Knowledge catalogue
arXiv:2605.17238v1 Announce Type: new Abstract: We study the dynamic joint assortment selection and positioning problem, where the attraction of each product depends on both its intrinsic appeal and i
arXiv:2605.18627v1 Announce Type: new Abstract: It has been recently shown that lifted STRIPS models can be learned correctly and efficiently from action traces alone; i.e., applicable action sequence
arXiv:2510.04006v2 Announce Type: replace Abstract: Data-driven machine learning (ML) models are reshaping weather forecasting and have shown the potential to accelerate and surpass traditional physic
arXiv:2605.17058v1 Announce Type: new Abstract: The combination of exponentially large action spaces, stochastic dynamics, and long-horizon decision-making under limited resources makes Sequential Sto
arXiv:2602.12978v2 Announce Type: replace-cross Abstract: Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at
arXiv:2605.18701v1 Announce Type: new Abstract: Blood-based biomarkers underpin clinical diagnosis and management, yet their interpretation relies largely on fixed population reference intervals that
arXiv:2605.17854v1 Announce Type: new Abstract: Conventional approaches to learning on graphs involve message passing along existing (i.e., positive) edges to update node features. However, these appr
arXiv:2605.18681v1 Announce Type: new Abstract: Explainable AI (XAI) techniques are increasingly important for the validation and responsible use of modern deep learning models, but are difficult to e
arXiv:2605.16834v1 Announce Type: cross Abstract: Multimodal pre-training demonstrates strong generalization performance, but this paradigm is often impractical in domains where paired data are scarce
arXiv:2602.21707v2 Announce Type: replace-cross Abstract: State-of-the-art learned reconstruction methods often rely on black-box modules that, despite their strong performance, raise questions about
arXiv:2605.17980v1 Announce Type: new Abstract: Diffusion-based methods demonstrate significant potential for remote sensing image super-resolution at large scaling factors, particularly in reference-
arXiv:2605.16857v1 Announce Type: new Abstract: Experience-driven learning has emerged as a promising paradigm for enabling agents to improve from interaction trajectories by accumulating and reusing
arXiv:2605.18624v1 Announce Type: cross Abstract: Machine learning-based malware detectors are widely deployed in antivirus and endpoint detection systems, yet their reliance on static features makes
arXiv:2505.19590v5 Announce Type: replace-cross Abstract: Training large language models (LLMs) for complex reasoning via Reinforcement Learning with Verifiable Rewards (RLVR) is effective but limited
arXiv:2605.18094v1 Announce Type: new Abstract: We study the Compositional Geometry Routing Problem (CGRP), a unified superclass of traditional routing problems that covers point-only, line-only, area
arXiv:2605.17359v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have shown strong potential for complex reasoning by coordinating specialized agents through struct
arXiv:2605.16755v1 Announce Type: cross Abstract: Learning permutations is fundamental to sorting, ranking, and matching, but existing differentiable methods based on entropy-regularized Sinkhorn prod
arXiv:2512.13506v4 Announce Type: replace Abstract: Statistical learning under distributional drift remains poorly characterized, especially in closed-loop settings where learning alters the data-gene
arXiv:2605.17779v1 Announce Type: new Abstract: Generative recommendation reformulates recommendation as next-token prediction over discrete semantic identifiers (IDs). A fundamental yet unexplored de
arXiv:2605.16615v1 Announce Type: new Abstract: In many applications, human and LLM evaluators use assessments of relevant criteria to create an overall evaluation for an item or individual. For examp
arXiv:2605.17003v1 Announce Type: cross Abstract: Reinforcement Learning (RL) post-training has emerged as the dominant paradigm for eliciting mathematical reasoning in Large Language Models (LLMs), y
arXiv:2605.12012v2 Announce Type: replace Abstract: Public-sector legal departments in the Netherlands face acute staff shortages, increased case volumes, and increased pressure to meet regulatory com
arXiv:2605.16474v1 Announce Type: cross Abstract: The integration of advertising auction mechanisms into large language model (LLM)-based chatbots presents a significant opportunity for commercializat
arXiv:2605.18541v1 Announce Type: new Abstract: Modeling hyperspectral imagery (HSI) across different sensors presents a fundamental challenge due to variations in wavelength coverage, band sampling,
arXiv:2605.16786v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly needed for interactive mobile applications, but high-quality models exceed the limited DRAM available on s
arXiv:2605.17333v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) typically samples multiple responses per prompt and assigns binary rewards based on individual cor
arXiv:2605.18211v1 Announce Type: cross Abstract: We introduce Graph-Augmented Sequence-to-Sequence (GA-S2S), a novel framework that integrates a T5-small encoder-decoder with a Relational Graph Atten
arXiv:2605.18641v1 Announce Type: new Abstract: Latent visual reasoning involves visual evidence more directly in multimodal reasoning by inserting continuous latent tokens before textual generation.
