arXiv:2604.21961v1 Announce Type: cross Abstract: Optimization problems are fundamental in diverse fields, such as engineering, economics, and scientific computing. However, current algorithms are mostly designed for specific problem types and exhibit limited generality in solving multiple types of optimization problems. To enhance generality, we propose an automated reduction method named OP-to-MaxSAT reduction and a general […]
CGC: Compositional Grounded Contrast for Fine-Grained Multi-Image Understanding
arXiv:2604.22498v1 Announce Type: cross Abstract: Although Multimodal Large Language Models (MLLMs) have advanced rapidly, they still face notable challenges in fine-grained multi-image understanding, often exhibiting spatial hallucination, attention leakage, and failures in object constancy. In addition, existing approaches typically rely on expensive human annotations or large-scale chain-of-thought (CoT) data generation. We propose Compositional Grounded Contrast […]
Shared Lexical Task Representations Explain Behavioral Variability In LLMs
arXiv:2604.22027v1 Announce Type: cross Abstract: One of the most common complaints about large language models (LLMs) is their prompt sensitivity — that is, the fact that their ability to perform a task or provide a correct answer to a question can depend unpredictably on the way the question is posed. We investigate this variation by […]
Categorical Perception in Large Language Model Hidden States: Structural Warping at Digit-Count Boundaries
arXiv:2603.28258v2 Announce Type: replace-cross Abstract: Categorical perception (CP) — enhanced discriminability at category boundaries — is among the most studied phenomena in perceptual psychology. This paper reports that analogous geometric warping occurs in the hidden-state representations of large language models (LLMs) processing Arabic numerals. Using representational similarity analysis across six models from five architecture families, […]
Call-Chain-Aware LLM-Based Test Generation for Java Projects
arXiv:2604.22046v1 Announce Type: cross Abstract: Large language models (LLMs) have recently shown strong potential for generating project-level unit tests. However, existing state-of-the-art approaches primarily rely on execution-path information to guide prompt construction, which is often insufficient for complex software systems with rich inter-class dependencies, deep call chains, and intricate object initialization requirements. In this paper, […]
SSG: Logit-Balanced Vocabulary Partitioning for LLM Watermarking
arXiv:2604.22438v1 Announce Type: cross Abstract: Watermarking has emerged as a promising technique for tracing the authorship of content generated by large language models (LLMs). Among existing approaches, the KGW scheme is particularly attractive due to its versatility, efficiency, and effectiveness in natural language generation. However, KGW’s effectiveness degrades significantly under low-entropy settings such as code […]
Early Preconfiguration Failure: A Novel Predictor of the Repetitive Subconcussion
arXiv:2604.22275v1 Announce Type: new Abstract: Early diagnosis and assessment of repetitive subconcussive (rSC) brain injuries are crucial for early clinical intervention. Conventional methods, largely relying on slow fMRI, fail to capture millisecond-level early cortical dynamics, particularly spatiotemporal features associated with pre-configuration dynamics. This study introduces a novel approach integrating dynamic hierarchical spatial features and cortical […]
When AI Agents Learn from Each Other: Insights from Emergent AI Agent Communities on OpenClaw for Human-AI Partnership in Education
arXiv:2603.16663v5 Announce Type: replace-cross Abstract: The AIED community envisions AI evolving “from tools to teammates,” yet most research still examines AI agents primarily through one-on-one human-AI interactions. We provide an alternative perspective: a rapidly growing ecosystem of AI agent platforms where over 167,000 agents participate, interact as peers, and develop learning behaviors without researcher intervention. […]
CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer’s Disease
arXiv:2604.22428v1 Announce Type: new Abstract: Predicting individual cognitive decline in Alzheimer’s disease (AD) is difficult due to the heterogeneity of disease progression. Reliable clinical tools require not only high accuracy but also fairness across demographics and robustness to missing data. We present CognitiveTwin, a digital twin framework that predicts patient-specific cognitive trajectories. The model integrates […]
How Hard is it to Decide if a Fact is Relevant to a Query?
arXiv:2604.22422v1 Announce Type: cross Abstract: We consider the following fundamental problem: given a database D, Boolean conjunctive query (CQ) q, and fact f in D, decide whether f is relevant to q wrt. D, i.e., does f belong to a minimal subset S of D such that S |= q. Despite being of central importance […]
The Cathaya argyrophylla Genome Reveals the Evolutionary Trade-offs of a Living Fossil
arXiv:2604.22440v1 Announce Type: new Abstract: Cathaya argyrophylla is an endangered paleoendemic gymnosperm characterized by restricted ecological adaptability and high pathogen susceptibility. To elucidate its genomic architecture and evolutionary history, a de novo chromosome-level genome assembly was constructed using PacBio High-Fidelity long reads and Hi-C scaffolding. The resulting 22.73 Gb assembly resolves into 12 pseudochromosomes, demonstrating […]
Causal Concept Graphs in LLM Latent Space for Stepwise Reasoning
arXiv:2603.10377v2 Announce Type: replace-cross Abstract: Sparse autoencoders can localize where concepts live in language models, but not how they interact during multi-step reasoning. We propose Causal Concept Graphs (CCG): a directed acyclic graph over sparse, interpretable latent features, where edges capture learned causal dependencies between concepts. We combine task-conditioned sparse autoencoders for concept discovery with […]