aiqbench analysis
This study investigates how persistent memory architectures influence the performance of LLM agents in programmatic Chinese-character drawing tasks. The findings indicate that while accumulated experience yields modest aggregate gains, specific memory designs like code banks offer advantages on complex items when paired with structural supervision. These results highlight critical trade-offs in designing agent memory systems for iterative problem-solving.
Persistent Memory and Iterative Improvement in an LLM Agent for Chinese-Character Drawing
arXiv:2610.24090v1 Announce Type: new Abstract: Does persistent memory accumulated from previous attempts help an LLM agent solve new problems, and does memory design affect that benefit? We study programmatic Chinese-character drawing across five agent conditions attempting the same 668 items. Accumulated memory produces modest aggregate gains despite substantial growth in the memory stores: free-form notes improve first-attempt success by 4.6 percentage points, while a design combining free-form memory with reusable code improves it by 2.8 points. The two designs do not differ significantly overall, but the code bank shows a suggestive advantage on later, harder items. A separate post-hoc comparison finds a much larger improvement when the code-bank agent receives privileged, target-specific structural supervision. The agent can therefore exploit highly informative structural guidance, although the privileged per-item information does not establish what could have been learned from experience alone. These results suggest that memory design may affect where accumulated experience becomes useful, even when its average benefit is modest.
Abstract:Does persistent memory accumulated from previous attempts help an LLM agent solve new problems, and does memory design affect that benefit? We study programmatic Chinese-character drawing across five agent conditions attempting the same 668 items. Accumulated memory produces modest aggregate gains despite substantial growth in the memory stores: free-form notes improve first-attempt success by 4.6 percentage points, while a design combining free-form memory with reusable code improves it by 2.8 points. The two designs do not differ significantly overall, but the code bank shows a suggestive advantage on later, harder items. A separate post-hoc comparison finds a much larger improvement when the code-bank agent receives privileged, target-specific structural supervision. The agent can therefore exploit highly informative structural guidance, although the privileged per-item information does not establish what could have been learned from experience alone. These results suggest that memory design may affect where accumulated experience becomes useful, even when its average benefit is modest.
Artificial Intelligence (cs.AI)
arXiv:2610.24090 [cs.AI]
(or arXiv:2610.24090v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.24090 Focus to learn more arXiv-issued DOI via DataCite
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