arXiv:2605.12987v2 Announce Type: replace Abstract: BACKGROUND: Coding Motivational Interviewing (MI) sessions is essential for understanding client behaviors and predicting outcomes, but it requires
arXiv:2409.15980v2 Announce Type: replace-cross Abstract: Traditional machine learning-based visual inspection systems require extensive data collection and repetitive model training to improve accura
arXiv:2410.13846v3 Announce Type: replace-cross Abstract: Scaling language models to handle longer contexts introduces substantial memory challenges due to the growing cost of key-value (KV) caches. M
arXiv:2309.05646v2 Announce Type: replace-cross Abstract: Distributed Denial of Service (DDoS) attacks remain a persistent threat to the availability of Internet services, edge networks, and cyber-phy
arXiv:2605.17898v1 Announce Type: new Abstract: Gaussian process (GP) inference in Python is dominated by libraries such as GPyTorch and GPflow, which are built on deep-learning frameworks and inherit
arXiv:2506.21499v2 Announce Type: replace-cross Abstract: Ultrasound Coherent Plane-Wave Compounding (CPWC) enhances image contrast by combining echoes from multiple steered transmissions. While incre
arXiv:2603.16947v2 Announce Type: replace-cross Abstract: Although vision-language navigation (VLN) has progressed rapidly, zero-shot VLN in continuous environments (VLN-CE) remains highly challenging
arXiv:2605.16675v1 Announce Type: new Abstract: We introduce LinAlg-Bench, a diagnostic benchmark evaluating 10 frontier large language models on structured linear algebra computation across a strict
arXiv:2605.16289v1 Announce Type: cross Abstract: Linguistic uncertainty is common in social media, but its relationship with engagement remains unclear across languages and topics. Using 2,258 Englis
arXiv:2604.00634v2 Announce Type: replace-cross Abstract: Panoptic segmentation is a key enabler for robotic perception, as it unifies semantic understanding with object-level reasoning. However, the
arXiv:2509.01629v3 Announce Type: replace-cross Abstract: We study the design of interpolation schedules in flow and diffusion-based generative models from both statistical and numerical perspectives.
arXiv:2605.17287v1 Announce Type: new Abstract: Driver gaze estimation serves as a fundamental metric for evaluating driver attentiveness in modern monitoring systems. Beyond being vulnerable to sudde
arXiv:2510.25799v2 Announce Type: replace Abstract: Human experts often struggle to select the best option from a large set of items with multiple competing objectives, a process bottlenecked by the d
arXiv:2605.17260v1 Announce Type: new Abstract: The fundamental challenge in scaling Video Large Language Models (Video LLMs) to long-form video lies in managing the explosion of visual-token context
arXiv:2603.00631v2 Announce Type: replace Abstract: LiTS is a modular Python framework for LLM reasoning via tree search. It decomposes tree search into three reusable components (Policy, Transition,
Live from Code with Claude London: we're launching self-hosted sandboxes (public beta) and MCP tunnels (research preview) in Claude Managed Agents. Run agents inside your own perimeter, with your secu
arXiv:2605.17986v1 Announce Type: cross Abstract: AI agents such as OpenClaw are increasingly deployed in local workflows with access to external tools. This creates indirect prompt-injection (IPI) ri
arXiv:2506.23978v3 Announce Type: replace-cross Abstract: While the Internet's core infrastructure was designed to be open and universal, today's application layer is dominated by closed, proprietary
arXiv:2605.16264v1 Announce Type: cross Abstract: Push notifications remain among the most direct channels through which digital platforms engage users, yet existing approaches have invested heavily i
llm-gemini 0.32 is an alpha release of Simon Willison's LLM Python library and CLI tool that provides access to Google's Gemini models , continuing work on major architectural changes to support newer
I don't have current information about this specific entry, so I'll describe what it likely covers based on the available details. This entry documents the release or update of llm-gemini version 0.32
arXiv:2605.18077v1 Announce Type: new Abstract: Communication is a key component in multi-agent reinforcement learning (MARL) for mitigating partial observability, yet prior approaches often rely on i
arXiv:2503.02574v2 Announce Type: replace-cross Abstract: In this paper, we argue that current safety alignment research efforts for large language models are hindered by many intertwined sources of n
arXiv:2503.02161v3 Announce Type: replace Abstract: Synthetic tabular data are increasingly being used to replace real data, serving as an effective solution that simultaneously protects privacy and a
arXiv:2605.17653v1 Announce Type: cross Abstract: Sub-billion-parameter Transformer language models are increasingly deployed on edge devices, where the privacy, latency, and operating-cost advantages
arXiv:2605.17205v1 Announce Type: new Abstract: Linguistic annotation of transcribed speech is essential for research in language acquisition, language disorders, and sociolinguistics, yet remains lab
arXiv:2605.16538v1 Announce Type: cross Abstract: This paper examines the opportunities, limitations, and practical considerations associated with the use of large language models (LLMs) in qualitativ
Chrome DevTools will add support for validating llms.txt files, enabling developers to check the validity and correctness of their llms.txt configurations directly within the browser's developer tools
arXiv:2603.20216v2 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models, offering sub-linear generation latency
arXiv:2605.18015v1 Announce Type: new Abstract: Production log analytics in self-hosted, resource-constrained environments requires natural-language access to massive log streams without the cost of r
arXiv:2503.14800v3 Announce Type: replace-cross Abstract: Effective long-term memory management is crucial for language models handling extended contexts. We introduce the Enhanced Ranked Memory Augme
arXiv:2605.17888v1 Announce Type: cross Abstract: Long-horizon prediction of three-dimensional (3D) wall-bounded turbulence with machine-learning methods remains a challenging task, due to the rapid a