20 papers
MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
Mao-xun Huang, Jerry Wang, Yi-Cheng Lai +3
The paper presents MANTA, a framework that lets large language model‑driven multi‑agent systems dynamically adjust their communication topology during inference, updating roles, li…
FIRE-Bench: Evaluating AI Agents on the Rediscovery of Scientific Insights
Zhen Wang, Fan Bai, Zhongyan Luo +9
Autonomous agents powered by large language models (LLMs) promise to accelerate scientific discovery end-to-end, but rigorously evaluating their capacity for verifiable discovery r…
HARP: Efficient Data Selection for Finetuning Large Language Models
Ning Wang, Zhengxin Zhang, Maosen Tang +3
Finetuning data selection requires balancing two competing goals: selecting examples that improve the downstream objective, and doing so without repeatedly finetuning models. Train…
Token-weighted Direct Preference Optimization with Attention
Chengyu Huang, Zhuohang Li, Sheng-Yen Chou +1
Direct Preference Optimization (DPO) aligns Large Language Models with human preferences without the need for a separate reward model. However, DPO treats all tokens in responses e…
Knowing but Not Showing: LLMs Recognize Ambiguity but Rarely Ask Clarifying Questions
Jinyan Su, Claire Cardie
User queries are often underspecified and may admit multiple valid interpretations. Rather than silently making assumptions about the user's intent, a helpful assistant should surf…
How Far Are We From True Auto-Research?
Zhengxin Zhang, Ning Wang, Sainyam Galhotra +1
Recent auto-research systems can produce complete papers, but feasibility is not the same as quality, and the field still lacks a systematic study of how good agent-generated paper…