GPT-5.6 Sol sets a new state of the art in cybersecurity on “The Last Ones” cyber range. We’re already seeing that capability translate into defensive outcomes: helping teams find, validate, and fix vulnerabilities in real-world code. Put it to work with Codex Security:

🌍 TL;DR: A training-free way to re-explore a recorded video from new camera angles just dropped. It handles viewpoint changes, hole-filling, and recovering earlier appearances entirely through a frozen model's own attention. Title: World in World: Explore the World with World Models URL: Points 🎥 Four evidence sources (source video, target-view scene, rendered geometry, retrieved history) get converted into clean K/V fed straight into the frozen model 🧭 CGAR routes queries toward reliable source-video tokens using persistent point correspondence and camera geometry 🎚 EWA independently tunes each auxiliary channel's contribution using existing attention outputs, with zero extra network evaluations 🏆 Top VBench score of 85.192 and lowest rotation error (2.8326°) against 6 baselines including ReCamMaster 🔧 Same framework also powers bullet-time rendering, video stabilization, editing, and motion transfer Getting this much versatility with zero retraining feels like a win for evidence engineering over model adaptation. #WorldModels# #VideoGeneration#

A new approach to physical world models — reason about "how the world changes" in language before generating any pixels. Title: PhiZero: A World Model Built Around Physical Language ❓ What is "Physical Language"? 💡 A discrete token sequence that directly represents state-transition patterns in a video — separate from visual appearance. Because dynamics and appearance are disentangled, the state transitions encoded from one video can be transferred to a completely different visual style. PhiZero's "Reason-then-render" paradigm predicts this physical language first, then generates video from it. ❓ Why did previous physical world models produce physically inconsistent results? 💡 They relied on direct pixel-space prediction, burying dynamics implicitly inside the model. Reconciling visual realism with physical consistency was difficult — leading to failures like a tennis ball hitting a rubber duck that doesn't react. The reasoning process was also opaque by design. ❓ What performance numbers did it achieve? 💡 State-of-the-art results across all six benchmarks: ・Physics-IQ IQ-Score: 41.2 (beats previous SOTA of 39.5) ・WorldModelBench: highest in physical compliance, commonsense, and overall score ・YoCausal causal reasoning: aggregated rank 2.0 (best) ・Tokenizer: just 256 tokens achieve PSNR 28.9 — 1/175th of Wan2.2-5B VAE's token count ❓ What does this mean for robotics and autonomous driving? 💡 Physical Language is appearance-agnostic, so zero-shot sim-to-real and human-to-robot transfer become possible. The team demonstrated encoding LIBERO simulation dynamics and replaying them as realistic video, and directly transferring human motion patterns to robot embodiments — all without task-specific training. #WorldModel# #EmbodiedAI#

Every time I fly to New York, I'm struck by how many trees there are in the densely-populated areas around the city. (Coming from the West Coast, one arrives over the Garden State, and the suitability of the sobriquet is quite apparent from the air.) It feels like this degree of tree cover in highly populated areas is atypical (the environs of places like Paris, London, Los Angeles, Melbourne, Tokyo, etc., look very different), so I asked the LLM to investigate. It found GHSL 2020 population and ESA WorldCover 2021 10m land-cover data, and concluded that New York is in fact quite unusual. As far as I can tell, it's because of some combination of: * A very favorable climate. (Trees grow quickly without irrigation.) * Marginal farmland. (Readily outcompeted by the Midwest in the 19th century.) * Together yielding reforestation before the advent of suburbs. * And a preference for development patterns that include trees. (Japan's climate is very hospitable, but one sees far fewer trees in the populated areas around the major cities -- forest and habitation are more disjoint.)

Seeing InternVideo-Next explicitly characterize its architecture as "Encoder–Predictor–Decoder (EPD)" and defining the Predictor as a "latent world model" confirms a clear convergence in the field. 📉 From our early work on Context AutoEncoder (CAE, — where we decoupled the Encoder, Predictor ("Regressor" in the paper), and Decoder — to Meta's I-JEPA/V-JEPA shifting entirely to latent prediction, and now this. It seems we are all validating the same core intuition: Understanding ≠ Pixel Reconstruction. 💡 Decoupling representation learning from high-frequency detail generation to build World Models in latent space is evidently the path forward. Glad to see our early intuition resonating with the latest SOTA. 🫡 #AI# #ComputerVision# #WorldModel# #JEPA# #CAE#

Hide an object behind a wall in a video generation model, and it might come back as something else entirely. This paper tackles that object permanence failure head-on. Title: Training Object Permanence in World Models URL: 📝 Overview The paper trains video models on object permanence and solidity — cognitive abilities human infants develop by six months old. It builds WROP, a benchmark of 150 synthetic task generators producing 1.5M training samples, and PWM-WROP, a 16B-parameter model fine-tuned on it. ❓ Problem Solved Models like Sora let objects vanish behind occluders and reappear as different objects, or pass straight through solid barriers. These failures undermine higher-level reasoning about collisions and cause and effect. 💡 Method & Approach 150 Blender-generated tasks are organized into six occlusion and solidity families. Structural parameters like object count and trajectory vary systematically while surface parameters like color and lighting are randomized, preventing models from succeeding through memorization. PWM-WROP fine-tunes NVIDIA's Cosmos3-Nano on this data. 📊 Results In 361 blind human pairwise comparisons, PWM-WROP ranked first among true-continuation models (Elo 1679.5), 224 points ahead of the runner-up. It topped every model on static occlusion tasks, but still struggled on solidity tasks like collisions. 🌍 Use Cases The team released the training data, model weights, and PWM, a native-PyTorch training stack for AWS Trainium2 — laying groundwork for physically grounded world models. #WorldModels# #VideoGeneration#

