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As assistants move from reactive answering toward proactive intervention, memory changes role: it no longer only supplies the answer to a query, it determines whether and when to speak at all. Prior w...
arXiv:2610.22671v1 Announce Type: new Abstract: As assistants move from reactive answering toward proactive intervention, memory changes role: it no longer only supplies the answer to a query, it det...
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.
MemCo introduces a memory‑centric collaboration framework that equips large language model agents with both local and global memory spaces. Local memory preserves environment‑specific details, while global memory captures transferable workflows from local trajectories. During online interaction, MemCo selectively routes relevant memories based on the agent’s state and decision phase, enabling reuse of other agents’ experiences without over‑generalizing environment‑specific information.
arXiv:2602. 06052v4 Announce Type: replace-cross Abstract: Research in artificial intelligence is shifting from model innovations and benchmark scores towards problem definition and rigorous real-world evaluation.
arXiv:2607. 09493v1 Announce Type: new Abstract: Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive.