collaborators

7 papers

cs.CL2026

NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness

NeoHorse Team, Guoliang Cao, Guohao Dai +34

Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into the next round of learning. We p…

cs.LG2026

Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks

Mengyu Zheng, Kai Han, Boxun Li +13

The software engineering capabilities of general-purpose agent harnesses remain underexplored, and existing benchmarks offer limited support for comparing these harnesses under con…

cs.LG2026

CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments

Yi Yu, Guangquan Hu, Chenghuang Shen +15

The increasing agentic capabilities of Large Language Models (LLMs) have enabled their deployment in real-world applications, such as cloud services, where customer-assistant inter…

cs.LG2026

VC-Soup: Value-Consistency Guided Multi-Value Alignment for Large Language Models

Hefei Xu, Le Wu, Yu Wang +4

As large language models (LLMs) increasingly shape content generation, interaction, and decision-making across the Web, aligning them with human values has become a central objecti…

cs.LG2026

Adapting Technical-Service LLM Agents with Latent Logic Augmentation, Robust Noise Reduction, and Hybrid Reward Modeling

Junzhuo Ma, Chenghuang Shen, Yi Yu +15

Technical-service LLM agents are entering production workflows, where value depends on whether engineers adopt generated replies. Service tickets hide decision logic, contain noisy…

cs.CL2025

ReXMoE: Reusing Experts with Minimal Overhead in Mixture-of-Experts

Zheyue Tan, Zhiyuan Li, Tao Yuan +13

Mixture-of-Experts (MoE) architectures have emerged as a promising approach to scale Large Language Models (LLMs). MoE boosts the efficiency by activating a subset of experts per t…

cs.CL2025

Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving

Yuxuan Zhou, Xien Liu, Chenwei Yan +8

Large language models (LLMs) have demonstrated remarkable performance on various medical benchmarks, but their capabilities across different cognitive levels remain underexplored.…