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inside-out-replication-canary-v1 | run_canary.py | meta-llama/Meta-Llama-3-8B-Instruct | {"n_questions": 5, "n_samples": 50, "temperature": 1.0, "judge_model": "Qwen/Qwen2.5-14B-Instruct"} | [] | Canary run: 5 questions per relation, 50 samples, Llama-3-8B. Full pipeline E2E test. | ["inside-out-replication", "canary", "factual-knowledge"] | {"experiment_name": "inside-out-replication", "job_id": "mll:26166", "cluster": "mll", "artifact_status": "final", "canary": true} | 2026-04-09T03:48:09.928223+00:00 | null | null | null | null | null | null | -1 | 2026-04-09T03:48:09.928223+00:00 |
inside-out-replication-results-v1 | full pipeline (02-09) | Llama-3-8B, Mistral-7B-v0.3, Gemma-2-9B | {"n_samples": 1000, "temperature": 1.0, "judge_model": "Qwen/Qwen2.5-14B-Instruct", "max_tokens": 64} | [] | Full Inside-Out replication: 3 models x 4 relations x 450 test questions x 1000 samples. Includes P(a|q), P_norm, P(True) V0/V1/V2, and probe scores. | ["inside-out-replication", "factual-knowledge", "hidden-states", "probe"] | {"experiment_name": "inside-out-replication", "cluster": "mll", "artifact_status": "final", "canary": false} | 2026-04-10T14:01:35.430354+00:00 | null | null | null | null | null | null | -1 | 2026-04-10T14:01:35.430354+00:00 |
self-consistency-correction-exp0-sanity-canary | exp0_sanity.py | Qwen/Qwen2.5-7B-Instruct, meta-llama/Llama-3.1-8B-Instruct | {"dtype": "bfloat16", "prompt_template": "Q: {question} A: ", "validator_suffix": ". Is this correct? Yes or No? Answer:"} | [] | Exp 0 sanity check (paper §7.1): raw and PMI-corrected generator scores vs validator scores on 8 hand-crafted cases, 2 local models. | ["self-consistency-correction", "canary", "exp0", "sanity-check"] | {"experiment_name": "self-consistency-correction", "job_id": "local:dgx-spark:exp0", "cluster": "local-dgx-spark", "artifact_status": "final", "canary": true} | 2026-04-13T05:04:47.094818+00:00 | null | null | null | null | null | null | -1 | 2026-04-13T05:04:47.094818+00:00 |
self-consistency-correction-exp5-diagnostic | exp5_diagnostic.py | Qwen/Qwen2.5-7B-Instruct, meta-llama/Llama-3.1-8B-Instruct | {"dtype": "bfloat16", "prompt_template": "Q: {question} A: ", "validator_suffix": ". Is this correct? Yes or No? Answer:"} | [] | Exp 5 Liechtenstein vs UK diagnostic (paper §7.6): raw, PMI, and exact-cluster generator scores vs validator scores on 6 hand-crafted cases with known equivalence classes. | ["self-consistency-correction", "exp5", "diagnostic", "cluster-correction"] | {"experiment_name": "self-consistency-correction", "job_id": "local:dgx-spark:exp5", "cluster": "local-dgx-spark", "artifact_status": "final", "canary": false} | 2026-04-13T05:04:54.127250+00:00 | null | null | null | null | null | null | -1 | 2026-04-13T05:04:54.127250+00:00 |
self-consistency-correction-exp23-correlation-discrimination | exp23_combined.py | Qwen/Qwen2.5-7B-Instruct, meta-llama/Llama-3.1-8B-Instruct | {"dtype": "bfloat16", "num_cases": 20, "validator_suffix": ". Is this correct? Yes or No? Answer:"} | [] | Experiments 2 & 3 combined (paper §7.3, §7.4): raw, PMI, negative-prompt, verbalization-prompt, and exact-cluster scores on 20 hand-crafted paraphrase-equivalence cases, 2 local models. Measures Spearman(score, V') and AUROC(score, is_correct_class). | ["self-consistency-correction", "exp2", "exp3", "correlation", "discrimination", "auroc"] | {"experiment_name": "self-consistency-correction", "job_id": "local:dgx-spark:exp23", "cluster": "local-dgx-spark", "artifact_status": "final", "canary": false} | 2026-04-13T05:13:48.317056+00:00 | null | null | null | null | null | null | -1 | 2026-04-13T05:13:48.317056+00:00 |
