Search papers, labs, and topics across Lattice.
100 papers published across 8 labs.
A single capable verifier can dramatically improve terminal agent action reliability, achieving higher success rates at lower costs than simply generating more trajectories.
Autonomous driving diffusion planners now learn to intrinsically adhere to driving rules and explain their decisions, significantly improving safety in challenging scenarios.
Learned world models can achieve real-time control in live games, outperforming behavioral cloning and maintaining high-frequency action loops.
Howard's policy iteration for deterministic MDPs is not strongly polynomial, exhibiting an exponential iteration complexity that separates it from the simplex method.
Sonar-only SLAM now achieves robust topological mapping by verifying loop closures with a sequence-aware approach, overcoming the inherent ambiguity of acoustic echoes.
A single capable verifier can dramatically improve terminal agent action reliability, achieving higher success rates at lower costs than simply generating more trajectories.
Autonomous driving diffusion planners now learn to intrinsically adhere to driving rules and explain their decisions, significantly improving safety in challenging scenarios.
Learned world models can achieve real-time control in live games, outperforming behavioral cloning and maintaining high-frequency action loops.
Howard's policy iteration for deterministic MDPs is not strongly polynomial, exhibiting an exponential iteration complexity that separates it from the simplex method.
Sonar-only SLAM now achieves robust topological mapping by verifying loop closures with a sequence-aware approach, overcoming the inherent ambiguity of acoustic echoes.
Radar novel-view synthesis finally unlocks Doppler, achieving 90.7% object detection recovery by separating scene geometry from sensor spread and enabling zero-shot configuration transfer.
Agents can now reliably navigate evolving environments by maintaining a probabilistic 4D belief that accounts for hidden target movements and revises predictions based on intermittent, visibility-aware observations.
Robots can now learn from physical failures in real-time, autonomously revising their own capabilities to achieve unprecedented long-horizon manipulation success.
An optimal threshold policy for irrevocable matching in growing trees loses at most twice the total-variation error of the true growth law, even when forecasts are misspecified.
Humans can directly control complex embodied agents using a handful of learned, composable skills discovered through self-play, bypassing the need for low-level action control.
Imagined failures in a world model can pinpoint exactly which robot skill components need new demonstrations, boosting success from 13% to 75% with minimal data.
Language, when self-evolving and treated as a physical representation, can unlock state-of-the-art physical plausibility in video world models, even surpassing proprietary systems.
Separating policy roles in offline RL unlocks significant performance gains by tailoring updates for value bootstrapping versus direct action execution.
LLM agents trained solely in rule-generated fictional worlds achieve state-of-the-art transfer to real-world search tasks, outperforming agents trained on actual data.
Tree search on on-policy trajectories dramatically boosts policy gradient learning by uncovering rare high-return paths, outperforming PPO on Atari and MuJoCo with minimal bias increase.
Neural predictors can be made dynamically feasible without ground-truth controls by a frozen, post-hoc operator that enforces learned dynamics manifolds via iterative control correction.
Hybrid RL for bus holding suffers when simulators misrepresent passenger journey completion, leading to worse outcomes than purely offline methods.
State representation quality, not goal representation, is the critical bottleneck in offline navigation GCRL, with simple positional encodings boosting performance significantly.
Autonomous agents can recursively self-improve LLM-generated games, achieving superhuman performance and drastically cutting generation costs by internalizing development experience.
A single video tokenizer can achieve superior semantic alignment and fidelity compared to much larger models by directly conditioning on frozen visual features.
Streaming world models can now maintain consistent representations across minutes-long revisits without quadratic attention costs by leveraging a novel retrieval-based memory bank.
Reconstructing revisited scenes in streaming world models is now possible with constant memory, outperforming full-softmax attention by 30% on peak memory.
A single visual interface, action views, bridges the gap between video generation models and diverse robotic embodiments, enabling unified action modeling and execution.
Most current robotic manipulation policies fail to retain crucial historical information, leading to significant performance drops in partially observable tasks, but a novel memory framework boosts success rates across diverse challenges.
A 4B grounding foundation model achieves state-of-the-art visual grounding, outperforming larger models and boosting downstream robotic and autonomous driving performance by up to 24.8% in OOD settings.
