The paper investigates when it is better to return an existing draft answer or revise it using retrieved evidence in retrieval‑augmented QA systems. By grading both the draft and its candidate revision with the same correctness judge, the authors define a paired effect called recoverability and train policies to predict it before revision. Experiments on 25,870 open‑domain questions show that a recoverability‑based scorer outperforms a draft‑correctness scorer across multiple Llama setups, improving accuracy–revision trade‑offs and closing a significant portion of the oracle gap, though it still applies harmful revisions in a substantial fraction of cases.
By Nicholas Kashani Motlagh, Tim Anderson, Jeremy Gwinnup, Grant Erdmann
CAMR (Compatibility-Aware Memory Replay) is an adaptive framework for continual learning in large language models that jointly manages supervision of new examples and replay of historical data. It uses compatibility checks and a learned memory gate to decide among three actions: general replay of incoming examples, reinforcing compatible related examples, and skipping supervision for mastered duplicates. Evaluated on Qwen3-8B-Base, CAMR outperforms existing methods on WikiBigEdit factual updates and achieves top scores on multiple TRACE tasks while preserving earlier-task performance across diverse domains.
By Jingpu Yang, Tianhao Li, Fengxian Ji, Yufeng Wang, Shuxin Zheng, Xiaoqing Zhang
The paper introduces LSREP, a Longitudinal State‑Replay Evaluation Protocol designed to assess how conversational memory evolves over time, incorporating ordered replay, lifecycle schedules, repeated probes, evolving reference answers, and mechanism‑fidelity checks. It applies LSREP to ICE v2, a local‑first memory middleware, and reports that on three ordinary‑density datasets ICE v2 achieves near‑zero mean quality difference from vector‑RAG while using fewer fragments but slightly more prompt tokens, yet fails catastrophically on a dense dataset. In a public diagnostic, ICE v2 underperforms pure vector‑RAG on LongMemEval, revealing significant multi‑session and temporal failures and a quality‑cost trade‑off rather than superior efficiency.
By Deepesh Sonar
arXiv:2606. 09877v1 Announce Type: new Abstract: LLM wiki systems compile knowledge into pre-filled KV caches for efficient inference, but assume a static corpus -- an assumption that fails whenever the underlying information landscape evolves.
By Juan M. Huerta
The paper investigates how temporal aggregation of token scores during decoding-time KV cache compression affects eviction decisions. It shows that using an exponential‑moving‑average (EMA) aggregation keeps ranking changes minimal for many scorer variants, while others like KeyDiff or recency significantly alter rankings and degrade performance. Building on this, the authors propose InertiaKV and its lazy variant, which achieve 1.34–1.46× faster decoding than full refresh, and also evaluate a score‑free approach that slightly improves quality while eliminating further scoring.
The paper introduces the Control Map, an evaluation framework for test‑time model updates that records the specific updates made before each answer and separately assesses acquisition, retention, locality, and performance in new settings. It demonstrates that update scheduling can dramatically affect accuracy, that learning an edit does not guarantee lasting or contained changes, and that no single editor consistently outperforms others across models and benchmarks. The authors argue that evaluations should report update histories and assess these dimensions separately.
By Zehao Zhao, Shangjian Yin, Rui Liu, Chongyang Bai, Mark Ibrahim, Kavosh Asadi, Yuchen Lu, Hang Cui, Hamed Firooz