Knowledge catalogue
arXiv:2605.25661v2 Announce Type: replace Abstract: Current mainstream methods of aligning diffusion models with human preferences typically employ VLM-based reward models. However, these reward model
arXiv:2606.25953v1 Announce Type: cross Abstract: Existing object-aware SLAM systems force a trade-off between real-time performance, multi-class support, and the generation of high-fidelity, semantic
arXiv:2606.25254v1 Announce Type: cross Abstract: Maternal-fetal US is the primary imaging modality for monitoring fetal development, yet accurate automated segmentation remains challenging due to the
arXiv:2606.25758v1 Announce Type: new Abstract: While test-time adaptation (TTA) empowers vision-language models to adapt without costly retraining, it remains highly vulnerable to out-of-distribution
arXiv:2606.24957v1 Announce Type: new Abstract: While speculative decoding improves inference throughput for multi-batch long-context Large Language Models (LLMs), its efficiency is often limited by a
arXiv:2606.25295v1 Announce Type: new Abstract: Mobile manipulation is a fundamental robotics task and has advanced rapidly in recent years, enabling robots to navigate, reach, and interact with objec
arXiv:2606.26003v1 Announce Type: new Abstract: Automatic speech and language technologies are still heavily biased toward high-resource languages, limiting their applicability to dialectal and low-re
arXiv:2606.25465v1 Announce Type: new Abstract: While image stylization has been studied extensively, video stylization remains a critical and largely unsolved challenge in the field of intelligent co
arXiv:2606.25838v1 Announce Type: new Abstract: Production vision pipelines silently degrade on blurry input, wasting compute on downstream OCR, retrieval, and vision-language model (VLM) calls that c
arXiv:2606.25197v1 Announce Type: new Abstract: Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximat
arXiv:2606.25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning. Existing UQ methods for regression tasks mainly operate und
arXiv:2606.25354v1 Announce Type: new Abstract: Test-time scaling improves language-model reasoning, but existing approaches often face a difficult trade-off: long chain-of-thought sampling remains si
arXiv:2606.25547v1 Announce Type: new Abstract: Image hiding aims to conceal image-level messages within cover images at the same resolution. Invertible neural networks (INN)-based image hiding has em
arXiv:2606.25732v1 Announce Type: new Abstract: Real-world single image dehazing is highly ill-posed due to spatially and spectrally varying scattering, while practical deployment demands lightweight
arXiv:2606.25324v1 Announce Type: new Abstract: The computational complexity of Transformers scales quadratically with the number of tokens, which significantly constrains the efficiency of vision mod
Richard Speed / The Register: Elastic announces a ~7% reduction in its workforce, and says “advances in AI and automation are letting us operate with leaner teams”; ESTC closed down 8.70% — CEO says a
arXiv:2606.25176v1 Announce Type: new Abstract: We study representation learning for individual human chess style: a per-player embedding learned from a player's move history such that inner products
arXiv:2606.26008v1 Announce Type: new Abstract: This chapter describes EMCAR, a novel software tool for programming robot motion that leverages the unique affordances of artistic practices such as pup
arXiv:2606.25010v1 Announce Type: cross Abstract: Neural scaling laws for transformer language models predict smooth improvements in pretraining loss with increasing parameters, but downstream capabil
arXiv:2606.24941v1 Announce Type: cross Abstract: Reviewing recorded interviews for affective cues such as composure, hesitation and agitation is slow and subjective, and cloud services that could aut
arXiv:2606.24910v1 Announce Type: cross Abstract: Voice control offers an intuitive alternative to manual drone piloting, yet most existing systems rely on rigid command vocabularies that fail to hand
arXiv:2606.25562v1 Announce Type: cross Abstract: This paper presents an energy-efficient hardware acceleration of the convolutional layers in the U-Net architecture for image segmentation, implemente
arXiv:2606.25894v1 Announce Type: new Abstract: Medical vision-language models typically generate diagnoses through single-pass inference without indicating which image regions support their conclusio
arXiv:2606.24960v1 Announce Type: new Abstract: Tailoring stroke rehabilitation requires assessing how movements are organized, not merely if they succeed. Currently, this assessment is a rate-limitin
arXiv:2606.17412v3 Announce Type: replace Abstract: Pathological images are inherently multi-scale, requiring pathologists to integrate evidence from global tissue architecture at low magnification to
arXiv:2602.22265v2 Announce Type: replace-cross Abstract: Modern vision generators transport a base distribution to data through time-indexed measures, implemented as deterministic flows (ODEs) or sto
arXiv:2512.00804v3 Announce Type: replace-cross Abstract: When answering user queries, LLMs often retrieve knowledge from external sources stored in retrieval-augmented generation (RAG) databases. The
arXiv:2606.25285v1 Announce Type: new Abstract: Post-Training Sparsity (PTS) has emerged as a crucial paradigm for compressing Large Language Models to facilitate efficient deployment on resource-cons
arXiv:2606.25001v1 Announce Type: new Abstract: Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or reduced logit-level membership inference. But if out
arXiv:2606.24915v1 Announce Type: new Abstract: End-to-end automatic speech recognition systems frequently hallucinate rare entities and domain-specific terms, especially in low-resource languages. Wh
