arXiv AI
Jul 7
Automating the Design of Embodied Agent Architectures
arXiv:2606. 30111v2 Announce Type: replace-cross Abstract: Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules.
Metis: Bridging Text and Code Memory for Self-Evolving Agents
arXiv:2606. 24151v1 Announce Type: cross Abstract: Self-evolving agents improve over time by distilling experience from past executions and reusing it in future tasks.
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arXiv AI
Jun 30
Automating the Design of Embodied AgentArchitectures
arXiv:2606. 30111v1 Announce Type: cross Abstract: Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules.
Hugging Face Trending Papers
Jun 29
Automating the Design of Embodied AgentArchitectures
Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules. This modularity exposes a large architectural design space, but current systems still rely on researcher intuition to choose where information is stored, how observations are processed, and how model calls are connected.
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.
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
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
Jul 21
Mechanistic Attention Guidance for Agent Memory Refinement
arXiv:2607. 17621v1 Announce Type: new Abstract: Existing self-evolving memory systems mainly improve agent memory based on textual outputs, such as task trajectories and reflections.
arXiv AI
Jun 3
AgentCL: Toward Rigorous Evaluation of Continual Learning in Language Agents
arXiv:2606. 02461v2 Announce Type: replace Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes.
arXiv AI
Jun 2
AGENTCL: Toward Rigorous Evaluation of Continual Learning in Language Agents
arXiv:2606. 02461v1 Announce Type: new Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes.
arXiv AI
Jun 4
Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline
arXiv:2606. 04315v1 Announce Type: new Abstract: LLM agents accumulate histories that outgrow their context windows, motivating a growing literature on memory systems.
arXiv AI
Aug 6
A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents
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 AI
1d ago
Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents
arXiv:2608. 15071v1 Announce Type: new Abstract: Learning from experience is critical for developing capable, self-improving large language model (LLM) agents.