arXiv:2601.17523v1 Announce Type: new Abstract: Sleep is thought to support memory consolidation and the recovery of optimal energetic regime by reorganizing synaptic connectivity, yet how plasticity across hierarchical brain circuits contributes to abstraction and energy efficiency remains unclear. Here we study a spiking multi-layer network alternating wake-like and deep-sleep-like states, with state-dependent dendritic integration and […]
Explaining Synergistic Effects in Social Recommendations
arXiv:2601.18151v1 Announce Type: cross Abstract: In social recommenders, the inherent nonlinearity and opacity of synergistic effects across multiple social networks hinders users from understanding how diverse information is leveraged for recommendations, consequently diminishing explainability. However, existing explainers can only identify the topological information in social networks that significantly influences recommendations, failing to further explain the […]
CooperBench: Why Coding Agents Cannot be Your Teammates Yet
arXiv:2601.13295v2 Announce Type: replace-cross Abstract: Resolving team conflicts requires not only task-specific competence, but also social intelligence to find common ground and build consensus. As AI agents increasingly collaborate on complex work, they must develop coordination capabilities to function as effective teammates. Yet we hypothesize that current agents lack these capabilities. To test this, we […]
Generative AI in Saudi Arabia: A National Survey of Adoption, Risks, and Public Perceptions
arXiv:2601.18234v1 Announce Type: cross Abstract: Generative Artificial Intelligence (GenAI) is rapidly becoming embedded in Saudi Arabia’s digital transformation under Vision 2030, yet public awareness, adoption, and concerns surrounding these tools remain underexplored. This study provides an early snapshot of GenAI engagement among Saudi nationals. Using a nationwide survey of 330 participants across regions, age groups, […]
Cognitive Platform Engineering for Autonomous Cloud Operations
arXiv:2601.17542v1 Announce Type: new Abstract: Modern DevOps practices have accelerated software delivery through automation, CI/CD pipelines, and observability tooling,but these approaches struggle to keep pace with the scale and dynamism of cloud-native systems. As telemetry volume grows and configuration drift increases, traditional, rule-driven automation often results in reactive operations, delayed remediation, and dependency on manual […]
TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment
arXiv:2601.18292v1 Announce Type: cross Abstract: In recent years, safety risks associated with large language models have become increasingly prominent, highlighting the urgent need to mitigate the generation of toxic and harmful content. The mainstream paradigm for LLM safety alignment typically adopts a collaborative framework involving three roles: an attacker for adversarial prompt generation, a defender […]
GCFX: Generative Counterfactual Explanations for Deep Graph Models at the Model Level
arXiv:2601.18447v1 Announce Type: cross Abstract: Deep graph learning models have demonstrated remarkable capabilities in processing graph-structured data and have been widely applied across various fields. However, their complex internal architectures and lack of transparency make it difficult to explain their decisions, resulting in opaque models that users find hard to understand and trust. In this […]
JaxARC: A High-Performance JAX-based Environment for Abstraction and Reasoning Research
arXiv:2601.17564v1 Announce Type: new Abstract: The Abstraction and Reasoning Corpus (ARC) tests AI systems’ ability to perform human-like inductive reasoning from a few demonstration pairs. Existing Gymnasium-based RL environments severely limit experimental scale due to computational bottlenecks. We present JaxARC, an open-source, high-performance RL environment for ARC implemented in JAX. Its functional, stateless architecture enables […]
ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule
arXiv:2601.18681v1 Announce Type: cross Abstract: We consider time discretization for score-based diffusion models to generate samples from a learned reverse-time dynamic on a finite grid. Uniform and hand-crafted grids can be suboptimal given a budget on the number of time steps. We introduce Adaptive Reparameterized Time (ART) that controls the clock speed of a reparameterized […]
GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design
arXiv:2601.17582v1 Announce Type: new Abstract: Biomolecular networks underpin emerging technologies in synthetic biology-from robust biomanufacturing and metabolic engineering to smart therapeutics and cell-based diagnostics-and also provide a mechanistic language for understanding complex dynamics in natural and ecological systems. Yet designing chemical reaction networks (CRNs) that implement a desired dynamical function remains largely manual: while a […]
POPE: Learning to Reason on Hard Problems via Privileged On-Policy Exploration
arXiv:2601.18779v1 Announce Type: cross Abstract: Reinforcement learning (RL) has improved the reasoning abilities of large language models (LLMs), yet state-of-the-art methods still fail to learn on many training problems. On hard problems, on-policy RL rarely explores even a single correct rollout, yielding zero reward and no learning signal for driving improvement. We find that natural […]
Discovery of Feasible 3D Printing Configurations for Metal Alloys via AI-driven Adaptive Experimental Design
arXiv:2601.17587v1 Announce Type: new Abstract: Configuring the parameters of additive manufacturing processes for metal alloys is a challenging problem due to complex relationships between input parameters (e.g., laser power, scan speed) and quality of printed outputs. The standard trial-and-error approach to find feasible parameter configurations is highly inefficient because validating each configuration is expensive in […]