Knowledge catalogue
arXiv:2605.28683v1 Announce Type: new Abstract: Existing benchmarks have laid the foundation for travel planning agents by establishing API-centric paradigms. However, as the capabilities of Autonomou
arXiv:2605.27882v1 Announce Type: cross Abstract: LLM-based agents score well on search benchmarks, yet real users consistently find results unsatisfying, revealing a persistent evaluation-experience
arXiv:2510.08555v2 Announce Type: replace Abstract: Existing controllable video generation methods are typically designed for rigid, task-specific settings, such as first-frame image-to-video, inpaint
arXiv:2605.28422v1 Announce Type: cross Abstract: Latent reasoning enables reasoning over continuous hidden states rather than explicit tokens, avoiding the language bottleneck and inference overhead
arXiv:2511.11896v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently shown strong potential in vulnerability detection (VD). However, accurately detecting vulnerabiliti
wait… if most people think 5.5 is better than 4.7, i assume that’s due to terminal coding benchmark… 4.8 is still outperformed by 5.5 Introducing Claude Opus 4.8: it builds on Opus 4.7 with sharper ju
We also shipped dynamic workflows in Claude Code (research preview), for tasks too big for one pass. Make sure to default to auto mode so Claude isn't stopping for permissions. It's token-intensive, s
arXiv:2403.16825v2 Announce Type: replace Abstract: We prove that a single-layer neural network trained with the online actor critic algorithm converges in distribution to a random ordinary differenti
arXiv:2602.22096v2 Announce Type: replace Abstract: Editable high-fidelity 4D scenes are crucial for autonomous driving, as they can be applied to end-to-end training and closed-loop simulation. Howev
We're releasing Paris 2.0, which, to our knowledge, is the world's first decentralized trained video generation model. We benchmarked it against a monolithic model trained on the same data and compute
We've raised 65 billion in Series H funding at a 965 billion post-money valuation, led by @AltimeterCap, Dragoneer, @Greenoaks, and @sequoia. This investment will help us advance our research and expa
arXiv:2605.27589v1 Announce Type: new Abstract: Video generation models are increasingly used as world simulators for tasks like driving and robotic manipulation. What matters in these settings is not
arXiv:2605.27851v1 Announce Type: new Abstract: Safety benchmark scores provide incomplete evidence of deployment readiness: aligned language models often adhere to rigid rules even when a situational
arXiv:2605.27673v1 Announce Type: new Abstract: Complex-valued Neural Networks (CVNNs) are often motivated by domains where information is naturally encoded in magnitude and phase. Yet complex-valued
arXiv:2605.28626v1 Announce Type: new Abstract: Hybrid interpretable models combine a transparent component with a black-box model by assigning some examples to the former and deferring the rest to th
Who's going first Introducing Claude Opus 4.8: it builds on Opus 4.7 with sharper judgment, more honesty about its own progress, and the ability to work independently for longer than its predecessors.
arXiv:2605.28187v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as scholar recommenders, shaping who is seen as an expert in academia. Existing audits remain Engli
arXiv:2605.27567v1 Announce Type: new Abstract: Causal discovery is a cornerstone of scientific reasoning, yet whether large language models can perform it reliably remains an open question. Recent be
This entry documents a Wordle game result (puzzle #1,803) where the player achieved a solution in 5 out of 6 allowed guesses. The color-coded emoji sequence shows the player's guess progression, with
arXiv:2506.22726v4 Announce Type: replace Abstract: Deep learning for human sensing on edge systems presents significant potential for smart applications. However, its training and development are hin
arXiv:2605.28390v1 Announce Type: new Abstract: Test-time skill evolving is regarded as a new paradigm for enhancing deployed agentic systems. Existing works mainly focus on hard-coded skill evolving
arXiv:2605.27586v1 Announce Type: cross Abstract: Ensuring agent behaviors in distributed open multi-agent systems remains challenging, especially as populations grow and unaligned agents may exist. W
arXiv:2605.28069v1 Announce Type: new Abstract: Adaptive context compression is vital for scaling Large Language Models (LLMs) to complex, multi-turn agent tasks. However, rule-based compression metho
A closer look at what we released today 🧵 ESMC is a language model trained on billions of protein sequences spanning the full diversity of life. Trained across 2.8 billion sequences, the model is expo
arXiv:2605.26747v1 Announce Type: new Abstract: Large Language Models (LLMs) have brought huge improvements to Artificial Intelligence (AI), which can be applied to general-purpose tasks. However, the
Steven Levy / Wired: A deep dive into how Anthropic's Claude Code and Peter Steinberger's OpenClaw unleashed the AI agent revolution that is rapidly transforming modern computing — The definitive stor
arXiv:2510.14542v2 Announce Type: replace-cross Abstract: We study deep state-space models (Deep SSMs) that contain linear quadratic-output (LQO) systems as internal blocks and present a compression m
I saw a developer asking on Reddit if there was any “sane way” to manage Cloud Run cold starts for AI across multiple regions. They were experiencing startup latencies of up to 20 seconds, a frustrati
arXiv:2605.26533v1 Announce Type: cross Abstract: Automated industrial inspection requires both precise defect localization and structured maintenance report generation; in current practice these task
DeepMind's AlphaFold3 and related tools have generated a comprehensive atlas containing over one billion predicted protein structures and additional billions of protein sequences, representing a major
arXiv:2512.11280v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable performance across a wide range of tasks, but their increasing parameter sizes significantly s
arXiv:2602.11460v2 Announce Type: replace Abstract: Large language models (LLMs) have shown great potential for healthcare applications. However, existing evaluation benchmarks provide minimal coverag
arXiv:2605.26396v1 Announce Type: new Abstract: Large multimodal models (LMMs) have rapidly advanced in perception and reasoning; however, it remains unclear whether these capabilities generalize to d
arXiv:2605.26203v1 Announce Type: cross Abstract: The success of deployed agents relies on their ability to handle open-ended user requests using their inherent capabilities, not only in solving reque
arXiv:2605.26596v1 Announce Type: new Abstract: The token-level extractive compressors widely used for general LM context are structurally inappropriate for LLM agents: across 17 (env, backbone, metho
arXiv:2605.26662v1 Announce Type: cross Abstract: This paper examines how estimates of AI use in scientific writing can be biased when evaluation methods ignore contextual differences across countries
arXiv:2605.26954v1 Announce Type: new Abstract: Safety evaluation of Large Language Models (LLMs) has largely focused on high-resource languages, leaving low-resource languages critically underserved.
