Knowledge catalogue
arXiv:2604.10962v1 Announce Type: new Abstract: Flow Matching (FM) policies have emerged as an efficient backbone for robotic control, offering fast and expressive action generation that underpins rec
arXiv:2412.07469v3 Announce Type: replace-cross Abstract: Causal discovery is a crucial initial step in establishing causality from empirical data and background knowledge. Numerous algorithms have be
arXiv:2604.11244v1 Announce Type: new Abstract: Advances in Multimodal Large Language Models (MLLMs) are transforming video captioning from a descriptive endpoint into a semantic interface for both vi
arXiv:2604.09743v1 Announce Type: cross Abstract: Multi-modal image registration plays a critical role in precision medicine but faces challenges from non-linear intensity relationships and local opti
arXiv:2604.07791v2 Announce Type: replace Abstract: Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have demonstrated significant potential in single-turn reasoning tasks. Wit
Digital certificates and certificate lifecycle management firm Sectigo Ltd. today announced the launch of Private PQC, a new feature that allows enterprises to issue and manage private post-quantum cr
Cloudflare Mesh provides secure, private network access for users, nodes, and autonomous AI agents. By integrating with Workers VPC, developers can now grant agents scoped access to private databases
arXiv:2509.22097v3 Announce Type: replace-cross Abstract: Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have be
The agentic era demands a new security era. Our mission is to be the world’s most trusted security partner in this new era, helping every organization accelerate their security transformation with the
arXiv:2604.09749v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) frequently hallucinate objects that are absent from the visual input, often because attention during decoding i
arXiv:2604.10299v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) rely on attention-based retrieval of safety instructions to maintain alignment during generation. Existing attack
arXiv:2505.15489v4 Announce Type: replace-cross Abstract: The impact of multimodal misinformation arises not only from factual inaccuracies but also from the misleading narratives that creators delibe
arXiv:2604.11711v1 Announce Type: new Abstract: Occlusion, where target structures are partially hidden by surgical instruments or overlapping tissues, remains a critical yet underexplored challenge f
arXiv:2604.11579v1 Announce Type: new Abstract: We address the problem of tactile localization, where the goal is to identify image regions that share the same material properties as a tactile input.
arXiv:2604.11231v1 Announce Type: new Abstract: Change detection is a fundamental task in remote sensing, aiming to quantify the impacts of human activities and ecological dynamics on land-cover chang
A Reddit post on r/StableDiffusion discussing the use of segmentation prediction within the Stable Diffusion ecosystem, likely covering how segmentation masks or maps can be generated and applied to g
arXiv:2604.11328v1 Announce Type: new Abstract: Automatic prompt optimization (APO) hinges on the quality of its evaluation signal, yet scoring every prompt candidate on the full training set is prohi
arXiv:2604.09624v1 Announce Type: new Abstract: Large language models (LLMs) are systematically overconfident: they routinely express high certainty on questions they often answer incorrectly. Existin
arXiv:2510.15850v2 Announce Type: replace-cross Abstract: Recent research has shown that optimization proxies can be trained to high fidelity, achieving average optimality gaps under 1% for large-scal
arXiv:2604.10734v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) substantially extends the knowledge boundary of large language models. However, it still faces two major challenges
arXiv:2604.11610v1 Announce Type: new Abstract: As LLM-based assistants become persistent and personalized, they must extract and retain useful information from past conversations as memory. However,
arXiv:2601.06540v2 Announce Type: replace-cross Abstract: This paper proposes a novel reinforcement learning framework, named Self-Organizing Dual-buffer Adaptive Clustering Experience Replay (SODACER
arXiv:2604.10609v1 Announce Type: new Abstract: Instance segmentation enables the analysis of spatial and temporal properties of cells in microscopy images by identifying the pixels belonging to each
arXiv:2604.09551v1 Announce Type: cross Abstract: Cross-domain recommendation (CDR) addresses the data sparsity and cold-start problems in the target domain by leveraging knowledge from data-rich sour
arXiv:2604.11548v1 Announce Type: new Abstract: The rise of OpenClaw in early 2026 marks the moment when millions of users began deploying personal AI agents into their daily lives, delegating tasks r
arXiv:2604.11122v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated immense potential in Earth observation. However, the massive visual tokens generated when p
arXiv:2604.10132v1 Announce Type: cross Abstract: Image Manipulation Localization (IML) aims to identify edited regions in an image. However, with the increasing use of modern image editing and genera
arXiv:2510.06687v2 Announce Type: replace-cross Abstract: Semantic segmentation serves as a cornerstone of scene understanding in autonomous driving but continues to face significant challenges under
arXiv:2509.23808v4 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) for LLM reasoning is often framed as balancing exploration and exploitation in action sp
arXiv:2604.09887v1 Announce Type: new Abstract: Medical vision-language datasets are often limited in size and biased toward negative findings, as clinicians report abnormalities mostly but might omit
arXiv:2604.10404v1 Announce Type: cross Abstract: Edge-based multimodal medical monitoring requires models that balance diagnostic accuracy with severe energy constraints. Continuous acquisition of EC
arXiv:2604.10091v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable performance in various domains, but they are constrained by massive computational and storage costs.
