arXiv Computer Vision
EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory
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MemVerse: Multimodal Memory for Lifelong Learning Agents
arXiv:2512. 03627v2 Announce Type: replace Abstract: Despite rapid progress in large-scale language and vision models, AI agents still suffer from a fundamental limitation: they cannot remember.
arXiv Computation and Language
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MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks
MemoryArena is a new evaluation gym that benchmarks agent memory in interdependent multi‑session tasks. Unlike prior benchmarks that test memorization or single‑session action in isolation, MemoryArena requires agents to acquire memory while interacting with the environment and then use that memory to guide future decisions across a range of tasks such as web navigation, planning, information search, and formal reasoning. The benchmark reveals that agents excelling on existing long‑context memory tests perform poorly here, highlighting a gap in current memory evaluation methods.
arXiv AI
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Dual Latent Memory for Visual Multi-agent System
arXiv:2602. 00471v2 Announce Type: replace Abstract: While Visual Multi-Agent Systems (VMAS) promise to enhance comprehensive abilities through inter-agent collaboration, empirical evidence reveals a counter-intuitive "scaling wall": increasing agent turns often degrades performance while exponentially inflating token costs.
arXiv Computation and Language
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SPyCE: Skill-Policy Co-evolution for Multimodal Agents
arXiv:2607. 13854v2 Announce Type: replace Abstract: Multimodal agents that think with images iteratively manipulate visual evidence and invoke tools across many steps.
arXiv AI
Sep 15
CoMem: Collective-Individual Memory Synergy for Evolutionary Multi-Agent Systems
CoMem introduces a memory architecture for multi-agent systems that blends private experience with shared knowledge. It includes Private Experience Sedimentation to retain useful individual memories, Collective Wisdom Curation to filter widely proven ideas for sharing, and Parallel Dual-Stream Retrieval to draw from both personal and group memories while maintaining diversity. Experiments on ALFWorld and PDDL benchmarks demonstrate that CoMem improves overall performance and reduces memory pollution.
arXiv Computation and Language
Sep 14
LifeMem: Enabling Lifelong Experience Reuse for LLM Agents
arXiv:2609.12655v1 Announce Type: new Abstract: Large language model agents are expected to continuously adapt to new tasks and environments over their lifetime by reusing past experience. However, e...