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Jicong Fan

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#small language model Preprint Aug 2026

The Retriever Should Remember: Experience-Amortized Reranking for Long-Term Agent Memory

Experiments on long-term conversational memory show that mixed observed-and-estimated reranking improves answer accuracy over semantic retrieval by up to 6.62% and remains effective when only 17.5% of candidates receive direct LLM relevance scores, thereby substantially reducing the inference overhead of LLM reranking.

Qi Feng, Chris Ding, Jicong Fan · 0 citations

Preprint Aug 2026

Mapping and Measuring the Behavioral Evolution of Large Language Models

An architecture-agnostic sufficient condition is established linking behavioral similarity to inference-prompt coverage, small excess population log-loss, and similar effective target distributions---a possible training-side account rather than an empirical explanation of the observed trends.

Dong Qiao, Chris Ding, Jicong Fan · 0 citations