Search papers, labs, and topics across Lattice.
83 papers published across 10 labs.
A simple visual harness transforms a general multimodal model into a perfect reasoner on ARC-AGI-3, completing all games with drastically fewer actions than humans.
Aligning video generation models to human intent and physical constraints is significantly harder than for text or images, necessitating a diverse suite of post-training techniques beyond simple fine-tuning.
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.
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.
A single-word instruction change can tank VLA performance by 98%, but a simple LLM-based rephrasing pipeline boosts success by up to 27% without retraining.
Current multimodal agents can build sophisticated perception-control pipelines but fail to reliably execute physical tasks, losing object states and mistaking unfinished goals for completion.
Generating 3D scenes where objects not only look plausible but also physically fit together, even when interactions are hidden.
A 300K-sample dataset and diagnostic benchmark unlock the challenging task of generating product-preserving, narrative-driven advertisement videos.
Fingertip touch dynamics, not just vision, unlock a 10.5% boost in dexterous robotic grasp success.
Frontier multimodal agents struggle to autonomously acquire and utilize task-relevant information in unfamiliar physical environments, failing 77% of the time on a new exploration benchmark.
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%.
A single 500-hour visual-tactile dataset boosts zero-shot contact prediction from 13% to 38% IoU and quadruples robot manipulation success rates, proving scale and consistency are paramount for embodied learning.
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.
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.
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.
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.
A 48.9M parameter robot policy matches systems 163x larger by prioritizing fine-grained, compressed visual representations over scale or generative priors.
LLM pruning can be task-aware: DIPrune's dual importance scoring jointly optimizes static feature saliency and dynamic semantic evolution, outperforming prior methods on LLaVA and Qwen-VL.
Generative flow matching directly refines 4D hand-object interactions during synthesis, integrating physical constraints and 2D evidence without post-hoc optimization.
Frozen medical AI models can now continuously learn and improve from real-world deployment experience without requiring weight updates or risking catastrophic forgetting.
Language instructions now directly control real-time, high-fidelity 360° video generation, enabling fluid interactive exploration from a single panorama.
MLLMs can now natively weave elastic visual representations, adaptively allocating detail to save 43% of tokens while preserving near-perfect performance.
Accelerating VLA models without sacrificing task success is now certifiable, guaranteeing over $85\%$ of reference performance with $10\times$ speedups.
Even state-of-the-art MLLMs fail to grasp intuitive visual reasoning, scoring less than 60% on tasks humans solve effortlessly at a glance.
OmniLLMs can now confess which specific evidence tokens rely on, enabling targeted hallucination correction without retraining.
Predicting subskill transition timing unlocks reliable execution of 4x longer action chunks in VLA models, drastically cutting robotic idle time by 5x.
Dual-arm robots can generalize to unseen task compositions by combining skill-conditioned parameters and arm-wise attention, achieving 39% success on physical hardware.
LLM interrogations reveal diffusion model's internal tokens capture scene semantics surprisingly early, even without explicit prompts, and correlate with generation quality.
LLM agents can now dynamically curate multimodal memory on-demand, achieving optimal performance-cost-latency trade-offs by intelligently choosing between pre-computed and runtime-generated evidence.
Explicitly linking multimodal patient data to biomedical knowledge graphs unlocks significant gains for clinical LLMs, with performance plummeting when these crucial connections are removed.
Multi-omics cancer analysis becomes practical even with missing data, achieving state-of-the-art reconstruction and classification via a novel representation learning approach.
Most reported gains in crop disease classification from environmental sensors are illusory, driven by simple session memorization rather than genuine multimodal learning.
Faithful concept bottleneck explanations are impossible without including all concepts, but probabilistic modeling and group lasso enable compact, faithful explanations.
Unreported sinkhole locations are effectively treated as right-censored, unlocking continuous, time-aware susceptibility predictions from multimodal data.
Generative image editing sliders now offer a smooth, predictable perceptual journey, eliminating jarring jumps and imperceptible stretches.
Spatial directionality, a critical but overlooked capability, is readily recoverable from frozen VLM encoders with a simple, parameter-free readout.
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.
