Continual learning requires large language models to acquire new knowledge and capabilities while retaining what they have learned. Fine-tuning and replay support acquisition and retention, but deciding whether incoming examples need supervision and which historical examples to revisit remains a challenge. We propose C...
Jing-Pu Yang, Tian-Hao Li, Feng-Xian Ji et al.· 0 citations
OrchBench is established as an efficient and interpretable benchmark for comparing and diagnosing multi-agent orchestration plans, finding that preserving task-critical information is more important than simply increasing the number of agents, and the benefits of parallelism diminish as coordination failures accumulate...
This work proposes HiGram, an evolving hierarchical graph memory framework with path-level localization and rewriting, which organizes the memory into coarse-to-fine architecture composed of upper-level nodes and MemoryUnits, thereby reducing the amount of irrelevant information during retrieval.
Xiawei Yue, Boran Wang, Xiaoqing Zhang et al.· 0 citations
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