3 citations · 3 across the 15 of their papers we have counts for
5 papers · 1 filter
Quantization Degradation in Large Language Models: A Signal-Noise Perspective
Chenxi Zhou, Pengfei Cao, Jinyu Ye +5
Post-training quantization reduces the deployment cost of large language models, yet how severely a quantized model degrades is not determined by bit-width alone. We systematically…
STAGE-Claw: Automated State-based Agent Benchmarking for Realistic Scenarios
Sirui Liang, Bohan Yu, Peiyu Wang +8
Large language models are increasingly used to power personal agents for everyday applications, but evaluating these agents remains a challenge. Existing benchmarks still rely on s…
Learning How to Remember: A Meta-Cognitive Management Method for Structured and Transferable Agent Memory
Sirui Liang, Pengfei Cao, Jian Zhao +4
Large language model (LLM) agents increasingly rely on accumulated memory to solve long-horizon decision-making tasks. However, most existing approaches store memory in fixed repre…
LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions
Xixun Lin, Yucheng Ning, Jingwen Zhang +21
Driven by the rapid advancements of Large Language Models (LLMs), LLM-based agents have emerged as powerful intelligent systems capable of human-like cognition, reasoning, and inte…
Large Language Models for Planning: A Comprehensive and Systematic Survey
Pengfei Cao, Tianyi Men, Wencan Liu +7
Planning represents a fundamental capability of intelligent agents, requiring comprehensive environmental understanding, rigorous logical reasoning, and effective sequential decisi…