World Models & Planning

LVLMs can be trained to actively mentalize spatial scenes from novel viewpoints, boosting their reasoning accuracy by up to 20% on complex tasks.

Predictive world models, not just static feedback, unlock substantial gains in recursive self-improvement across diverse domains, from code to scientific discovery.

Existing 3D asset generators can now produce objects that not only look good but also move and function realistically, passing physics tests for stability and collision-free articulation.

Innate robot reflexes, designed for solitary tasks, actively impede the evolution of effective communication strategies in social foraging.

One million parallel environments are insufficient without intelligent sampling: Success Guided Sampling unlocks mega-scale RL for robots by focusing training on the edge of learned capabilities.

Frozen LLMs can autonomously achieve recursive self-improvement by learning and refining explicit, executable world models from interaction data.

A single visual world model can now execute complex procedural tasks end-to-end, adapting its generation to the actual outcome of each predicted action.

Robot world models can now achieve 6.25% interaction defect rates, a 7-fold reduction, by training on counterfactual futures and a novel embodied video reward.

Policy optimization for adversarial MDPs now matches state-of-the-art regret bounds by stabilizing local updates with regularized Q-functions, eliminating the horizon gap.

Robots can achieve 70% success on complex bimanual tasks using only code for decision-making, bypassing runtime vision-language models entirely.

Visual transition reasoning, not just diverse data, unlocks significant multimodal LLM improvements, with just 2,000 examples boosting 22 benchmarks.

A reconstruction-free JEPA successfully disentangles invariant and variant latent factors, offering a path to robust world models without observational decoders.

Reverse-mode gradients slash Bayesian inference time for expensive simulators, especially when parameters vastly outnumber outputs.

Reasoning models can learn to plan by reverse-engineering solution ideas from hindsight, even when initially unable to solve the problem.

Optimal regret in adversarial MDPs with stochastic hard constraints is finally achieved, bridging the gap between existing upper and lower bounds by achieving $\widetilde{\mathcal{O}}(\sqrt{T}/\rho + 1/(d\rho))$ regret.

A frozen video tokenizer's latent can be factored into controllable and reward-relevant components, unlocking significant downstream planning gains.

Structured linear dynamical systems can be learned from drastically undersampled observations ($p=o(1)$) with trajectory lengths far shorter than previously thought possible.

Generated robot futures can actively harm performance if temporally misaligned, but a new control-based conditioning method recovers substantial success by learning to trust and align them.

Local contact reasoning, not global scene modeling, unlocks 30%+ accuracy gains in rigid-body physics simulation.

Static 3D worlds now possess vivid, looping motion, seamlessly transitioning from any viewpoint without explicit motion capture.

Uncertainty in loss distributions cripples standard risk-averse ML; this work shows credal sets can reliably prevent catastrophic decisions without sacrificing average performance.

Fine-grained embodied interactions between multiple agents can be effectively modeled by synchronizing ego-stream generation with cross-view action consistency and shared environment memory.

Learned cable simulators can achieve 15%+ prediction error reduction and halve segment length drift by biasing attention with physical distances.

Humanoid robots can now catch thrown objects with 65% success from 2.5m using only egocentric RGB-D, a leap in reactive whole-body control.

Agents can learn explicit, reusable mental models of partners to dramatically improve multi-agent interaction quality and Theory-of-Mind capabilities.

AI agents can learn to climb leaderboards in complex adversarial games by refining executable policies, but struggle with intricate rule sets

Latent world models scale predictably with compute via a second-order power law, enabling accurate forecasting of robotic planning performance and zero-shot task execution on real hardware.

The hardest-to-distinguish competitor, not the average, dictates the speed of decentralized adaptive sensing.

Autoregressive video pretraining unlocks the full potential of long contexts for real-time robot control, boosting success rates by 15% and enabling complex manipulation previously impossible.

A single neural network now predicts energy use for millions of diverse buildings with physics-validity and accuracy rivaling black-box models, all while being orders of magnitude smaller.

Average-reward RL for general multichain MDPs is now possible without model knowledge or reduction to discounted problems.

Generative AI now predicts complex 3D urban wind and temperature fields with LES-level accuracy in seconds, unlocking rapid, turbulence-aware urban design.

A scalar adjoint method slashes the computational cost of fine-tuning flow policies in RL, achieving state-of-the-art results on complex manipulation tasks.

Even perfect AI agents fail to guarantee safety in realistic, time-sensitive, and partially observable environments, fundamentally challenging agent-agnostic alignment approaches.

Logarithmic regret is achieved in adaptive control of unknown, changing systems by exploiting the unique energy signatures of admissible plant changes during exploitation.

The optimal strategy for the cow-path problem is provably cyclic, meaning no search algorithm can outperform a fixed path traversal order.

A frozen graph backbone effectively preserves past structural knowledge in continual multi-agent reinforcement learning, dramatically reducing forgetting and boosting transfer across tasks with distinct graph structures.

Quadrupedal robots can achieve up to 20% lower energy consumption by learning speed-dependent gaits with a hierarchical RL policy that explicitly optimizes cost of transport.

Planning in visual world models can be accelerated by $5\times$ by pruning $50\%$ of spatial tokens based on their influence on the planning cost gradient, without sacrificing performance on most control tasks.

Co-evolved communication protocols exhibit surprising robustness when transferred from 2D to 3D physics, with performance scaling linearly with episode length and social cues acting predominantly as jam-assistance.

Interactive video generation can be sped up by 1.4x by intelligently caching transformer residuals based on detected control transitions, without sacrificing quality.

A simulation flywheel recursively bootstraps embodied data, generating richer scenes and tasks for robot pretraining and evaluation.

Most video world models fail basic physics tests, with even the best models exhibiting significant physical inconsistencies across mechanics, optics, and fluids.

Agents can learn to act reliably in new environments by generating their own practice tasks and distilling reusable heuristics from self-identified failures, achieving significant performance gains without human supervision or pre-defined benchmarks.

Embodied agents can now reliably achieve complex, multi-stage goals in dynamic environments by learning to predict and ground targets from decoupled visual context, even when the goal is out of view.

Spatiotemporal time series forecasting now boasts valid confidence intervals even with imputed labels and temporal dependencies, overcoming a critical limitation of existing PPI.

Sequential hypothesis testing without explicit densities is now possible with anytime-valid error control and near-optimal performance.

Occupancy measure formulation enables decision-focused learning in MDPs with closed-form gradients, overcoming KKT-based scalability bottlenecks and achieving state-of-the-art regret.

Unlocking broad downstream generalization for 3D atomistic systems, Atom-JEPA achieves state-of-the-art property prediction by learning from unlabeled structures alone.

Interactive AI worlds can now speak and react in real-time with spatially accurate sound, bridging the sensory gap in generative environments.