Search papers, labs, and topics across Lattice.
100 papers published across 13 labs.
An 8x token reduction and 11.1x latency improvement in video generation is achieved by training autoencoders that are generation-aware, not just reconstruction-aware.
Current image generation models struggle with rendering dense, multi-region text, with fidelity dropping by over 14 points when clarity is prioritized.
A quadtree visual tokenizer slashes token count for autoregressive image generation by adaptively focusing representational power on intricate image regions.
Stored patch vectors from visual document retrieval systems can be inverted to reconstruct original pages, revealing sensitive information and enabling source page identification with high accuracy.
Decoupling denoising and context writing parameters in autoregressive video models unlocks unprecedented long-horizon generation capabilities, extending continuous output to 24 hours from just 5-second training rollouts.
An 8x token reduction and 11.1x latency improvement in video generation is achieved by training autoencoders that are generation-aware, not just reconstruction-aware.
Current image generation models struggle with rendering dense, multi-region text, with fidelity dropping by over 14 points when clarity is prioritized.
A quadtree visual tokenizer slashes token count for autoregressive image generation by adaptively focusing representational power on intricate image regions.
Stored patch vectors from visual document retrieval systems can be inverted to reconstruct original pages, revealing sensitive information and enabling source page identification with high accuracy.
Decoupling denoising and context writing parameters in autoregressive video models unlocks unprecedented long-horizon generation capabilities, extending continuous output to 24 hours from just 5-second training rollouts.
Global point set alignment is solved exactly by finding the farthest vertex of a novel "permutation polygon," resolving a long-standing open problem in assignment geometry.
Generating 3D scenes where objects not only look plausible but also physically fit together, even when interactions are hidden.
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.
Evolutionary search discovers a 26x smaller, yet more accurate, model for satellite-based chlorophyll prediction than expert-designed alternatives.
A 300K-sample dataset and diagnostic benchmark unlock the challenging task of generating product-preserving, narrative-driven advertisement videos.
Fresh, shallow network activations can correct diffusion model feature forecasts, unlocking significant speedups and improved fidelity without any parameter updates.
Probabilistic forecasting models can now achieve 65% CRPS reduction with drastically fewer samples by directly learning the distribution shift between successive predictions.
Generative AI now predicts complex 3D urban wind and temperature fields with LES-level accuracy in seconds, unlocking rapid, turbulence-aware urban design.
Fingertip touch dynamics, not just vision, unlock a 10.5% boost in dexterous robotic grasp success.
Transformers learn translation, rotation, and scale invariance through distinct, identifiable mechanisms, paving the way for structured equivariant function learning.
Inertial activity recognition models can now learn rich, global motion structure from unlabeled data by discovering and grouping motion primitives, achieving state-of-the-art performance with orders of magnitude less data.
Diffusion models can achieve arbitrarily low error amplification at low noise levels, decoupling sampler performance from worst-case Lipschitz bounds.
Spatially dependent data breaks standard conformal prediction; this method achieves exact coverage and oracle efficiency by sequentially whitening residuals, even with nonrandom sampling.
Kinetic Langevin diffusions slash posterior sampling time for imaging inverse problems by 10x while maintaining reconstruction quality, even with complex diffusion model priors.
Embodied agents can now remember objects they never touched by tracking their resting locations and inferring moves from repeated evidence, boosting downstream task accuracy by over 10%.
Interactive video generation systems can now robustly handle latent dependencies by transforming unstructured inputs into executable constraints, achieving a 10.3-point average improvement.
A single generative model recovers temporally consistent 3D hand motion from video by jointly learning 2D-3D pose dynamics, bypassing unreliable per-frame estimates.
Sparse video reasoning can outperform dense observation models by learning to extract crucial temporal evidence from limited frames, even without explicit VQA annotations.
Interactive video generation can be sped up by 1.4x by intelligently caching transformer residuals based on detected control transitions, without sacrificing quality.
A single image can now yield complete, high-fidelity 3D scenes, indoors and out, by adapting object-centric priors with adaptive chunking and explicit 2D-3D correspondence.
Inserted objects can now be made "alive" and interact realistically within videos, solving a long-standing challenge in video editing.
Diffusion models reveal feature generation order quantitatively, exposing architectural differences and suggesting ordered training benefits.
Deterministic dermoscopic preprocessing actually degrades performance in leakage-controlled evaluations, suggesting current automated analysis might be over-engineering for minimal gains.
Rare event probabilities in diffusion models can now be estimated orders of magnitude faster, unlocking their use in high-stakes scientific simulation.
The geometric intuition behind SVD is revealed as the direct algorithmic engine driving PCA, kernel methods, and PageRank, unifying their proofs and implementations.
Edge CNN adaptation can now achieve near-zero activation memory overhead by decoupling backward computations from full-width layer inputs.