arXiv:2604.09563v1 Announce Type: new Abstract: AI systems produce large volumes of logs as they interact with tools and users. Analysing these logs can help understand model capabilities, propensitie
arXiv:2603.09355v1 Announce Type: cross Abstract: Under the multiplicative noise scaling (MNS) condition, original Nesterov acceleration is provably sensitive to noise and may diverge when gradient no
arXiv:2604.11200v1 Announce Type: cross Abstract: Changes in input distribution can induce shifts in the average predictions of machine learning models. Such prediction shifts may impact downstream bu
arXiv:2604.11152v1 Announce Type: new Abstract: This intermediate technical report introduces the SHARE family of base models and the MIRROR user interface. The SHARE models are the first causal langu
arXiv:2604.11050v1 Announce Type: cross Abstract: We extract 21-emotion vector sets from twelve small language models (six architectures x base/instruct, 1B-8B parameters) under a unified comprehensio
arXiv:2604.09696v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) are a natural computational model for on-sensor and near-sensor vision, where event driven processors must operate unde
arXiv:2510.07972v3 Announce Type: replace Abstract: Query-product relevance prediction is vital for AI-driven e-commerce, yet current LLM-based approaches face a dilemma: SFT and DPO struggle with lon
arXiv:2604.11275v1 Announce Type: new Abstract: Spatio-temporal systems often exhibit highly heterogeneous and non-intuitive responses to localized disruptions, limiting the effectiveness of conventio
Nous Research posted a brief status update on X (formerly Twitter) indicating that a previously reported issue has been resolved, thanking their community for their patience during the downtime or dis
arXiv:2604.09890v1 Announce Type: cross Abstract: Across multiple language pairings (English o {Spanish, French, German, Mandarin, Japanese, Urdu, Cantonese}), we find reasoning errors in translation.
arXiv:2604.10373v1 Announce Type: cross Abstract: From adversarial robustness to multi-agent learning, many machine learning tasks can be cast as finite-sum min-max optimization or, more generally, as
arXiv:2305.09958v5 Announce Type: replace Abstract: Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nod
arXiv:2604.11225v1 Announce Type: cross Abstract: Recent Vision Language Models (VLMs) have demonstrated strong performance across a wide range of multimodal reasoning tasks. This raises the question
arXiv:2604.11263v1 Announce Type: cross Abstract: Accurate and efficient surrogate models for aerodynamic surface pressure fields are essential for accelerating aircraft design and analysis, yet deter
arXiv:2604.10436v1 Announce Type: new Abstract: Accurate semantic understanding of complex traffic signs-including those with intricate layouts, multi-lingual text, and composite symbols-is critical f
arXiv:2510.17516v4 Announce Type: replace-cross Abstract: Large language model (LLM) simulations of human behavior have the potential to revolutionize the social and behavioral sciences, if and only i
arXiv:2604.10548v1 Announce Type: new Abstract: Obstacle avoidance is a fundamental vision-based task essential for enabling quadrotors to perform advanced applications. When planning the trajectory,
arXiv:2604.10334v1 Announce Type: new Abstract: Structured Illumination Microscopy (SIM) enables rapid, high-contrast optical sectioning of fresh tissue without staining or physical sectioning, making
Simply retrieving a reasoning trace looks a lot like human reasoning, until it's time to navigate uncharted territory. If you memorized all reasoning traces of humans from 10,000 BC, you could automat
arXiv:2604.09874v1 Announce Type: new Abstract: Simulating how organized groups (e.g., corporations) make decisions (e.g., responding to a competitor's move) is essential for understanding real-world
arXiv:2604.11090v1 Announce Type: new Abstract: Simulation trained legged locomotion policies often exhibit performance loss on hardware due to dynamics discrepancies between the simulator and the rea
arXiv:2604.02460v2 Announce Type: replace Abstract: Recent work reports strong performance from multi-agent LLM systems (MAS), but these gains are often confounded by increased test-time computation.
arXiv:2604.10027v1 Announce Type: new Abstract: Large language models (LLMs) suffer from hallucination and context forgetting. Prior studies suggest that attention drift is a primary cause of these pr
arXiv:2604.10674v1 Announce Type: cross Abstract: Reinforcement learning (RL) has been widely used to train LLM agents for multi-turn interactive tasks, but its sample efficiency is severely limited b
arXiv:2604.03088v3 Announce Type: replace-cross Abstract: LLM agents increasingly adopt skills as a reusable unit of composition. While skills are shared across diverse agent platforms, current system
arXiv:2604.11466v1 Announce Type: cross Abstract: Large Language Model (LLM) agents offer a potentially-transformative path forward for generative social science but face a critical crisis of validity
Elon Musk shared or engaged with a post on X (formerly Twitter) claiming that slavery has been a universal feature of human history across cultures and civilizations, while framing the abolition of sl
arXiv:2604.10821v1 Announce Type: new Abstract: High-dimensional and complex discrete distributions often exhibit multimodal behavior due to inherent discontinuities, posing significant challenges for