arXiv:2504.11075v2 Announce Type: replace Abstract: Infants often exhibit goal-directed behaviors, such as reaching for a sensory stimulus, even when no external reward criterion is provided. These intrinsically motivated behaviors facilitate spontaneous exploration and learning of the body and environment during early developmental stages. Although computational modeling can offer insight into the mechanisms underlying such behaviors, […]
SemanticForge: Repository-Level Code Generation through Semantic Knowledge Graphs and Constraint Satisfaction
arXiv:2511.07584v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed software development by enabling automated code generation, yet they frequently suffer from systematic errors that limit practical deployment. We identify two critical failure modes: textitlogical hallucination (incorrect control/data-flow reasoning) and textitschematic hallucination (type mismatches, signature violations, and architectural inconsistencies). These errors stem from the […]
Matters of Life and Death in Computational Cell Biology
arXiv:2511.07847v1 Announce Type: new Abstract: Nearly all cell models explicitly or implicitly deal with the biophysical constraints that must be respected for life to persist. Despite this, there is almost no systematicity in how these constraints are implemented, and we lack a principled understanding of how cellular dynamics interact with them and how they originate […]
TrackStudio: An Integrated Toolkit for Markerless Tracking
arXiv:2511.07624v1 Announce Type: cross Abstract: Markerless motion tracking has advanced rapidly in the past 10 years and currently offers powerful opportunities for behavioural, clinical, and biomechanical research. While several specialised toolkits provide high performance for specific tasks, using existing tools still requires substantial technical expertise. There remains a gap in accessible, integrated solutions that deliver […]
Chain-of-Thought Hijacking
arXiv:2510.26418v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieve higher task performance with more inference-time computation, and prior works suggest this scaled reasoning may also strengthen safety by improving refusal. Yet we find the opposite: the same reasoning can be used to bypass safeguards. We introduce Chain-of-Thought Hijacking, a jailbreak attack on reasoning models. […]
Adaptive Graph Learning with Transformer for Multi-Reservoir Inflow Prediction
arXiv:2511.07649v1 Announce Type: cross Abstract: Reservoir inflow prediction is crucial for water resource management, yet existing approaches mainly focus on single-reservoir models that ignore spatial dependencies among interconnected reservoirs. We introduce AdaTrip as an adaptive, time-varying graph learning framework for multi-reservoir inflow forecasting. AdaTrip constructs dynamic graphs where reservoirs are nodes with directed edges reflecting […]
GAMA: A Neural Neighborhood Search Method with Graph-aware Multi-modal Attention for Vehicle Routing Problem
arXiv:2511.07850v1 Announce Type: new Abstract: Recent advances in neural neighborhood search methods have shown potential in tackling Vehicle Routing Problems (VRPs). However, most existing approaches rely on simplistic state representations and fuse heterogeneous information via naive concatenation, limiting their ability to capture rich structural and semantic context. To address these limitations, we propose GAMA, a […]
Speech Separation for Hearing-Impaired Children in the Classroom
arXiv:2511.07677v1 Announce Type: cross Abstract: Classroom environments are particularly challenging for children with hearing impairments, where background noise, multiple talkers, and reverberation degrade speech perception. These difficulties are greater for children than adults, yet most deep learning speech separation models for assistive devices are developed using adult voices in simplified, low-reverberation conditions. This overlooks both […]
Informed Correctors for Discrete Diffusion Models
arXiv:2407.21243v5 Announce Type: replace-cross Abstract: Discrete diffusion has emerged as a powerful framework for generative modeling in discrete domains, yet efficiently sampling from these models remains challenging. Existing sampling strategies often struggle to balance computation and sample quality when the number of sampling steps is reduced, even when the model has learned the data distribution […]
Diffusion Guided Adversarial State Perturbations in Reinforcement Learning
arXiv:2511.07701v1 Announce Type: cross Abstract: Reinforcement learning (RL) systems, while achieving remarkable success across various domains, are vulnerable to adversarial attacks. This is especially a concern in vision-based environments where minor manipulations of high-dimensional image inputs can easily mislead the agent’s behavior. To this end, various defenses have been proposed recently, with state-of-the-art approaches achieving […]
WaterMod: Modular Token-Rank Partitioning for Probability-Balanced LLM Watermarking
arXiv:2511.07863v1 Announce Type: new Abstract: Large language models now draft news, legal analyses, and software code with human-level fluency. At the same time, regulations such as the EU AI Act mandate that each synthetic passage carry an imperceptible, machine-verifiable mark for provenance. Conventional logit-based watermarks satisfy this requirement by selecting a pseudorandom green vocabulary at […]
TurboSAT: Gradient-Guided Boolean Satisfiability Accelerated on GPU-CPU Hybrid System
arXiv:2511.07737v1 Announce Type: cross Abstract: While accelerated computing has transformed many domains of computing, its impact on logical reasoning, specifically Boolean satisfiability (SAT), remains limited. State-of-the-art SAT solvers rely heavily on inherently sequential conflict-driven search algorithms that offer powerful heuristics but limit the amount of parallelism that could otherwise enable significantly more scalable SAT solving. […]