An AI model just rediscovered Kepler's law from latent space, with zero knowledge of orbital mechanics. And the same core architecture spans molecular dynamics, cell prediction, and even generating cancer treatment hypotheses. Title: JEPA-Anything: Learning Predictive Models across Different Worlds URL: In one sentence: it decomposes a target's representation into K orthogonal subspaces, each with its own dedicated predictor — "Orthogonal Predictive Factorization" (OPF) — and applies this single mechanism across radically different domains, from vision and biology to clinical data, control, and molecular dynamics. 🔭 Highlight 1: Rediscovering a physical law from latent space Trained only on orbital motion data, its latent frequency modes recovered Kepler's law f=(2π)⁻¹a⁻³/². The fitted slope was -1.4991 against a theoretical -1.5, with R²=0.9999999. 🧬 Highlight 2: Generating and validating a cancer treatment hypothesis Factor analysis on liver cancer data proposed combining IL-18 and CD73 blockade, which then showed the strongest tumor cell killing in actual patient-derived organoids. ⚛️ Highlight 3: Consistently strong across molecular dynamics and cell prediction It achieved the lowest error across 100-step molecular simulations of water, quartz, paracetamol, and benzene, and also beat prior methods on single-cell perturbation prediction. It's striking that one core architecture spans such wildly different scientific domains this well. #WorldModels# #AIforScience#

🤖 Turns out robot dexterity scales with data too, and now there's hard evidence for it. Title: GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation URL: 🧩 Overview AgiBot Research Team built GE-Act 2.0, a world-action model that predicts future visual states to guide robot actions, training every component from scratch on manipulation data instead of inheriting a pretrained video generator. ⚙️ Problem it solves Prior systems reused reconstruction-optimized video generators that don't guarantee action-relevant information, and it was unclear how to pretrain visual generation and inverse dynamics separately before connecting them. 🛠 Methodology Three pieces make it work: a 64x-compressed control-oriented autoencoder (CoAE), a Single-Step Visual Planner that generates a full future frame in one forward pass, and KASO, which fixes the "validity gap" between predicted futures and real actions via top-k selection. 📊 Results Scaling training data from 300 to 30,000 hours lifted the G1-OP robot's success rate from 17.1% to 44.1%, a 27.0-point gain. Even G2-90D, with under 2% of the data, gained 17.7 points, showing real cross-embodiment transfer. Object, color, and position grounding all exceed 90% accuracy. #Robotics# #WorldModels#

AGIBOT releases GE-Act 2.0 — the first native World Action Model to validate a pretraining and scaling path for embodied AI. 📖 Explore the project: Trained entirely from scratch on embodied manipulation data: visual representation, future generation, and action prediction, all from random initialization. No inherited video generators. No task-specific fine-tuning. Put straight to a ruthless real-robot zero-shot test — unseen scenes, unseen objects, 100 atomic tasks, 20 skill categories, and two robot embodiments: ✅ Data scaled 100×: from 300 to 30,000 hours ✅ Task success climbs from 17.1% to 44.1% on G1-OP — with no sign of saturation ✅ New skills emerge at scale: folding towels, nesting paper cups, uncapping pens, arranging flowers ✅ Cross-embodiment transfer: G2-90D, under 2% of the training data, still gains 17.7 percentage points ✅ Failure data becomes a training asset — 2,000 hours of failed manipulations and deployment rollouts A capable model envisions reality before it acts. #AGIBOT# #EmbodiedAI# #WorldModel# #PhysicalAI# #Robotics#

🎮 Code agents thrive because code is executable and verifiable. This paper brings the same idea to game development to accelerate world model training. Title: Agentic Game Development as a Verifiable Trajectory Data Engine for Scaling World Models URL: 🧩 Overview The paper identifies a "verifiability bottleneck": fuzzy proxies like CLIP scores are noisy and gameable as reward signals. It proposes tapping game engines' collision, physics, and navmesh checks as a dense, trustworthy reward source instead. ❗ Problem it solves Spatial generation models (video, 3D, world models) lack reliable reward signals, so scaling compute doesn't reliably translate into real capability — what the authors call the "unverifiability tax." 🛠 Method RLHEV combines dense engine-verified rewards with sparse human acceptance judgments. AWoMo runs a Propose-Render-Verify-Repair-Review loop as a developer agent. UWDP unifies development traces into a single structured protocol for training data. 📊 Results On UnitySceneBench, full RLHEV beats the fuzzy-proxy baseline by +0.098. Cross-engine generalization (Unity to Godot) rises from 0.15 to 0.35. Downstream, it boosts D4RL Gym-MuJoCo by +48.43% and R2R navigation by +0.79%. #WorldModels# #ReinforcementLearning#