Knowledge catalogue
arXiv:2605.29564v1 Announce Type: new Abstract: When using reinforcement learning (RL) for contact-rich robotic manipulation, vision can provide task-relevant information that accelerates learning bey
VIDEO: @BlueOrigin major New Glenn static fire anomaly at Launch Complex-36 📷 @JerryPikePhoto/@NASASpaceflight Media ANOMALY: @BlueOrigin have suffered a CATASTROPHIC EXPLOSION AT LAUNCH COMPLEX-36 📷
arXiv:2605.29483v1 Announce Type: new Abstract: Wearable devices enable continuous monitoring of physiological signals such as ECG and PPG, but existing mHealth systems are largely limited to task-spe
Watch your avatar speak Spanish, English and Japanese https://x.com/DotCSV/status/2059676610400231599?s=20 Sigo jugando con Omni! Efectivamente el modelo desbloquea un montón de casos de uso (e.g. tra
We’re taking steps to accelerate defensive progress in biology: - Launching Rosalind Biodefense to help trusted builders develop new biodefense and pandemic preparedness capabilities. - Expanding trus
arXiv:2605.28918v1 Announce Type: new Abstract: For sparse, structured reinforcement-learning tasks with semantic reward-function interfaces, LLM-generated reward shaping is better framed as debugging
When we say “LiteParse runs everywhere,” we mean it. Our WASM package is lightweight, minimal, and built for browser and edge runtimes, which makes it a perfect fit for @cloudflare Workers. Using WebA
When you are talking to an LLM, you are speaking to a synthesized work of interactive fiction, not a real being. ChatGPT, Claude, and Sydney are not their neural networks. If any LLM claims to be cons
arXiv:2605.30051v1 Announce Type: new Abstract: A key part of developing large language model (LLM)-powered, automated tutoring tools is student simulation, i.e., using LLMs to role-play as students,
Windows users, this one’s for you. Computer use now works on Windows, so Codex can take action on your Windows computer. And with Windows support for Codex in the ChatGPT mobile app, you can start, re
This resource discusses how healthcare organizations can build learning health systems to succeed under CMS TEAM (Transforming Episode Accountability Models) and advance value-based care (VBC) initiat
This entry documents a Wordle game result where Anthropic solved puzzle #1,804 in 4 attempts, using color-coded feedback (⬛ = incorrect letter, 🟨 = correct letter wrong position, 🟩 = correct letter co
65B private round More than double the size of the largest IPO ever We've raised 65 billion in Series H funding at a $965 billion post-money valuation, led by @AltimeterCap, Dragoneer, @Greenoaks, and
arXiv:2510.07208v2 Announce Type: replace Abstract: Thompson Sampling is one of the most widely used and studied bandit algorithms, known for its simple structure, low regret performance, and solid th
arXiv:2605.28448v1 Announce Type: new Abstract: Optical tweezers (OT) provide piconewton-scale manipulation for delicate biomedical tasks, where visuo-haptic feedback can improve operator awareness by
arXiv:2605.27789v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems are often compared by asking a large language model (LLM) judge which answer is better. For multi-hop RAG,
arXiv:2605.28703v1 Announce Type: cross Abstract: Baldwinian and Lamarckian evolution have existed for a long time in evolutionary algorithms (EAs) without ever dominating the academic literature or p
arXiv:2511.02398v2 Announce Type: replace Abstract: We present a novel decentralized algorithm for coverage control in unknown spatial environments modeled by Gaussian Processes (GPs). To trade-off be
arXiv:2605.28664v1 Announce Type: cross Abstract: Safety detection models require examples of HHH (Helpful, Harmless, Honest)-violating outputs for robust generalization, however such examples are sca
arXiv:2605.28290v1 Announce Type: new Abstract: We study bandit learning in matching markets, where players and arms constitute the two market sides, and the players' utilities are linear in the arm c
arXiv:2604.04295v3 Announce Type: replace Abstract: Automated patent claim validation demands low error tolerance. However, existing approaches face a rigidity-resource dilemma: lightweight encoders c
arXiv:2605.27531v1 Announce Type: cross Abstract: Specification synthesis, the task of automatically inferring formal specifications from program implementations and natural language, is important for
This appears to be a casual, informal social media post using slang terminology, likely discussing plans to attend an event or venue while making light of potential conflicts with a bouncer. The post
arXiv:2605.28730v1 Announce Type: new Abstract: Designing a transit network requires many sequential route extension decisions, but their quality is often visible only after the full network is assemb
arXiv:2605.28337v1 Announce Type: new Abstract: A new variant of the classic capacitated facility location problem, which considers incompatibilities between customers, has recently been introduced in
arXiv:2605.28222v1 Announce Type: new Abstract: We study quality-latency-resource trade-offs in a documentation-grounded retrieval-augmented generation (RAG) system that uses Low-Rank Adaptation (LoRA
Claude: Anthropic adds dynamic workflows to Claude Code, enabling hundreds of subagents to run in parallel for complex engineering tasks such as framework migrations — Early access users and teams ins
arXiv:2605.28021v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs ma
