The paper introduces Contextual Belief Management (CBM) to address how LLMs should manage accumulating information over long interactions, showing that reinforcement learning significantly improves be…

This paper proposes using Answer-Set Programming (ASP) to implement and evaluate CARCASS abstractions, demonstrating a promising method for constructing powerful abstractions for Reinforcement Learnin…

This survey provides a comprehensive analysis of Reasoning Language Model (RLM) adoption across 28 scientific disciplines, revealing significant disparities in RLM maturity across different scientific…

The paper proposes a novel hybrid authorization framework that combines roles and First-Order Logic to enforce fine-grained, triple-level access control for autonomous agents interacting with knowledg…

This paper synthesizes over 150 scattered studies and reports to provide the first comprehensive primer on post-training reasoning data, organizing the field around data objects, utility, construction…

The paper introduces PRAXIS, a novel algorithm that efficiently approximates the computation of 'Rashomon sets' for decision trees, significantly reducing memory and runtime complexity.

The paper introduces Post-Deterministic Distributed Systems (PDDS) as a new model to coordinate autonomous infrastructure where participants, including stochastic agents, produce divergent reasoning p…

The paper establishes that for quantifier-free dependence logic formulas, the property of k-coherence is equivalent to first-order rewritability, and analyzes the computational complexity of checking…

EviLink addresses the ambiguity of schema linking in Text-to-SQL by treating it as an uncertainty-aware inference over multiple plausible SQL paths, significantly improving recall and efficiency.

The paper proposes a Multi-Phase Inference Mechanism (MIM) to formalize how diverse world models arise, reframing alignment as making heterogeneous representations mutually processable rather than for…

The paper proposes a flexible meta-programming framework to declaratively operationalize and explore varied temporal logics, such as TEL, MEL, and DEL, within standard Answer Set Programming systems.

This paper unifies the fragmented field of Tree-of-Thoughts (ToT) reasoning by mapping LLM-based search processes onto a formal taxonomy derived from classical heuristic search theory.

The paper proposes PaW, a co-training framework that uses standard RL rollouts to provide auxiliary world model supervision directly during policy training, significantly improving language agent perf…

The paper investigates the limits of self-evolution in LLM reasoning under closed-loop settings, finding that while self-improvement is significant, it consistently falls short of perfect oracle super…

The paper introduces Context-Dependent Argumentation Frameworks (CDAFs) to model how an agent strategically manipulates the success of arguments by choosing the external evaluation context.

The paper proposes a framework to model moral reasoning as an ethical distribution (ethical pluralism) rather than a single binary judgment, achieving high classification accuracy by integrating norma…

The paper introduces a diagnostic testbed, PAVE, to evaluate how LLMs arbitrate between their internal knowledge and retrieved evidence during fact-checking, revealing that this arbitration is unrelia…

The paper analyzes the entropy dynamics of Chain-of-Thought (CoT) reasoning, identifying a transition from an exploratory Uncertainty Region to a stable Confidence Region, which enables superior early…

The study demonstrates that domain adaptation primarily reshapes the linguistic explanatory framework of language models, causing shifts in cosmological stance secondarily, rather than directly modify…

This paper introduces the first LLM-generated, domain-independent heuristics for symbolic AI planning, using evolutionary search to surpass the performance of hand-engineered state-of-the-art methods.