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Logical Reasoning with Outcome Reward Models for Test-Time Scaling
Ramya Keerthy Thatikonda, Wray Buntine, Ehsan Shareghi
https://arxiv.org/abs/2508.19903 https://

Logical Reasoning with Outcome Reward Models for Test-Time Scaling
Logical reasoning is a critical benchmark for evaluating the capabilities of large language models (LLMs), as it reflects their ability to derive valid conclusions from given premises. While the combination of test-time scaling with dedicated outcome or process reward models has opened up new avenues to enhance LLMs performance in complex reasoning tasks, this space is under-explored in deductive logical reasoning. We present a set of Outcome Reward Models (ORMs) for deductive reasoning. To tra…

Investigating Advanced Reasoning of Large Language Models via Black-Box Interaction
Congchi Yin, Tianyi Wu, Yankai Shu, Alex Gu, Yunhan Wang, Jun Shao, Xun Jiang, Piji Li
https://arxiv.org/abs/2508.19035

Investigating Advanced Reasoning of Large Language Models via Black-Box Interaction
Existing tasks fall short in evaluating reasoning ability of Large Language Models (LLMs) in an interactive, unknown environment. This deficiency leads to the isolated assessment of deductive, inductive, and abductive reasoning, neglecting the integrated reasoning process that is indispensable for humans discovery of real world. We introduce a novel evaluation paradigm, \textit{black-box interaction}, to tackle this challenge. A black-box is defined by a hidden function that maps a specific set…

Unifying Deductive and Abductive Reasoning in Knowledge Graphs with Masked Diffusion Model
Yisen Gao, Jiaxin Bai, Yi Huang, Xingcheng Fu, Qingyun Sun, Yangqiu Song
https://arxiv.org/abs/2510.11462

Unifying Deductive and Abductive Reasoning in Knowledge Graphs with Masked Diffusion Model
Deductive and abductive reasoning are two critical paradigms for analyzing knowledge graphs, enabling applications from financial query answering to scientific discovery. Deductive reasoning on knowledge graphs usually involves retrieving entities that satisfy a complex logical query, while abductive reasoning generates plausible logical hypotheses from observations. Despite their clear synergistic potential, where deduction can validate hypotheses and abduction can uncover deeper logical patte…

Finite Axiomatizability by Disjunctive Existential Rules
Marco Calautti, Marco Console, Andreas Pieris
https://arxiv.org/abs/2508.11946 https://arxiv.org/p…

Finite Axiomatizability by Disjunctive Existential Rules
Rule-based languages lie at the core of several areas of central importance to databases and artificial intelligence such as deductive databases and knowledge representation and reasoning. Disjunctive existential rules (a.k.a. disjunctive tuple-generating dependencies in the database literature) form such a prominent rule-based language. The goal of this work is to pinpoint the expressive power of disjunctive existential rules in terms of insightful model-theoretic properties. More precisely, g…

Toward Mechanistic Explanation of Deductive Reasoning in Language Models
Davide Maltoni, Matteo Ferrara
https://arxiv.org/abs/2510.09340 https://arxiv.org/…

Toward Mechanistic Explanation of Deductive Reasoning in Language Models
Recent large language models have demonstrated relevant capabilities in solving problems that require logical reasoning; however, the corresponding internal mechanisms remain largely unexplored. In this paper, we show that a small language model can solve a deductive reasoning task by learning the underlying rules (rather than operating as a statistical learner). A low-level explanation of its internal representations and computational circuits is then provided. Our findings reveal that inducti…

Generative Logic: A New Computer Architecture for Deterministic Reasoning and Knowledge Generation
Nikolai Sergeev
https://arxiv.org/abs/2508.00017 https://

Generative Logic: A New Computer Architecture for Deterministic Reasoning and Knowledge Generation
We present Generative Logic (GL), a deterministic architecture that begins from user-supplied axiomatic definitions -- written in a minimalist Mathematical Programming Language (MPL) -- and systematically explores their deductive neighborhood. Definitions are compiled into a distributed grid of simple Logic Blocks (LBs) that exchange messages; any time several expressions unify under an inference rule, a new fact is emitted with full provenance to its sources, yielding replayable, auditable pro…