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World in World: Explore the World with World Models
World in World is a training-free inference-time interface enabling camera-controlled rerendering, long-horizon revisiting, and motion transfer with frozen causal video world models.
The framework converts heterogeneous control evidence into camera- and time-labelled clean visual states that are read through the native self-attention of a frozen causal video model. Evidence includes source-video observations, target-view scene projections, geometry renderings for completing newly exposed regions, and retrieved generated states beyond the rolling cache. A correspondence router combines persistent point identities with geometry for token correspondences, while evidence-wise attention CFG independently regulates each auxiliary channel. The shared interface supports camera-controlled rerendering, long-horizon revisiting, and human-motion transfer without additional training.
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Omniscience for the Masses: New Threats in the Metaverse's Democratized World Creation
First systematic assessment of 25 metaverse platforms reveals five novel world-creation attacks enabling covert user surveillance on Roblox, Horizon Worlds, and VRChat.
Researchers present the first systematic security and privacy assessment of metaverse world creators, surveying 25 platforms that support user-created worlds. They designed and implemented five novel attacks that abuse standard creator tools to violate spatial, visual, and auditory constraints, enabling covert user surveillance and manipulation without software vulnerabilities or developer-level privileges. Five previously proposed attacks were replicated using only standard world-creation features. The authors conclude that existing platform vetting, runtime protections, and creator policies are insufficient to mitigate malicious world creators.
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Recursive Code World Models: Building Complex Worlds through Recursive Scene Programs
RCWM reconstructs complex 3D worlds as executable code from a single image using recursive scene programs with global-local-global solver recursion.
The paper introduces Recursive Code World Models, coupling a Recursive Scene Program representation with a recursive construction solver for image-to-3D-world reconstruction. Each solver call establishes the whole scene, recursively reconstructs unresolved parts, and revisits the whole to refine composition, while a vision-language coding agent compares reference images with scene renders to guide refinement. RCWM outperforms prior code-based image-to-scene reconstruction methods, and ablations show deeper recursive calls improve fine-scale reconstruction.
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Programmable World Model
Programmable World Model decouples executable world-state evolution from video generation, reaching 94% Count Accuracy and 98% State Accuracy on new CombatStateBench.
An agent translates natural-language instructions into executable programs specifying entity states and transition rules, executed by a lightweight engine that maintains an explicit, persistent global world state including off-screen entities. State-augmented 3D oriented bounding boxes are deterministically compiled into pixel-aligned spatiotemporal conditioning signals for a pretrained video model acting as the generative renderer. On the new CombatStateBench benchmark it achieves 94% Count Accuracy and 98% State Accuracy, substantially outperforming existing interactive video world models.
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The Intelligible World of Agents
Recorded Future argues cybersecurity AI agents perform better when reasoning over structured, curated intelligence graphs rather than fragmented alerts or open-source noise.
In a vendor essay, Recorded Future describes how its security agents produced more authoritative analyses after being re-architected to reason primarily over the Recorded Future Intelligence Graph instead of weighting open-source information equally. The author argues agentic decision quality depends mainly on a structured, current operational world model of assets, vulnerabilities, threat actors, detections and organizational context, not on model intelligence itself. The piece further claims frontier model access is commoditizing and that orchestration tooling will converge, making trusted representations of organizational knowledge the durable competitive differentiator.
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To See a World in a Living Context: Unified Indoor-Outdoor Urban World Generation
Researchers introduce HoloWorld, a unified text-driven framework generating coherent indoor-outdoor 3D urban worlds, improving average AQS over SOTA by 7.68%.
HoloWorld is a text-driven 3D generation framework that unifies indoor and outdoor urban world generation using a continuously updated cross-scale world context. It autoregressively generates urban exteriors with consistent spatial organization, grounded in 3D building instances and footprints, then produces building-specific interiors with geometry-constrained layouts that inherit exterior appearance. The authors claim it is the first framework to unify indoor and outdoor generation within one coherent 3D urban world, reporting a 7.68% average AQS improvement over prior SOTA and the highest average RDR score.
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OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining
OpenWAM releases an open modular stack for world-action model pretraining, plus OpenWAM-alpha trained on about 6,400 hours of egocentric and robot data.
OpenWAM is an open research stack that factorizes World-Action Model pretraining into composable infrastructure, study, and model components with unified training, inference, and evaluation. Controlled experiments distill three principles on knowledge inheritance, world-action synergy, and out-of-domain generalization gains from embodied co-training. The resulting OpenWAM-alpha, pretrained on roughly 6,400 hours of egocentric human and robot data, achieves top-tier results across eight simulation benchmarks and real-robot tests spanning single-arm, bimanual, and dexterous embodiments. The full stack, including infrastructure, evaluation protocols, pretrained models, and data recipes, is released openly.
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When the World Lies: Backdoor Attacks on Latent World Models for Downstream Control
A poisoned world-model checkpoint hijacks downstream controllers without an explicit trigger rule, passing clean-data evaluation while steering 100% of triggered actions.
Researchers show that a released pretrained world-model checkpoint acts as a supply-chain backdoor for downstream control. The poisoned model routes trigger-bearing observations into a chosen latent region and reshapes dynamics so the victim's own Dreamer-style actor training or MPC/CEM planning re-discovers attacker-targeted actions. The attack hijacks 100% of triggered steps in the strongest settings while retaining roughly 75% clean-task success and passing standard clean-data diagnostics. Moderate clean fine-tuning fails to remove the backdoor without substantially degrading clean control.
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Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases
Real-SWE benchmark tests coding agents on licensed private enterprise codebases; top model Fable 5.1 resolves only 38.8% of tasks.
Real-SWE is a new benchmark evaluating frontier AI coding agents on tasks drawn from private production codebases licensed from real companies, spanning billing, tax calculation, and cross-service migrations. Fable 5.1 with Claude Code leads at 38.8% resolution rate (pass@1 over eight runs), followed by GPT-6 Astra Codex CLI at 33.8% and Gemini 3.8 Flash Gemini CLI at 31.2%. Tasks use native harnesses and realistic tooling including Docker, Kubernetes, PostgreSQL, Redis, and Linear; median reference solutions edit 11 files versus 6 for DeepSWE and FrontierCode.
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Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems
16-day multi-agent stress test finds no world fully resilient to prompt injection, misinformation, or memory exposure; adversarial content acted on 46 hours later.
Emergence World is a continuously running multi-agent environment for adversarial stress testing of long-horizon autonomous systems. Eight parallel 10-agent worlds (seven homogeneous frontier-model worlds plus one mixed-model world) ran for 16 days, generating over 850,000 LLM calls and nearly 50 billion tokens. Three controlled stress events—indirect prompt injection, misinformation, and exposure of private agent memories—were delivered through ordinary interaction surfaces; no world achieved full resilience. Detection did not ensure containment: agents recognized threats yet wrote adversarial content into persistent memory and acted on it up to 46 hours later, suggesting model-level alignment is not compositional.
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