A novel ResNet-BiLSTM model achieves 85.78% F1 for multi-label perceptual bug detection in video games, proving temporal modeling is key for identifying diverse visual glitches.
Privacy-preserving federated learning achieves segmentation performance on skin lesions comparable to centralized methods, without centralizing sensitive patient data.
Unfrozen video-language models implicitly know when they've seen enough evidence, a signal that can be extracted and used to boost answer accuracy by nearly 10% without retraining.
Multimodal geometric scores are surprisingly sensitive to the *direction* of modality degradation, not just its magnitude, revealing a critical geometric dependency previously overlooked.
Pinpoint the exact birth-death plane regions driving topological data differences with guaranteed error control, moving beyond global statistical tests.
Interactive AI worlds can now speak and react in real-time with spatially accurate sound, bridging the sensory gap in generative environments.
A 48.9M parameter robot policy matches systems 163x larger by prioritizing fine-grained, compressed visual representations over scale or generative priors.
Latent-based diffusion model watermarking is fundamentally brittle to geometric distortions, with a theoretically derived perturbation bound explaining its limited robustness.
Satellite images are not $n^2$ independent samples; their effective sample size plummets to $\Theta(n^2/r^2)$ due to spatial correlation, fundamentally altering generalization analysis.
End-to-end driving models can learn interpretable safety costs over reachable futures, drastically reducing collisions without fine-tuning.
Existing autoregressive post-training weights, often discarded after diffusion model conversion, can be directly applied to significantly boost dLLM performance, even outperforming dedicated diffusion post-training when combined.
Generative flow matching directly refines 4D hand-object interactions during synthesis, integrating physical constraints and 2D evidence without post-hoc optimization.
Robot world models can now predict photorealistic RGB *and* accurate metric depth, bridging the gap between visually plausible rollouts and geometrically consistent 3D environments.
Automated code generation from 3DGS papers not
Generating molecular graphs in a pretrained VAE's latent space via flow matching unlocks significant efficiency gains and enables property-guided generation without retraining.
A novel initialization technique for diffusion samplers bypasses initial steps by directly sampling a softened clean-space posterior, achieving order-of-magnitude gains on rare-mode inverse problems.
LLM interrogations reveal diffusion model's internal tokens capture scene semantics surprisingly early, even without explicit prompts, and correlate with generation quality.
Causal representation learning now achieves block disentanglement under weaker intervention assumptions, directly enabling label-free visual state estimation in embodied agents.
Hierarchical JEPAs unlock efficient long-horizon visual planning by learning distinct latent spaces for different temporal scales, dramatically boosting success rates and reducing compute.
Aligning DLM generation with reference distributions via feature-space MMD significantly boosts performance on perplexity and reasoning tasks without retraining the entire model.
Bosonic Gaussian states can be identified with unprecedented accuracy using a novel warm-start strategy combined with non-Gaussian measurements, outperforming classical Gaussian estimation.
Learned corner selection for cooperative guards dramatically boosts deployment efficiency and agent utilization in partially observable environments, outperforming established methods without sacrificing formal guarantees.
Local AI models now predict user well-being from passive computer activity, offering explainable feedback directly within a workplace sensing application.
Deterministic weather foundation models can be efficiently transformed into powerful probabilistic forecasters, generating skillful 15-day forecasts in minutes.
Nighttime anomaly detection is significantly enhanced by fusing M-band and DNB satellite signals, achieving higher accuracy and broader applicability than thermal-only methods.
Explicitly modeling the temporal rate of change in spatio-temporal graph models unlocks superior prediction accuracy and pattern preservation for propagating phenomena.
Physics-based secure key distribution can now sustainably support federated learning for medical imaging, reducing key material needs by 35x without sacrificing accuracy.
Most reported gains in crop disease classification from environmental sensors are illusory, driven by simple session memorization rather than genuine multimodal learning.
Unreported sinkhole locations are effectively treated as right-censored, unlocking continuous, time-aware susceptibility predictions from multimodal data.
Wi-Fi sensing accuracy jumps to 92.83% by decomposing CSI into trajectory-guided tokens that capture complex temporal dynamics.
Generative image editing sliders now offer a smooth, predictable perceptual journey, eliminating jarring jumps and imperceptible stretches.
Flowcharts break silently across aspect ratios, but a new agentic pipeline with VLM critics now guarantees connectivity and editability.
Robots can now learn complex manipulation tasks from a single demonstration video by recursively accessing fine-grained sub-event details, boosting success rates by up to 9%.
Generative models are becoming so good at mimicking real images that content alone is no longer a reliable indicator of authenticity, necessitating a shift to reconstruction-based verification.
NeuroCBIR achieves near-perfect subject re-identification in 3D brain MRIs and encodes clinically relevant information for downstream tasks, all within seconds per scan.