Todd Spangler / Variety: Amazon MGM Studios announces the GenAI Creators' Fund, greenlights three AI animated series for Prime Video, and launches an AI production platform with AWS — Amazon MGM Studi
arXiv:2511.02525v2 Announce Type: replace-cross Abstract: The capacitated location-routing problems (CLRPs) are classical problems in combinatorial optimization, which require simultaneously making lo
arXiv:2503.21510v3 Announce Type: replace-cross Abstract: Ensuring that predictions of machine learning (ML) classification models are accompanied by uncertainty estimates is one of the main pillars o
arXiv:2605.26321v1 Announce Type: new Abstract: AI agents are beginning to complete valuable, long-horizon business operations tasks, but training and evaluation environments for enterprise work still
Announcing ESMFold2, our new state-of-the-art structure prediction model capable of predicting structure from single sequences or MSAs. ESMFold2 improves on benchmarks of protein-protein interaction a
arXiv:2605.26172v1 Announce Type: new Abstract: When language models use test-time sampling, they generate multiple reasoning trajectories and select an answer by majority vote. We show that these tra
arXiv:2605.26918v1 Announce Type: new Abstract: Video generation models (VGMs) are rapidly entering classrooms, yet existing benchmarks evaluate only perceptual quality, intrinsic faithfulness, generi
arXiv:2605.27025v1 Announce Type: new Abstract: Hate speech annotation is costly, subjective, and prone to annotator disagreement, making large-scale dataset construction challenging. We systematicall
arXiv:2605.26179v1 Announce Type: cross Abstract: Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands exten
Amazon Web Services Inc. today introduced a new offering designed to help retailers integrate artificial intelligence features into their online stores. AWS Agentic Shopping Assistant, or ASA, combine
arXiv:2602.21636v2 Announce Type: replace Abstract: Abridged: Clinicians commonly interpret 3D medical images by examining multiple anatomical planes rather than relying on volumetric views. In clinic
arXiv:2605.27067v1 Announce Type: new Abstract: Automatic movie trailer generation must select shots from a full-length film and synchronize them with background music. Existing methods either relegat
arXiv:2602.13626v3 Announce Type: replace Abstract: The expanding integration of Large Language Models (LLMs) into recommender systems poses critical challenges to evaluation reliability. This paper i
arXiv:2605.26283v1 Announce Type: new Abstract: Modern deep learning offers powerful tools for automated retinal screening, but it remains unclear how different visual model families compare in realis
arXiv:2509.21106v2 Announce Type: replace Abstract: Search-augmented large language models (LLMs) have advanced information-seeking tasks by integrating retrieval into generation, reducing users' cogn
arXiv:2605.26772v1 Announce Type: new Abstract: Large reasoning models (LRMs) generate chain-of-thought (CoT) traces before producing final outputs, introducing a dynamic internal state that may compl
arXiv:2605.26562v1 Announce Type: new Abstract: While previous research in multivariate time series forecasting has focused on developing complex holistic models, this work advocates for a shift towar
arXiv:2605.26937v1 Announce Type: cross Abstract: Parametric knowledge in large language models (LLMs) is a cornerstone of their success, yet remains poorly understood. Existing knowledge benchmarks t
arXiv:2601.08146v3 Announce Type: replace-cross Abstract: Existing circuit discovery methods rely on templated tasks with clean counterfactuals, limiting their use on diverse natural text. We adapt Co
arXiv:2603.03194v2 Announce Type: replace Abstract: Current code-agent benchmarks primarily evaluate localized issue resolution within a single target repository, leaving under-tested many software en
arXiv:2605.27050v1 Announce Type: new Abstract: We present BhashaSetu, a linguistically enriched English--Marathi parallel dataset addressing persistent data limitations in low-resource neural machine
arXiv:2605.27020v1 Announce Type: cross Abstract: The rapid advancement of diffusion-based image generation models has raised serious concerns regarding potential copyright and privacy infringements i
arXiv:2605.26193v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) has long been a hot research topic in data mining due to its various applications. Recent studies challenge the e