The paper introduces a new retrieval method called Retrieval as Traversal, which treats memory retrieval as a persistent local traversal through an address space, separating content from address. It proposes a cost‑regularised indexing objective that balances information value against retrieval cost, encouraging complementary memories to be co‑retrievable while discouraging redundant ones. A simple fixed‑geometry implementation was tested on MiniGrid Memory, achieving a mean return of 0.95 after 0.59M steps with a five‑token context, outperforming Gaussian and categorical absolute addressing baselines.
By Daniel Strau{\ss}, Zhenshan Bing, Alois Knoll
arXiv:2610.15642v1 Announce Type: new
Abstract: Under a limited context budget, a long-term agent must recall not merely memories that are individually relevant, but evidence that is collectively suf...
By Qi Feng, Jicong Fan
arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.
By Eric Hanchen Jiang, Zhi Zhang, Yuchen Wu, Levina Li, Dong Liu, Xiao Liang, Rui Sun, Yubei Li, Edward Sun, Haozheng Luo, Zhaolu Kang, Aylin Caliskan, Kai-Wei Chang, Ying Nian Wu
arXiv:2606. 30067v1 Announce Type: cross Abstract: We introduce Neural Subspace Reallocation (NSR), which reframes continual learning as memory management over parameter subspaces.
By Byeong Hoon Yoon
We introduce Neural Subspace Reallocation (NSR), which reframes continual learning as memory management over parameter subspaces. Instead of treating Low-Rank Adaptation (LoRA) modules as disposable per-task adapters, NSR manages them as compressible, retrievable memory units on a frozen backbone through a recurring cycle: (1) compress learned LoRAs via SVD, (2) reserve them in a TaskKnowledgeBank, (3) recall related past LoRAs by embedding similarity to warm-start new or returning tasks, and (4) reallocate the active subspace accordingly, with distillation protecting prior tasks.
Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks. Yet nearly all existing approaches, from graph-structured memories to reflective insight stores, access memory through fixed, hand-designed heuristics.
arXiv:2607. 08032v1 Announce Type: new Abstract: Large language models, and the agents built on them, spend an ever-growing share of their compute and memory on remembering: caching attention keys and values, carrying long prompts, maintaining recurrent state, and storing what happened in previous turns and sessions.
By Ashwin Gerard Colaco, Nada Lahjouji
The paper introduces LRE (Learned Relevance Eviction), a lightweight, CPU‑only, language‑model‑free scorer that learns which parts of an agent’s interaction history are task‑critical and preserves them verbatim. In experiments, LRE matches or surpasses baseline eviction policies on accuracy‑cost trade‑offs, recovers 93% of full‑history accuracy, reduces worst‑case prompt size by 52%, and outperforms dense and token‑pruning encoders in conversational memory while being 295–1569× smaller. The method also achieves superior budgeted answer quality on LoCoMo reading and can be trained annotation‑free, recovering 95% of supervised scorer performance.
By Nusrat Jahan Lia, Aritra Mazumder
arXiv:2610.26433v2 Announce Type: replace
Abstract: We study whether geometric relationships between conversation turns, memory texts, and queries can predict answer correctness before generation. We...
By Iliya Zhechev, Radoslav Ralev, Chris Coleman, Srijith Rajamohan, Jen Agarwal, Tyler Hutcherson, Simba Khadder
The paper introduces TokenWorth, a lightweight framework that learns the value of past experiences for self-improving agents operating under strict resource constraints. By using sparse execution feedback, TokenWorth guides both retrieval and memory management without updating the core model, enabling agents to adaptively reuse only the most beneficial experiences. Experiments on LifelongAgentBench show that TokenWorth outperforms existing lifelong-memory methods in task success while minimizing output tokens within the given budgets.
By Guannan Lai, Zi-Yang Bai, Jun-Peng Jiang, Han-Jia Ye
arXiv:2608. 02560v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) imposes a prefill cost proportional to retrieved context length, and -- with Transformer backbones -- a KV-cache that grows with each generated token.
By Anusha Madan Gopal, Aras Pirbadian, Kristofor D. Carlson, M Anthony Lewis, Jonathan Tapson
The paper examines how a memory‑augmented agent can determine the value of its memories using only retrieval logs. It shows that top‑k retrieval fails to assign any value to memories regardless of the number of episodes logged, and that a single question can determine at most one memory’s value. To address this, the authors propose LEDGER, which estimates determined Shapley values with confidence intervals, merges memories with identical histories, and strategically retrieves to increase determinability, achieving a substantial rise in determined memories on HotpotQA and LoCoMo.