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cs.CL2026

UXBench: Benchmarking User Experience in AI Assistants

Mengze Hong, Xia Zeng, Zeyang Lei +26

UXBench is a user‑centric benchmark that uses real interaction logs to evaluate how well AI assistants align with user preferences and generate engaging dialogue, featuring three t…

cs.CL2026

Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent

Lei Bai, Zongsheng Cao, Yang Chen +50

The paper introduces Agents-A1, a 35B mixture-of-experts agent model that attains trillion-parameter-level performance by extending the length of reasoning horizons and integrating…

cs.CL2026

Self-Harness: Harnesses That Improve Themselves

Hangfan Zhang, Shao Zhang, Kangcong Li +5

The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment. Because different models exhibit d…

cs.CL2026

ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

Zhuohan Ge, Haoyang Li, Yubo Wang +3

While Large Language Models (LLMs) have demonstrated potential in healthcare, they often struggle with the complex, non-linear reasoning required for accurate clinical diagnosis. E…

cs.CL2026

TriEx: A Game-based Tri-View Framework for Explaining Internal Reasoning in Multi-Agent LLMs

Ziyi Wang, Chen Zhang, Wenjun Peng +2

Explainability for Large Language Model (LLM) agents is especially challenging in interactive, partially observable settings, where decisions depend on evolving beliefs and other a…

cs.CL2026

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents

Heng Zhou, Zelin Tan, Zhemeng Zhang +15

When an LLM-based agent improves on a task, is the gain from the model itself or from the reasoning paradigm wrapped around it? We study this question by comparing six inference-ti…