Background The European Commission is using the Digital Markets Act DMA to address Google’s dominant position in online search. Since March 2024, Google has be...
Conventional frame-based cameras capture rich contextual information but suffer from limited temporal resolution and motion blur in dynamic scenes. Event camera...
This paper focuses on the alignment of flow matching models with human preferences. A promising way is fine-tuning by directly backpropagating reward gradients ...
This paper presents a method for image relighting that enables precise and continuous control over multiple illumination attributes in a photograph. We formulat...
The rapid progress of Artificial Intelligence Generated Content (AIGC) tools enables images, videos, and visualizations to be created on demand for webpage desi...
High-level autonomous driving requires motion planners capable of modeling multimodal future uncertainties while remaining robust in closed-loop interactions. A...
Whether language models can systematically generalize remains actively debated. Yet empirical performance is jointly shaped by multiple factors such as training...
LLM-as-judge frameworks are increasingly used for automatic NLG evaluation, yet their per-instance reliability remains poorly understood. We present a two-prong...
Many SLT systems quietly assume that brief chunks of signing map directly to spoken-language words. That assumption breaks down because signers often create mea...
Video generation has advanced rapidly, with recent methods producing increasingly convincing animated results. However, existing benchmarks-largely designed for...
MLP is a heavily used backbone in modern deep learning (DL) architectures for supervised learning on tabular data, and AdamW is the go-to optimizer used to trai...
Over the past year, spatial intelligence has drawn increasing attention. Many prior works study it from the perspective of visual-spatial intelligence, where mo...
The reliability of a machine vision system for autonomous driving depends heavily on its training data distribution. When a vehicle encounters significantly dif...
We study post-training interpretability for Support Vector Machines (SVMs) built from truncated orthogonal polynomial kernels. Since the associated reproducing ...
The efficient spatial allocation of primitives serves as the foundation of 3D Gaussian Splatting, as it directly dictates the synergy between representation com...
3D policy learning promises superior generalization and cross-embodiment transfer, but progress has been hindered by training instabilities and severe overfitti...
Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans. Vision-language models (VLMs) have made tremendous p...
Node embeddings act as the information interface for graph neural networks, yet their empirical impact is often reported under mismatched backbones, splits, and...
This paper presents Prism, the first symbolic superoptimizer for tensor programs. The key idea is sGraph, a symbolic, hierarchical representation that compactly...
Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decision suppor...
The impossibility of simultaneously cloning non-orthogonal states lies at the foundations of quantum theory. Even when allowing for approximation errors, clonin...
It is increasingly important that LLM agents interact effectively and safely with other goal-pursuing agents, yet, recent works report the opposite trend: LLMs ...
Looped transformers promise test-time compute scaling by spending more iterations on harder problems, but it remains unclear which architectural choices let the...
Speculative decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose outputs that a stronger target model verif...
We study the problem of learning minimax policies in zero-sum matrix games. Fiegel et al. (2025) recently showed that achieving last-iterate convergence in this...
The LLM-as-a-judge paradigm has become the operational backbone of automated AI evaluation pipelines, yet rests on an unverified assumption: that judges evaluat...
Artificial Intelligence is increasingly introduced into systems engineering activities, particularly within requirements engineering, where quality assessment a...
Humor is one of the few cognitive tasks where getting the reasoning right matters as much as getting the answer right. While recent work evaluates humor underst...
Machine learning in high-stakes domains such as healthcare requires not only strong predictive performance but also reliable uncertainty quantification (UQ) to ...
Simulating group-level user behavior enables scalable counterfactual evaluation of merchant strategies without costly online experiments. However, building a tr...
Agentic workflows carry out complex tasks by orchestrating multiple large language models (LLMs) and tools. Serving such workflows at a target throughput with l...
Sparse attention has been proposed as a way to alleviate the quadratic cost of transformers, a central bottleneck in long-context training. A promising line of ...
LLMs are proving to be adept at machine translation although due to their generative nature they may at times overgenerate in various ways. These overgeneration...
This work simulates the developmental process of cortical neurogenesis, initiating from a single stem cell and governed by gene regulatory rules derived from mo...
This beta technical report asks how reusable experience should be represented so that it can function as effective test-time control and as a substrate for iter...
To navigate a space, the brain makes an internal representation of the environment using different cells such as place cells, grid cells, head direction cells, ...
Open-weight Small Language Models(SLMs) can provide faster local inference at lower financial cost, but may not achieve the same performance level as commercial...
Pareto optimization via evolutionary multi-objective algorithms has been shown to efficiently solve constrained monotone submodular functions. Traditionally whe...
Head straight for orbit with GeForce NOWhttps://www.nvidia.com/en-us/geforce-now/ — no space helmet required. PRAGMATA, Capcom’s long‑awaited sci‑fi action adve...
In data-sensitive domains such as healthcare, cross-silo federated learning (CFL) allows organizations to collaboratively train AI models without sharing raw da...
Vibe coding inherently assumes iterative refinement of LLM-generated code through feedback loops. While effective for conventional software tasks, its reliabili...
As agent systems move into increasingly diverse execution settings, trajectory-level safety evaluation and diagnosis require benchmarks that evolve with them. A...
The communication bottleneck in federated learning (FL) has spurred extensive research into techniques to reduce the volume of data exchanged between client dev...
In many real-world settings, problem instances that need to be solved are quite similar, and knowledge from previous optimization runs can potentially be utiliz...
Mixture-of-Experts (MoE) models have become the dominant architecture for large-scale language models, yet on-premises serving remains fundamentally memory-boun...
In modern data-streaming systems, alongside traditional programs, a new type of entity has emerged that can interact with streaming data: AI agents. Unlike trad...
Overview OpenAI’s Trusted Access for Cyber is built on a simple premise: advanced cyber capabilities should reach defenders broadly, but access must scale with...
Long video understanding is inherently challenging for vision-language models (VLMs) because of the extensive number of frames. With each video frame typically ...
