All entries

arXiv:2605.11993v1 Announce Type: new Abstract: Movie subtitle translation is inherently multimodal, yet text-only systems often miss visual cues needed to convey emotion, action, and social nuance, e

arXiv:2503.16072v4 Announce Type: replace-cross Abstract: Toxicity detection has become core safety infrastructure for online moderation, dataset filtering, and deployed language-model systems. Yet mo

arXiv:2605.11380v1 Announce Type: new Abstract: Learning transferable representations for electroencephalography (EEG) remains challenging because EEG signals are inherently multi-channel and non-stat

arXiv:2605.11744v1 Announce Type: new Abstract: Transformer-based large language models face severe scalability challenges in long-context generation due to the computational and memory costs of full-

arXiv:2605.05971v2 Announce Type: replace Abstract: Long-context language modeling is increasingly constrained by the Key-Value (KV) cache, whose memory and decode-time access costs scale linearly wit

arXiv:2605.12391v1 Announce Type: cross Abstract: We present a novel method for extracting moving objects from TESS data using machine learning. Our approach uses two stacked 3D U-Nets with skip conne

arXiv:2506.01568v3 Announce Type: replace Abstract: Being able to solve a task in diverse ways makes agents more robust to task variations and less prone to local optima. In this context, constrained

arXiv:2605.12312v1 Announce Type: new Abstract: Random delays weaken the temporal correspondence between actions and subsequent state feedback, making it difficult for agents to identify the true prop

NVIDIA's video analytics AI agents analyze and process large volumes of video data through natural language tasks to provide critical insights , powered by vision language models, large language model

arXiv:2304.10891v2 Announce Type: replace-cross Abstract: Transformer-based models are becoming a central paradigm in autonomous driving because they can capture long-range spatial dependencies, multi

arXiv:2605.12220v1 Announce Type: new Abstract: Safe autonomous agents and mobile robots need fast real time 3D perception, especially for vulnerable road users (VRUs) such as pedestrians. We introduc

arXiv:2605.11020v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) is typically formulated as maximizing entropy subject to matching the distribution of expert trajectories. Classica

arXiv:2605.12380v1 Announce Type: new Abstract: Reinforcement learning is structurally harder than supervised learning because the policy changes the data distribution it learns from. The resulting fr

Vercel introduced a trusted sources feature for deployment protection that allows teams to control which sources can trigger deployments, enhancing security by restricting deployment access to verifie

This post likely shares a workflow link from ComfyUI, a node-based interface for AI image generation and processing. Users can access and import the workflow through the provided link to use it in the

A user experiments with serious Star Trek: The Next Generation styled video content using LTX-2.3, a video generation model with available LoRA style adapters. LTX-2.3 is a major upgrade to video gene

TST has two moving parts that can be ablated independently: the input-side averaging and the output-side multi-hot cross-entropy. Both, on their own, outperform the matched-FLOPs baseline. Their effec

TST works in two phases. In phase 1, which covers the first 20-40% of training, the model reads contiguous bags of k tokens, with input embeddings averaged within each bag, and predicts the next bag o

arXiv:2605.11735v1 Announce Type: new Abstract: The efficient operation of modern cellular networks hinges on the accurate analysis of spatio-temporal traffic data. Mastering these patterns is essenti

arXiv:2510.03853v4 Announce Type: replace Abstract: We present UGround, a extbf{U}nified visual extbf{Ground}ing paradigm that dynamically selects intermediate layers across extbf{U}nrolled transforme

arXiv:2605.12237v1 Announce Type: new Abstract: Vision-Language Models (VLMs) increasingly operate on ultra-high-resolution (UHR) Earth observation imagery, yet they remain vulnerable to a severe scal

