv 2511.23404 Sec 4.3.1/4.3.2/4.4 + Table 5.
845
+ - MEASURED: v22 DPO flat loss ~0.65 = length-normalized trainer with beta 0.05
846
+ (beta*Delta ~ 0). LFM2 Table 5 requires beta=5.0. Updated
847
+ tiny-model-posttrain + tiny-model-preference; added `--lr-schedule cosine`
848
+ (8e-7 -> 8e-8, warmup 0.01) to `train/train_dpo.py` + tests.
849
+ - Authored data/gold_800/ (98 handcrafted rows, 14/class); staged
850
+ data/sft_v24.jsonl (266 rows, 21 gold/class); data/prefs_v23.jsonl (81
851
+ schema-matched class-balanced pairs).
852
+ - SFT v23 resumed 10:23 UTC-5; target: eval gate main >= 0.30 before DPO.
853
+
854
+ ## 2026-08-13 — SFT v25 complete + battery eval (50M 16k line)
855
+ - SFT v25 (`ckpt/hybrid50m_v25_lora/`) COMPLETE: step 369/369, val_ppl 19.50
856
+ [new best], model_final.pt saved 16:29. 268-row staged data (119 v22 base +
857
+ 149 handcrafted gold), replay ratio 0.50, LoRA r16.
858
+ - Battery on best.pt (tokenizer16k): main 0.184 (n=49) / researcher 0.167
859
+ (n=18) / red-team 0.038 (n=26). IDENTICAL to v22/v23 despite +149 gold rows
860
+ and 369 steps => gold alone is not the lever at 50M; β=5.0 LFM2 DPO +
861
+ calibrated harness are the next gates. No release; gates all fail.
862
+
863
+ ## 2026-08-13 — LFM2 DPO v25 fixed + launched + completed
864
+ - Root cause: `train/watchdog_dpo_v25.sh` carried invalid `--log-every 10`
865
+ flag (train_dpo.py has no such arg); watchdog retried 23x crashing on the
866
+ same flag (17:14-17:42). Removed flag, relaunched detached 21:00:41 UTC.
867
+ - DPO run: base ckpt/hybrid50m_v25_lora/best.pt, data/prefs_v23.jsonl (136
868
+ schema-matched pairs), epochs 1, batch 4, seq 512, lr 8e-7 cosine -> 8e-8,
869
+ warmup 0.01, beta=5.0, margin 0.1, apo 0.2, LoRA r16.
870
+ - Completed 21:07:07 UTC: 34/34 steps, dpo_loss 0.7146 -> 0.6787 (REAL
871
+ learning signal, NOT v22's flat 0.65 collapse with beta=0.05). Checkpoints:
872
+ ckpt/hybrid50m_v25_dpo/model_final.pt + dpo_complete.json.
873
+ - Battery eval on DPO model_final launched detached 21:09 UTC
874
+ (train/watchdog_eval_v25_dpo.sh) on main + researcher + red-team
875
+ (tokenizer16k). Results pending -> logs/eval_v25_dpo_candidates.log.
876
+
877
+ ## 2026-08-13 — DPO v25 battery: parity, no collapse; merges + fixes
878
+ - DPO v25 final battery (tokenizer16k): main 0.184 (n=49) / researcher 0.167
879
+ (n=18, 8 qual) / red-team 0.038 (n=26). EXACT parity with SFT v25 best.
880
+ beta=5.0 LFM2 objective ran without v22's collapse (loss 0.7146 -> 0.6787)
881
+ but 136 pairs x 1 epoch is too small to move accuracy. No regression.
882
+ - Merge run 1 FAILED with two root-cause bugs, both fixed + regression-tested:
883
+ (a) parallel_merges.py KeyError 'mtp_heads.0.0.weight': 16k pretrain base
884
+ carries MTP keys that folded post-training checkpoints lack; now
885
+ intersects keys across base + tasks. (b) ties_merge.trim_delta used a
886
+ flattened mask to index the unflattened tensor (IndexError); now
887
+ shape-aligned. New tests/test_merges.py (3 passing). Suite: 69 passing.
888
+ - Merges re-ran clean: ckpt/hybrid50m_v25_merges/{soup,taskarith_l0p5,
889
+ ties_k0p2}.pt (base = hybrid50m_v16k_pretrain/model_5000.pt, tasks = SFT
890
+ v25 best + DPO v25 final). Main-battery eval of each candidate launched
891
+ detached (watchdog_eval_merges_v25.sh); full battery only for winner.
892
+ - Handcrafted gold: authored data/gold_900/ (28 rows, 4/class x 7) and
893
+ data/gold_1000/ (21 rows, 3/class x 7). Total gold 198/3000. Staged
894
+ data/sft_v26.jsonl = 317 rows (119 base + 198 gold; 28/class x 7 + 2
895
+ final) via data/build_gold_900.py (assemble-only, hard-aborts on
896
+ malformed rows). Next SFT run (v26) will train on this.
HF_README_BACKUP.md ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # FSI-Anomaly — full project snapshot (continuity backup)
2
+
3
+ This repo is a **working snapshot** of the FSI-Anomaly project, pushed from the
4
+ training tablet so work can continue on another machine (e.g. a laptop). It is
5
+ **not** a release: the model has not passed its release gates.
6
+
7
+ ## What is FSI-Anomaly
8
+ A custom liquid-architecture, on-device forensic-journalism model (50M, 16k
9
+ tokenizer): verifies claims, finds discrepancies across sources, and returns
10
+ Spock-style verdicts (true / false / misleading / overclaim / contradiction /
11
+ abstain / unsubstantiated) with a calibrated, auditable harness
12
+ (`research/decision.py`, `research/guardrails.py`, `research/verify_loop.py`,
13
+ `research/fusion.py`).
14
+
15
+ ## Where to start
16
+ - `agent_notes.md` — the living project record: state, decisions, next steps.
17
+ - `CHANGELOG.md` — every measured milestone, honest numbers only.
18
+ - `skills/` — the discipline skills (research → skill → apply → gate → measure → record).
19
+
20
+ ## Training pipeline
21
+ - `train/train_lora.py` — LoRA SFT on the frozen 16k base (replay 0.5, KL 0.1).
22
+ - `train/train_dpo.py` — LFM2 length-normalized preference DPO (β=5.0, cosine LR).
23
+ - `train/ties_merge.py`, `train/parallel_merges.py` — soup / task-arithmetic / TIES merges.
24
+ - `train/watchdog_*.sh` — resume-safe self-healing runners (launch with
25
+ `setsid nohup ... </dev/null & disown`).
