AI

Diagonal-Tiled Mixed-Precision Attention for Efficient Low-Bit MXFP Inference

Researchers have developed a new low-bit mixed-precision attention kernel called Diagonal-Tiled Mixed-Precision Attention (DMA) that significantly speeds up large language model inference on NVIDIA B200 GPUs while maintaining generation quality. The technique uses microscaling floating-point (MXFP) data format and kernel fusion to address the high computational costs of transformer-based models.

Customized User Plane Processing via Code Generating AI Agents for Next Generation Mobile Networks

Researchers propose using generative AI agents to create customized user plane processing blocks for 6G mobile networks based on text-based service requests. The study evaluates factors affecting AI code generation accuracy for network-specific tasks, finding that AI agents can successfully generate desired processing functions under suitable conditions.

SkillX: Automatically Constructing Skill Knowledge Bases for Agents

Researchers introduce SkillX, an automated framework for building reusable skill knowledge bases for AI agents that addresses inefficiencies in current self-evolving paradigms. The system uses multi-level skill design, iterative refinement, and exploratory expansion to create plug-and-play skill libraries that improve task success and execution efficiency across different agents and environments.

When Does Multimodal AI Help? Diagnostic Complementarity of Vision-Language Models and CNNs for Spectrum Management in Satellite-Terrestrial Networks

Researchers developed SpectrumQA, a benchmark comparing vision-language models (VLMs) and CNNs for spectrum management in satellite-terrestrial networks. The study reveals task-dependent complementarity: CNNs excel at spatial localization while VLMs uniquely enable semantic reasoning capabilities that CNNs lack entirely.

Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems

Researchers introduce LLMA-Mem, a memory framework for LLM multi-agent systems that balances team size with lifelong learning capabilities. The study reveals that larger agent teams don't always perform better long-term, and smaller teams with better memory design can outperform larger ones while reducing costs.

Can LLMs Learn to Reason Robustly under Noisy Supervision?

Researchers propose Online Label Refinement (OLR) to improve AI reasoning models' robustness under noisy supervision in Reinforcement Learning with Verifiable Rewards. The method addresses the critical problem of training language models when expert-labeled data contains errors, achieving 3-4% performance gains across mathematical reasoning benchmarks.

LightThinker++: From Reasoning Compression to Memory Management

Researchers developed LightThinker++, a new framework that enables large language models to compress intermediate reasoning thoughts and manage memory more efficiently. The system reduces peak token usage by up to 70% while improving accuracy by 2.42% and maintaining performance over extended reasoning tasks.

Stabilizing Unsupervised Self-Evolution of MLLMs via Continuous Softened Retracing reSampling

Researchers propose Continuous Softened Retracing reSampling (CSRS) to improve the self-evolution of Multimodal Large Language Models by addressing biases in feedback mechanisms. The method uses continuous reward signals instead of binary rewards and achieves state-of-the-art results on mathematical reasoning benchmarks like MathVision using Qwen2.5-VL-7B.

Testing the Limits of Truth Directions in LLMs

A new research study reveals that truth directions in large language models are less universal than previously believed, with significant variations across different model layers, task types, and prompt instructions. The findings show truth directions emerge earlier for factual tasks but later for reasoning tasks, and are heavily influenced by model instructions and task complexity.

V-Reflection: Transforming MLLMs from Passive Observers to Active Interrogators

Researchers introduce V-Reflection, a new framework that transforms Multimodal Large Language Models (MLLMs) from passive observers to active interrogators through a 'think-then-look' mechanism. The approach addresses perception-related hallucinations in fine-grained tasks by allowing models to dynamically re-examine visual details during reasoning, showing significant improvements across six perception-intensive benchmarks.

SoLA: Leveraging Soft Activation Sparsity and Low-Rank Decomposition for Large Language Model Compression

Researchers propose SoLA, a training-free compression method for large language models that combines soft activation sparsity and low-rank decomposition. The method achieves significant compression while improving performance, demonstrating 30% compression on LLaMA-2-70B with reduced perplexity from 6.95 to 4.44 and 10% better downstream task accuracy.

ClawSafety: "Safe" LLMs, Unsafe Agents

Causality Laundering: Denial-Feedback Leakage in Tool-Calling LLM Agents

Researchers have identified a new security vulnerability called 'causality laundering' in AI tool-calling systems, where attackers can extract private information by learning from system denials and using that knowledge in subsequent tool calls. They developed the Agentic Reference Monitor (ARM) system to detect and prevent these attacks through enhanced provenance tracking.

Build on Priors: Vision--Language--Guided Neuro-Symbolic Imitation Learning for Data-Efficient Real-World Robot Manipulation

Researchers have developed a neuro-symbolic framework that enables robots to learn complex manipulation tasks from as few as one demonstration, without requiring manual programming or large datasets. The system uses Vision-Language Models to automatically construct symbolic planning domains and has been validated on real industrial equipment including forklifts and robotic arms.

AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub

Researchers released AgenticFlict, a large-scale dataset analyzing merge conflicts in AI coding agent pull requests on GitHub. The study of 142K+ AI-generated pull requests from 59K+ repositories found a 27.67% conflict rate, highlighting significant integration challenges in AI-assisted software development.

ROSClaw: A Hierarchical Semantic-Physical Framework for Heterogeneous Multi-Agent Collaboration

Researchers introduce ROSClaw, a new AI framework that integrates large language models with robotic systems to improve multi-agent collaboration and long-horizon task execution. The framework addresses critical gaps between semantic understanding and physical execution by using unified vision-language models and enabling real-time coordination between simulated and real-world robots.

Hallucination Basins: A Dynamic Framework for Understanding and Controlling LLM Hallucinations

Researchers introduce a geometric framework for understanding LLM hallucinations, showing they arise from basin structures in latent space that vary by task complexity. The study demonstrates that factual tasks have clearer separation while summarization tasks show unstable, overlapping patterns, and proposes geometry-aware steering to reduce hallucinations without retraining.

SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization

Researchers have developed SecPI, a new fine-tuning pipeline that teaches reasoning language models to automatically generate secure code without requiring explicit security instructions. The approach improves secure code generation by 14 percentage points on security benchmarks while maintaining functional correctness.

The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading

New research reveals that while AI tools boost short-term worker productivity, sustained use erodes the underlying skills that enable those gains. The study identifies an 'augmentation trap' where workers can become less productive than before AI adoption due to skill deterioration over time.

Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw

Researchers conducted the first real-world safety evaluation of OpenClaw, a widely deployed AI agent with extensive system access, revealing significant security vulnerabilities. The study found that poisoning any single dimension of the agent's state increases attack success rates from 24.6% to 64-74%, with even the strongest defenses still vulnerable to 63.8% of attacks.

CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks

Researchers have developed CoopGuard, a new defense framework that uses cooperative AI agents to protect Large Language Models from sophisticated multi-round adversarial attacks. The system employs three specialized agents coordinated by a central system that maintains defense state across interactions, achieving a 78.9% reduction in attack success rates compared to existing defenses.

Robust LLM Performance Certification via Constrained Maximum Likelihood Estimation

Researchers propose a new constrained maximum likelihood estimation (MLE) method to accurately estimate failure rates of large language models by combining human-labeled data, automated judge annotations, and domain-specific constraints. The approach outperforms existing methods like Prediction-Powered Inference across various experimental conditions, providing a more reliable framework for LLM safety certification.

Gradual Cognitive Externalization: A Framework for Understanding How Ambient Intelligence Externalizes Human Cognition

Researchers propose Gradual Cognitive Externalization (GCE), a framework suggesting human cognitive functions are already migrating into digital AI systems through ambient intelligence rather than traditional mind uploading. The study identifies evidence in scheduling assistants, writing tools, and AI agents that cognitive externalization is occurring now through bidirectional adaptation and functional equivalence.

Combee: Scaling Prompt Learning for Self-Improving Language Model Agents

Researchers have developed Combee, a new framework that enables parallel prompt learning for AI language model agents, achieving up to 17x speedup over existing methods. The system allows multiple AI agents to learn simultaneously from their collective experiences without quality degradation, addressing scalability limitations in current single-agent approaches.

Evolutionary Search for Automated Design of Uncertainty Quantification Methods

Researchers developed an LLM-powered evolutionary search method to automatically design uncertainty quantification systems for large language models, achieving up to 6.7% improvement in performance over manual designs. The study found that different AI models employ distinct evolutionary strategies, with some favoring complex linear estimators while others prefer simpler positional weighting approaches.