Robbie Whelan / Wall Street Journal: UK chip startup Fractile raised a $220M Series B led by Factorial Funds, Accel and Founders Fund to make specialized logic and memory chips for inference — Factori

arXiv:2605.11491v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become an effective paradigm for improving the reasoning ability of large language models. How

arXiv:2605.11911v1 Announce Type: new Abstract: Predictive Coding (PC) is an influential account of cortical learning. Much of recent work has focused on comparing PC to Backpropagation (BP) to find w

arXiv:2505.19770v5 Announce Type: replace-cross Abstract: We present a fine-grained theoretical analysis of the performance gap between two-stage reinforcement learning from human feedback~(RLHF) and

arXiv:2511.18152v3 Announce Type: replace Abstract: Deep unfolding networks (DUNs) combine the interpretability of model-based methods with the learning ability of deep networks, yet remain limited fo

arXiv:2605.12088v1 Announce Type: new Abstract: Multi-reference image generation aims to synthesize images from textual instructions while faithfully preserving subject identities from multiple refere

arXiv:2605.12169v1 Announce Type: new Abstract: With the recent surge of generative models, diffusion-based approaches have become mainstream for view synthesis tasks, either in an explicit depth-warp

arXiv:2605.11059v1 Announce Type: cross Abstract: We study the large-depth limit of transformers trained with AdamW, by modelling the hidden-state dynamics as an interacting particle system (IPS) coup

arXiv:2605.11856v1 Announce Type: cross Abstract: Multimodal large language models are increasingly expected to perform thinking with images, yet existing visual latent reasoning methods still rely on

arXiv:2605.11170v1 Announce Type: new Abstract: Noise-based certified machine unlearning currently faces a hard ceiling: the noise magnitude required to certify unlearning typically destroys model uti

arXiv:2605.11710v1 Announce Type: cross Abstract: Object-centric representations promise a key property for few-shot learning: Rather than treating a scene as a single unit, a model can decompose it i

arXiv:2605.11258v1 Announce Type: cross Abstract: Autonomous science promises to augment scientific discovery, particularly in complex fields like biomedicine. However, this requires AI systems that c

arXiv:2605.11634v1 Announce Type: new Abstract: Although Vision Language Models (VLMs) have seen tremendous progress across all kinds of use cases, they still fall behind in answering questions regard

arXiv:2605.11166v1 Announce Type: new Abstract: Political and social identities structure how people evaluate political information, a finding decades deep in political science and routinely discarded

arXiv:2605.11782v1 Announce Type: new Abstract: Visual impairment affects hundreds of millions of people worldwide, severely limiting their ability to navigate urban environments safely and independen

arXiv:2605.11131v1 Announce Type: new Abstract: Accurate medical image segmentation is an integral part of the medical image analysis pipeline that requires the ability to merge local and global infor

Ollama v0.23.4 is a release version of Ollama, a tool for running large language models locally. This patch release likely includes bug fixes, performance improvements, and refinements to existing fea

arXiv:2605.11865v1 Announce Type: cross Abstract: Standard Bradley--Terry (BT) reward models are limited when human preferences are pluralistic. Although soft preference labels preserve disagreement i

arXiv:2605.11196v1 Announce Type: new Abstract: Linear attention reduces the quadratic cost of softmax attention to O(T), but its memory state grows as O(T) in Frobenius norm, causing progressive inte

arXiv:2605.11913v1 Announce Type: new Abstract: Differentiable vector graphics have enabled powerful gradient-based optimization of vector primitives directly from raster images. However, existing fra

Veeam Software Group GmbH today debuted new features that will make it easier for enterprises to back up their records and protect them from hackers. Some of the capabilities are rolling to the compan

Veeam Software Group GmbH used VeeamON 2026 in New York City this week to punctuate its shift from “the backup company” to a data and artificial intelligence trust platform for the agentic era. With a

arXiv:2605.11334v1 Announce Type: cross Abstract: LLM-as-Judge systems are widely deployed for automated evaluation, yet practitioners lack reliable methods to know when a judge's verdict should be tr

arXiv:2605.10974v1 Announce Type: new Abstract: Certified verification of transformer attention requires bounding the softmax function over interval constraints on the pre-softmax scores. Existing ver

arXiv:2605.11864v1 Announce Type: cross Abstract: Listwise reranking is a key yet computationally expensive component in vision-centric retrieval and multimodal retrieval-augmented generation (M-RAG)