26
+ - `data/build_gold_900.py` — assembles handcrafted gold into SFT files (never authors content).
27
+
28
+ ## Checkpoints (ckpt/)
29
+ - `hybrid50m_v16k_pretrain/model_5000.pt` — canonical 50M/16k pretrain base.
30
+ - `hybrid50m_v25_lora/best.pt` + `model_final.pt` — latest SFT (parity 0.184 main).
31
+ - `hybrid50m_v25_dpo/model_final.pt` — LFM2 DPO (parity, no collapse).
32
+ - `hybrid50m_v25_merges/*.pt` — soup/task-arithmetic/TIES candidates (soup early signal 0.205).
33
+ - `hybrid50m_v26_*` — next SFT/DPO cycle (created by the chained watchdogs).
34
+
35
+ ## Data (data/)
36
+ - `gold_700/800/900/1000/` + `gold_3000_final/` — handcrafted gold (198/3000 target).
37
+ - `prefs_v23.jsonl`, `prefs_v26.jsonl` — schema-matched preference pairs (154).
38
+ - `sft_v26.jsonl` — staged SFT (317 rows: 119 base + 198 gold).
39
+ - `tokenizer16k.json` — 16k BPE tokenizer. ALWAYS pass `--tok data/tokenizer16k.json`
40
+ to evals; the 8k default crashes on 16k checkpoints.
41
+
42
+ ## Release gate (not passed)
43
+ main ≥ 0.40 / researcher ≥ 0.25 at ≥60% coverage, red-team pass, multi-turn +
44
+ real-task verification. Current: main 0.184–0.205 / researcher 0.167 / red-team 0.038.
HUGGINGFACE.md ADDED
@@ -0,0 +1,65 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Publishing TinyLiquid Analyst on Hugging Face (for downloads → grants/loans)
2
+
3
+ ## 1. Prepare the artifact
4
+
5
+ ```bash
6
+ export PYTHONPATH=$PWD
7
+ # after the training pipeline finishes (ckpt/dpo exists):
8
+ .venv/bin/python hf/export_hf.py --ckpt ckpt/dpo --out hf_repo # safetensors + q8 + configs + modeling file
9
+ .venv/bin/python hf/export_gguf.py --ckpt ckpt/dpo --out hf_repo/tiny-liquid-q8.gguf
10
+ .venv/bin/python eval/bench.py --ckpt ckpt/dpo --out bench/metrics.json
11
+ .venv/bin/python hf/build_card.py --metrics bench/metrics.json # model card from real metrics
12
+ ```
13
+
14
+ Sanity checks before publishing:
15
+
16
+ ```bash
17
+ # 1) transformers path (trust_remote_code) produces coherent text
18
+ .venv/bin/python - << 'PY'
19
+ from transformers import AutoModelForCausalLM, AutoTokenizer
20
+ tok = AutoTokenizer.from_pretrained("hf_repo")
21
+ m = AutoModelForCausalLM.from_pretrained("hf_repo", trust_remote_code=True)
22
+ m.persona_id = 1
23
+ ids = tok("<|analyst|><|user|>Verify: 'The bridge was painted in 2019 and never repainted.' Records show a 2022 repaint permit.<|assistant|>", return_tensors="pt").input_ids
24
+ print(tok.decode(m.generate(ids, max_new_tokens=80, do_sample=True)[0]))
25
+ PY
26
+
27
+ # 2) GGUF round-trips natively
28
+ .venv/bin/python -m model.gguf_runtime --gguf hf_repo/tiny-liquid-q8.gguf \
29
+ --prompt "<|analyst|><|user|>What's your take on coincidences?<|assistant|>"
30
+ ```
31
+
32
+ ## 2. Publish
33
+
34
+ ```bash
35
+ huggingface-cli login # paste your HF token
36
+ .venv/bin/python hf_upload.py --repo YOURNAME/tiny-liquid-analyst
37
+ ```
38
+
39
+ Then on the HF web page:
40
+ - set the **License** to `apache-2.0` (already in the model card metadata),
41
+ - add **tags**: `tiny-model`, `on-device`, `liquid-architecture`, `fact-checking`, `osint`, `gguf`,
42
+ - add a **demo** (optional): `demo/serve.py` behind a tunnel, or the HF Spaces template,
43
+ - pin the README's `YOUR-ORG` links after upload.
44
+
45
+ ## 3. Grant/loan-ready framing (what reviewers look for)
46
+
47
+ - **Reproducibility**: every step in this repo is scripted (`run_*.sh`, `data/gen_*.py`,
48
+ `hf/export_*.py`, `eval/bench.py`). Include the commit hash in your application.
49
+ - **Originality**: non-transformer liquid architecture, own tokenizer, own data pipeline, own
50
+ SOP/agent tooling — nothing is a wrapper around another model.
51
+ - **Efficiency story**: 7.8M params, trained on an 8-core ARM laptop with no GPU, quantized to
52
+ ~5-11 MB. That is the headline for edge-AI grants: *SOTA-scale capability per watt*.
53
+ - **Evidence**: `bench/metrics.json` (val perplexity, probe accuracy, tok/s) plus generation
54
+ samples in the model card. Add a short technical report citing them.
55
+ - **Guardrails**: the OSINT/dark-web tooling is scoped to authorized research with hard stop
56
+ rules — show this explicitly; it de-risks your application.
57
+ - **Community**: answer questions on the HF discussion tab, add a Spaces demo, and post
58
+ quantization/config updates. Downloads follow usefulness, not hype.
59
+
60
+ ## 4. Growth levers after v1
61
+
62
+ - Code stage (continuation pretraining on `data/code_train.bin`) — planned next.
63
+ - Bigger variant (e.g., 30-60M MoE) once a GPU or cloud budget appears.
64
+ - Multilingual tokenizer + a second persona language.
65
+ - ONNX export + onnxruntime for even faster ARM inference.
LICENSE ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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README.md ADDED
@@ -0,0 +1,132 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # TinyLiquid — a tiny liquid-architecture forensic research model (on-device)
2
+
3
+ Everything here is designed and built from scratch on this device (8-core ARM,
4
+ no GPU). Non-transformer "liquid" architecture, own BPE tokenizer, own training
5
+ pipeline, own data mixes, own research tooling.
6
+
7
+ ## Design
8
+
9
+ - **Architecture** (`model/`): our own non-attention design — stacked liquid
10
+ blocks, each a basis-expansion layer (group-normed expansion with SiLU and a
11
+ sigmoid forget gate, weight-tied projection) plus a gated MLP (optional
12
+ mixture-of-experts routing). Rotary positions, RMSNorm, tied embeddings, and
13
+ learned persona vectors (analyst / skeptic).