arXiv:2506.21091v2 Announce Type: replace Abstract: Stereo matching has become an increasingly important component of modern autonomous systems. Developing deep learning-based stereo matching models t
arXiv:2606.25317v1 Announce Type: new Abstract: An efficient and accurate system for detecting errors in procedural tasks is crucial for supporting human needs in daily life, as it can provide instant
arXiv:2603.10277v3 Announce Type: replace Abstract: In this paper, we propose a fast method for estimating the condition number of sparse matrices using graph neural networks (GNNs). For efficient dep
arXiv:2602.11988v2 Announce Type: replace-cross Abstract: A widespread practice in software development is to tailor coding agents to repositories using context files, such as AGENTS.md. Although this
arXiv:2606.25436v1 Announce Type: cross Abstract: Dialogue systems based on large language models (LLMs) have advanced significantly in recent years. However, dialectal variation remains a major chall
arXiv:2606.25530v1 Announce Type: cross Abstract: Software performance optimization is a notoriously complex and manual task. Despite the growing use of Large Language Models (LLMs) for code refinemen
Explore how the GitHub Copilot agentic harness delivers strong results across multiple benchmarks and leading token efficiency, while maintaining flexibility to choose among more than 20 models. The p
arXiv:2606.25284v1 Announce Type: new Abstract: Camera-based monitoring systems are increasingly adopted in healthcare settings for the continuous assessment of patient movement and activities. Howeve
arXiv:2606.25177v1 Announce Type: new Abstract: Cognitive workload monitoring is important for adaptive rehabilitation and assistive interfaces, where task difficulty, pacing, and feedback should be a
arXiv:2606.25629v1 Announce Type: new Abstract: Robot navigation in safety-critical scenarios faces significant challenges from unforeseen semantic events, where collisions arise primarily from the un
Analysis of ancient proteins from 23 fossilized teeth belonging to at least 20 Homo naledi individuals revealed no male genetic markers, suggesting the assemblage may represent females only. The findi
arXiv:2606.24964v1 Announce Type: new Abstract: Understanding the features of large language models (LLMs) is a central goal of interpretability. LLMs are commonly assumed to use superposition to repr
arXiv:2603.09359v2 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) have shown promise in addressing the ill-posed deconvolution problem in computed tomography perfusion (CTP)
excited to be speaking at @aiDotEngineer World Fair next week on Improving Agents, Continual Learning, and why we think a large part of it is...Data Mining! Trace Mining is how we understand agent beh
Secure software supply chain solution provider Chainguard Inc. today expanded its Chainguard Repository product with malware scanning, policy enforcement and visibility features that now cover Java pa
LucidLink Corp., the maker of a cloud network-attached storage system based on object storage technology, today extended its distributed file system technology into agentic artificial intelligence wit
arXiv:2606.25941v1 Announce Type: cross Abstract: Increasing demand for precise and reliable control in complex scenarios has led to the development of increasingly sophisticated controllers, includin
Replit's integrations catalog allows developers to browse and discover third-party tools, services, and extensions that can be connected to their Replit projects to enhance functionality and streamlin
arXiv:2602.00585v2 Announce Type: replace Abstract: Information-seeking agents have emerged as a powerful paradigm for knowledge-intensive tasks, yet today's systems remain specialized for the open we
arXiv:2606.25606v1 Announce Type: new Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for ea
arXiv:2606.25923v1 Announce Type: new Abstract: A digital twin (DT) is a virtual model of a real-world system that can assist decision-making by simulating scenarios induced by different policies. How
arXiv:2606.18874v2 Announce Type: replace Abstract: AI systems can increasingly automate scientific workflows, but the reasoning that links prior evidence, generated ideas, experiments and final claim
arXiv:2606.24994v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) for language-model reasoning can fail at both extremes of task difficulty: easy prompts often prod
arXiv:2606.25237v1 Announce Type: cross Abstract: We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-shot) items can arrive continuously at a rapid pace. Con
Aisha Malik / TechCrunch: Facebook brings back Facebook Creator Studio as a stand-alone app with built-in AI chatbot to help creators grow their audiences through personalized guidance — Facebook anno
Meta is bringing back the Facebook Creator Studio page manager, now 'reimagined' as a standalone AI companion app. The new app aims to make it easier for creators to connect with their audiences and s
arXiv:2606.25394v1 Announce Type: new Abstract: Finding minimal arithmetic circuits for polynomials over finite fields is a combinatorially hard problem central to algebraic complexity theory. We form
arXiv:2606.24902v1 Announce Type: cross Abstract: The 'First Proof' benchmark [1] posed ten research-level mathematics questions to the strongest publicly available LLMs and found them consistently wr
arXiv:2606.25701v1 Announce Type: new Abstract: Conventional vision-language models are largely object-centric, focusing on detecting and describing individual entities. In safety-critical X-ray bagga
arXiv:2606.26010v1 Announce Type: new Abstract: Robust and accurate odometry estimation is essential in modern robotics. In environments characterized by highly dynamic motion and sensor noise, odomet