9 papers · 1 filter
Coupled Harness--Model Evolution for Scientific Equation Discovery
Pengze Li, Jiaquan Zhang, Encheng Su +8
Large language models (LLMs) are increasingly used to discover scientific equations from experimental data. Such a discovery system has two parts that must work together: a library…
Geometry-aware Incremental Neural Operator for Long-Horizon PDE prediction
Jiaquan Zhang, Shuxu Chen, Haifan Meng +6
Neural operators have shown strong potential for learning solution operators of partial differential equations (PDEs). However, long-horizon autoregressive prediction remains chall…
Lightweight LLM Agent Memory with Small Language Models
Jiaquan Zhang, Chaoning Zhang, Shuxu Chen +9
Although LLM agents can leverage tools for complex tasks, they still need memory to maintain cross-turn consistency and accumulate reusable information in long-horizon interactions…
Weak-Link Optimization for Multi-Agent Reasoning and Collaboration
Haoyu Bian, Chaoning Zhang, Jiaquan Zhang +4
LLM-driven multi-agent frameworks address complex reasoning tasks through multi-role collaboration. However, existing approaches often suffer from reasoning instability, where indi…
CAP-CoT: Cycle Adversarial Prompt for Improving Chain of Thoughts in LLM Reasoning
Shuxu Chen, Yitian Zhou, Jiaquan Zhang +6
Chain-of-Thought (CoT) prompting has emerged as a simple and effective way to elicit step-by-step solutions from large language models (LLMs). However, CoT reasoning can be unstabl…
Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization
Xudong Wang, Chaoning Zhang, Jiaquan Zhang +8
Large Language Model (LLM)-driven Multi-Agent Systems (MAS) have demonstrated strong capability in complex reasoning and tool use, and heterogeneous agent pools further broaden the…
AI-for-Science Low-code Platform with Bayesian Adversarial Multi-Agent Framework
Zihang Zeng, Jiaquan Zhang, Pengze Li +2
Large Language Models (LLMs) demonstrate potentials for automating scientific code generation but face challenges in reliability, error propagation in multi-agent workflows, and ev…
Learning Global Hypothesis Space for Enhancing Synergistic Reasoning Chain
Jiaquan Zhang, Chaoning Zhang, Shuxu Chen +9
Chain-of-Thought (CoT) has been shown to significantly improve the reasoning accuracy of large language models (LLMs) on complex tasks. However, due to the autoregressive, step-by-…
Understanding Chain-of-Thought in Large Language Models via Topological Data Analysis
Chenghao Li, Chaoning Zhang, Yi Lu +10
With the development of large language models (LLMs), particularly with the introduction of the long reasoning chain technique, the reasoning ability of LLMs in complex problem-sol…