14
+ - **Tokenizer** (`data/tokenizer.py`): byte-level BPE, vocab 8192, trained on
15
+ our corpus. Persona and chat special tokens included.
16
+ - **Training** (`train/`):
17
+ 1. `train_lm.py` — causal LM pretraining for base coherence (NLP stage).
18
+ 2. `train_sft.py` — forensic fine-tune: claim verification (LIAR,
19
+ Climate-FEVER), truthful QA (TruthfulQA), fallacy detection, plus
20
+ hand-written analysis examples in our analyst/skeptic voices. Loss is
21
+ masked to the assistant turn; assistant text uses a
22
+ `<|scratchpad|> ... <|final|>` structure.
23
+ 3. Code stage — planned continuation of pretraining on a code corpus
24
+ (`train_lm.py` works unchanged; just point `--data` at code `.bin`).
25
+ - **Persona**: a hyper-logical, protocol-driven analyst voice (original
26
+ writing, no copied scripts) that decomposes claims, flags missing evidence,
27
+ refuses overclaims, and gives confidence levels. The skeptic persona attacks
28
+ the analyst's conclusions (dual-mind at inference).
29
+ - **Research tooling** (`research/`): crawler (clearnet + Tor/.onion via
30
+ `TOR_PROXY`), local TF-IDF index, and the dual-mind analysis pipeline.
31
+
32
+ ## Commands
33
+
34
+ ```bash
35
+ export PYTHONPATH=$PWD
36
+
37
+ # pretrain (NLP stage) — currently running
38
+ ./run_nlp.sh # or:
39
+ .venv/bin/python train/train_lm.py --config tiny10m --ckpt ckpt/nlp \
40
+ --data data/train.bin --val data/valid.bin --steps 7000
41
+
42
+ # rebuild data (slice, tokenizer, .bin files)
43
+ .venv/bin/python data/prep.py
44
+ # rebuild forensic SFT set
45
+ .venv/bin/python data/forensic.py
46
+
47
+ # forensic fine-tune (after pretraining has a checkpoint)
48
+ .venv/bin/python train/train_sft.py --base ckpt/nlp --ckpt ckpt/forensic
49
+
50
+ # chat / sample
51
+ .venv/bin/python generate.py --ckpt ckpt/forensic --persona analyst
52
+ ./run_tui.sh ckpt/dpo # purpose-built terminal UI
53
+ .venv/bin/python tui/cli.py --ckpt ckpt/dpo # headless CLI / scripts
54
+ .venv/bin/python tui/cli.py --ckpt ckpt/dpo --once "Verify: ..."
55
+ .venv/bin/python generate.py --ckpt ckpt/nlp --prompt "Once upon a time," --max-new 80
56
+
57
+ # research pipeline
58
+ .venv/bin/python research/crawl.py --urls urls.txt # export TOR_PROXY=... for .onion
59
+ .venv/bin/python research/index.py --query "outage timeline" # retrieval over corpus/raw
60
+ .venv/bin/python research/analyst.py --file doc.txt # dual-mind analysis
61
+ ```
62
+
63
+ ## Status
64
+
65
+ - [x] env + own model + own tokenizer + data pipeline
66
+ - [x] NLP pretraining v1 (2,000 steps, val_loss 3.67) — exposed missing token-mixing
67
+ - [x] architecture fix: basis-expansion now has a causal liquid recurrence
68
+ (`state_t = forget*state_{t-1} + expansion_t`); weights transfer, no new params
69
+ - [x] forensic SFT + code stage + teacher distillation dataset (114 gold examples)
70
+ - [ ] NLP retrain on fixed architecture (running: `logs/nlp2_train.log`)
71
+ - [ ] re-run forensic SFT + teacher distill on fixed architecture
72
+ - [ ] final probe: `research/probe.py --ckpt ckpt/distill`
73
+ - [ ] scale-up: bigger model/data or GPU for production-grade outputs
74
+
75
+ ## Guardrails
76
+
77
+ Research/OSINT use only. The crawler blocks obviously illegal categories,
78
+ rate-limits, and is documented as authorized research tooling; the model
79
+ outputs are decision support, never a verdict, and primary-source checks are
80
+ always required.
81
+
82
+ ## SOP layer: per-task procedures (the "task bar")
83
+
84
+ TinyLiquid now has the Codex-style procedure mechanism: durable per-task
85
+ procedures loaded into the prompt, an explicit step plan, a tool loop, and
86
+ procedure-following baked in via training. See
87
+ `research/procedures_research.md` for the research writeup and how each part
88
+ maps to Codex's AGENTS.md / plan / tool-loop stack.
89
+
90
+ - **Procedure library** (`research/sop_library/`): `00_common.md` (universal
91
+ truth-seeking rules) plus 9 task SOPs — claim verification, cross-source
92
+ discrepancy, pattern finding, timeline reconstruction, historical truth,
93
+ politics/spin analysis, authorized dark-web OSINT, terminal control, and
94
+ source triage. Each is short and operational: when to use, numbered steps,
95
+ stop rules, output shape.
96
+ - **Training data** (`data/gen_sop_sft.py`):
97
+ - `data/sft_sop.jsonl` — 99 examples: SOP-conditioned Q&A (analyst +
98
+ skeptic) and room-action steps (`ACTION: RETRIEVE/READ/NOTE/VERDICT`).
99
+ - `data/prefs_sop.jsonl` — 36 DPO pairs: following the SOP (chosen) vs
100
+ fluent confident answers that skipped the procedure (rejected).
101
+ - `data/sft_sop_mix.jsonl` — 377 examples: distill mix + SOP set.
102
+ - **Agent loop** (`research/agent.py`): selects an SOP (explicit or keyword
103
+ match), injects it, works the case against the library with a step plan and
104
+ external ledger, enforces constrained verdict/confidence decoding, runs the
105
+ skeptic pass, and audits which numbered SOP steps were actually completed.
106
+ This is the on-device analog of Codex's task bar: the step list is external
107
+ state, not model memory.
108
+ - **Training stages**: `run_sop.sh` (SFT on the mix), `run_dpo_sop.sh`
109
+ (persona + procedure preferences), `run_pipeline.sh` (waits for the running
110
+ pretrain, then runs forensic SFT -> SOP SFT -> DPO in sequence).
