arXiv:2509.21012v3 Announce Type: replace-cross Abstract: In-context Learning (ICL) is an emerging few-shot learning paradigm based on modern Language Models (LMs), yet its inner mechanism remains unclear. In this paper, we investigate the mechanism through a novel perspective of information removal. Specifically, we demonstrate that in the zero-shot scenario, LMs encode queries into non-selective representations in […]
RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation
arXiv:2601.08430v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has driven substantial progress in reasoning-intensive domains like mathematics. However, optimizing open-ended generation remains challenging due to the lack of ground truth. While rubric-based evaluation offers a structured proxy for verification, existing methods suffer from scalability bottlenecks and coarse criteria, resulting in a supervision […]
MAnchors: Memorization-Based Acceleration of Anchors via Rule Reuse and Transformation
arXiv:2502.11068v2 Announce Type: replace-cross Abstract: Anchors is a popular local model-agnostic explanation technique whose applicability is limited by its computational inefficiency. To address this limitation, we propose a memorization-based framework that accelerates Anchors while preserving explanation fidelity and interpretability. Our approach leverages the iterative nature of Anchors’ algorithm which gradually refines an explanation until it […]
Spectral Representation-based Reinforcement Learning
arXiv:2512.15036v2 Announce Type: replace-cross Abstract: In real-world applications with large state and action spaces, reinforcement learning (RL) typically employs function approximations to represent core components like the policies, value functions, and dynamics models. Although powerful approximations such as neural networks offer great expressiveness, they often present theoretical ambiguities, suffer from optimization instability and exploration difficulty, […]
AGFS-Tractometry: A Novel Atlas-Guided Fine-Scale Tractometry Approach for Enhanced Along-Tract Group Statistical Comparison Using Diffusion MRI Tractography
arXiv:2507.10601v2 Announce Type: replace Abstract: Diffusion MRI (dMRI) tractography is currently the only method for in vivo mapping of the brain’s white matter (WM) connections. Tractometry is an advanced tractography analysis technique for along-tract profiling to investigate the morphology and microstructural properties along the fiber tracts. Tractometry has become an essential tool for studying local […]
PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice
arXiv:2601.16669v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are increasingly applied to legal domain-specific tasks, evaluating their ability to perform legal work in real-world settings has become essential. However, existing legal benchmarks rely on simplified and highly standardized tasks, failing to capture the ambiguity, complexity, and reasoning demands of real legal practice. Moreover, […]
Tracing Mathematical Proficiency Through Problem-Solving Processes
arXiv:2512.00311v2 Announce Type: replace-cross Abstract: Knowledge Tracing (KT) aims to model student’s knowledge state and predict future performance to enable personalized learning in Intelligent Tutoring Systems. However, traditional KT methods face fundamental limitations in explainability, as they rely solely on the response correctness, neglecting the rich information embedded in students’ problem-solving processes. To address this […]
KV Admission: Learning What to Write for Efficient Long-Context Inference
arXiv:2512.17452v3 Announce Type: replace-cross Abstract: Long-context LLM inference is bottlenecked by the quadratic attention complexity and linear KV cache growth. Prior approaches mitigate this via post-hoc selection or eviction but overlook the root inefficiency: indiscriminate writing to memory. In this paper, we formalize KV cache management as a causal system of three primitives: KV Admission, […]
Teaching LLMs to Ask: Self-Querying Category-Theoretic Planning for Under-Specified Reasoning
arXiv:2601.20014v1 Announce Type: new Abstract: Inference-time planning with large language models frequently breaks under partial observability: when task-critical preconditions are not specified at query time, models tend to hallucinate missing facts or produce plans that violate hard constraints. We introduce textbfSelf-Querying Bidirectional Categorical Planning (SQ-BCP), which explicitly represents precondition status (textttSat/textttViol/textttUnk) and resolves unknowns via […]
Exploring Transformer Placement in Variational Autoencoders for Tabular Data Generation
arXiv:2601.20854v1 Announce Type: cross Abstract: Tabular data remains a challenging domain for generative models. In particular, the standard Variational Autoencoder (VAE) architecture, typically composed of multilayer perceptrons, struggles to model relationships between features, especially when handling mixed data types. In contrast, Transformers, through their attention mechanism, are better suited for capturing complex feature interactions. In […]
Cognition Envelopes for Bounded AI Reasoning in Autonomous UAS Operations
arXiv:2510.26905v2 Announce Type: replace Abstract: Cyber-physical systems increasingly rely on Foundational Models such as Large Language Models (LLMs) and Vision-Language Models (VLMs) to increase autonomy through enhanced perception, inference, and planning. However, these models also introduce new types of errors, such as hallucinations, overgeneralizations, and context misalignments, resulting in incorrect and flawed decisions. To address […]
LLM Multi-Agent Systems: Challenges and Open Problems
arXiv:2402.03578v3 Announce Type: replace-cross Abstract: This paper explores multi-agent systems and identify challenges that remain inadequately addressed. By leveraging the diverse capabilities and roles of individual agents, multi-agent systems can tackle complex tasks through agent collaboration. We discuss optimizing task allocation, fostering robust reasoning through iterative debates, managing complex and layered context information, and enhancing […]