Claims Browser

SpatialCrafter outperforms state-of-the-art methods for explorable image-to-scene generation and mitigates long-term drift while remaining robust under rapid camera motion and extreme viewpoint changes

"Extensive experiments on both synthetic and real-world datasets show that SpatialCrafter outperforms state-of-the-art methods, mitigates long-term drift, and remains robust and consistent under rapid camera motion and extreme viewpoint changes."

Using a global 3D proxy with Point-anchored Sparse Structure Flow enables spatially aligned and geometrically consistent 3D proxy generation for high-fidelity image-to-scene generation

"we propose a Point-anchored Sparse Structure~(PaSS) Flow module that predicts a spatially aligned and geometrically consistent 3D proxy"

Google DeepMind is conducting the world's first double-blind AI evaluations

"Piloting the world's first double-blind AI evaluations"

Nvidia is acquiring Hugging Face for $12.9 billion

"Nvidia is buying Hugging Face for $12.9B (via The Information)."

Quotient couplings can be lifted to aligned representatives without additional cost

"quotient couplings can be lifted to aligned representatives without additional cost"

Structure-aware couplings substantially improve sample quality at small integration budgets in graph and molecular generation

"these structure-aware couplings substantially improve sample quality at small integration budgets"

The proposed molecular models remain competitive under conventional many-step sampling when scaled up

"our scaled-up molecular models remain competitive under conventional many-step sampling"

ClusterAttention is a general training-free speedup method for bidirectional attention layers that uses fast recursive clustering adapting to the geometry of keys and queries

"This paper introduces ClusterAttention, a general training-free speedup of bidirectional attention layers. Existing sparse attention methods either rely on structure in the input, such as order in language or spatial proximity in images, or use slow clustering processes amortized over several forward passes. ClusterAttention instead uses a fast recursive clustering method that adapts to the geometry of the keys and queries in each attention head to produce useful clusters."

ClusterAttention achieves the same latency per query-key interaction as dense attention on GPUs by using fixed power-of-two cluster sizes

"We utilize this by setting all clusters to be a fixed size that is a power of two, allowing the block-sparse attention to run at the same latency per query-key interaction as dense attention on GPUs."

ClusterAttention speeds up TabPFN-3 by two to six times while retaining at least 99% of dense accuracy on large-scale tabular data

"On large-scale tabular data ClusterAttention speeds up TabPFN-3 arXiv:2605.13986 by two to six times, while retaining at least 99% of the dense accuracy."

ClusterAttention is the first training-free method that can be successfully applied in the setting of unstructured input and a single forward pass

"To our knowledge, it is the first training-free method that can be successfully applied in the setting of unstructured input and a single forward pass."

For video generation with Wan 2.1-14B T2V, ClusterAttention achieves output closer to dense attention and a larger speedup (1.8x versus 1.4x) compared to SVOO without offline calibration

"For video generation with Wan 2.1-14B T2V arXiv:2503.20314 , ClusterAttention achieves output closer to dense attention and a larger speedup (1.8x versus 1.4x) compared to SVOO arXiv:2603.18636 , a leading method developed specifically for this domain, both run without offline calibration."

Tight clusters can lead to larger errors than random clusters in sparse attention, but when excluded clusters are compensated through their centroids, the error shrinks with tighter clusters

"We also derive an expression for the output error in sparse attention, that explains the counterintuitive experimental finding that tight clusters can lead to larger errors than random clusters. We then derive the error when excluded clusters are compensated through their centroids, and show that this error shrinks with tighter clusters."

Image-to-video generation models have made remarkable progress in subject consistency and temporal coherence, enabling high quality video synthesis in recent years

"In recent years, image-to-video (I2V) generation models have made remarkable progress in subject consistency and temporal coherence, enabling high quality video synthesis."

I2V systems have a temporal vulnerability where unsafe semantics can emerge from semantic composition over time rather than from a single frame

"we investigate three attack scenarios and uncover a temporal vulnerability in I2V systems: unsafe semantics may emerge not from a single frame, but from semantic composition over time."

TempJail improves attack success rate over prior state-of-the-art methods by 23.3% under GPT-5.2 evaluation and 22.0% under human evaluation on commercial models

"Experiments on closed-source commercial models, including Kling, Seedance, Veo and PixVerse, show that TempJail improves attack success rate over prior state-of-the-art methods by 23.3\% under GPT-5.2 evaluation and 22.0\% under human evaluation."

Two key challenges in temporal jailbreak attacks are temporal abstraction and semantic camouflage

"We further identify two key challenges in such attacks: temporal abstraction and semantic camouflage."

Representation learning has reached good performances as a pretraining method for downstream tasks and as a first step towards unsupervised speech modeling

"Representation learning has attracted great atten- tion and managed to reach good performances as a pretraining method for downstream tasks or as a first step towards unsu- pervised speech modeling"

Pre-training with limited data exhibits different scaling behavior than web-scale language modeling, where increasing parameters beyond an optimal point causes overfitting rather than improved performance

"Pre-training under limited data requires a different view of scaling than web-scale language modeling. With a fixed data budget but relatively abundant compute, increasing parameter count helps only up to an optimal scale; beyond that point, models overfit and generalization worsens."