arXiv AI
5d ago
Practice Makes Unsafe: Skill Misevolution in Self-Improving LLM Agents
arXiv:2608. 12851v1 Announce Type: new Abstract: Self-improving LLM agents convert successful trajectories into persistent cross-task state.
TAME: A Trustworthy Test-Time Evolution of Agent Memory with Systematic Benchmarking
arXiv:2602. 03224v2 Announce Type: replace Abstract: Test-time evolution of agent memory represents a pivotal paradigm for advancing AGI, as it strengthens complex reasoning through experience accumulation without requiring parameter updates.
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arXiv AI
Jun 6
AdaMEM: Test-Time Adaptive Memory for Language Agents
arXiv:2606. 05684v1 Announce Type: new Abstract: A central challenge for language agents is utilizing past experience to adapt to dynamic test-time conditions.
Hugging Face Trending Papers
Jun 23
Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning
Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experience learning methods mostly rely on single-agent loops, where the same agent executes tasks, summarizes outcomes, and determines memory content.
arXiv AI
Jun 6
Beyond Similarity: Trustworthy Memory Search for Personal AI Agents
arXiv:2606. 06054v1 Announce Type: new Abstract: Personal AI agents increasingly rely on long-term memory to provide persistent personalization across sessions.
arXiv AI
5d ago
MindMemOS: A Portable and Self-Evolving Memory Operating Layer for AI Agents
arXiv:2608. 12428v1 Announce Type: new Abstract: Memory is a core component of AI agents, enabling them to accumulate experience, maintain personalization, and adapt over long-term interactions.
arXiv AI
Jul 10
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
arXiv:2607. 08716v1 Announce Type: new Abstract: In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act.
arXiv Machine Learning
Jul 21
Self-Evolving Just-In-Time Memory for Proactive Embodied Safety
arXiv:2607. 16247v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) have empowered embodied agents to execute complex household tasks, they struggle to proactively handle dynamically emerging hazards during closed-loop interactions.
arXiv AI
Jun 9
MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution
arXiv:2606. 07603v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning capabilities, yet most LLM-based agents are statically deployed and unable to improve through task interactions.
arXiv AI
Jun 16
EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective
arXiv:2605. 18421v2 Announce Type: replace-cross Abstract: Recent benchmarks for Large Language Model (LLM) agents mainly evaluate reasoning, planning, and execution.
Hugging Face Trending Papers
Jul 9
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed.
arXiv AI
Jul 14
The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory
arXiv:2607. 10608v1 Announce Type: new Abstract: Memory is becoming a core component of long-horizon AI agents, allowing agents to reuse past experience when operating web browsers, software tools, and other interactive environments.
Hugging Face Trending Papers
Jul 1
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. However, memory is not always beneficial: retrieved memories often induce a critical issue of sycophancy, causing agents to over-align with the user at the cost of factual accuracy or objective reasoning.