Researchers developed a multi-agent pipeline based on the Model Context Protocol to convert natural-language documentation into standardized formats for continuous compliance in critical infrastructure.
Industry experts in China report that the rapid adoption of artificial intelligence is driving the emergence of a token-based economy for pricing and delivering digital services.
Electronic components distributor Hottech predicted its net profit for the first half of 2026 would rise by up to 376.03% year-on-year.
Former autonomous driving engineers are applying closed-loop data systems to develop smart sleep technology for bedrooms.
Huawei plans to debut its next-generation Atlas 950 SuperPoD computing cluster and the world's first AI agent-enabled smartphone at the upcoming World Artificial Intelligence Conference.
China's State Council has approved the 15th Five-Year Plan for Expanding Consumption, which outlines measures to boost inbound tourism and expand visa-free entry.
This study introduces a self-validating framework to analyze and mitigate safety hazards and hallucinations in LLM-assisted safety engineering tools.
The paper proposes Narration-of-Thought, a system prompting technique that structures chain-of-thought reasoning to improve ethical decision-making and reduce bias in large language models.
Researchers developed liteOdyssey, a lightweight framework that guides general-purpose language models through diagnostic reasoning for rare diseases without altering model weights.
A new multi-agent workflow utilizing large language models has been developed to autonomously formalize complex theorems in theoretical physics.
Researchers have introduced a three-stage curriculum learning framework designed to efficiently distill chain-of-thought reasoning from large language models into smaller student models.
CriterAlign is a criterion-centric evaluation framework designed to improve pairwise code preference judging by aligning rationales across specific criteria.
Researchers propose a hierarchical framework that utilizes a centralized large language model for strategic planning to coordinate specialized reinforcement learning policies in multi-agent environments.
Researchers have introduced a proactive memory agent designed to mitigate behavioral state decay in long-horizon tasks by actively retrieving relevant context.
The paper introduces SearchGen-20K and SearchGen-Bench to address the world-knowledge limitations of visual generators by integrating search capabilities.
The study presents a mechanistic taxonomy and diagnostic pipeline to track how failures like reward hacking and policy collapse emerge during reinforcement learning from human feedback.
The Large Sparse Reconstruction Model demonstrates how expanding transformer context windows can improve the recovery of fine-grained textures in feed-forward 3D reconstruction.
Researchers have introduced Concept-as-Tree, a synthetic data framework designed to improve the personalization capabilities of vision-language models by generating high-quality positive and negative samples.
VEGAS is a training-free evaluation metric that uses viewer gaze data to measure how well generated video captions align with human visual attention.
This study investigates how pre-trained code models internally represent programming types by probing their hidden states across different programming languages.
A new benchmark called PredicateLongBench has been introduced to systematically analyze the specific factors that make long-context tasks difficult for large language models.
CausalDS is a new benchmark designed to evaluate the causal reasoning and data analysis capabilities of language model agents.
A new framework maps large language model personality traits within weight space using low-rank adapters to measure and control model personas.
Researchers have developed a content protection method that exploits context compression to prevent LLM-based web crawlers from scraping online information.