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.
Hugging Face Trending Papers
3d ago
MemCodex: Self-Programming Hierarchical Memory for Language Agents
Agent memory faces heterogeneous access needs: a single-hop question may require one piece of evidence, whereas a multi-hop question must combine evidence from multiple sources. Predefined memory work...
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.
arXiv Computation and Language
Sep 21
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 Computation and Language
3d ago
MemCodex: Self-Programming Hierarchical Memory for Language Agents
arXiv:2609.39765v1 Announce Type: new Abstract: Agent memory faces heterogeneous access needs: a single-hop question may require one piece of evidence, whereas a multi-hop question must combine evide...
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
3d ago
Rep2Skill: Representation-Guided Skill Self-Evolution for LLM Agents
Rep2Skill introduces a representation-guided framework that enables large language model agents to self-evolve their textual skills by analyzing internal representation trajectories from agent rollouts. The method identifies execution turns that deviate from successful dynamics and uses these signals, together with execution contexts, as actionable feedback for targeted skill revision. Experiments with two open-source LLMs across two agent environments demonstrate that Rep2Skill consistently outperforms purely text-based approaches, showing that incorporating internal representations can enhance agent self-improvement.
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.