Paper

ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning

Unreadunread

AI summary

arXiv:2606. 10532v1 Announce Type: new Abstract: Memory is essential for enabling large language model (LLM) agents to handle long-horizon reasoning tasks.

Reading status

Priority

About this paper

Authors: Yunhan Jiang, Wenbin Duan, Shasha Guo, Liang Pang, Xiaoqian Sun, Huawei Shen
Released: 2026-06-10 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, rate, control, language, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.10532

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.