4 papers
Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Tianxin Wei, Noveen Sachdeva, Benjamin Coleman +12
Statefulness is essential for large language model (LLM) agents to perform long-term planning and problem-solving. This makes memory a critical component, yet its management and ev…
PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents
Minghao Yan, Bo Peng, Benjamin Coleman +11
Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. This limits adaptation in prac…
AgenticTagger: Structured Item Representation for Recommendation with LLM Agents
Zhouhang Xie, Bo Peng, Zhankui He +11
High-quality representations are a core requirement for effective recommendation. In this work, we study the problem of LLM-based descriptor generation, i.e., keyphrase-like natura…
PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution
Minghao Yan, Bo Peng, Benjamin Coleman +13
Large Language Models (LLMs) have emerged as powerful operators for evolutionary search, yet the design of efficient search scaffolds remains ad hoc. While promising, current LLM-i…