Individual token selection and metadata caching unlock sparse attention for diffusion transformers without sacrificing generation quality, achieving up to $2.32\times$ speedups.
Most generative image models fail to produce usable automotive instructional imagery, with only two Google-based generators achieving >14% usable outputs, underscoring the critical role of the image generator over prompt engineering.
Unknown traffic objects are detected with 2.6% higher recall than RT-DETR by fusing semantic and visual prompts symmetrically, enabling real-time open-world detection without NMS.
Folding multi-branch redundancy slashes speculative decoding costs in diffusion LLMs, unlocking up to 2x faster generation.
Directly learning robot action policies from raw video without explicit action labels unlocks scalable world action models.
Autoregressive video models can now perform explicit, goal-directed reasoning by jointly predicting the final outcome and intermediate steps, achieving state-of-the-art performance with 75% fewer training steps.
Current omnimodal video embeddings struggle with physical reasoning, yet specific physics can be recovered and leveraged to significantly improve the physical fidelity of generated videos.
Composing aesthetic preference with explicit rubric rewards unlocks substantial gains in text-to-image generation, outperforming existing open-source models by a significant margin.
State-of-the-art text-to-image models, despite photorealism, still exhibit pervasive world-consistency failures that existing metrics miss.
Generative 3D reconstruction now scales from objects to cityscapes with arbitrary resolution, matching per-scene optimization quality without per-scene tuning.
Imperfect diffusion models can now achieve exact target distribution sampling at inference time by correcting inherent path and endpoint errors.
Persistent world models can now track objects even when they leave the agent's view, thanks to a novel actor-observer decoupling mechanism.
Real-time 3D Gaussian avatar animation is now achievable at 60fps on mobile by distilling complex neural inference into a linear blendshape approximation, reducing CPU cost by 1000x.
Explicitly disentangling and structuring spatial geometry latents via CR-GEO and routed optimization unlocks significantly more effective 3D reasoning from 2D images, pushing SPAR-Bench and SPBench performance to new heights.
Adversarial distillation achieves state-of-the-art few-step visual generation by learning log-density ratios directly via classification, eliminating the need for auxiliary score fitting.
Standard UAD evaluations for brain MRI are brittle, with a single axis-order mismatch capable of collapsing voxel AUROC from 0.873 to 0.583, while slice-level AUROC remains largely unaffected.
Achieve precise, intuitive 3D control over video generation by moving rigid object meshes, eliminating depth ambiguity and viewpoint inconsistencies.
Dynamically recomputing embeddings at each denoising step in diffusion transformers halves training compute while achieving state-of-the-art FID scores.
Topology-preserving 3D generation now achieves 98% fewer tokens and 58.5% faster inference by encoding shapes as overlapping slice latents instead of expensive voxels.
Multimodal sampling with Wasserstein gradient flows and forward diffusion is fundamentally limited by slow mixing, requiring exponential time to bridge well-separated modes.
A single CatBoost model detects wildfires 26 times earlier than GOES FDC and VIIRS, and achieves significantly higher F1 scores, even at night.
Adversarial domain adaptation that explicitly conditions on predicted weather unlocks robust street-view perception even with non-street-view training data.
Malware analysis just got a new tool: S4 models unlock long-range dependency detection in execution flows, outperforming traditional deep learning for multi-class classification.
A novel Four-Dimensional Architecture Selection Framework correctly recommends 70% of deployed autonomous driving architectures, offering critical guidance for system design.
Adversarial training on Fourier phase perturbations unlocks over 20% domain generalization gains in medical imaging by decoupling spatial structure from domain-specific textures.
Discrete neural samplers achieve fixed-point convergence and drastically reduce mode collapse by leveraging Gibbs sampling within a masked diffusion framework.
Diffusion models can achieve fairness and diversity simultaneously without sensitive attribute labels by using a frozen model adapter and a vision-language embedding space.
Geographic context unlocks precise PCS anomaly detection, revealing infrastructure health issues previously missed by purely visual methods.
Artists can now directly sculpt 3D object motion relative to a moving camera, overcoming the inherent ambiguity of 2D controls in video generation.
Most video generation models fail to render text accurately, with even top performers exhibiting a 25% word error rate on a new benchmark designed for this critical oversight.
Current egocentric video generation models fail to execute multi-step manipulation tasks realistically, often skipping crucial actions and exhibiting poor physical dynamics.
Transformer and RNN tractography models achieve state-of-the-art performance by introducing a novel generation-validation phase for streamline-level supervision, overcoming the limitations of local loss functions.
End-to-end learning of temporally predictable latent spaces for world models unlocks superior future scene understanding without the need for separate representation and prediction training stages.
Human mobility generation can now be achieved without target trajectories by learning relative movement transitions, not absolute destinations, across cities.