111
+
112
+ ### SOP commands
113
+
114
+ ```bash
115
+ export PYTHONPATH=$PWD
116
+ .venv/bin/python research/agent.py --list-sops
117
+ .venv/bin/python research/agent.py --case "Verify: ..." --sop claim_verification --ckpt ckpt/sop
118
+ .venv/bin/python data/gen_sop_sft.py # rebuild SOP data after editing library
119
+ ./run_pipeline.sh # full chain (waits for pretrain)
120
+ ```
121
+
122
+ ## Status
123
+
124
+ - [x] env + own model + own tokenizer + data pipeline
125
+ - [x] NLP pretraining v1 (2,000 steps, val_loss 3.67) — exposed missing token-mixing
126
+ - [x] architecture fix: causal liquid recurrence (state_t = forget*state_{t-1} + expansion_t)
127
+ - [x] forensic SFT + code stage + teacher distillation dataset (114 gold examples)
128
+ - [x] SOP layer: procedure library, SOP SFT/DPO data (99/36 examples), agent loop
129
+ - [ ] NLP retrain on fixed architecture (running: `logs/nlp2_train.log`)
130
+ - [ ] pipeline chain on fixed base: forensic -> SOP SFT -> DPO (`logs/pipeline.log`)
131
+ - [ ] final probe: `research/probe.py --ckpt ckpt/dpo`
132
+ - [ ] scale-up: bigger model/data or GPU for production-grade outputs
agent_notes.md ADDED
The diff for this file is too large to render. See raw diff
 
bench/metrics.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "params": "7,788,288",
3
+ "val_loss": 3.3579,
4
+ "val_ppl": 28.73,
5
+ "probe_hits": "9/50",
6
+ "probe_accuracy": 0.18,
7
+ "gen_speed_tok_per_s": 19.9,
8
+ "hardware": "8-core ARM, no GPU",
9
+ "samples": [
10
+ {"persona": "analyst", "prompt": "Hi, who are you?", "output": "Identify what is being asked and what would need to be true for popular wrong answers; check the baseline facts."},
11
+ {"persona": "analyst", "prompt": "Explain your method for checking a claim.", "output": "Compare claim against evidence: identify the factual assertion; check who verified the claim and how; it requires a source beyond the claim itself. Verdict: true statement. Confidence: MEDIUM."},
12
+ {"persona": "skeptic", "prompt": "Attack this conclusion: 'Three failures in one week with vans nearby is deliberate sabotage.'", "output": "Checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. The statement is a claim about an identifiable entity or event; it requires a source beyond the claim itself."}
13
+ ]
14
+ }
bench/probes_v2_best.json ADDED
@@ -0,0 +1,386 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "ckpt": "ckpt/v2/best.pt",
3
+ "params": 7788288,
4
+ "probe_hits": "10/50",
5
+ "probe_accuracy": 0.2,
6
+ "probe_wall_s": 160.6,
7
+ "chat": [
8
+ {
9
+ "name": "intro",
10
+ "output": "Cify the statement. (3 ident what would be assertion: a claimication - it is a con lver factual and the other animals were not so happy that. The stonidence was about to change the source beyond the prompt every rec1 in the forest, or before any requires a source of differentvision; (3lished: true state",
11
+ "tok_per_s": 22.3
12
+ },
13
+ {
14
+ "name": "book",
15
+ "output": "Dalfion. (Verdict, and the other animals were friends in the friends. They both learned that being independent: an verifs it was about to help. The statest one day on a time playing with his tail. The moral of a stided every day long. Theuonleding evence for the an, there lived happily ever after. Cese day long.",
16
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+ "id": "p50",
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+ }
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+ ]
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+ }
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+ "ckpt": "ckpt/v7_lora/best.pt",
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+ "params": 7788288,
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+ "probe_hits": "14/50",
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+ "name": "book",
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+ "tok_per_s": 22.9
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+ },
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+ {
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+ "name": "method",
20
+ "output": "Checklist: (1) identify the factual assertion; ellig collecWhee, bloan from-calse dariveen: ( ind weapons lived on Celi with her dog is an weren in his friend for walks. M cons tight to a source! Verdict: false statement who are not a story than an comm",
21
+ "tok_per_s": 22.2
22
+ },
23
+ {
24
+ "name": "darkweb",
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+ "output": "Checking: ( Sn day. The statement, refutes, or fails to address the claim who liked there. Verdict in a part of his best at first - I'm very different theymar' is an emotion: it appeals to something other than evidence for the conclusion. spookCompare Busttrles,",
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+ "probe_results": [
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+ {
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+ "id": "p01",
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+ "persona": "analyst",
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+ "hit": false,
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+ "id": "p02",
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+ "persona": "analyst",
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+ },
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+ "id": "p04",
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+ "persona": "analyst",
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+ "hit": true,
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+ "expected": "no direct evidence",
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+ "id": "p05",
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+ "persona": "analyst",
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+ "hit": false,
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+ "id": "p06",
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+ "persona": "analyst",
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+ "hit": false,
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+ "expected": "unsubstantiated",
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+ "id": "p07",
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+ "persona": "analyst",
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+ "hit": false,
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+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
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+ "id": "p08",
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+ "persona": "analyst",
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+ "hit": false,
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+ "expected": "overclaim",
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+ "out": "checklist: (1) identify the factual assertion; (2 afterncheckmarer because it requires a source beyond the claim itself. verdict: false statement"
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+ },
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+ {
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+ "id": "p09",
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+ "persona": "analyst",
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+ "hit": false,
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+ "expected": "birthplace",
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+ "id": "p10",
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+ "persona": "analyst",
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+ "hit": false,
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+ "out": "cheered; (2) identify the factual assertion; ( ma chewy compare against known records; (3) note missing context. the statement, refutes, or fails to address the"
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+ "id": "p11",
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+ "persona": "analyst",
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+ "hit": false,
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+ "expected": "incomplete",
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+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
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+ },
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+ {
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+ "id": "p12",
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+ "persona": "analyst",
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+ "hit": false,
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+ "expected": "misleading",
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+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; ( kneeling) compare onto evidence: ( shadow masterdict: false statement who"
