Knowledge catalogue
arXiv:2604.21360v1 Announce Type: new Abstract: Test-time adaptation (TTA) has emerged as a promising paradigm for vision-language models (VLMs) to bridge the distribution gap between pre-training and
arXiv:2604.21357v1 Announce Type: new Abstract: This paper proposes ReaGeo, an end-to-end geocoding framework based on large language models, designed to overcome the limitations of traditional multi-
arXiv:2604.21454v1 Announce Type: cross Abstract: Reasoning in large language models is often treated as a monolithic capability, but its observed gains may arise from more basic operations. We study
arXiv:2604.21139v1 Announce Type: new Abstract: Language models must bind entities to the attributes they possess and maintain several such binding relationships within a context. We study how multipl
arXiv:2604.20874v1 Announce Type: cross Abstract: Every system that maintains a large language model conversation beyond a single session faces two inescapable constraints: the context window is finit
arXiv:2604.21764v1 Announce Type: new Abstract: Reasoning LLMs often spend substantial tokens on long intermediate reasoning traces (e.g., chain-of-thought) when solving new problems. We propose to su
Tool Attention Is All You Need // Tool Attention Is All You Need // New research proposes a practical fix for the hidden 'MCP tax.' The work introduces a dynamic tool gating mechanism built on an Inte
arXiv:2604.21696v1 Announce Type: new Abstract: Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as t
arXiv:2604.21840v1 Announce Type: cross Abstract: Modern phishing campaigns increasingly evade snapshot-based URL classifiers using interaction gates (e.g., checkbox/slider challenges), delayed conten
arXiv:2604.21193v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across a wide range of NLP tasks, yet they remain prone to factual ina
arXiv:2604.08016v2 Announce Type: replace Abstract: Regardless of its foundational role in human discovery and sense-making, abductive reasoning--the inference of the most plausible explanation for an
arXiv:2403.18151v2 Announce Type: replace-cross Abstract: Objective: Cytology plays a crucial role in lung cancer diagnosis. Pulmonary cytology involves cell morphological characterization in the spec
@benswerd there are tradeoffs but we've spoken to folks doing both inside and outside and the split is pretty much 50:50 in our experience. I've found @hwchase17's list of tradeoffs the most accurate
arXiv:2604.19856v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise for generating Register-Transfer Level (RTL) code from natural language specifications, but single-shot gene
arXiv:2604.19926v1 Announce Type: new Abstract: Large language models can generate plausible game code, but turning this capability into iterative creative improvement remains difficult. In practice,
At Google Cloud, every day is Developer Day, but none so much as day 2 of Google Cloud Next, when we hold the developer keynote. This year’s topic? An in-depth look at Gemini Enterprise Agent Platform
arXiv:2604.19776v1 Announce Type: new Abstract: Tuberculosis (TB) is one of the world's deadliest infectious diseases, and in South Africa, it contributes a significant burden to the country's health
arXiv:2501.07399v2 Announce Type: replace Abstract: Consistent maps are key for most autonomous mobile robots, and they often use SLAM approaches to build such maps. Loop closures via place recognitio
arXiv:2604.20689v1 Announce Type: new Abstract: Dexterous robotic manipulation requires comprehensive perception across all phases of interaction: pre-contact, contact initiation, and post-contact. Su
arXiv:2604.20006v1 Announce Type: new Abstract: Personalized agents that interact with users over long periods must maintain persistent memory across sessions and update it as circumstances change. Ho
arXiv:2604.20707v1 Announce Type: new Abstract: Digital twins of natural systems must remain aligned with physical systems that evolve over time, are only partially observed, and are typically modeled
arXiv:2604.19834v1 Announce Type: new Abstract: Functional fitness movements are widely used in training, competition, and health-oriented exercise programs, yet consistently enforcing repetition (rep
arXiv:2411.16719v4 Announce Type: replace Abstract: Domain randomization through synthesis is a powerful strategy to train networks that are unbiased with respect to the domain of the input images. Ra
arXiv:2604.20174v1 Announce Type: new Abstract: Reinforcement learning (RL) policies are typically trained for fixed objectives, making reuse difficult when task requirements change. We study inferenc
arXiv:2604.19792v1 Announce Type: new Abstract: This paper presents OpenCLAW-P2P v6.0, a comprehensive evolution of the decentralized collective-intelligence platform in which autonomous AI agents pub
arXiv:2604.19795v1 Announce Type: new Abstract: We introduce prism{} (extbf{P}robabilistic extbf{R}etrieval with extbf{I}nformation-extbf{S}tratified extbf{M}emory), an evolutionary memory substrate f
arXiv:2604.20311v1 Announce Type: cross Abstract: Micro-video popularity prediction (MVPP) aims to forecast the future popularity of videos on online media, which is essential for applications such as
arXiv:2604.20087v1 Announce Type: new Abstract: Skills have become the de facto way to enable LLM agents to perform complex real-world tasks with customized instructions, workflows, and tools, but how
Written by: JP Glab, Tufail Ahmed, Josh Kelley, Muhammad Umair Introduction Google Threat Intelligence Group (GTIG) identified a multistage intrusion campaign by a newly tracked threat group, UNC6692,
arXiv:2604.20216v1 Announce Type: new Abstract: Many applications of LLM-based text regression require predicting a full conditional distribution rather than a single point value. We study distributio
