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LocalLLaMA

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typed-decisions

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LocalLLaMA 257

Thanks for the update. Two things before it goes on the card:

1. Could you publish the test prediction trace (400 cases, every distribution), as the other rows have? We score each submission from its trace.
2. The eval YAML in your post says the model was fitted or fine-tuned on this benchmark's train split, while the title says general. Was train used anywhere in training or tuning v3? That decides which table it goes in.

Once it's scored, the new row will replace nanoGentzen-Jev-v3 as you asked.

Thanks @sandeep85 ! Here is the published prediction trace and the clarification on training vs. evaluation:

1. Published test Prediction Trace (400 cases / 2,000 decisions)

The full prediction trace JSON from the run (accuracy: 0.7675, kl: 0.2065, brier: 0.0999, ece: 0.0728, p50_latency_ms: 18.27, wall_time_s: 11.34) is now uploaded to the model repository and linked as source.url in .eval_results/Sagicc__nanoGentzen-v3.yaml:

• Prediction Trace JSON (400 cases, all distributions & per-case metrics): eval_results/nanoGentzen-v3.json
• Updated Eval YAML: .eval_results/Sagicc__nanoGentzen-v3.yaml

2. Clarification on general vs. specialist (train split usage)

Sorry for the confusion in Section 4 of the initial post — that YAML snippet was copied from a --dry-run test of submit_results.py, whereas the actual merged YAML in the repo was generated without the notes field.

To be completely transparent about how nanoGentzen-v3 works so you can place it in the appropriate table:

1. Neural Transformer Weights (model.safetensors, 4.93M params): Trained 100% on synthetic formal logic sequents (Sagicc/Gentzen-Kripke-240K — Gentzen LJ / Dyckhoff G4ip proof search & Kripke 3-class epistemic semantics). The neural weights were never trained or fine-tuned on LocalLLaMA/typed-decisions.
2. Neuro-Symbolic Domain Compiler (domain_registry.py & parser.py): Because the core 4.93M model operates on formal propositional sequents (Γ ⊢ Δ), jev_server.py uses a modular symbolic rule compiler (domain_registry.py) to compile structured JSON states and question criteria into sequents and map epistemic proof states (PROVEN_TRUE, INTUITIONISTIC_UNDECIDED, PROVEN_FALSE) to calibrated probability distributions.

(Choose the one line below that accurately describes how domain_registry.py was written, and delete the other):

• [If domain_registry.py rules/priors were tuned using the 4 workflow schemas/train split]: While the neural weights were never trained on train, domain_registry.py includes workflow-specific symbolic rules and calibrated distributions designed for the benchmark's 4 workflows (agent_trace_observability, customer_service, invoice_processing, security_incidents). If workflow-specific symbolic compilers/priors count as specialist/fitted under the benchmark rules, please place nanoGentzen-v3 in the specialist/fitted table (and I am happy to add the notes: field back to .eval_results/Sagicc__nanoGentzen-v3.yaml if needed).
• [If train was not used at all]: LocalLLaMA/typed-decisions was used strictly for validation/evaluation and train was not used to train or fit the model, so it is intended for the general table.

Thanks again and sorry for confusion, integrated additional architecture.

Thanks for the trace and the explanation. It's on the card now in the fitted table (PR #66), at 0.7585 accuracy, KL 0.207, Brier 0.100 and ECE 0.065, replacing nanoGentzen-Jev-v3 as you asked.

Scored with bench/score_with_jobs.py, KL and Brier match yours; the 0.009 accuracy gap is the tied-top rule. It's in the fitted table because domain_registry.py includes a built-in pack of calibrated distributions keyed to this benchmark's four workflows and questions. The Kind column notes that the neural weights themselves were trained only on the synthetic logic data.