7 papers · 1 filter
AgentKVShift: Efficient KV Cache Reuse for Agentic Memory Systems
Nilesh Prasad Pandey, Jason Kong, Lanxiang Hu +5
Memory-augmented LLM agents maintain context across hundreds of interactions through agentic memory systems that actively curate retrieved content with LLM-generated metadata such…
AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications
Yujie Zhao, Boqin Yuan, Junbo Huang +9
Large Language Models (LLMs) are increasingly used as autonomous agents in complex, long-horizon applications, where effective memory is critical for sustained performance. Yet exi…
Towards Interpretable and Inference-Optimal COT Reasoning with Sparse Autoencoder-Guided Generation
Daniel Zhao, Abhilash Shankarampeta, Lanxiang Hu +2
We propose a novel method that leverages sparse autoencoders (SAEs) and clustering techniques to analyze the internal token representations of large language models (LLMs) and guid…
General Modular Harness for LLM Agents in Multi-Turn Gaming Environments
Yuxuan Zhang, Haoyang Yu, Lanxiang Hu +2
We introduce a modular harness design for LLM agents that composes of perception, memory, and reasoning components, enabling a single LLM or VLM backbone to tackle a wide spectrum…
lmgame-Bench: How Good are LLMs at Playing Games?
Lanxiang Hu, Mingjia Huo, Yuxuan Zhang +6
Playing video games requires perception, memory, and planning, exactly the faculties modern large language model (LLM) agents are expected to master. We study the major challenges…
GameArena: Evaluating LLM Reasoning through Live Computer Games
Lanxiang Hu, Qiyu Li, Anze Xie +4
Evaluating the reasoning abilities of large language models (LLMs) is challenging. Existing benchmarks often depend on static datasets, which are vulnerable to data contamination a…