arXiv Computation and Language September 15, 2026 By Kyle Richardson, Cullen Anderson, Pranav Balakrishnan, Takuto Ban, Daksha Ladia, Ankita Gupta, Marisa Hudspeth

From Token Probabilities to Semantic Constraints: Towards Declarative Probabilistic Evaluation of Language Models

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llms

arXiv Machine Learning

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Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification

arXiv:2601. 22642v2 Announce Type: replace Abstract: Large Language Models (LLMs) show remarkable capabilities, yet their stochastic next-token prediction creates logical inconsistencies and reward hacking that formal symbolic systems avoid.

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llms fine-tuning benchmarks

arXiv AI

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RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models

arXiv:2510. 19698v3 Announce Type: replace Abstract: Large Language Models (LLMs) can propose rules in natural language, sidestepping the need for a predefined predicate space in traditional rule learning.

By Yang Yang, Hua XU, Zhangyi Hu, Yutao Yue

llms

arXiv AI

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Entropy-Aware Token Rejection for Improving Speculative Decoding

arXiv:2512.23765v2 Announce Type: replace-cross Abstract: Speculative decoding (SD) accelerates large language model (LLM) inference by using a lightweight draft model to propose tokens and a stronge...

By Tiancheng Su, Meicong Zhang, Guoxiu He

llms benchmarks

arXiv AI

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CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement

arXiv:2608. 11631v1 Announce Type: new Abstract: In open-domain human-computer interaction scenarios, large language models (LLMs) frequently encounter user queries that are ambiguous or incomplete.

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llms reinforcement-learning fine-tuning safety

arXiv Machine Learning

Aug 11

Rethinking Reasoning with MDLMs: Early Exits, Post-hoc Reasoning, and Beyond

arXiv:2510. 19990v2 Announce Type: replace Abstract: The reasoning paradigm, where language models reason before answering, has enabled breakthroughs on tasks such as mathematical problem-solving.

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llms diffusion reinforcement-learning fine-tuning

arXiv AI

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Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models

arXiv:2606. 09856v1 Announce Type: cross Abstract: Post-training Large Language Models (LLMs) for reasoning typically focuses on deductive tasks such as mathematics and coding where correctness is verifiable.

By Liyi Zhang, Akshay K. Jagadish, Brenden M. Lake, Thomas L. Griffiths

llms agents fine-tuning benchmarks safety

One email a morning, machine-written

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Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification

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