5 papers
What Is This Memory Useful For? Measuring Task-Conditioned Utility in Long-Term Agent Memory
Chih-Hsuan Yang, Anjir Ahmed Chowdhury, Feng Yan +1
A stored experience may answer one future question and offer little help with another. We measure this task-conditioned utility rather than assigning each memory a single importanc…
LAM: Efficient Lossy Agent Memory Framework With A Retrieval-Score Error Bound
Baixi Sun, Le Chen, Anjir Ahmed Chowdhury +10
Agent memory grows as agents read inputs, reason, and call tools. Longer histories increase inference cost and eventually exceed the context window. LLM-based summarization reduces…
Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages
Chih-Hsuan Yang, Anjir Ahmed Chowdhury, Cheng-Hau Yang +8
A solution can end with a wrong answer and still help another solver. In multi-agent reasoning, evaluating only a message's proposed answer can therefore miss its contribution to t…
Know When To Fold 'Em: Token-Efficient LLM Synthetic Data Generation via Multi-Stage In-Flight Rejection
Anjir Ahmed Chowdhury, Syed Zawad, Feng Yan
While synthetic data generation with large language models (LLMs) is widely used in post-training pipelines, existing approaches typically generate full outputs before applying qua…
PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts
Anjir Ahmed Chowdhury, Syed Zawad, Xiaolong Ma +2
Parameter-Efficient Fine-Tuning (PEFT) is widely used for adapting Large Language Models (LLMs) for various tasks. Recently, there has been an increasing demand for fine-tuning a s…