Closed-system multi-step LLM reasoning is subject to an information-theoretic bound where mutual information with evidence decreases, preserving accuracy while eroding faithfulness, with EGSR recovering it on SciFact and FEVER.
arXiv preprint arXiv:2402.13950 , year=
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ReSS extracts decision paths from trees as scaffolds to guide LLM reasoning generation, fine-tunes the LLM on the resulting dataset with scaffold-invariant augmentation, and reports up to 10% gains on medical and financial tabular benchmarks with new faithfulness metrics.
Introduces the Mechanism Plausibility Scale, a four-level framework separating generative sufficiency from mechanistic plausibility in LLM-based agent-based models.
Fine-tuning Qwen2.5-3B with GRPO on GSM8K to use Prolog yields competitive zero-shot MMLU performance but exposes an accuracy-auditability trade-off interpreted as reward hacking.
citing papers explorer
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The Reasoning Trap: An Information-Theoretic Bound on Closed-System Multi-Step LLM Reasoning
Closed-system multi-step LLM reasoning is subject to an information-theoretic bound where mutual information with evidence decreases, preserving accuracy while eroding faithfulness, with EGSR recovering it on SciFact and FEVER.
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ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold
ReSS extracts decision paths from trees as scaffolds to guide LLM reasoning generation, fine-tunes the LLM on the resulting dataset with scaffold-invariant augmentation, and reports up to 10% gains on medical and financial tabular benchmarks with new faithfulness metrics.
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Mechanism Plausibility in Generative Agent-Based Modeling
Introduces the Mechanism Plausibility Scale, a four-level framework separating generative sufficiency from mechanistic plausibility in LLM-based agent-based models.
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Training Language Models to Use Prolog as a Tool
Fine-tuning Qwen2.5-3B with GRPO on GSM8K to use Prolog yields competitive zero-shot MMLU performance but exposes an accuracy-auditability trade-off interpreted as reward hacking.