self-consistency-correction-exp1-assumptions | exp1_assumptions.py | Qwen/Qwen2.5-7B-Instruct, meta-llama/Llama-3.1-8B-Instruct | {"num_cases": 20} | ["latkes/self-consistency-correction-exp23-correlation-discrimination"] | Experiment 1 (paper §7.2): empirical tests of Assumption 2 (V' constant across paraphrases) and Assumption 3 (cluster score equals validator log-odds). Derived from the exp23 artifact, no new inference. | ["self-consistency-correction", "exp1", "assumption-tests"] | {"experiment_name": "self-consistency-correction", "job_id": "local:dgx-spark:exp1", "cluster": "local-dgx-spark", "artifact_status": "final", "canary": false} | 2026-04-13T05:13:55.348889+00:00 | null | null | null | null | null | null | -1 | 2026-04-13T05:13:55.348889+00:00 |
self-consistency-correction-ambigqa | exp_ambigqa.py | Qwen/Qwen2.5-7B-Instruct, meta-llama/Llama-3.1-8B-Instruct | {"dtype": "bfloat16", "num_cases": 10, "validator_suffix": ". Is this correct? Yes or No? Answer:"} | ["juand-r/rankalign#longform:data/ambigqa/with_negatives"] | 5-score self-consistency-correction comparison on 10 hand-clustered AmbigQA questions from the rankalign longform branch. | ["self-consistency-correction", "ambigqa", "rankalign", "generator-validator-gap", "self-consistency"] | {"experiment_name": "self-consistency-correction", "job_id": "local:dgx-spark:ambigqa", "cluster": "local-dgx-spark", "artifact_status": "final", "canary": false} | 2026-04-13T14:50:30.312276+00:00 | null | null | null | null | null | null | -1 | 2026-04-13T14:50:30.312276+00:00 |
inside-out-replication-v2-metrics | 07_compute_metrics.py | Llama-3-8B / Mistral-7B-v0.3 / Gemma-2-9B | {} | ["inside-out-replication-v2-external-scores", "inside-out-replication-v2-probe-scores"] | Headline K and K* metrics for Inside-Out replication V2 (Gekhman et al. 2025). One row per (model, relation, scoring_method) on the test split. K = mean fraction of correct>incorrect answer pairs ranked correctly; K* = fraction of questions with perfect ranking (K=1). | ["inside-out-replication-v2", "metrics", "hidden-knowledge"] | {"experiment_name": "inside-out-replication-v2", "job_id": "mll:27608-27621", "cluster": "mll", "artifact_status": "final", "canary": false} | 2026-05-16T17:54:54.390757+00:00 | null | null | null | null | null | null | -1 | 2026-05-16T17:54:54.390757+00:00 |
inside-out-replication-v2-external-scores | 04_external_scores.py | Llama-3-8B / Mistral-7B-v0.3 / Gemma-2-9B | {} | ["inside-out-replication-v2-judge-labels"] | Per (question, answer) external scores with judge labels for Inside-Out V2. Covers P(a|q), P_norm(a|q), P(True) and two verification-prompt variants. Used to compute external K/K*. | ["inside-out-replication-v2", "external-scores"] | {"experiment_name": "inside-out-replication-v2", "job_id": "mll:27608-27621", "cluster": "mll", "artifact_status": "final", "canary": false} | 2026-05-16T17:56:33.031332+00:00 | null | null | null | null | null | null | -1 | 2026-05-16T17:56:33.031332+00:00 |