Robots can adapt to unseen tasks purely through simulated imagination, boosting real-world manipulation success from 20% to 77% without requiring new human demonstrations or external environment rollouts.
Robot policies can now test and refine manipulation plans entirely within a learned visual simulator across ten distinct embodiments, gaining up to 21.4 percentage points in task success without any target-domain fine-tuning.
LLM agents abandon their own declared plans up to 78% of the time, but enforcing faithful execution via pattern-specific scaffolding nearly doubles benchmark success rates.
Robots no longer need human intervention to learn from their mistakes: foundation-model-guided exploration over modular skills can autonomously synthesize corrective rollouts that distill directly into low-level policy weights.
Multi-action weakly-coupled MDPs can shatter the theoretical $O(1/\sqrt{N})$ performance barrier to reach an $O(1/N)$ optimality gap simply by ditching heuristic priority ordering in favor of locally linear mean-field dynamics.
Multi-agent generative models can now provably satisfy complex, coupled hard constraints without centralized runtime coordination—even zero-shot scaling to agent counts never seen during training.
Causal generative models can now recover true joint distributions and counterfactual queries even when not a single complete record exists in the dataset.
Standard 5-second planning horizons miss over 90% of real-world passenger instructions, exposing a severe architectural failure mode in language-conditioned autonomous driving.
Predictive SSL can provably distinguish intrinsic process stochasticity from random observation noise—resolving a foundational puzzle behind why non-generative latent prediction captures genuine world dynamics.
Nonpreemptive queueing breaks standard myopic priority rules because optimal decisions fundamentally depend on the remaining horizon, yet certainty-equivalence Bellman planning can still match the theoretical lower bound at $\widetilde{O}(\sqrt{d/T})$ regret.
Closed-loop vision verification is no longer bottlenecked by intractable generative surrogates: compact stochastic world models cut parameter counts by 130× while resolving over 80% of previously unverified RGB state spaces.
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Video world models can now maintain persistent scene memory without ever-growing storage by warping and fusing low-rank features into a fixed-size HexMemory.
Co-evolved robot communication protocols fail to transfer robustly to the physical world, even with sensory calibration, revealing that ecological and navigational context is as crucial as the signaling process itself.
Decoupling structured semantics from frame-level actions and distilling autoregressive rollouts via shared, compressed memory tokens solves the long-standing trade-off between real-time responsiveness and multi-step drift in interactive world models.
Massive 100K-node routing problems can be solved in tens of seconds without learned heuristics simply by optimizing a compressed surrogate space to seed downstream solvers.
LLMs fail at chemical planning not from deficient reasoning capacity, but from an acute representation alignment bottleneck that disappears when structured through intermediate molecule and reaction abstractions.
Latent world models often fail at planning not because their dynamics predictions are inaccurate, but because distorted control geometry makes the latent optimization landscape hostile to sampling-based search.
Value-driven optimistic exploration has historically crumbled under deep network approximations, but diagnosing and repairing failure modes in uncertainty propagation allows pure model-free RL to beat complex model-based exploration baselines.
Model-based planning over dynamic spatial graphs cuts epidemic prediction error by 29% while guaranteeing the hard, per-period resource feasibility that standard RL controllers routinely violate.
Complex dynamical systems across diverse regimes share low-dimensional functional subspaces, allowing conditional mean operators for unseen distributions to be solved in closed form rather than learned from scratch.
Enforcing contractive dynamical structures onto learned latent disturbance representations delivers predictive disturbance accommodation without sacrificing the exponential stability guarantees essential for real-world adaptive control.
A single agent can disentangle true mechanisms from computational twin errors by jointly learning them, unlocking more accurate mechanistic discovery in complex biological systems.
A single diffusion operator can jointly predict intervention outcomes and select optimal measurements by treating decision quality as an interface property, outperforming Bayesian experimental design in cost-efficiency.
Predictive pretraining for parametric PDEs unlocks a 51.4% generalization improvement on unseen governing parameters by explicitly modeling latent dynamics geometry.
Iterative intent denoising enables decentralized multi-agent pathfinding agents to coordinate complex joint actions, overcoming the limitations of one-shot sampling and achieving near-perfect task completion.
Enterprise AI agents can now generate valid, high-fidelity tabular data for training and evaluation without needing access to sensitive schemas or real systems, bypassing critical industry bottlenecks.
