Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to parallelize. World modeling offers a natural intermediate proxy that allows agents to query lower-cost, more controllable feedback before committing to real actions.
arXiv:2608. 02713v1 Announce Type: cross Abstract: Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to parallelize.
Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by acting as silicon samples that unite agent-based...
arXiv:2609.24911v1 Announce Type: new Abstract: Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by...
arXiv:2608. 04232v1 Announce Type: new Abstract: Biological systems must regulate competing needs under limited perceptual bandwidth, where sharpening one estimate costs the capacity to sharpen the others.
The paper introduces Imagine-then-Plan (ITP), a framework that lets agents learn by interacting with a learned world model to generate multi-step imagined trajectories. ITP features an adaptive lookahead mechanism that balances ultimate goals with task progress, producing richer signals about future outcomes. Experiments on various benchmarks show that ITP outperforms existing baselines, and analyses suggest the adaptive lookahead improves reasoning for complex tasks.
RS-Claw-Evolution is an environment-feedback-driven framework designed to enhance lightweight remote sensing agents for long-horizon tasks. It improves agents through three stages—interaction evolution, experience evolution, and decision evolution—using executable code, failure-aware trajectory generation, and reinforcement learning with multi-dimensional rewards. On Earth-Bench, a Qwen3-4B agent trained with this framework reaches 65.9% accuracy, surpassing larger baselines and approaching GPT-5 performance.
EvoBelief is a self‑evolving world model that represents environment knowledge as falsifiable hypotheses, allowing an LLM agent to update its beliefs during deployment. The system generates hypotheses when unexpected outcomes or unknowns arise, validates them through observations and controlled experiments, and then uses the confirmed rules to guide predictions or plans. Across benchmarks such as ALFWorld, MiniGrid, and DiscoveryWorld, EvoBelief improves task performance, revises stale rules, and discovers quantitative laws, outperforming baseline frozen world models.
The paper introduces Population-Centered Evolution (PCE), a new principle for improving self‑improving AI agents by leveraging patterns of success, failure, and prior modifications across the entire population rather than focusing solely on selected parent agents. PCE distills task records and modification histories into a population block that guides enhancements to a chosen parent’s harness. Experiments show that PCE raises success rates on SWE‑bench Verified and Polyglot benchmarks, narrowing the gap between population coverage and best‑agent performance.
arXiv:2603.28200v2 Announce Type: replace-cross Abstract: Guiding collective motion in biological groups is a fundamental challenge in understanding social interaction rules. In this study, we propos...
arXiv:2511. 04500v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in high-stakes domains and as imitators of human behavior in the social and behavioral sciences.
SwarmWorld demonstrates that homogeneous language‑model agents can self‑organize into evolving technological societies without assigned roles or direct communication. In a spatial environment, agents explore, process resources, construct artifacts, and write executable controllers that are later evaluated by a deterministic simulator. The resulting societies develop broader, more resilient technological portfolios than isolated search, with agents differentiating into exploration, construction, maintenance, and coordination roles as the world matures.