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Papers for March 8, 2026

10 papers found

Leif Van Holland, Domenic Zingsheim, Mana Takhsha, Hannah Dröge, Patrick Stotko, Markus Plack, Reinhard Klein 3/5/2026 arxiv

computer vision

High-quality 3D streaming from multiple cameras is crucial for immersive experiences in many AR/VR applications. The limited number of views - often due to real-time constraints - leads to missing information and incomplete surfaces in the rendered images. Existing approaches typically rely on simpl...

Keywords: transformer, inpainting, real-time, 3D streaming, multi-camera, spatio-temporal embeddings, adaptive patch selection, AR/VR

Junjie Fang, Wendi Chen, Han Xue, Fangyuan Zhou, Tian Le, Yi Wang, Yuting Zhang, Jun Lv, Chuan Wen, Cewu Lu 3/5/2026 arxiv

robotics

Scaling imitation learning is fundamentally constrained by the efficiency of data collection. While handheld interfaces have emerged as a scalable solution for in-the-wild data acquisition, they predominantly operate in an open-loop manner: operators blindly collect demonstrations without knowing th...

Keywords: imitation learning, data collection, AR visual foresight, remote inference, online finetuning, robot-free, smartphone, policy iteration

Shai Yehezkel, Shahar Yadin, Noam Elata, Yaron Ostrovsky-Berman, Bahjat Kawar 3/5/2026 arxiv

computer vision

Recent diffusion models enable high-quality video generation, but suffer from slow runtimes. The large transformer-based backbones used in these models are bottlenecked by spatiotemporal attention. In this paper, we identify that a significant fraction of token-to-token connections consistently yiel...

Keywords: CalibAtt, sparse attention, calibrated sparsity, diffusion models, video generation, spatiotemporal attention, transformer, runtime acceleration

Shangwen Sun, Alfredo Canziani, Yann LeCun, Jiachen Zhu 3/5/2026 arxiv

natural language processing

We study two recurring phenomena in Transformer language models: massive activations, in which a small number of tokens exhibit extreme outliers in a few channels, and attention sinks, in which certain tokens attract disproportionate attention mass regardless of semantic relevance. Prior work observ...

Keywords: massive activations, attention sinks, Transformers, pre-norm, implicit parameters, ablation study, interpretability

Lizhi Yang, Ryan M. Bena, Meg Wilkinson, Gilbert Bahati, Andy Navarro Brenes, Ryan K. Cosner, Aaron D. Ames 3/5/2026 arxiv

machine learning

Traditional safety-critical control methods, such as control barrier functions, suffer from semantic blindness, exhibiting the same behavior around obstacles regardless of contextual significance. This limitation leads to the uniform treatment of all obstacles, despite their differing semantic meani...

Keywords: Poisson safety function, Laplace guidance field, control barrier function, model predictive control, semantic perception, instance segmentation, persistent object tracking, multi-sensor fusion

Khai Nguyen, Petros Ellinas, Anvita Bhagavathula, Priya Donti 3/5/2026 arxiv

machine learning

To scale the solution of optimization and simulation problems, prior work has explored machine-learning surrogates that inexpensively map problem parameters to corresponding solutions. Commonly used approaches, including supervised and self-supervised learning with either soft or hard feasibility en...

Keywords: amortized optimization, cheap labels, self-supervised refinement, supervised pretraining, surrogate models, nonconvex optimization, power-grid operation, stiff dynamical systems

Helena Casademunt, Bartosz Cywiński, Khoi Tran, Arya Jakkli, Samuel Marks, Neel Nanda 3/5/2026 arxiv

machine learning

Large language models sometimes produce false or misleading responses. Two approaches to this problem are honesty elicitation -- modifying prompts or weights so that the model answers truthfully -- and lie detection -- classifying whether a given response is false. Prior work evaluates such methods ...

Keywords: censored LLMs, honesty elicitation, lie detection, Qwen3, few-shot prompting, linear probes, fine-tuning, DeepSeek R1

Balakumar Sundaralingam, Adithyavairavan Murali, Stan Birchfield 3/5/2026 arxiv

robotics

Effective robot autonomy requires motion generation that is safe, feasible, and reactive. Current methods are fragmented: fast planners output physically unexecutable trajectories, reactive controllers struggle with high-fidelity perception, and existing solvers fail on high-DoF systems. We present ...

Keywords: cuRoboV2, B-spline, TSDF, ESDF, signed distance field, GPU-native, topology-aware kinematics, differentiable inverse dynamics

Siddharth Boppana, Annabel Ma, Max Loeffler, Raphael Sarfati, Eric Bigelow, Atticus Geiger, Owen Lewis, Jack Merullo 3/5/2026 arxiv

machine learning

We provide evidence of performative chain-of-thought (CoT) in reasoning models, where a model becomes strongly confident in its final answer, but continues generating tokens without revealing its internal belief. Our analysis compares activation probing, early forced answering, and a CoT monitor acr...

Keywords: chain-of-thought, performative reasoning, activation probing, attention probing, early exit, MMLU, GPQA-Diamond, model interpretability

Shahriar Noroozizadeh, Xiaobin Shen, Jeremy C. Weiss, George H. Chen 3/5/2026 arxiv

machine learning

Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as precision medicine and individualized policy-making. Yet, the survival analysis setting poses unique challenges for HTE estimation due to censoring, unobserved counterf...

Keywords: heterogeneous treatment effect, survival analysis, right-censoring, benchmark, causal inference, synthetic datasets, semi-synthetic, twin study
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