arXiv Computer Vision
Authoring for Living Worlds: Tool-Constrained LLM Agents for Executable Multi-Actor Scenarios
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arXiv AI
Jun 16
Orchestrated Reality: From Role-Play to Living, Playable Game Worlds -- LLM-Driven World Simulation as a Parameterized-Action POMDP
arXiv:2606. 16014v1 Announce Type: cross Abstract: Many games rely on storytelling combined with systems that track levelling, NPC behaviour, and consequence simulation; bridging tightly-authored narrative with deeply-simulated worlds -- most acute in sandbox and open-world settings -- has been prohibitively expensive.
arXiv AI
Aug 21
ChronoAgentic: A Code-based Multi-Agent World Simulator for Physically Grounded Simulation Construction
arXiv:2605. 14398v3 Announce Type: replace Abstract: Video-based world models generate visually plausible rollouts, but since they infer dynamics in latent states, they enforce no explicit physical constraints: contacts drift, shapes distort, and motion loses consistency.
arXiv AI
Jun 17
OmniDrive: An LLM-Choreographed Multi-Agent World Model with Unified Latent Co-Compression for Multi-View Driving Video Generation
arXiv:2606. 17536v1 Announce Type: cross Abstract: Generative world models for autonomous driving face two unresolved tensions: heterogeneous control injection, where free-form language, HD-maps, trajectories, and camera poses reside in incompatible representational spaces, and post-hoc cross-view fusion, where per-camera latents fail to encode global 3-D geometry.
arXiv AI
Sep 10
PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving
PlannerForge is a unified LLM‑agent framework that covers the entire scenario‑based testing pipeline for autonomous driving systems, from scenario generation to ADS assessment, and adds ADS enhancement and benchmarking stages. It was evaluated with ten off‑the‑shelf LLMs across all tasks and five prompt conditions, achieving best‑per‑task scores between 0.88 and 1.00 and matching commercial APIs with open‑source models such as Qwen3.6:35B. The end‑to‑end chaining retains 83% of seed queries for commercial backends and 78% for open‑source, outperforming existing tools like Scenario Factory 2.0 and BM25 in natural‑language generation, attribute realization, and physically valid edits. whyItMatters":"PlannerForge demonstrates that a single LLM‑based system can streamline and improve the fragmented scenario‑based testing workflow for autonomous driving, achieving high performance without domain‑specific fine‑tuning."
arXiv AI
Oct 7
WorldSolver: Can LLM Agents Simulate the Physical Dynamics via Solver Generation?
arXiv:2610.08720v1 Announce Type: new Abstract: LLM-based agents are increasingly advancing scientific and engineering problem solving, with physics simulation emerging as a challenging yet practical...
Hugging Face Trending Papers
Sep 8
Hi-FLoop: Hierarchical State-Feedback Loops for Multi-Timescale World Modeling
Hi-FLoop introduces a hierarchical state‑feedback framework for multi‑agent traffic simulation that reconciles decision time scales over an 8‑second rollout. The model uses eight scene‑level Worlds to maintain joint hypotheses, with an 8‑second Goal, 2‑second Preview, and 1‑second Control hierarchy, and commits only executed prefixes every 0.5 seconds to preserve factual consistency. A joint preview interaction graph and a prefix‑frozen A‑to‑B cascade enable sparse interaction refinement and accurate state recovery, achieving an overall score of 0.689987 on the H‑D public‑validation split and strong oracle‑minADE performance. whyItMatters":"The paper presents a novel multi‑timescale approach that improves consistency and realism in long‑horizon traffic simulations, as evidenced by its competitive evaluation metrics."