2 citations · 2 across the 18 of their papers we have counts for
12 papers · 1 filter
Cost-Effective Communication: An Auction-based Method for Language Agent Interaction
Yijia Fan, Jusheng Zhang, Kaitong Cai +4
Multi-agent systems (MAS) built on large language models (LLMs) often suffer from inefficient "free-for-all" communication, leading to exponential token costs and low signal-to-noi…
ORACLE: Optimizing Reasoning Abilities of Large Language Models via Constraint-Led Synthetic Data Elicitation
Zhuojie Yang, Wentao Wan, Keze Wang
Training large language models (LLMs) with synthetic reasoning data has become a popular approach to enhancing their reasoning capabilities, while a key factor influencing the effe…
AgriWorld:A World Tools Protocol Framework for Verifiable Agricultural Reasoning with Code-Executing LLM Agents
Zhixing Zhang, Jesen Zhang, Hao Liu +4
Foundation models for agriculture are increasingly trained on massive spatiotemporal data (e.g., multi-spectral remote sensing, soil grids, and field-level management logs) and ach…
Why Keep Your Doubts to Yourself? Trading Visual Uncertainties in Multi-Agent Bandit Systems
Jusheng Zhang, Yijia Fan, Kaitong Cai +6
Vision-Language Models (VLMs) enable powerful multi-agent systems, but scaling them is economically unsustainable: coordinating heterogeneous agents under information asymmetry oft…
Reflective Confidence: Correcting Reasoning Flaws via Online Self-Correction
Qinglin Zeng, Jing Yang, Keze Wang
Large language models (LLMs) have achieved strong performance on complex reasoning tasks using techniques such as chain-of-thought and self-consistency. However, ensemble-based app…
Large Language Models as Discounted Bayesian Filters
Jensen Zhang, Jing Yang, Keze Wang
Large Language Models (LLMs) demonstrate strong few-shot generalization through in-context learning, yet their reasoning in dynamic and stochastic environments remains opaque. Prio…