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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | https://arxiv.org/abs/2510.04817 |
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| _version_ | 1866911194047053824 |
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| author | Madahar, Abhinav |
| author_facet | Madahar, Abhinav |
| contents | Controllers for structured LM reasoning (e.g., Chain-of-Thought, self-consistency, and Tree-of-Thoughts) often entangle what to try next with how to execute it, exposing only coarse global knobs and yielding brittle, compute-inefficient, and hard-to-audit behavior. We introduce Natural Language Edge Labelling (NLEL), a labeller-tuner overlay that attaches a free-form natural-language directive to each search edge and translates it into a schema-bounded control vector for decoding, search (branch quotas, exploration $β$), generation bundle size, retrieval mixtures, and verification passes. A labeller $Λ$ emits labels from the parent state and a compact context; a tuner $Ψ$ maps $(P, L, C)\to Π$, with strict schema validation and trust-region projection around safe defaults. Downstream selection remains ToT-style with score $S=μ+βσ$ and depth-annealed $β$. We show NLEL strictly generalizes CoT/ToT, prove an anytime-monotonicity property for top-$k$ selection under label-conditioned bundles, and bound selector shortfall by control-vector distortion, providing decision-relevant justification for guards like trust regions and verification passes. We instantiate $Ψ$ as a prompt-only JSON Parameter Emitter and preregister an evaluation on GSM8K, MATH (subset), StrategyQA, and ARC-Challenge with compute-aware reporting (success@compute, tokens-per-success) and ablations over $Λ$, $Ψ$, trust-region radius, and control quantization; preregistered forecasts anticipate accuracy gains at comparable token budgets and improved success@compute under constraints. NLEL offers an interpretable, model-agnostic interface that separates intent from execution for controllable, auditable LM inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04817 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Natural Language Edge Labelling: Decoupling Intent from Execution in Structured LM Reasoning Madahar, Abhinav Artificial Intelligence Controllers for structured LM reasoning (e.g., Chain-of-Thought, self-consistency, and Tree-of-Thoughts) often entangle what to try next with how to execute it, exposing only coarse global knobs and yielding brittle, compute-inefficient, and hard-to-audit behavior. We introduce Natural Language Edge Labelling (NLEL), a labeller-tuner overlay that attaches a free-form natural-language directive to each search edge and translates it into a schema-bounded control vector for decoding, search (branch quotas, exploration $β$), generation bundle size, retrieval mixtures, and verification passes. A labeller $Λ$ emits labels from the parent state and a compact context; a tuner $Ψ$ maps $(P, L, C)\to Π$, with strict schema validation and trust-region projection around safe defaults. Downstream selection remains ToT-style with score $S=μ+βσ$ and depth-annealed $β$. We show NLEL strictly generalizes CoT/ToT, prove an anytime-monotonicity property for top-$k$ selection under label-conditioned bundles, and bound selector shortfall by control-vector distortion, providing decision-relevant justification for guards like trust regions and verification passes. We instantiate $Ψ$ as a prompt-only JSON Parameter Emitter and preregister an evaluation on GSM8K, MATH (subset), StrategyQA, and ARC-Challenge with compute-aware reporting (success@compute, tokens-per-success) and ablations over $Λ$, $Ψ$, trust-region radius, and control quantization; preregistered forecasts anticipate accuracy gains at comparable token budgets and improved success@compute under constraints. NLEL offers an interpretable, model-agnostic interface that separates intent from execution for controllable, auditable LM inference. |
| title | Natural Language Edge Labelling: Decoupling Intent from Execution in Structured LM Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.04817 |