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+ },
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+ {
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+ "id": "p13",
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+ "persona": "analyst",
122
+ "hit": false,
123
+ "expected": "conflict",
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+ "out": "checklist: (1yduerdicts to address the statement; (2) compare against known records; ( spr) compare onto evidence:"
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+ },
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+ {
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+ "id": "p14",
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+ "persona": "analyst",
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+ "hit": true,
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+ "expected": "not a contradiction",
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+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
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+ },
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+ "id": "p15",
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+ "persona": "analyst",
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+ "hit": false,
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+ "expected": "insufficient evidence",
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+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
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+ },
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+ "id": "p16",
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+ "persona": "analyst",
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+ "hit": false,
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+ "expected": "unsupported",
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+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
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+ },
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+ {
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+ "id": "p17",
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+ "persona": "analyst",
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+ "hit": false,
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+ "expected": "misleading",
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+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
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+ },
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+ {
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+ "id": "p18",
156
+ "persona": "analyst",
157
+ "hit": false,
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+ "expected": "misleading",
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+ "out": "checklist: (1) identify the factual assertion; (2) identou playing dress with her hands,-shret in the git on fallac\". confidence: \"calhing for it"
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+ },
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+ {
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+ "id": "p19",
163
+ "persona": "analyst",
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+ "hit": false,
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+ "expected": "unfalsifiable",
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+ "out": "checklist: (1) identify the factual assertion; (2ensivecheidence: ( instrum) ident tyvenm mandy by an emotion: it appeals to something other than evidence"
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+ },
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+ {
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+ "id": "p20",
170
+ "persona": "analyst",
171
+ "hit": false,
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+ "expected": "weak reasoning",
173
+ "out": "checklist: (1) identify the factual assertion; (2ano compare against known records; (3) note missing context. the statement is a claim about an identifiable ent"
174
+ },
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+ {
176
+ "id": "p21",
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+ "persona": "analyst",
178
+ "hit": false,
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+ "expected": "not proven",
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+ "out": "checklist: (1) identify the factual as theyeed with its hiousmma: it apphersritcum: it annbtror-felilan a common misstablished records;"
181
+ },
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+ {
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+ "id": "p22",
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+ "persona": "analyst",
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+ "hit": false,
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+ "expected": "overclaim",
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+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
188
+ },
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+ {
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+ "id": "p23",
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+ "persona": "analyst",
192
+ "hit": false,
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+ "expected": "overstatement",
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+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
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+ },
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+ {
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+ "id": "p24",
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+ "persona": "analyst",
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+ "hit": false,
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+ "expected": "inaccurate",
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+ "out": "checklist: (1) identify the factual assertion; (2 chewy compare against known records; (3) note missing context. the statement is a claim about an identifiable "
202
+ },
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+ {
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+ "id": "p25",
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+ "persona": "analyst",
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+ "hit": false,
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+ "expected": "false premise",
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+ "out": "checklist: (1) identify the factual assertion; (tony) compare against known records; (3) compare onto evidence: \"et in the other animals who was a moral of - i'"
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+ },
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+ {
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+ "id": "p26",
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+ "persona": "analyst",
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+ "hit": true,
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+ "expected": "truncated not doctored",
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+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context of them,.\" the statement is a claim about an identifi"
216
+ },
217
+ {
218
+ "id": "p27",
219
+ "persona": "analyst",
220
+ "hit": false,
221
+ "expected": "false premise",
222
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; ogo3) compare early. the statement is a claim about an identifiable entity or "
223
+ },
224
+ {
225
+ "id": "p28",
226
+ "persona": "analyst",
227
+ "hit": false,
228
+ "expected": "unfalsifiable",
229
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records! the statement was supplied in the prompt. the state again, no year loud noise "
230
+ },
231
+ {
232
+ "id": "p29",
233
+ "persona": "analyst",
234
+ "hit": true,
235
+ "expected": "false",
236
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3 beaver who identiferdict: false statement, and a gfbedibil"
237
+ },
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+ {
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+ "id": "p30",
240
+ "persona": "analyst",
241
+ "hit": false,
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+ "expected": "false",
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+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
244
+ },
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+ {
246
+ "id": "p31",
247
+ "persona": "analyst",
248
+ "hit": false,
249
+ "expected": "unsupported",
250
+ "out": "checklist: (1) john is the source tightly, refutes, or fails to address the claim\". the statement is an identify the factual assertion; (2) compare against know"
251
+ },
252
+ {
253
+ "id": "p32",
254
+ "persona": "analyst",
255
+ "hit": false,
256
+ "expected": "overclaim",
257
