The paper introduces MERIT, a benchmark that evaluates the marginal benefit of long‑term memory for tool‑using large language model agents while explicitly accounting for cost. MERIT provides episodic tool‑use tasks across three domains, verifies dependence on earlier‑episode facts, and measures memory operations in tokens and dollars. Experiments on GPT‑4.1‑mini, Claude Haiku 4.5, and Claude Sonnet 5 show that memory can significantly improve task success, but its utility varies widely across models and memory implementations, and full replay is rarely cost‑effective.
By Shweta Mishra, Shashank Mishra
The paper introduces the Harmful Memorization Index (HMI) to measure when large language models (LLMs) fail to adapt to small semantic changes in coding tasks, producing code that is superficially similar to the original solution. It proposes an automated code‑rewriting pipeline that generates semantically different variants of tasks by altering core logic elements, and uses HMI to combine similarity metrics with performance degradation. Experiments on MBPP+ and BigCodeBench show that HMI does not simply grow with model size, is higher for certain logic‑edit types, and that supervised fine‑tuning can increase HMI while PPO offers a better accuracy–HMI trade‑off.
By Lizhe Zhang, Wentao Chen, Li Zhong, Letian Peng, Zilong Wang, Jingbo Shang
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
arXiv:2504. 04718v2 Announce Type: replace-cross Abstract: Recent studies have demonstrated that test-time compute scaling effectively improves the performance of small language models (sLMs).
By Minki Kang, Jongwon Jeong, Jaewoong Cho
Large language models can reveal memorized training sequences when prompted with a prefix, but real-world systems often use additional context such as instructions or retrieved documents. This study measures how such contextual conditioning affects memorization by comparing suffix probabilities with empty prompts versus retrieved contexts across three instruction-tuned models. The results show that context does not simply erase memorization; instead, it preserves a core set of robustly extractable samples while also shifting the boundary of marginal cases, sometimes suppressing and sometimes enabling new exposures.
By Ali Satvaty, Narjes Sharafi, Jirui Qi, Suzan Verberne, Fatih Turkmen
arXiv:2510. 00492v3 Announce Type: replace Abstract: The reliability of large language models (LLMs) during test-time scaling is often assessed with \emph{external verifiers} or \emph{reward models} that distinguish correct reasoning from flawed logic.
By Dong Bok Lee, Seanie Lee, Sangwoo Park, Minki Kang, Jinheon Baek, Dongki Kim, Dominik Wagner, Jiongdao Jin, Heejun Lee, Tobias Bocklet, Jinyu Wang, Jingjing Fu, Sung Ju Hwang, Jiang Bian, Lei Song