A single verification prompt is insufficient; learning a reliability score from multiple, complementary perspectives unlocks robust self-verification for VLMs.
Most web-browsing LLM agents struggle to find obscure, multi-hop information across diverse modalities and languages, even when explicitly designed for web interaction.
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.
Multimodal agents can achieve >90% reasoning latency reduction and comparable task performance by selecting from pre-defined reasoning steps instead of generating them freely.
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.
Robot agents achieve 14% higher success rates and use 65% fewer tokens by composing primitives and performing local retries within an interactive code execution framework.
Dynamically recomputing embeddings at each denoising step in diffusion transformers halves training compute while achieving state-of-the-art FID scores.
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.
Unlearning PII from VLMs is now feasible without sacrificing retained knowledge by directly manipulating attention-value representations.
Adversarial domain adaptation that explicitly conditions on predicted weather unlocks robust street-view perception even with non-street-view training data.
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.
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.
Multimodal RL models trained without visual input can still achieve high accuracy on visual reasoning tasks when images are present at inference, but prolonged visual training can paradoxically erode grounding.
A simple visual harness transforms a general multimodal model into a perfect reasoner on ARC-AGI-3, completing all games with drastically fewer actions than humans.
Semantic communication via VLM orchestrators unlocks robust, long-horizon coordination for multi-robot systems, outperforming prior single-robot approaches on complex manipulation tasks.
Synthetic scenes with automatically grounded object identities unlock substantial real-world perceptual gains in MLLMs via on-policy self-distillation.
A unified proactive generation framework enables LLMs to form reusable factual memories from streaming video without compromising real-time perception.
VideoLLMs forget up to 40% of temporal context by the output layer, but a simple inference-time injection can recover it.
A 0.9B parameter omni-modal embedder achieves state-of-the-art text retrieval quality by distilling a frozen text backbone's embeddings into new modalities, bypassing the common modality-extension performance drop.
Unified LLMs can now achieve state-of-the-art 3D generation and understanding by treating geometry as an explicit sequence of sparse octree tokens, without compromising core language abilities.
Ultra-long video grounding is solved by evolving reusable agent skills and tools from VLM reasoning, bypassing parameter updates and separate planning models.
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.
State-of-the-art VLMs struggle to execute scientific research workflows, revealing a critical gap in their ability to interact with specialized software and produce verifiable results.
Multimodal models can achieve significant image generation improvements by learning from their own critiques during test-time compute, effectively acting as their own recursive self-improvement mechanism.
Visual perception agents can achieve significant performance gains by actively seeking information beyond a single glance, outperforming static models by up to 30 points on challenging benchmarks.
Clinical MLLMs can be significantly improved by optimizing for AUROC via pairwise instance rankings, reversing accuracy-based prompt evolution's performance degradation.
Standard image ViTs, when adapted with masked Siamese learning on "super images," unlock state-of-the-art video representation learning with orders of magnitude less pretraining.
Current VLMs, despite impressive general capabilities, fail to grasp the nuanced social dynamics required for robot navigation, lagging behind simple rule-based systems.
VLA models fail not by ignoring instructions, but by misbinding them to incorrect action trajectories, a problem solved by training with counterfactually paired demonstrations.
Spherical interpolation between old and new model embeddings recovers backward-compatibility for vision-language retrieval, outperforming orthogonal alignment by finding optimal query directions in the interstitial space.
Aligning video generation models to human intent and physical constraints is significantly harder than for text or images, necessitating a diverse suite of post-training techniques beyond simple fine-tuning.
A single continuous flow model now unifies language and vision generation, outperforming hybrid and discrete models under matched budgets and achieving strong benchmarks with drastically less data.
VLMs struggle to assess physical plausibility, failing to bridge visual recognition with genuine physical understanding even when perception and reasoning are intact.
Immersive AR/VR experiences can finally have stereo audio that dynamically tracks visual sources, thanks to a novel framework that binds visual motion to sound localization.
Visual grounding can now be achieved at flash speed with precise localization by treating it as parallel evidence extraction, not sequential generation.
Unsupervisedly discover and generate specific subtypes of visual data, like distinct eyewear styles or retinal diseases, by disentangling salient factors from common content within foundation model latents.
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.
