Knowledge catalogue
arXiv:2503.16251v2 Announce Type: replace-cross Abstract: Federated Learning (FL) has gained prominence in machine learning applications across critical domains by enabling collaborative model trainin
arXiv:2604.18757v1 Announce Type: cross Abstract: The retina provides a unique, noninvasive window into Alzheimer's disease (AD) and dementia, capturing early structural changes through morphometric f
Rivian and Volkswagen Group's joint venture put Devin to work on testing and ticket triage across a software platform that will power up to 30M vehicles. Devin handles autonomous ticket triage in Slac
arXiv:2604.19146v1 Announce Type: new Abstract: Particle accelerator beamline optimization is a high-dimensional control problem traditionally requiring significant expert intervention. We present RLA
arXiv:2604.19639v1 Announce Type: cross Abstract: Modern world models are becoming too complex to admit explicit dynamical descriptions. We study safety-critical contextual control, where a Planner mu
arXiv:2506.14387v3 Announce Type: replace Abstract: Adapting LLMs with new knowledge is increasingly important, but standard fine-tuning often erodes aligned epistemic abstention: the ability to ackno
arXiv:2604.18835v1 Announce Type: cross Abstract: We propose a scalable, multifactorial experimental framework that systematically probes LLM sensitivity to subtle semantic changes in pairwise documen
arXiv:2604.19219v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training among multiple parties without centralizing raw data. There are two main paradigms in FL:
Karen Freifeld / Reuters: Sources: Micron is pushing the US Congress to pass the “MATCH Act”, which would put new export restrictions on equipment its Chinese rivals use to make chips — Micron Technol
arXiv:2604.19710v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models offer a promising autonomous driving paradigm for leveraging world knowledge and reasoning capabilities, especially
arXiv:2601.02993v4 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has become a key paradigm for reducing factual hallucinations in Large Language Models (LLMs), yet little is kn
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:2508.08080v2 Announce Type: replace Abstract: Symbolic Regression (SR) is a well-established framework for generating interpretable or white-box predictive models. Although SR has been successfu
arXiv:2604.18905v1 Announce Type: new Abstract: The collaboration between humans and robots is critical in many robotic applications, especially in those requiring physical human-robot interaction (pH
arXiv:2604.19295v1 Announce Type: new Abstract: Test-time training (TTT) adapts model parameters on unlabeled test instances during inference time, which continuously extends capabilities beyond the r
Terrible This is insane… The Virginia redistricting amendment on the ballot today is framed as a vote to 'restore fairness in the upcoming elections.' In reality, it turns a state that Kamala barely w
arXiv:2510.08240v2 Announce Type: replace Abstract: Harnessing the power of LLMs requires a delicate dance between being helpful and harmless. This creates a fundamental tension between two competing
arXiv:2412.01763v3 Announce Type: replace-cross Abstract: We study a censored variant of the data-driven newsvendor problem, where the decision-maker must select an ordering quantity that minimizes ex
arXiv:2604.19064v1 Announce Type: new Abstract: In vision-and-language navigation (VLN), self-improvement from policy-induced experience, using only standard VLN action supervision, critically depends
arXiv:2601.09926v3 Announce Type: replace Abstract: Current approaches to proactive assistance move beyond the ask-and-respond paradigm by anticipating user needs. In practice, they either burden user
arXiv:2604.19645v1 Announce Type: new Abstract: An earlier paper (Hong, Potteiger, and Zapata 2026) established that an unoptimized GPT 4.1 prompt predicts fan-reported experience ratings within one p
arXiv:2604.18850v1 Announce Type: cross Abstract: AI systems are increasingly embedded in multi-user social environments, yet most alignment frameworks conceptualize interaction as a dyadic relationsh
Gary Marcus expresses skepticism or concern about an artificial intelligence claim or incident that he finds implausible, suggesting that even young children would recognize the flaw in the reasoning
arXiv:2604.18967v1 Announce Type: new Abstract: Chest X-ray (CXR) radiology report generation (RRG) models have shown rapid progress, yet their clinical utility remains uncertain due to limited evalua
arXiv:2604.19070v1 Announce Type: new Abstract: Zero-shot reasoning on text-rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure wi
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.