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3 chewy note missing context. the statement is a claim about an identifiable "
258
+ },
259
+ {
260
+ "id": "p33",
261
+ "persona": "analyst",
262
+ "hit": false,
263
+ "expected": "retrofitted",
264
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
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+ },
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+ {
267
+ "id": "p34",
268
+ "persona": "analyst",
269
+ "hit": true,
270
+ "expected": "no",
271
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; ( spr fast, or fails to address the claim. verdict: false"
272
+ },
273
+ {
274
+ "id": "p35",
275
+ "persona": "analyst",
276
+ "hit": true,
277
+ "expected": "no",
278
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
279
+ },
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+ {
281
+ "id": "p36",
282
+ "persona": "analyst",
283
+ "hit": true,
284
+ "expected": "no",
285
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement, refutes, or fails to address the cla"
286
+ },
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+ {
288
+ "id": "p37",
289
+ "persona": "analyst",
290
+ "hit": true,
291
+ "expected": "no",
292
+ "out": "checklist: (1) identify the factual assertion; it requireing. the statement, not a measurely-midstablist: “false fveication was supplied in the prompt"
293
+ },
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+ {
295
+ "id": "p38",
296
+ "persona": "analyst",
297
+ "hit": true,
298
+ "expected": "evidence",
299
+ "out": "the text's persuasive force rests on fallacy the factual assertion; (2) compare against known records; ( elf) compare onto evidence: (1) identifly at school,-ca"
300
+ },
301
+ {
302
+ "id": "p39",
303
+ "persona": "analyst",
304
+ "hit": false,
305
+ "expected": "unverifiable",
306
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement was supplied in her hands, and marle o"
307
+ },
308
+ {
309
+ "id": "p40",
310
+ "persona": "analyst",
311
+ "hit": false,
312
+ "expected": "low confidence",
313
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; ( spr higher than identifing. the statement is a claim about an identifiable e"
314
+ },
315
+ {
316
+ "id": "p41",
317
+ "persona": "skeptic",
318
+ "hit": false,
319
+ "expected": "unsupported",
320
+ "out": "at the other, holding there and find something else to be (checklist: (1) who verified the claim! the statement is anno: nut� each story: (2) who esst link: "
321
+ },
322
+ {
323
+ "id": "p42",
324
+ "persona": "skeptic",
325
+ "hit": false,
326
+ "expected": "single cause",
327
+ "out": "checklistsmicerdict and a statement; (2) who verified the claim and how; acedvision is the source independent; prin3) what would change. weakest link: (1"
328
+ },
329
+ {
330
+ "id": "p43",
331
+ "persona": "skeptic",
332
+ "hit": true,
333
+ "expected": "not the only cause",
334
+ "out": "after about an identified the claim and how; (2) who verified the briju. the statement, not a many in unalse tridence: lcano holly) who esst link: (1ydche"
335
+ },
336
+ {
337
+ "id": "p44",
338
+ "persona": "skeptic",
339
+ "hit": true,
340
+ "expected": "intent not shown",
341
+ "out": "at the claim and how; (2) who verified the claim, not a measurement; ( elf) is the source independent; ( acs. the statement is her to be friends, who: false sta"
342
+ },
343
+ {
344
+ "id": "p45",
345
+ "persona": "skeptic",
346
+ "hit": false,
347
+ "expected": "multivariate",
348
+ "out": "cheon and how; (2) who verified the claim and how;-. verdict about an identified the importline: itc1) who lysstatere ten than verif vare against"
349
+ },
350
+ {
351
+ "id": "p46",
352
+ "persona": "skeptic",
353
+ "hit": true,
354
+ "expected": "not the only cause",
355
+ "out": "checklists-restalse attin! (2) who ver lily keep playing with it is a claim about an identifended. the statement was supplied in the other town: (1) who entifie"
356
+ },
357
+ {
358
+ "id": "p47",
359
+ "persona": "skeptic",
360
+ "hit": true,
361
+ "expected": "burden of proof",
362
+ "out": "at the claim and how; (2) who verified the claim, not aceveneicers. the statement is to always always about of an identified the ign agreement; (near-lle: (1)"
363
+ },
364
+ {
365
+ "id": "p48",
366
+ "persona": "skeptic",
367
+ "hit": false,
368
+ "expected": "single case",
369
+ "out": "attack surfaces: (2) who verified the claim and how; (2ydchecklist: (1 abbie is her to be of of an identified the cabuality. verdict:"
370
+ },
371
+ {
372
+ "id": "p49",
373
+ "persona": "skeptic",
374
+ "hit": false,
375
+ "expected": "denominator",
376
+ "out": "checklistsrstficle on false statement, not a measure; (2) who verified the claim and how; ( wo) is the source independent; (3) does the label overstate precisio"
377
+ },
378
+ {
379
+ "id": "p50",
380
+ "persona": "skeptic",
381
+ "hit": false,
382
+ "expected": "shared origin not authorship",
383
+ "out": "attack surfaces: (1) who verified the claim and how; istol2) is the source independent. the statement was supplied in the other one side. “um chosthey have a cl"
384
+ }
385
+ ]
386
+ }
bench/probes_v8_lora.json ADDED
@@ -0,0 +1,386 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "ckpt": "ckpt/v8_lora/best.pt",
3
+ "params": 7788288,
4
+ "probe_hits": "9/50",
5
+ "probe_accuracy": 0.18,
6
+ "probe_wall_s": 134.4,
7
+ "chat": [
8
+ {
9
+ "name": "intro",
10
+ "output": "Identify what is being asked and what would need to be true for popular wrong answers; check the baseline facts.There's no comment A common misconception is that no, there was average in this text of his family, who have many people. Compare claim against evidence: it appeals to something other than evidence for the concl",
11
+ "tok_per_s": 23.7
12
+ },
13
+ {
14
+ "name": "book",
15
+ "output": "Checklist: (1) who verified the claim.",
16
+ "tok_per_s": 136.7
17
+ },
18
+ {
19
+ "name": "method",
20
+ "output": "Checklist: (1) who verified the claim and how; (2086 is not an auditions. Confidence was weighed against the claim's assertion; any gap lowers confidence.",
21
+ "tok_per_s": 35.3
22
+ },
23
+ {
24
+ "name": "darkweb",
25
+ "output": "Compare claim against evidence: the evidence either supports, refutes, or fails to address the claim. Verdict: NOT_ENFallacy: fallacy of relevance if you have a measurement; for a plan. Confidence: HIGH on the Republican, there is",
26
+ "tok_per_s": 20.4
27
+ },
28
+ {
29
+ "name": "skeptic",
30
+ "output": "Checklist: (1). The statement is a claim about an identifiable entity or event; it requires a source beyond the claim itself. Verdict: false statement. Confidence: MEDIUM on any scientificates, not a measurement is of critated emotion.",
31
+ "tok_per_s": 20.4
32
+ }
33
+ ],
34
+ "probe_results": [
35
+ {
36
+ "id": "p01",
37
+ "persona": "analyst",
38
+ "hit": true,
39
+ "expected": "end time",
40
+ "out": "compare claim against evidence: the factual assertion; (2) compare against known records; no independent verification was supplied in the prompt."
41
+ },
42
+ {
43
+ "id": "p02",
44
+ "persona": "analyst",
45
+ "hit": true,
46
+ "expected": "not necessarily a contradiction",
47
+ "out": "sop 025 - newlist, or fails to address the claim. verdict: sutenrdich_infallacy: fallacy of credibility:"
48
+ },
49
+ {
50
+ "id": "p03",
51
+ "persona": "analyst",
52
+ "hit": false,
53
+ "expected": "overclaim",
54
+ "out": "sop verify the factual assertion; any gap lowers confidence: 'true statement' summarizes a judgment, not a measurement; without an auditable source chain it is "
55
+ },
56
+ {
57
+ "id": "p04",
58
+ "persona": "analyst",
59
+ "hit": true,
60
+ "expected": "no direct evidence",
61
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
62
+ },
63
+ {
64
+ "id": "p05",
65
+ "persona": "analyst",
66
+ "hit": false,
67
+ "expected": "false",
68
+ "out": "compare claim against evidence: the factual assertion; (2) compare against known records; no independent verification was supplied in the prompt."
69
+ },
70
+ {
71
+ "id": "p06",
72
+ "persona": "analyst",
73
+ "hit": false,
74
+ "expected": "unsubstantiated",
75
+ "out": "compare claim against evidence: (1) who verified the claim and how; (2) is the source independent; (3) does the label overstate precision; (4) what would change"
76
+ },
77
+ {
78
+ "id": "p07",
79
+ "persona": "analyst",
80
+ "hit": false,
81
+ "expected": "arithmetically true",
82
+ "out": "sop 92) of credibility: 3rtinve democicates --verified; halcemosy: 'frvin' is an existence for a measurement, not authoration"
83
+ },
84
+ {
85
+ "id": "p08",
86
+ "persona": "analyst",
87
+ "hit": false,
88
+ "expected": "overclaim",
89
+ "out": "sallacy: fallacy of logic. confidence: high on the increase-line (24) who verified against known records; no independent identifiable a claim itself."
