aiqbench analysis
ERSkill introduces a novel framework that treats memory retrieval mechanisms as evolvable components rather than static modules, significantly enhancing LLM agent performance on heterogeneous queries. By co-evolving a router with executable skill primitives and utilizing an experience trie to manage retrieval paths, the system achieves substantial gains in accuracy and evidence construction across major benchmarks. This research advances the core architecture of agentic systems by making memory access dynamic and adaptive to specific task requirements.
ERSkill: Evolving for Skill-Guided Adaptive Memory Retrieval
arXiv:2608.12720v2 Announce Type: replace Abstract: While Large Language Model (LLM) agents increasingly rely on long-term memory for persistent interactions, the retrieval mechanisms governing this memory are rarely treated as evolvable components. This static approach limits performance on heterogeneous memory queries, which often demand diverse evidence construction strategies. To address this, we introduce \textbf{ERSkill}, a retrieval-centric framework for evolving, skill-guided memory access. ERSkill compiles interaction histories into a structured memory store and represents retrieval behaviors as executable skills composed of fundamental primitives. At inference time, a trained router dynamically matches each query to a suitable retrieval skill to construct tailored evidence for answer generation. ERSkill co-evolves the skill set and the router during training. It employs an experience trie to efficiently record explored retrieval paths, alongside a double-frontier mechanism that separates oracle-side capability expansion from router-validated deployment. Experiments across multiple agent memory benchmarks demonstrate that ERSkill substantially outperforms strong non-evolving and evolving baselines. Notably, it improves the overall average across F1, BLEU-1, and LLM-judge scores by 31.3\% with Qwen3-Next-80B-A3B-Instruct and by 21.4\% with GPT-5.4-nano.
Abstract:While Large Language Model (LLM) agents increasingly rely on long-term memory for persistent interactions, the retrieval mechanisms governing this memory are rarely treated as evolvable components. This static approach limits performance on heterogeneous memory queries, which often demand diverse evidence construction strategies. To address this, we introduce \textbf{ERSkill}, a retrieval-centric framework for evolving, skill-guided memory access. ERSkill compiles interaction histories into a structured memory store and represents retrieval behaviors as executable skills composed of fundamental primitives. At inference time, a trained router dynamically matches each query to a suitable retrieval skill to construct tailored evidence for answer generation. ERSkill co-evolves the skill set and the router during training. It employs an experience trie to efficiently record explored retrieval paths, alongside a double-frontier mechanism that separates oracle-side capability expansion from router-validated deployment. Experiments across multiple agent memory benchmarks demonstrate that ERSkill substantially outperforms strong non-evolving and evolving baselines. Notably, it improves the overall average across F1, BLEU-1, and LLM-judge scores by 31.3\% with Qwen3-Next-80B-A3B-Instruct and by 21.4\% with GPT-5.4-nano.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2608.12720 [cs.CL]
(or arXiv:2608.12720v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.12720 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 264 words
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