arXiv:2501.04410v2 Announce Type: replace Abstract: User simulation is an emerging interdisciplinary topic with multiple critical applications in the era of Generative AI. It involves creating an inte
arXiv:2604.03261v2 Announce Type: replace Abstract: The rise of generative AI is posing increasing risks to online information integrity and civic discourse. Most concretely, such risks can materialis
arXiv:2602.12735v2 Announce Type: replace-cross Abstract: Effectively retrieving, reasoning, and understanding multimodal information remains a critical challenge for agentic systems. Traditional Retr
arXiv:2604.18627v1 Announce Type: new Abstract: Ensuring human safety is of paramount importance in warehouse environments that feature mixed traffic of human workers and autonomous mobile robots (AMR
arXiv:2411.18275v2 Announce Type: replace Abstract: Vision-language models (VLMs) have significantly advanced autonomous driving (AD) by enhancing reasoning capabilities. However, these models remain
arXiv:2507.21563v4 Announce Type: replace-cross Abstract: Recommendation systems often suffer from data sparsity caused by limited user-item interactions, which degrade their performance and amplify p
arXiv:2601.18891v3 Announce Type: replace Abstract: Caribou across the Arctic has declined in recent decades, motivating scalable and accurate monitoring approaches to guide evidence-based conservatio
arXiv:2604.19206v1 Announce Type: new Abstract: The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely ha
Ads Advisor is an agentic conversational experience built with Gemini in Google Ads designed to help maximize performance based on business goals. The tool can help users get personalized answers, und
arXiv:2604.17679v1 Announce Type: new Abstract: Autonomous navigation requires planning to reach a goal safely and efficiently in complex and potentially dynamic environments. Graph search-based algor
arXiv:2604.16446v1 Announce Type: new Abstract: Optical Music Recognition (OMR) aims to convert printed or handwritten music score images into editable symbolic representations. This paper presents an
arXiv:2604.17763v1 Announce Type: cross Abstract: This paper presents a controlled quasi-experimental developer study examining whether a layer-based security training package is associated with impro
arXiv:2604.17046v1 Announce Type: new Abstract: Collisions between cyclists and pedestrians at urban intersections remain a persistent source of injuries, yet few systems attempt real-time warnings to
arXiv:2511.22003v2 Announce Type: replace-cross Abstract: Limited overlap between treated and control groups is a key challenge in observational analysis. Standard approaches like trimming importance
arXiv:2509.24478v2 Announce Type: replace Abstract: Modern neural networks have greatly improved performance across speech recognition benchmarks. However, gains are often driven by frequent words wit
arXiv:2604.17803v1 Announce Type: cross Abstract: Post-training Large Language Models requires diverse, high-quality data which is rare and costly to obtain, especially in low resource domains and for
arXiv:2510.16458v2 Announce Type: replace Abstract: Natural Language Inference (NLI) datasets often exhibit human label variation. To better understand these variations, explanation-based approaches a
arXiv:2604.17325v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) by incorporating retrieved documents and/or generated conte
arXiv:2604.16622v1 Announce Type: new Abstract: Backchannels (e.g., `yeah', `mhm', and `right') are short, non-interruptive feedback signals whose lexical form and prosody jointly convey pragmatic mea
arXiv:2604.18489v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise in lyric-to-melody generation, but models trained with Supervised Fine-Tuning (SFT) often produce musically
arXiv:2505.23114v3 Announce Type: replace Abstract: Human preference data is essential for aligning large language models (LLMs) with human values, but collecting such data is often costly and ineffic
arXiv:2604.18130v1 Announce Type: new Abstract: Digital marketplaces processing billions of dollars annually represent critical infrastructure in sociotechnical ecosystems, yet their performance optim
arXiv:2507.20993v3 Announce Type: replace Abstract: We study how to learn treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text. These poli
APIs and limited releases for AI models are not a safety policy, they’re a business model (which is totally ok as long as you’re transparent about it). Especially on cyber-security, they give a false
arXiv:2604.05983v2 Announce Type: replace-cross Abstract: We present Arch (AI-native Register-transfer Clocked Hardware), a hardware description language for micro-architecture specification and AI-as
arXiv:2603.29068v3 Announce Type: replace Abstract: This paper presents ARCS (Autoregressive Circuit Synthesis), a system for amortized analog circuit generation. ARCS produces complete, SPICE-simulat
arXiv:2604.18468v1 Announce Type: new Abstract: Closed-loop simulation is a core component of autonomous vehicle (AV) development, enabling scalable testing, training, and safety validation before rea
arXiv:2511.02356v2 Announce Type: replace-cross Abstract: Despite extensive safety alignment, Large Language Models (LLMs) remain vulnerable to jailbreak attacks. However, existing methods generally l
arXiv:2601.13707v2 Announce Type: replace Abstract: Hallucinations in large vision--language models (LVLMs) often arise when language priors dominate over visual evidence, leading to object misidentif
arXiv:2604.18187v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have made significant progress in audio understanding, yet they primarily operate as perception-and-answer systems
arXiv:2510.15339v3 Announce Type: replace Abstract: Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However
arXiv:2604.16702v1 Announce Type: new Abstract: Road traffic accidents are a leading cause of fatalities worldwide. In the US, human error causes 94% of crashes, resulting in excess of 7,000 pedestria
arXiv:2604.18572v1 Announce Type: new Abstract: The Platonic Representation Hypothesis suggests that neural networks trained on different modalities (e.g., text and images) align and eventually conver
arXiv:2604.16324v1 Announce Type: new Abstract: The activation memory required for exact backpropagation scales linearly with network depth, context length, and feature dimensionality, forming an O(L