6 citations · 6 across the 5 of their papers we have counts for
5 papers
First Things First: Teaching LLM-Based Agents to Prioritize Must-Haves before Nice-to-Haves
Tianjie Ju, Xinyue Xu, Wanxuan Sun +4
Recent progress in multimodal large language models (MLLMs) has fueled significant enthusiasm in their potential to act as autonomous agents for real-world tasks. However, scenario…
DiG-Plan: Mitigating Early Commitment for Tool-Graph Planning via Diffusion Guidance
Yansi Li, Zhuosheng Zhang
Generating executable tool plans requires selecting appropriate subsets from tool libraries, a combinatorial search problem with an exponentially large solution space. However, we…
Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
Chenyu Zhou, Huacan Chai, Wenteng Chen +18
Large language model (LLM) agents are increasingly built less by changing model weights than by reorganizing the runtime around them. Capabilities that earlier systems expected the…
DRQA: Dynamic Reasoning Quota Allocation for Controlling Overthinking in Reasoning Large Language Models
Kaiwen Yan, Xuanqing Shi, Hongcheng Guo +3
Reasoning large language models (RLLMs), such as OpenAI-O3 and DeepSeek-R1, have recently demonstrated remarkable capabilities by performing structured and multi-step reasoning. Ho…
GuideBench: Benchmarking Domain-Oriented Guideline Following for LLM Agents
Lingxiao Diao, Xinyue Xu, Wanxuan Sun +2
Large language models (LLMs) have been widely deployed as autonomous agents capable of following user instructions and making decisions in real-world applications. Previous studies…