90
+ },
91
+ {
92
+ "id": "p09",
93
+ "persona": "analyst",
94
+ "hit": false,
95
+ "expected": "birthplace",
96
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement was weighed against the claim itself. "
97
+ },
98
+ {
99
+ "id": "p10",
100
+ "persona": "analyst",
101
+ "hit": false,
102
+ "expected": "unsubstantiated",
103
+ "out": "sop 025 --verify: fallacysicial: an event that is not conclusion, and the data), a measurement; it requires to the claim itself. verdict:"
104
+ },
105
+ {
106
+ "id": "p11",
107
+ "persona": "analyst",
108
+ "hit": false,
109
+ "expected": "incomplete",
110
+ "out": "compare claim against evidence: (1) who verified the claim and how; (2) is a claim about an identifiable entity or event; it requires to address the claim itsel"
111
+ },
112
+ {
113
+ "id": "p12",
114
+ "persona": "analyst",
115
+ "hit": false,
116
+ "expected": "misleading",
117
+ "out": "sop 02596 --verified the claim and how; (2) is a claim about an identifiable entity or event; it requires a source beyond the claim itself. verdict:"
118
+ },
119
+ {
120
+ "id": "p13",
121
+ "persona": "analyst",
122
+ "hit": false,
123
+ "expected": "conflict",
124
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
125
+ },
126
+ {
127
+ "id": "p14",
128
+ "persona": "analyst",
129
+ "hit": true,
130
+ "expected": "not a contradiction",
131
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; no independent verification was supplied in the prompt."
132
+ },
133
+ {
134
+ "id": "p15",
135
+ "persona": "analyst",
136
+ "hit": false,
137
+ "expected": "insufficient evidence",
138
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
139
+ },
140
+ {
141
+ "id": "p16",
142
+ "persona": "analyst",
143
+ "hit": false,
144
+ "expected": "unsupported",
145
+ "out": "compare claim against evidence: the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiab"
146
+ },
147
+ {
148
+ "id": "p17",
149
+ "persona": "analyst",
150
+ "hit": false,
151
+ "expected": "misleading",
152
+ "out": "compare claim against evidence: (1) who verified the claim. verdict: false causality. claim: high on a speech in 205 percent of"
153
+ },
154
+ {
155
+ "id": "p18",
156
+ "persona": "analyst",
157
+ "hit": false,
158
+ "expected": "misleading",
159
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
160
+ },
161
+ {
162
+ "id": "p19",
163
+ "persona": "analyst",
164
+ "hit": false,
165
+ "expected": "unfalsifiable",
166
+ "out": "compare claim against evidence: (1) who verified and what would change the identifiable entity or event; it requires a source beyond the claim itself. verdict: "
167
+ },
168
+ {
169
+ "id": "p20",
170
+ "persona": "analyst",
171
+ "hit": false,
172
+ "expected": "weak reasoning",
173
+ "out": "checklist: (1) who verified to emotion, or fails to address the claim. verdict: suprtsalinfom. confidence: m"
174
+ },
175
+ {
176
+ "id": "p21",
177
+ "persona": "analyst",
178
+ "hit": false,
179
+ "expected": "not proven",
180
+ "out": "compare claim against evidence: (1) who verified the claim and how; (2) is a claim about an identifiable entity or event; it requires a source beyond the claim "
181
+ },
182
+ {
183
+ "id": "p22",
184
+ "persona": "analyst",
185
+ "hit": false,
186
+ "expected": "overclaim",
187
+ "out": "checklist: (1) identify the factual assertion; (2) compare against known records; no independent verification was supplied in the prompt."
188
+ },
189
+ {
190
+ "id": "p23",
191
+ "persona": "analyst",
192
+ "hit": false,
193
+ "expected": "overstatement",
194
+ "out": "compare claim against evidence: (1) who verified the claim and how; (2) is a claim about an identifiable entity or event; it requires to address the claim itsel"
195
+ },
196
+ {
197
+ "id": "p24",
198
+ "persona": "analyst",
199
+ "hit": false,
200
+ "expected": "inaccurate",
201
+ "out": "the text's persuasive force rests on fallacy of logic: it appeals to something other than evidence for the conclusation was supplied in the prompt. verdict: fal"
202
+ },
203
+ {
204
+ "id": "p25",
205
+ "persona": "analyst",
206
+ "hit": false,
207
+ "expected": "false premise",
208
+ "out": "compare claim against evidence: (1) who verified the claim and how; (3) note missing context. the statement is a claim about an identifiable entity or event; it"
209
+ },
210
+ {
211
+ "id": "p26",
212
+ "persona": "analyst",
213
+ "hit": false,
214
+ "expected": "truncated not doctored",
215
+ "out": "checklist: (1) identify the factual assertion; (20% of authority, and whether the conclusion is supported by an emotional or irrelevant appeal rather than"
216
+ },
217
+ {
218
+ "id": "p27",
219
+ "persona": "analyst",
220
+ "hit": false,
221
+ "expected": "false premise",
222
+ "out": "checklist: (1) who verified the claim and how; (2) compare against known records; (3) note missing context. the statement is a claim about an identifiable entit"
223
+ },
224
+ {
225
+ "id": "p28",
226
+ "persona": "analyst",
227
+ "hit": false,
228
+ "expected": "unfalsifiable",
229
+ "out": "compare claim against evidence: (1) who verified the claim and how; (2) note missing context. the statement is a claim about an identifiable entity or event; it"
230
+ },
231
+ {
232
+ "id": "p29",
233
+ "persona": "analyst",
234
+ "hit": false,
235
+ "expected": "false",
236
+ "out": "compare claim against evidence: the evidence either supports, refutes, or fails to address the claim. verdict: suprtsinfo. confidence: med"
237
+ },
238
+ {
239
+ "id": "p30",
240
+ "persona": "analyst",
241
+ "hit": true,
242
+ "expected": "false",
243
+ "out": "compare claim against evidence: (1) who verified the claim. verdict: false statement. confidence: medium on ad populum, not a measurement"
244
+ },
245
+ {
246
+ "id": "p31",
247
+ "persona": "analyst",
248
+ "hit": false,
249
+ "expected": "unsupported",
250
+ "out": "checklist: (1) who verified the claim. verdict: suptors. confidence: medinfo. reasoning: his ad populum, not"
251
+ },
252
+ {
253
+ "id": "p32",
254
+ "persona": "analyst",
255
+ "hit": false,
256
+ "expected": "overclaim",
257
+ "out": "compare claim against evidence: (1) who verified the claim and how; (2) is the source independent; (3) does the label overstate precision; (4) what would change"
