activity
20172026
most citedEnhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge

17 citations · 42 across the 30 of their papers we have counts for

collaborators
Showing cs.CLShow all

15 papers · 1 filter

cs.CL2025

NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering

Kaiwen Shi, Zheyuan Zhang, Zhengqing Yuan +4

Diet plays a central role in human health, and Nutrition Question Answering (QA) offers a promising path toward personalized dietary guidance and the prevention of diet-related chr…

cs.CL2025

AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering

Zheyuan Zhang, Kaiwen Shi, Zhengqing Yuan +6

Large language models (LLMs) and agent-based frameworks have advanced rapidly, enabling diverse applications. Yet, with the proliferation of models and agentic strategies, practiti…

cs.CL2025

Food4All: An Agentic Framework and Benchmark for Food Resource Navigation with Adaptive User Understanding

Yiyang Li, Weixiang Sun, Tianyi Ma +3

Food assistance referral requires conversational agents to translate underspecified, often noisy help-seeking dialogues into locally valid resource recommendations. We present Food…

cs.CL2025

OjaKV: Context-Aware Online Low-Rank KV Cache Compression

Yuxuan Zhu, David H. Yang, Mohammad Mohammadi Amiri +3

The expanding long-context capabilities of large language models are constrained by a significant memory bottleneck: the key-value (KV) cache required for autoregressive generation…

cs.CL2025

EfficientLLM: Efficiency in Large Language Models

Zhengqing Yuan, Weixiang Sun, Yixin Liu +13

Large Language Models (LLMs) have driven significant progress, yet their growing parameter counts and context windows incur prohibitive compute, energy, and monetary costs. We intr…

cs.CL2025

Cross-Examiner: Evaluating Consistency of Large Language Model-Generated Explanations

Danielle Villa, Maria Chang, Keerthiram Murugesan +2

Large Language Models (LLMs) are often asked to explain their outputs to enhance accuracy and transparency. However, evidence suggests that these explanations can misrepresent the…