Knowledge catalogue
Mike Wheatley / SiliconANGLE: Dell reports Q1 AI server revenue up 757% YoY to 16.1B and raises its FY 2027 AI server revenue forecast to 60B, up from its prior projection of $50B — Dell Technologies
Jordan Novet / CNBC: Dell reports Q1 revenue up 88% YoY to 43.84B, vs. 35.43B est., and forecasts FY 2027 revenue above estimates; DELL jumps 38%+ after hours — Dell reported its fastest pace of reven
arXiv:2605.28148v1 Announce Type: cross Abstract: The rapid development of LLMs coupled with the introduction of Model Context Protocol (MCP) has revolutionized how intelligent agents interact with AP
arXiv:2605.28421v1 Announce Type: new Abstract: Reinforcement learning has become a central paradigm for advancing reasoning in large language models, yet most existing methods still depend on stronge
arXiv:2605.27809v1 Announce Type: new Abstract: Despite recent progress in backdoor attacks, existing methods remain susceptible to post-training defenses that erase the backdoor through fine-tuning o
arXiv:2605.28163v1 Announce Type: cross Abstract: Multilingual Large Language Models (mLLMs) leaderboards report per-language accuracy but rarely explain why disparities emerge, leaving systemic biase
Deploying a Hermes Agent with Fly, Modal, OpenRouter, & Cloudflare 02:43 Managed vs VPS 06:08 Architecture 15:14 Setup 22:23 Deployment 28:00 Access / OIDC 39:57 Hermes and Open WebUI 52:07 Cloudflare
arXiv:2605.27661v1 Announce Type: new Abstract: We describe our preliminary design of a real-time asynchronous event-based monocular odometry for planetary exploration. Operating under strict computat
arXiv:2605.27470v1 Announce Type: cross Abstract: Graph anomaly detection aims to identify anomaly nodes in attributed graphs and plays an important role in real-world applications. However, existing
arXiv:2605.28355v1 Announce Type: new Abstract: The boundary between real and diffusion-generated time series is becoming increasingly difficult to draw, yet detection in this domain remains underexpl
arXiv:2605.27559v1 Announce Type: cross Abstract: Multi-stage LLM pipelines that perform multi-agent debate, intrinsic self-correction, or retrieval-augmented verification exhibit puzzling aggregate b
'Developers can update Claude’s instructions mid-task without breaking the prompt cache or routing the update through a user turn' wtf? how?? Introducing Claude Opus 4.8: it builds on Opus 4.7 with sh
arXiv:2605.27656v1 Announce Type: cross Abstract: Online recruitment platforms require recommendation methods capable of retrieving relevant job opportunities from large and heterogeneous collections
arXiv:2605.27784v1 Announce Type: new Abstract: LLM agents are governed by long-lived natural-language prompt policies, but individually reasonable standing rules can interact in uninspected ways. We
arXiv:2605.27931v1 Announce Type: new Abstract: Scientific diagrams are essential for communicating complex methodologies in academic papers. A natural way for researchers to specify such diagrams is
Cal Newport discusses whether recent AI advances represent a breakthrough in mathematical problem-solving capabilities, examining the implications of AI systems' improved performance on complex mathem
Did this actually happen? It seems very suspicious. 'An AI consultant tells Axios one of their clients recently spent half a billion dollars in a single month after failing to put usage limits on Clau
arXiv:2605.27418v1 Announce Type: cross Abstract: The imminent integration of autonomous vehicles and mobile robots in urban settings presents a critical safety challenge for future intelligent transp
arXiv:2601.04765v4 Announce Type: replace-cross Abstract: We study how syntactic and semantic information is encoded in inner layer representations of Large Language Models (LLMs), focusing on the ver
arXiv:2512.02019v3 Announce Type: replace-cross Abstract: Diffusion models excel at sampling from complex, unnormalized distributions. In this work, we extend Maximum Entropy Reinforcement Learning (M
arXiv:2605.27487v1 Announce Type: cross Abstract: Handwritten text generation (HTG) conditioned on writer style has been widely studied for Latin scripts, but remains underexplored for low-resource an
arXiv:2605.28456v1 Announce Type: new Abstract: Existing Visual Speech Recognition (VSR) systems commonly rely on left-to-right autoregressive decoding, which can force premature decisions on visually
DiffusionBlocks is a novel training method that enables neural networks to be trained sequentially, one architectural block at a time, rather than end-to-end. This approach, developed by Sakana AI, le
arXiv:2603.00309v2 Announce Type: replace Abstract: The increasingly popular agentic AI paradigm promises to harness the power of multiple, general-purpose large language model (LLM) agents to collabo
arXiv:2605.28335v1 Announce Type: new Abstract: Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine atta
arXiv:2605.27957v1 Announce Type: new Abstract: Disasters cause severe societal impacts, demanding rapid coordination of heterogeneous AI tools, from satellite analysis to flood prediction and damage
arXiv:2605.28491v1 Announce Type: new Abstract: We study real-time audio-responsive character control as a deployment-faithful problem: strictly causal, bounded-latency streaming that must generate co
arXiv:2605.27571v1 Announce Type: new Abstract: Modern analytics systems are fundamentally reactive, requiring users to define queries over increasingly complex and continuously evolving data. In real
arXiv:2605.27823v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulti
arXiv:2605.27649v1 Announce Type: new Abstract: Multilingual LLMs are increasingly used when instruction, source content, and required response languages do not coincide. Existing benchmarks have expa
arXiv:2605.28532v1 Announce Type: new Abstract: Tool-using agents often incur substantial computational cost due to long reasoning chains and iterative tool usage. In practical scenarios, many tasks b
arXiv:2605.28787v1 Announce Type: cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows. For more than a decade, semantic metadata like schema.