inside-out-replication-v2-probe-scores | 06_train_probe.py | Llama-3-8B / Mistral-7B-v0.3 / Gemma-2-9B | {} | ["inside-out-replication-v2-samples"] | Internal (probe) scores: logistic-regression probe on hidden states, best layer chosen by dev K. Trained probe .pkl files and per-layer selection JSON attached to this repo. | ["inside-out-replication-v2", "probe", "internal-scores"] | {"experiment_name": "inside-out-replication-v2", "job_id": "mll:27608-27621", "cluster": "mll", "artifact_status": "final", "canary": false} | 2026-05-16T17:56:44.628880+00:00 | null | null | null | null | null | null | -1 | 2026-05-16T17:56:44.628880+00:00 |
inside-out-replication-v2-samples | 02_sample_answers.py | Llama-3-8B / Mistral-7B-v0.3 / Gemma-2-9B | {} | [] | Raw sampled answers: 1 greedy + 1000 temperature samples per question (temp=1.0 test/dev, temp=2.0 train). Full outputs, no truncation. | ["inside-out-replication-v2", "samples"] | {"experiment_name": "inside-out-replication-v2", "job_id": "mll:27608-27621", "cluster": "mll", "artifact_status": "final", "canary": false} | 2026-05-16T17:57:46.361548+00:00 | null | null | null | null | null | null | -1 | 2026-05-16T17:57:46.361548+00:00 |
inside-out-replication-v2-judge-labels | 03_run_judge.py | Qwen/Qwen2.5-14B-Instruct (judge) | {} | ["inside-out-replication-v2-samples"] | Judge labels for deduplicated sampled answers. Method is exact_match where the normalized answer equals gold, else a Qwen2.5-14B per-relation CoT judge producing grade A/B/C/D. | ["inside-out-replication-v2", "judge-labels"] | {"experiment_name": "inside-out-replication-v2", "job_id": "mll:27608-27621", "cluster": "mll", "artifact_status": "final", "canary": false} | 2026-05-16T17:59:58.913529+00:00 | null | null | null | null | null | null | -1 | 2026-05-16T17:59:58.913529+00:00 |
cybernetic-agents-validation-step1-characterization-v1-pass1 | scripts/upload_step1.py | n/a (human/agent characterization, not model inference) | {"rubric": "experiments/E3-step1-vsm-rubric.md (frozen pre-scoring)", "coders": "subA-pass1 (MAST-7), subB-pass1 (mainstream), subC-pass1 (VSM-axis)", "second_pass": "pending"} | [] | Step 1 of the cybernetic-agents-validation program: mechanism-based characterization of 16 real LLM-agent systems against Beer's VSM (S1-S5, S3*, algedonic, recursion). Each (system,function) scored Present/Partial/Absent strictly from source code/docs, IGNORING the systems' own VSM/cybernetics labels, with an SE-paral... | ["cybernetic-agents-validation", "vsm", "characterization", "agent-architecture", "step1", "pass1", "partial"] | {"experiment_name": "cybernetic-agents-validation", "artifact_status": "partial", "canary": false} | 2026-05-16T22:04:36.579960+00:00 | null | null | null | null | null | null | -1 | 2026-05-16T22:04:36.579960+00:00 |
