arXiv AI
Jul 24
NVIDIA-labs OO Agents: Native Python Object-Oriented Agents
arXiv:2607. 20709v1 Announce Type: new Abstract: Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs.
Object-Centric Environment Modeling for Agentic Tasks
arXiv:2607. 02846v1 Announce Type: new Abstract: Large language model (LLM) agents can improve through accumulated experience, but free-form textual memories become difficult to maintain, validate, and reuse as interactions grow.
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arXiv AI
Aug 3
OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems
arXiv:2607. 28629v1 Announce Type: new Abstract: The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents.
arXiv AI
Jul 3
OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration
arXiv:2607. 01531v1 Announce Type: new Abstract: Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks.
arXiv AI
3d ago
Schema: Discovering Unknown Environments via Agentic Program Induction
arXiv:2609.39140v1 Announce Type: new Abstract: Learning to complete tasks in unfamiliar environments with unknown rules remains a key challenge for LLM agents. Current LLM agents often record their...
arXiv AI
Aug 24
AgentMercury: Your Agent Can Synthesize Verifiable Environments for Business Scenarios at scale
AgentMercury is a scalable framework that synthesizes executable environments from high‑level business scenarios instead of task‑specific benchmarks. It creates a persistent world with entities, services, tools, and invariants, allowing diverse tasks and interaction trajectories to emerge naturally. The authors generated 4,783 environments across 14 industries and 50 countries, and training reinforcement‑learning agents on them improved performance on enterprise workflows and out‑of‑domain benchmarks, while the construction process itself can be learned to increase authoring success.
Hugging Face Trending Papers
Aug 20
EnvHarness: Awakening Static Worlds for Agent Learning
LLM agents learn by interacting with environments, yet these environments are hand-built and static: blind to an agent's weaknesses, and quickly left behind as it improves. While recent environment generation methods attempt to address this, they require domain-specific pipelines, rely on expensive or unreliable verifiers, and still produce static environments.
arXiv Computation and Language
Sep 10
Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks
arXiv:2609.09233v1 Announce Type: cross Abstract: How can language model agents effectively leverage libraries of reusable knowledge to solve long-horizon tasks? Recent work has increasingly focused...
arXiv AI
Sep 25
Breaking the Environment Wall: Evolving LLM Agent Environments for Recursive Self-Improvement
The paper introduces Env‑Rethink, a 27B post‑trained model system designed to help large language model agents better interact with complex, evolving environments. It builds Collection Maps and Event Logs to organize scattered information, uses offline trajectory learning to detect noise, and generates virtual event histories to evolve environments for more challenging tasks. Experiments show that Env‑Rethink improves downstream task performance by over 15.1% rubric pass rate across nine models on 30 tasks.
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 Machine Learning
Aug 31
Aligning Agentic World Models via Knowledgeable Experience Learning
arXiv:2601.13247v2 Announce Type: replace-cross Abstract: Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural ground...
arXiv AI
Jul 16
OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3
arXiv:2607. 01531v2 Announce Type: replace Abstract: Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks.
Hugging Face Trending Papers
Jul 1
OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration
Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks. World models learned with deep networks are flexible but data-hungry and transfer poorly beyond their training distribution.