arXiv:2605.28108v1 Announce Type: new Abstract: A long-lived LLM agent, such as OpenClaw, earns its value by acting on a user's preferences and constraints across sessions, not just the current reques
arXiv:2601.18116v2 Announce Type: replace Abstract: We argue that multi-document reasoning is constrained not only by how much text a model can read, but also by how limited query-time evidence budget
arXiv:2605.28103v1 Announce Type: new Abstract: We present a unified experiment, analysis, and benchmark study of multivariate time-series (MTS) anomaly detection. Ten family-representative detectors
arXiv:2605.27715v1 Announce Type: new Abstract: Large reasoning models (LRMs) achieve strong mathematical reasoning performance in English, but remain much less reliable in many low- and medium-resour
Big migrations and refactors are some of a team's most important work, and the easiest to push off to a 'better time' since they'd tie up engineers for a quarter. With dynamic workflows, Claude can no
arXiv:2605.27380v1 Announce Type: cross Abstract: Cross-lingual biomedical entity linking (BEL) maps mentions in any language to unique identifiers in a biomedical knowledge base (KB), supporting clin
arXiv:2605.28632v1 Announce Type: cross Abstract: Cryptographic watermarking is a leading defense for attributing text generated by large language models (LLMs). Existing schemes, including KGW, Unigr
arXiv:2605.27969v1 Announce Type: new Abstract: Post-trained language-model assistants are often optimized to avoid under-answering, encouraging complete, helpful, cautious, and proactive responses. W
arXiv:2605.27962v1 Announce Type: new Abstract: This paper describes our approach for the 8th UG2+ Workshop (CVPR 2026) Track~2, which targets semantic segmentation of outdoor scenes degraded by five
arXiv:2605.28089v1 Announce Type: new Abstract: BuddyBench introduces a privacy-constrained multi-task benchmark for pediatric social-communication personalization. Unlike existing neurodevelopmental
Agent evaluation is most powerful when you combine fast-moving online signals with stable offline baselines. To understand whether your agent is truly improving over time, you need a fixed benchmark a
arXiv:2605.27860v1 Announce Type: new Abstract: Retrieval-augmented generation combined with reinforcement learning has shown promise for grounding large language models in trustworthy medical evidenc
arXiv:2605.28778v1 Announce Type: new Abstract: LLMs' linguistically expressed confidence should faithfully reflect their intrinsic uncertainty. While recent work shows LLMs struggle to use epistemic
arXiv:2605.27835v1 Announce Type: cross Abstract: We introduce CAREF, a parameter-efficient fine-tuning framework that jointly optimizes predictive accuracy and explanation faithfulness via calibratio
arXiv:2605.28330v1 Announce Type: new Abstract: Chance-constrained Model Predictive Path Integral (MPPI) control is increasingly adopted for navigation in dynamic environments to explicitly bound coll
arXiv:2605.27449v1 Announce Type: cross Abstract: In the field of multimodal fact checking, the accuracy of retrieving evidence from different modalities has a significant impact on the downstream cla
arXiv:2605.28128v1 Announce Type: new Abstract: Chinese word segmentation is especially fragile in non-standard text, where language learner errors and other character-level divergences disrupt the wo
arXiv:2605.28516v1 Announce Type: cross Abstract: Simulation-based inference with neural posterior estimation (NPE) often yields overconfident and unreliable posteriors under limited simulation budget
arXiv:2605.28062v1 Announce Type: new Abstract: We describe ConvMemory, a small 3.6M-parameter learned reranker for conversational long-term memory retrieval, trained with cross-encoder teacher superv
arXiv:2605.27654v1 Announce Type: cross Abstract: Generative translation systems are cultural technologies because they decide how socially meaningful cues are rendered within culturally specific gram
arXiv:2605.28369v1 Announce Type: new Abstract: E-commerce platforms have begun recruiting crowdsourced jurors to adjudicate massive volumes of transaction disputes. Unlike formal legal judgment, E-co
arXiv:2605.27954v1 Announce Type: new Abstract: Agentic large language models are increasingly used to solve real-world tasks by reasoning over goals, invoking tools, and interacting with external env
arXiv:2605.27831v1 Announce Type: new Abstract: We study decentralized online convex optimization when agents communicate over a graph and messages may be compressed. Classical decentralized online me
'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
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: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:2605.27823v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulti
arXiv:2605.27904v1 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:2605.27566v1 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:2605.28409v1 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
arXiv:2605.28312v1 Announce Type: cross Abstract: Event-based vision sensors offer asynchronous, high-temporal-resolution measurements that are attractive for low-latency robotic perception, but many
arXiv:2605.27671v1 Announce Type: cross Abstract: Safety defenses for large language models (LLMs) are typically trained and evaluated on single-turn prompts, yet real attacks often unfold as indirect