arXiv:2605.27935v1 Announce Type: new Abstract: Recent mechanistic studies suggest that large language models (LLMs) may utilize their depth inefficiently in standard single-turn tasks. Whether this s
arXiv:2605.27772v1 Announce Type: cross Abstract: Audio large language models (Audio LLMs) demonstrate strong performance on speech understanding tasks, yet their ability to understand paralinguistic
arXiv:2605.28129v1 Announce Type: new Abstract: Clinical foundation models are evaluated with factual or exam-style medical QA, but treatment decisions must change when patient context changes. We int
arXiv:2605.28277v1 Announce Type: new Abstract: Whether large language models (LLMs) construct internal spatial world models from pure-text descriptions remains contested, and whether such capabilitie
arXiv:2605.28515v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become an integral part of software development, especially with the advent of agentic capabilities. Yet, many front
arXiv:2605.27773v1 Announce Type: cross Abstract: When a language model sees a document contradicting its training knowledge, it must choose: follow the document or trust itself. Prior work proved thi
arXiv:2601.17363v2 Announce Type: replace-cross Abstract: This study investigates whether readers prefer AI-generated short stories in Italian over one written by a renowned Italian author. In a blind
arXiv:2605.27923v1 Announce Type: cross Abstract: The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical ma
Barbara Moens / Financial Times: Doc: the EU is preparing emergency powers to intervene in Europe's chip supply chains during shortages, including by forcing chipmakers to override contracts — Chipmak
arXiv:2602.16872v2 Announce Type: replace Abstract: Optical Character Recognition (OCR) is a fundamental task for digitizing information, serving as a critical bridge between visual data and textual u
arXiv:2509.04192v2 Announce Type: replace Abstract: A Markov logic network (MLN) M determines a probability distribution P_n^M on the set mathbf{W}_n of structures, or ``possible worlds'', with domain
Windsurf is a software application available for download from windsurf.com, allowing users to try the program. This appears to be a promotional post from the official Windsurf social media account en
arXiv:2605.27904v1 Announce Type: new Abstract: Time series forecasting in real-world settings often depends not only on historical observations, but also on external context that must be actively dis
arXiv:2605.28678v1 Announce Type: new Abstract: Speculative reasoning has recently been proposed as a means to accelerate reasoning-intensive generation in large multimodal models, but its effectivene
arXiv:2605.28544v1 Announce Type: new Abstract: Pretrained foundation models have become an important basis for end-to-end autonomous driving. In contrast to vision-language models pretrained primaril
arXiv:2603.21465v2 Announce Type: replace Abstract: Developing efficient CUDA kernels is a fundamental yet challenging task in the generative AI industry. Recent research leverages Large Language Mode
arXiv:2307.06240v2 Announce Type: replace-cross Abstract: The Drone Swarm Search project is an environment, based on extsc{PettingZoo}, that is to be used in conjunction with multi-agent (or single-ag
arXiv:2605.28018v1 Announce Type: new Abstract: Given the real-time demands of UAV tracking, many methods simplify the backbone to reduce computation, but this often weakens feature representation and
arXiv:2605.28269v1 Announce Type: new Abstract: Dynamic topic modeling is widely used to analyze evolving trends in scientific literature, medical records, and social media. Traditional topic models r
arXiv:2605.27566v1 Announce Type: new Abstract: Progress in neural combinatorial optimization for Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is currently hindered by a methodological tension
arXiv:2605.27428v1 Announce Type: new Abstract: Edge deployments of generative inference increasingly face two practical realities: per-device per-model performance is often unknown at deployment time
arXiv:2510.11170v2 Announce Type: replace-cross Abstract: With the rise of reasoning language models and test-time scaling methods as a paradigm for improving model performance, substantial computatio
arXiv:2605.27846v1 Announce Type: new Abstract: Large Reasoning Models are typically trained via reinforcement learning from verifiable rewards (RLVR). However, existing approaches adopt fixed weights
arXiv:2602.02150v2 Announce Type: replace-cross Abstract: Test-time reinforcement learning generates multiple candidate answers via repeated rollouts and performs online updates using pseudo-labels co
arXiv:2605.28272v1 Announce Type: new Abstract: Real-time synthesis of high-fidelity 3D character motion from audio is a pivotal component for next-generation interactive avatars and virtual assistant
Economy then vs now: GDP growth: 2024 2.8% 2026 1.6% Inflation: 2024 2.9% 2026 3.8% Wages: 2024 rising faster than inflation 2026 rising slower than inflation Unemployment rate: 2024 4% 2026 4.3% Jobs
arXiv:2605.28510v1 Announce Type: cross Abstract: Large language models (LLMs) for code completion and generation are increasingly used in software development, yet they may reproduce training example
arXiv:2605.28409v1 Announce Type: new Abstract: Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for