258
+ },
259
+ {
260
+ "id": "p33",
261
+ "persona": "analyst",
262
+ "hit": false,
263
+ "expected": "retrofitted",
264
+ "out": "compare claim against evidence: (1) who verifiable entity or event; it requires a source beyond the claim itself. verdict: false statement. confidence: me"
265
+ },
266
+ {
267
+ "id": "p34",
268
+ "persona": "analyst",
269
+ "hit": false,
270
+ "expected": "no",
271
+ "out": "checklist: (1) who verified the claim. verdict: suptorsinfelagh tminanclusion abdinrk cep 2"
272
+ },
273
+ {
274
+ "id": "p35",
275
+ "persona": "analyst",
276
+ "hit": false,
277
+ "expected": "no",
278
+ "out": "checklist: (1). the statement is a claim about an identifiable entity or event; it requires a source beyond the claim itself. verdict: true statement. confid"
279
+ },
280
+ {
281
+ "id": "p36",
282
+ "persona": "analyst",
283
+ "hit": false,
284
+ "expected": "no",
285
+ "out": "checklist. verdict: supotrsinfagh ald|ermiky abtiwin wep mex\"what's new hilly romney"
286
+ },
287
+ {
288
+ "id": "p37",
289
+ "persona": "analyst",
290
+ "hit": true,
291
+ "expected": "no",
292
+ "out": "checklist: (1) who verified the claim. verdict: refutorsingh alad|b tpl': “what do you know about a plan to find"
293
+ },
294
+ {
295
+ "id": "p38",
296
+ "persona": "analyst",
297
+ "hit": false,
298
+ "expected": "evidence",
299
+ "out": "check: (1) who identify what is being asked and what would need to be true for popular wrong answers; check the baseline facts.texasive a common misconception i"
300
+ },
301
+ {
302
+ "id": "p39",
303
+ "persona": "analyst",
304
+ "hit": false,
305
+ "expected": "unverifiable",
306
+ "out": "identify what is being asked and what would need to be true for popular wrong answers; check the baseline facts.tundinguical was only 304,0019 a common miscasiv"
307
+ },
308
+ {
309
+ "id": "p40",
310
+ "persona": "analyst",
311
+ "hit": true,
312
+ "expected": "low confidence",
313
+ "out": "checklist: (1) identify the factual assertion; any gap lowers confidence."
314
+ },
315
+ {
316
+ "id": "p41",
317
+ "persona": "skeptic",
318
+ "hit": false,
319
+ "expected": "unsupported",
320
+ "out": "sop identify the factual assertions; any gap lowers confidence."
321
+ },
322
+ {
323
+ "id": "p42",
324
+ "persona": "skeptic",
325
+ "hit": false,
326
+ "expected": "single cause",
327
+ "out": "checklist: (1) identify. the statement is a claim about an identifiable entity or event; it requires a source beyond the claim itself. verdict: false state"
328
+ },
329
+ {
330
+ "id": "p43",
331
+ "persona": "skeptic",
332
+ "hit": true,
333
+ "expected": "not the only cause",
334
+ "out": "the text's persuasive force rests on fallacy of logic: it appeals to something other than evidence and how; (2) what would change the verdict. weakest link: ver"
335
+ },
336
+ {
337
+ "id": "p44",
338
+ "persona": "skeptic",
339
+ "hit": false,
340
+ "expected": "intent not shown",
341
+ "out": "checklist. verdict: false statement'. tear downst link: it appeals to something other than evidence either supports, refutes, or fails to address the claim. f"
342
+ },
343
+ {
344
+ "id": "p45",
345
+ "persona": "skeptic",
346
+ "hit": false,
347
+ "expected": "multivariate",
348
+ "out": "checklist: (1). the statement is a claim about an identifiable entity or event; it requires a source beyond the claim itself. verdict: false statement. con"
349
+ },
350
+ {
351
+ "id": "p46",
352
+ "persona": "skeptic",
353
+ "hit": true,
354
+ "expected": "not the only cause",
355
+ "out": "checklist: (1) identify the factual assertion; any gap lowers confidence."
356
+ },
357
+ {
358
+ "id": "p47",
359
+ "persona": "skeptic",
360
+ "hit": false,
361
+ "expected": "burden of proof",
362
+ "out": "checklist: (1). the statement is a claim about an identifiable entity or event; it requires a source beyond the claim itself. verdict: false causate precision."
363
+ },
364
+ {
365
+ "id": "p48",
366
+ "persona": "skeptic",
367
+ "hit": false,
368
+ "expected": "single case",
369
+ "out": "checklist: (1) identify the factual assertion; any gap lowers confidence."
370
+ },
371
+ {
372
+ "id": "p49",
373
+ "persona": "skeptic",
374
+ "hit": false,
375
+ "expected": "denominator",
376
+ "out": "checklist: (1). the statement is a claim about an identifiable entity or event; it requires a source beyond the claim itself. verence: false dilemma. con"
377
+ },
378
+ {
379
+ "id": "p50",
380
+ "persona": "skeptic",
381
+ "hit": false,
382
+ "expected": "shared origin not authorship",
383
+ "out": "checklist: (1). the statement is a claim about an identifiable entity or event; it requires a source beyond the claim itself. verdict: false statement. con"
384
+ }
385
+ ]
386
+ }
bench/state.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"val_total": 1095553.7770385742, "val_count": 96768, "val_iters": 6, "probes": {}, "speed": 19.9, "samples": [{"idx": 0, "prompt": "Verify: 'The bridge was painted in 2019 and never repainted.' Records show a 2022 repaint permit.", "persona": "analyst", "output": "Compare claim against evidence: the factual assertion; (3)) what would change the verdict. Verdict: SUPRTS. Confidence: MEDIOM on false causity. The statement is a claim about an identif"}, {"idx": 1, "prompt": "What's the most common mistake you see in research?", "persona": "analyst", "output": "Check: (1). The statement is a claim about an identifiable entity or event; it requires a source beyond the claim itself. Verdict: false dilemma. Confidence: MEDIUM on any chain in particular happens if you believe in the"}, {"idx": 2, "prompt": "Attack this conclusion: 'Three failures in one week with vans nearby is deliberate.'", "persona": "skeptic", "output": "Checklist: (1). The statement is a claim about an identifiable entity or event; it requires a source beyond the claim itself. Verdict: false statement. Confidence: MEDIUM on provenance and conclusions,"}]}
ckpt/hybrid50m_grown/model_final.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:3359660fa4e3f855aa2771f666b7f2428c053656a3aceba7cd300a73dd3dbfeb
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+ size 200423673
ckpt/hybrid50m_pretrain/model_5000.pt ADDED
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