Knowledge catalogue
arXiv:2605.23027v1 Announce Type: new Abstract: Recent research has demonstrated the potential of reinforcement learning in effective multi-robot collaboration, particularly in social dilemmas where r
arXiv:2503.06684v3 Announce Type: replace Abstract: Recent advances in diffusion-based text-to-image generation have demonstrated promising results through visual condition control. However, existing
arXiv:2605.23531v1 Announce Type: new Abstract: Low-light images exhibit severe noise, contrast loss, and semantic ambiguity, making enhancement a joint problem of denoising and detail recovery. We pr
PixlStash 1.3 is a Python-based image management and tagging web app that improves grid loading performance and introduces JoyCaption integration for AI-powered image captioning. The update adds suppo
arXiv:2507.05311v2 Announce Type: replace-cross Abstract: In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enli
Plugins can also affect the empty state of the new menu - the latest datasette-agent adds a form for kicking off a new agent conversation - live demo (if you sign in with GitHub) on https://agent.data
arXiv:2605.23856v1 Announce Type: new Abstract: Robot policy learning benefits from world-action models that capture environment dynamics, but pixel-level prediction entangles dynamics with nuisance f
arXiv:2605.22891v1 Announce Type: new Abstract: Evaluation in scientific reconstruction is dominated by pointwise metrics - RMSE, MAE, per-event resolution - under the implicit assumption that lower e
arXiv:2605.23168v1 Announce Type: cross Abstract: When practitioners fine-tune LLMs on unvetted datasets, an adversary can exploit the data supply chain through task-level poisoning: inserting a small
arXiv:2603.21437v2 Announce Type: replace Abstract: Transformer-based embedding models frequently exhibit geometric pathologies, such as anisotropy and length-induced representation collapse, which ca
Pope Leo XIV warned of the risks of AI and unconstrained technological power in his first major papal document released on Monday. Magnifica Humanitas is the pope's manifesto on 'safeguarding the huma
Pope Leo has warned that the artificial intelligence revolution is being driven primarily by profit motives rather than ethical considerations, characterizing this as a form of 'idolatry of profit.' T
Pope Leo XIV today issued an encyclical that contains an extensive discussion of artificial intelligence and its risks. The approximately 43,000-word document is entitled “Magnifica Humanitas,” which
New York Times: Pope Leo XIV presents Magnifica humanitas, his encyclical on AI, calling for AI regulation, protection for children against hypersexualized AI images, and more — The document marks a p
arXiv:2605.23170v1 Announce Type: cross Abstract: Position-controlled evaluation is standard for retrieval tasks such as Needle-in-a-Haystack and RULER, but mainstream reasoning benchmarks do not cont
arXiv:2605.23522v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become an effective way to improve prompt alignment and perceptual quality in diffusion and flow-matching generators.
arXiv:2605.22855v1 Announce Type: cross Abstract: Personalized pricing negotiations are a challenging testbed for LLM agents because successful interaction does not guarantee profitable decision makin
arXiv:2605.23603v1 Announce Type: cross Abstract: We introduce the Preisach Attention Layer (PAL), a novel sequence modelling architecture grounded in the classical Preisach hysteresis operator from m
Product >> Brand 2 years ago: 'Elon destroyed the Tesla brand globally! It'll never recover!' Today: #1 selling EV in California: Tesla Model Y #1 selling EV in the U.S.: Tesla Model Y #1 selling EV i
arXiv:2603.02897v2 Announce Type: replace Abstract: Recent advances in generative image compression (GIC) have delivered remarkable improvements in perceptual quality. However, many GICs rely on large
arXiv:2601.15224v2 Announce Type: replace-cross Abstract: Estimating task progress requires reasoning over long-horizon dynamics rather than recognizing static visual content. While modern Vision-Lang
arXiv:2605.04118v2 Announce Type: replace-cross Abstract: Recent advances in de novo protein binder design have enabled increasing experimental validation, yet reported in silico metrics remain diffic
arXiv:2605.23351v1 Announce Type: new Abstract: We study adversarial multi-armed bandits with and without delayed feedback under a safety-aware goal: achieving minimax-optimal worst-case regret while
arXiv:2605.23182v1 Announce Type: new Abstract: Pure exploration in episodic Reinforcement Learning has primarily focused on Best Policy Identification (BPI), which seeks to identify a (near)-optimal
arXiv:2605.23574v1 Announce Type: new Abstract: Long-horizon language agents can make many plausible local tool calls yet fail to persist until a requested count is actually complete. We study this ga
arXiv:2605.22834v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems depend critically on document chunking quality for retrieving relevant context. Fixed chunking segments doc
arXiv:2601.03715v2 Announce Type: replace-cross Abstract: Reinforcement learning drives recent advances in LLM reasoning and agentic capabilities, yet current approaches struggle with both exploration
arXiv:2605.23550v1 Announce Type: cross Abstract: We study nonsmooth difference-of-convex programs whose subtracted convex term is a finite maximum of smooth convex functions. In this setting, standar
arXiv:2605.23028v1 Announce Type: cross Abstract: Machine learning methods rely on data. However, gathering suitable data can be challenging due to availability constraints, cost, or the need for doma
arXiv:2510.11195v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) increases the reliability and trustworthiness of the LLM response and reduces hallucination by eliminatin