arXiv:2604.19749v1 Announce Type: new Abstract: Equipping LLMs with external tools effectively addresses internal reasoning limitations. However, it introduces a critical yet under-explored phenomenon
arXiv:2604.20444v1 Announce Type: cross Abstract: Embodied intelligence has advanced rapidly in recent years; however, bimanual manipulation-especially in contact-rich tasks remains challenging. This
arXiv:2604.19998v1 Announce Type: new Abstract: Evaluating AI-generated reviews by verdict agreement is widely recognized as insufficient, yet current alternatives rarely audit which concerns a system
arXiv:2604.19465v1 Announce Type: cross Abstract: Understanding how complex systems respond to perturbations, such as whether they will remain stable or what their most sensitive patterns are, is a fu
arXiv:2507.09861v2 Announce Type: replace-cross Abstract: Visually Rich Document Understanding (VRDU) has become a pivotal area of research, driven by the need to automatically interpret documents tha
arXiv:2604.19238v1 Announce Type: new Abstract: Real-world image super-resolution (Real-SR) has been revolutionized by leveraging the powerful generative priors of large-scale diffusion and flow-based
arXiv:2604.19747v1 Announce Type: new Abstract: Sparse-view 3D reconstruction is essential for modeling scenes from casual captures, but remain challenging for non-generative reconstruction. Existing
arXiv:2604.19342v1 Announce Type: new Abstract: Generative AI-powered by Large Language Models (LLMs)-is increasingly deployed in industry across healthcare decision support, financial analytics, ente
arXiv:2604.15034v2 Announce Type: replace Abstract: Recent advances in LLM based agent systems have shown promise in tackling complex, long horizon tasks. However, existing agent protocols (e.g., A2A
arXiv:2604.18740v1 Announce Type: new Abstract: Purpose: Automated C-arm positioning ensures timely treatment in patients requiring emergent interventions. When a conventional Deep Learning (DL) appro
arXiv:2604.19052v1 Announce Type: new Abstract: Understanding a discourse requires tracking entities and the relations that hold between them. While Large Language Models (LLMs) perform well on relati
arXiv:2604.19488v1 Announce Type: new Abstract: Large language models (LLMs) have achieved substantial advances in logical reasoning, yet they continue to lag behind human-level performance. In-contex
arXiv:2410.16431v4 Announce Type: replace Abstract: The semantic similarity between sample expressions measures the distance between their latent 'meaning'. These meanings are themselves typically rep
arXiv:2604.19457v1 Announce Type: new Abstract: Long-horizon enterprise agents make high-stakes decisions (loan underwriting, claims adjudication, clinical review, prior authorization) under lossy mem
arXiv:2604.19314v1 Announce Type: new Abstract: Recovering corrupted images is one of the most challenging problems in image processing. Among various restoration tasks, blind image deblurring has bee
arXiv:2604.19522v1 Announce Type: new Abstract: Bimanual mobile manipulation requires a seamless integration between high-level semantic reasoning and safe, compliant physical interaction - a challeng
arXiv:2604.18791v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models fail systematically on long-horizon manipulation tasks despite strong short-horizon performance. We show that this
arXiv:2604.18874v1 Announce Type: new Abstract: Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack
arXiv:2601.11632v2 Announce Type: replace Abstract: Multi-modal Large Language Models (MLLMs) for Visual Question Answering (VQA) often suffer from dual limitations: knowledge hallucination and insuff
arXiv:2603.20530v2 Announce Type: replace-cross Abstract: Target localization is a prerequisite for embodied tasks such as navigation and manipulation. Conventional approaches rely on constructing exp
arXiv:2512.15577v2 Announce Type: replace Abstract: In this paper, we focus on online zero-shot monocular 3D instance segmentation, a novel practical setting where existing approaches fail to perform
arXiv:2506.06211v2 Announce Type: replace-cross Abstract: Puzzlehunts are a genre of complex, multi-step puzzles lacking well-defined problem definitions. In contrast to conventional reasoning benchma
arXiv:2512.19442v3 Announce Type: replace-cross Abstract: Diffusion-based generative models have greatly impacted the speech processing field in recent years, exhibiting high speech naturalness and sp
arXiv:2604.18722v1 Announce Type: new Abstract: Cross-lingual transfer in NLP is often hindered by the ``script barrier'' where differences in writing systems inhibit transfer learning between languag
arXiv:2604.18976v1 Announce Type: new Abstract: While Large Language Models (LLMs) are widely used, they remain susceptible to jailbreak prompts that can elicit harmful or inappropriate responses. Thi
arXiv:2604.18758v1 Announce Type: new Abstract: Low-resource machine translation requires methods that differ from those used for high-resource languages. This paper proposes a novel in-context learni
The Never Ending Lore of Harness w/@Vtrivedy10⚡️ Viv brought some amazing perspective around harness design, evals, file systems, RL Envs and so much. 0:00:00 - INTRO 0:03:30 - PhD at Temple Universit
arXiv:2604.19447v1 Announce Type: new Abstract: We present a dual-track pipeline for detecting biblical allusions in literary fiction and apply it to the novels of Cormac McCarthy. A bottom-up embeddi
arXiv:2512.07761v3 Announce Type: replace Abstract: Large language models have seen widespread adoption, yet they remain vulnerable to multi-turn jailbreak attacks, threatening their safe deployment.
Succeeding in the agentic era requires a transformation in your data strategy: moving from human-scale to agent-first workloads, evolving from reactive intelligence to proactive action, and shifting f