cybernetic-agents-validation-step1-characterization-v2-2pass | scripts/merge_passes.py + scripts/upload_step1_v2.py | n/a (independent human/agent characterization) | {"rubric": "experiments/E3-step1-vsm-rubric.md (frozen pre-scoring)", "passes": 2, "coders": "subA/B/C pass1 + subA/B/C pass2 (independent)", "kappa_unweighted": 0.44, "kappa_linear_weighted": 0.79, "exact_agreement": "81/128", "hard_flips": "2/128"} | ["latkes/cybernetic-agents-validation-step1-characterization-v1-pass1"] | Step 1 FINAL: two independent passes scoring 16 real LLM-agent systems against Beer's VSM (S1-S5,S3*,algedonic,recursion) from source code, ignoring the systems' own VSM labels. 128 cells x 2 passes = 256 rows. Inter-coder reliability: exact 63% (81/128), only 2/128 hard Present<->Absent flips, linear-weighted Cohen ka... | ["cybernetic-agents-validation", "vsm", "characterization", "agent-architecture", "step1", "final", "two-pass", "inter-coder"] | {"experiment_name": "cybernetic-agents-validation", "artifact_status": "final", "canary": false} | 2026-05-16T22:20:41.959844+00:00 | null | null | null | null | null | null | -1 | 2026-05-16T22:20:41.959844+00:00 |
cybernetic-agents-validation-step2-failure-mapping | scripts/step2_failure_mapping.py | n/a (statistical analysis of public mcemri/MAD) | {"n_traces": 1242, "n_systems": 7, "permutations": 10000, "seed": 20260516} | ["mcemri/MAD", "latkes/cybernetic-agents-validation-step1-characterization-v2-2pass"] | Step 2 (pre-registered, zero compute, plan=experiments/E3-step2-plan.md): do MAST-7 documented failures concentrate on the VSM functions a system lacks? RESULT: Delta_real=-0.01021 (negative — opposite of the paper's prediction), permutation p=0.6023 (threshold 0.05), Delta_SE_rival=-0.00061; verdict='NO analytic teeth... | ["cybernetic-agents-validation", "vsm", "step2", "failure-mapping", "mast", "final", "negative-result"] | {"experiment_name": "cybernetic-agents-validation", "artifact_status": "final", "canary": false} | 2026-05-16T23:16:02.647723+00:00 | null | null | null | null | null | null | -1 | 2026-05-16T23:16:02.647723+00:00 |
inside-out-replication-v2-corrected-metrics-v1 | 07_compute_metrics.py (corrected Option-C pipeline) | Meta-Llama-3-8B-Instruct, Mistral-7B-Instruct-v0.3, gemma-2-9b-it | {"train_judge_cap": 30, "probe": "logreg C=1.0", "best_layers": {"llama3-8b": 11, "mistral-7b": 12, "gemma2-9b": 24}} | [] | Corrected K/K* (judge bug fixed, paper-faithful Option-C train) vs paper | ["inside-out", "replication-v2", "corrected", "option-c", "K-metric"] | {"experiment_name": "inside-out-replication-v2", "job_id": "mll:39939-40005", "cluster": "mll", "artifact_status": "final", "canary": false} | 2026-05-19T13:14:52.075798+00:00 | null | null | null | null | null | null | -1 | 2026-05-19T13:14:52.075798+00:00 |
inside-out-replication-v2-pamqfix-canary-v1 | 04_external_scores.py (A.8.1 in-context fix) | Mistral-7B-Instruct-v0.3 | {"n_qids": 25, "rows": 5832, "fix_commits": ["60dc183", "aa99611", "6a12f03"]} | [] | Canary: corrected P(a|q)/P_norm externals on a 25-qid subset of mistral/P264/test | ["inside-out", "replication-v2", "pamqfix", "canary", "A.8.1", "externals"] | {"experiment_name": "inside-out-replication-v2", "job_id": "mll:40264-40265", "cluster": "mll", "artifact_status": "final", "canary": true} | 2026-05-19T21:56:52.703803+00:00 | null | null | null | null | null | null | -1 | 2026-05-19T21:56:52.703803+00:00 |