arXiv:2605.22833v1 Announce Type: cross Abstract: Chronic osteomyelitis presents substantial prognostic challenges due to its high recurrence risk and complex postoperative recovery trajectories. Trad
Ralph Loops are powerful, but wrapping a naive loop in a shell script is a total token burner in production. Pinecone Principal Engineer Jen Hamon breaks down why standard loops collapse: ❌ The Bug: P
arXiv:2605.22937v1 Announce Type: new Abstract: Inference-time scaling can reduce errors in structured query generation, but methods to allocate the compute for query code generation remains underexpl
arXiv:2605.22864v1 Announce Type: new Abstract: The maximum softmax probability (MSP) represents a default approach when evaluating uncertainty quantification for language model generation with struct
arXiv:2605.22836v1 Announce Type: cross Abstract: Rapid earthquake magnitude estimation is crucial for effective early warning systems that can save lives and reduce economic damage. In this paper, we
arXiv:2605.11215v2 Announce Type: replace-cross Abstract: Pre-training large language models on massive GPU clusters has made hardware faults routine rather than rare, driving the need for resilient t
arXiv:2605.23656v1 Announce Type: new Abstract: Training high-capacity vision models from scratch requires substantial computational resources. To improve training efficiency of a wide target model, e
arXiv:2605.23179v1 Announce Type: new Abstract: Agentic AI orchestrators reduce the interface and assembly costs of composing information systems capabilities across organizational boundaries, seeming
arXiv:2605.23415v1 Announce Type: cross Abstract: Reinforcement learning has long struggled with poor sample efficiency. One promising approach to mitigate this problem is leveraging group-invariant M
arXiv:2605.23285v1 Announce Type: cross Abstract: How network structure determines function is a fundamental question, and it can be investigated by graph ensembles with precisely controlled structura
arXiv:2510.00915v4 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) replaces costly human labeling with automated verifiers. To reduce verifier hacking, man
arXiv:2605.23673v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become important machine learning tools for graph analysis, and its explainability is crucial for safety, fairness, an
arXiv:2605.23241v1 Announce Type: new Abstract: Relational databases (RDBs) remain the cornerstone of modern data systems and support diverse predictive tasks. Recent relational deep learning (RDL) me
arXiv:2605.22889v1 Announce Type: new Abstract: Remote robotic-assisted endovascular intervention offers a promising approach to reduce clinician radiation exposure and physical strain, while extendin
Reuters: Report: the EU plans to fine Google a high triple-digit million euro amount as part of a 2025 probe over concerns it favors its own services in search results — The European Union is planning
arXiv:2605.22886v1 Announce Type: cross Abstract: AI-native wireless receivers based on deep learning exhibit remarkable performance under stationary channel conditions, yet their resilience to distri
arXiv:2605.23472v1 Announce Type: new Abstract: Vision foundation models pretrained on web-scale data have recently shown strong transfer capabilities on many downstream tasks, but their effectiveness
arXiv:2605.23747v1 Announce Type: new Abstract: Material segmentation, the pixel-wise classification of physical surface properties, remains a challenging problem in computer vision, requiring physico
arXiv:2605.23672v1 Announce Type: new Abstract: Reconstructing dynamic 3D scenes from monocular videos is a fundamental yet highly challenging task, as real-world motions often involve both long-term
arXiv:2605.22875v1 Announce Type: new Abstract: We present extbf{Research Math Agents (RMA)}, an agentic framework for automated reasoning on research-level mathematical problems. Unlike prior studies
arXiv:2605.23068v1 Announce Type: new Abstract: Reliable visual understanding in robot-assisted and minimally invasive surgery (RMIS/MIS) demands more than accurate masks: in clinical practice, clinic
arXiv:2605.23863v1 Announce Type: new Abstract: This study presents a closed-loop robotic strawberry harvesting system that combines a robust vision module, simulation-trained deep reinforcement learn
arXiv:2605.22986v1 Announce Type: cross Abstract: Learning reward functions from demonstrations assumes that demonstrations provide adequate supervision over all features -- or task-relevant aspects o
arXiv:2502.13731v5 Announce Type: replace Abstract: This paper addresses a key limitation in existing counterfactual inference methods for Markov Decision Processes (MDPs). Current approaches assume a
arXiv:2605.23175v1 Announce Type: cross Abstract: Proprietary large language models (LLMs) face risks of intellectual property (IP) violation, as adversaries can replicate an LLM by collecting input-o
arXiv:2605.23115v1 Announce Type: new Abstract: Bike-sharing models trained on historical station-hour data may degrade when deployed in later years because travel patterns change over time. This pape
arXiv:2605.23406v1 Announce Type: new Abstract: End-to-end autonomous driving solutions, which directly process multimodal sensory data and output fine-grained control commands, have gradually become
arXiv:2403.12401v2 Announce Type: replace Abstract: Neural Representations for Videos(NeRV) have emerged as a promising paradigm for video compression by representing videos as compact neural networks
arXiv:2605.23467v1 Announce Type: new Abstract: Message-passing neural networks (MPNNs) often suffer from an information bottleneck when capturing long-range dependencies, leading to the oversquashing
arXiv:2603.23565v2 Announce Type: replace-cross Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be com