arXiv AI June 30, 2026 By Peyman Hosseini, Ondrej Bohdal, Ahmed Alajrami, Andrea Maracani, Ignacio Castro, Matthew Purver, Mete Ozay, Savas Ozkan, Taha Ceritli

DuoMem: Towards Capable On-Device Memory Agents via Dual-Space Distillation

Read the original on arXiv AI →

arXiv:2606. 29961v1 Announce Type: cross Abstract: Large Language Model (LLM)-based agents can solve complex procedural tasks by interacting with environments over multiple turns, but this ability typically depends on large models, long contexts, and repeated inference calls.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 10

Agent Memory Distillation: Empowering Small LLM Agents with Hierarchical Teacher Memory

arXiv:2608. 07169v1 Announce Type: new Abstract: Memory systems have shown promise for improving agent performance, but their potential remains largely unexplored for small language models, which struggle to generate sufficient successful trajectories on their own.

By Taeil Kim, Kangsan Kim, Sung Ju Hwang

llms agents efficiency benchmarks

arXiv Machine Learning

Jul 7

Effective Distillation to Hybrid xLSTM Architectures

arXiv:2603. 15590v2 Announce Type: replace Abstract: There have been numerous attempts to distill quadratic attention-based large language models (LLMs) into sub-quadratic linearized architectures.

By Lukas Hauzenberger, Niklas Schmidinger, Thomas Schmied, Anamaria-Roberta Hartl, David Stap, Pieter-Jan Hoedt, Maximilian Beck, Sebastian B\"ock, G\"unter Klambauer, Sepp Hochreiter

llms efficiency

arXiv Computer Vision

Sep 24

EmbodiedMemory-Bench: Benchmarking Embodied Memory for Long-Horizon Embodied Tasks

arXiv:2609.28236v1 Announce Type: new Abstract: Long-horizon embodied interaction requires agents to retain and continually update information about the environment as they observe, act, and encounte...

By Lizhou Liang, Xinyu Zhong, Miao Pan, Xiaohe Zhou, Xuanyu Liu, Qinfeng Li, Peng Li, Jintao Chen, Xuhong Zhang, Wenqi Zhang

agents robotics multimodal benchmarks

arXiv Machine Learning

Sep 22

PSD: Pseudo Self-Distillation of Memory Representation Capabilities for LLM Agents

arXiv:2609.23449v1 Announce Type: cross Abstract: Memory systems are becoming a core component of LLM agents, but constructing and maintaining memory remains expensive because it relies on repeated c...

By Pirzada Suhail, Menglin Xia, Xuchao Zhang, Mayukh Das, Chetan Bansal, Saravan Rajmohan

llms agents efficiency

arXiv AI

Jul 24

Student-Centered Distillation Narrows the Agentic Gap Between Small and Large LLMs

arXiv:2509. 14257v3 Announce Type: replace-cross Abstract: Large Language Model agents achieve strong performance on multi-step reasoning and tool-use tasks, but their impressive capabilities typically rely on extremely large backbones.

By Yuanjie Lyu, Chengyu Wang, Jun Huang, Tong Xu

llms agents reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jul 28

TokenMem: Faithful Knowledge Injection for Frozen LLMs

arXiv:2607. 22625v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge, but suffers from knowledge conflicts: when retrieved information contradicts parametric memory, the shared self-attention pathway produces unpredictable outputs.

By Chengzhang Yu, Chenyang Zheng, Zening Lu, Yingru He, Yutong Huang, Yiming Zhang, Yue Xu, Zhanpeng Jin

llms rag benchmarks

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Agent Memory Distillation: Empowering Small LLM Agents with Hierarchical Teacher Memory

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EmbodiedMemory-Bench: Benchmarking Embodied Memory for Long-Horizon Embodied Tasks

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