inside-out-replication-v2-pamqfix-metrics-v1 | 07_compute_metrics.py (A.8.1 in-context pipeline) | Meta-Llama-3-8B-Instruct, Mistral-7B-Instruct-v0.3, gemma-2-9b-it | {"fix_commits": ["60dc183", "aa99611", "6a12f03", "6b65e92"], "harness": "sbatch/pamqfix/"} | [] | FULL re-score of P(a|q)/P_norm with paper A.8.1 in-context tokenization across all 12 (model,relation) cells, test split | ["inside-out", "replication-v2", "pamqfix", "A.8.1", "externals", "final", "K-metric"] | {"experiment_name": "inside-out-replication-v2", "job_id": "mll:40407,40408,40416,40433,40530", "cluster": "mll", "artifact_status": "final", "canary": false} | 2026-05-20T12:12:07.570011+00:00 | null | null | null | null | null | null | -1 | 2026-05-20T12:12:07.570011+00:00 |
inside-out-replication-v2-gemma-attn-ablation-v1 | 04_external_scores.py + gemma_ablation.sbatch | google/gemma-2-9b-it | {"n_qids": 25, "rows_per_arm": 7398, "relation": "P26", "fix_commits": ["eda9792"]} | [] | Gemma 2x2 ablation: attn_impl x query_pre_attn_scalar; negative result on P26 | ["inside-out", "replication-v2", "gemma", "ablation", "attn-implementation", "query-pre-attn-scalar", "negative-result"] | {"experiment_name": "inside-out-replication-v2", "job_id": "mll:40561,40570", "cluster": "mll", "artifact_status": "final", "canary": true} | 2026-05-20T18:38:09.711382+00:00 | null | null | null | null | null | null | -1 | 2026-05-20T18:38:09.711382+00:00 |
inside-out-replication-v2-gemma-old-tokenizer-v1 | 04_external_scores.py + INSIDE_OUT_GEMMA_TOKENIZER_REVISION | google/gemma-2-9b-it | {} | [] | Tokenizer revision ablation (old vs current); negative result on P26 | ["inside-out", "replication-v2", "gemma", "tokenizer", "ablation", "negative-result"] | {"experiment_name": "inside-out-replication-v2", "job_id": "mll:40692,40693", "cluster": "mll", "artifact_status": "final", "canary": true} | 2026-05-20T22:20:10.165488+00:00 | null | null | null | null | null | null | -1 | 2026-05-20T22:20:10.165488+00:00 |
inside-out-replication-v2-gemma-attn-ablation-large-v1 | 04_external_scores.py + gemma_ablation_large.sbatch | google/gemma-2-9b-it | {"n_qids": 100, "rows_per_arm": 35612, "relation": "P26"} | [] | Gemma 2x2 attn x scalar ablation at N=100; negative result confirmed | ["inside-out", "replication-v2", "gemma", "ablation", "attn-implementation", "query-pre-attn-scalar", "negative-result", "n100"] | {"experiment_name": "inside-out-replication-v2", "job_id": "mll:40687,40688", "cluster": "mll", "artifact_status": "final", "canary": true} | 2026-05-20T23:58:52.197727+00:00 | null | null | null | null | null | null | -1 | 2026-05-20T23:58:52.197727+00:00 |
inside-out-replication-v2-pamqfix-scores-full | 04_external_scores.py (A.8.1) + _export/build_full_scores.py | Meta-Llama-3-8B-Instruct, Mistral-7B-Instruct-v0.3, gemma-2-9b-it | {} | [] | Full corrected raw per-(q,a) external scores + labels, all 12 cells, test split. Recompute K/K* from this. | ["inside-out", "replication-v2", "raw-scores", "external", "K-metric", "full"] | {"experiment_name": "inside-out-replication-v2", "job_id": "mll:40408+40416+40433 (scores), 40884 (export)", "cluster": "mll", "artifact_status": "final", "canary": false} | 2026-05-21T05:40:43.940041+00:00 | null | null | null | null | null | null | -1 | 2026-05-21T03:32:46.819138+00:00 |
RACA-PROJECT-MANIFEST
Central registry of all datasets in the latkes organization.
- Total Datasets Tracked: 22
- Last Updated: 2026-05-21T05:40:45.733147+00:00
Usage
from datasets import load_dataset
manifest = load_dataset("latkes/RACA-PROJECT-MANIFEST", split="train")
print(f"Tracking {len(manifest)} datasets")
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