9 papers
Enabling Agents to Communicate Entirely in Latent Space
Zhuoyun Du, Runze Wang, Huiyu Bai +6
While natural language is the de facto communication medium for LLM-based agents, it presents a fundamental constraint. The process of downsampling rich, internal latent states int…
One Interaction Is Worth a Thousand Guesses: Benchmarking the Interactive Capabilities of Deep Research Agents
Yingchaojie Feng, Qiang Huang, Xiaoya Xie +4
Deep research agents powered by Large Language Models (LLMs) can perform multi-step reasoning, web exploration, and long-form report generation. However, existing systems remain la…
Linear Dynamics in the RLVR Training of Large Language Models
Tianle Wang, Jiayu Liu, Zhongyuan Wu +4
Reinforcement learning with verifiable rewards (RLVR) has driven significant performance gains in reasoning-oriented large language models (LLMs), yet its internal training dynamic…
ReLE: A Scalable System and Structured Benchmark for Diagnosing Capability Anisotropy in Chinese LLMs
Rui Fang, Jian Li, Wei Chen +4
Large Language Models (LLMs) have achieved rapid progress in Chinese language understanding, yet accurately evaluating their capabilities remains challenged by benchmark saturation…
EmbodiedBrain: Expanding Performance Boundaries of Task Planning for Embodied Intelligence
Ding Zou, Feifan Wang, Mengyu Ge +17
The realization of Artificial General Intelligence (AGI) necessitates Embodied AI agents capable of robust spatial perception, effective task planning, and adaptive execution in ph…
Planner and Executor: Collaboration between Discrete Diffusion And Autoregressive Models in Reasoning
Lina Berrayana, Ahmed Heakl, Muhammad Abdullah Sohail +3
Current autoregressive language models (ARMs) achieve high accuracy but require long token sequences, making them costly. Discrete diffusion language models (DDLMs) enable parallel…