SUMONE is a lightweight summarizer designed to compress long interaction histories of language‑model agents. It is trained via masked reconstruction, where a frozen reconstructor predicts randomly masked parts of the original trajectory; reconstruction quality plus a length penalty yields a task‑agnostic reward for optimizing the summarizer with GRPO. At inference, only the summarizer remains, enabling plug‑and‑play context compression that reduces inference cost by roughly 78 % on DeepSearchQA and 75 % on SWE‑bench without affecting the underlying agent.
By Jizhe Zhang, Ang Li, Chenheng Zhang, Yisen Wang
arXiv:2607. 20734v1 Announce Type: new Abstract: As LLMs become more capable, they are increasingly deployed as collaborative agents, taking on user-delegated tasks through iterative interaction.
By Jihoon Tack, Philippe Laban, Jennifer Neville
arXiv:2608. 04095v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used as personalized assistants in high-stakes domains such as financial advising, yet it remains unclear whether they can maintain and update an individualized user model over long horizons.
By Ben Wang, Kang Zhou, Lifan Guo, Feng Chen, Chi Zhang
The paper introduces Dual-Path Parametric Memory (DPPM), a method for long‑term personalization of language models that leverages interaction history without requiring it in the inference context. DPPM uses two complementary paths: an Evidence path that pools historical representations to retain earlier evidence, and a Delta path that sequentially updates an associative state to capture changes. By fusing these outputs into history‑conditioned LoRA adapters, DPPM achieves superior performance on PersonaMem‑v2 (54.22%) and PrefEval (86.79%) compared to existing baselines.
By Yuhao Chen, Shuochen Liu, Jiayao Shi, Jian Hong, Chen Cheng, Xinyun Ding, Tao Wang, Ya Li, Quan Liu, Tong Xu
EdgeMem is a new agent-memory method that preserves original interaction turns and organizes them using complementary content, temporal, and episodic cues via a multi‑anchor hypergraph. It performs lightweight local processing, returning source evidence directly and reserving LLM use only for final answer generation. Experiments on LoCoMo and LongMemEval‑S demonstrate strong retrieval and memory‑grounded question answering, with EdgeMem achieving the highest strict‑judge score among seven systems on LoCoMo while requiring no generative‑LLM calls for construction and retrieval.
By Zeyang Cui, Jiannong Cao, Zhiyuan Wen, Bo Yuan, Junlan Feng, Shengyuan Chen
arXiv:2606. 13316v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a central technique for improving long-horizon reasoning in Large Language Models (LLMs).
By Xucong Wang, Ziyu Ma, Yong Wang, Shidong Yang, Hailang Huang, Renda Li, Pengkun Wang, Xiangxiang Chu
Long‑horizon LLM agents use context compaction to replace an overflowing interaction history with a natural‑language summary. The paper shows that these summaries mix factual environment data with the compressor’s reasoning, making it hard for the agent to distinguish the two. The authors introduce Source‑Judged Compaction (SJC), a training‑free post‑processing step that scores each summary entry for a source in the environment or previous summary and removes unsupported entries, improving cross‑run reliability by 2.1%–8.9% on AppWorld and OfficeBench without increasing token usage.
By Guanghui Min, Chen Chen
arXiv:2609.40118v1 Announce Type: new
Abstract: As LLM capabilities advance, agents are tackling increasingly complex tasks over longer horizons. Their growing interaction histories make memory compa...
By Jingbo Yang, Kwei-Herng Lai, Xiaowen Wang, Zhaoxuan Tan, Pei Zhou, Mengting Wan, Yaar Harari, Evgeniy Gabrilovich, Shiyu Chang
arXiv:2608.12990v2 Announce Type: replace
Abstract: Long-horizon LLM agents must preserve information from past interactions to support future tasks. Existing memory systems typically rely on eager c...
By Dongfang Li, Zixuan Liu, Junmai Wang, Jiahe Huang, Fuhao Li, Lin Zhang, Bonian Jia, Baotian Hu, Min Zhang
The paper introduces the Multi-Session Personalized Tool Calling (MPT) benchmark, containing 4,695 instances across 459 multi‑session histories that test Preference Recall, Induction, and Transfer. It proposes PRefine, a test‑time memory method that refines a user’s latent preference via a generate‑verify‑refine loop. Experiments with five LLMs show that PRefine outperforms existing memory systems and even full‑history prompting on Preference Transfer, suggesting that personalized agents should encode behavior as preferences rather than merely storing past interactions.
By Yejin Yoon, Minseo Kim, Taeuk Kim
arXiv:2606. 20632v2 Announce Type: replace-cross Abstract: Multi-LLM systems use multiple language models to deliberate, judge each other's outputs, or coordinate as agents.
By Luyang Zhang, Jialu Wang, Fei Xue, Yi-Yun Chu
The paper investigates how long‑horizon language model agents encode memory‑management signals before taking actions. By examining hidden states just prior to each action, the authors find that the model already signals the need for compression and recall, independent of context length or interaction progress, and that these signals vary across model depth. They propose the Preaction Memory with Evidence Retrieval (PaMER) framework, which uses state‑guided compression and selective evidence retrieval to reduce context consumption while preserving task performance.
By Mingxuan Wang, Guorun Yao, Fei Luo, Yinglong Guo, Chao Ning, Bo Wang, Hongyue Chen, Yanbiao Ma, Jungong Han