Cainew

This appears to be a reference to AI image generation models, combining Stable Diffusion 1.5 with DALL-E 2 technology. The item likely discusses advancements or comparisons in AI-powered image synthesis capabilities.

Granite Embedding Multilingual R2 is an open-source, Apache 2.0 licensed embedding model supporting multiple languages with a 32K token context window. This multilingual embedding solution enables more comprehensive semantic understanding across languages.

Arkon: Enterprise AI Knowledge Hub & MCP Server. Self-hosted knowledge base for teams to manage RAG contexts, access policies, and AI skills. Connect Claude and other LLMs via Model Context Protocol (MCP) for automated, secure organizational knowledge integration.

✨ The agentic HTML editor — your local AI agent writes the HTML, you ship it. 🚀 75 Skills × 9 Surfaces (magazine · deck · poster · XHS / tweet · prototype · data report · Hyperframes) 🛡️ Sandboxed preview · 📤 1-click to WeChat / X / Zhihu / HTML / PNG 🔑 Zero API key — Claude Code / Cursor / Codex / Gemini / Copilot / OpenCode / Qwen / Aider.

Production LLM call layer for AI agents and tools: keep OpenAI/Anthropic/AI SDK/LiteLLM, hot-swap models with MDA presets, and add cache, retries, circuit breakers, key rotation, singleflight, and Python/TypeScript/Rust parity.

Keep Claude Code alive and autonomous without -p. Heartbeat hook + inbox/outbox pattern.

Validate, repair, and retry LLM structured outputs. 13 repair strategies for common JSON malformations, JSON Schema validation, and retry-with-feedback prompts.

Most meeting tools give you notes. Spellar AI gives you memory. It joins your calls, captures every word, and builds context across all your meetings. Ask what a client said three calls ago. Find decisions from last week. See what’s still open. Organize by client, use templates, and choose the AI you trust — OpenAI, Anthropic, Perplexity, Gemini and more!

Naptick is a smart bedside AI sleep companion designed for founders, professionals, light sleepers, and anyone struggling with nighttime stress or doomscrolling. It combines circadian light therapy, 1000+ adaptive soundscapes, room condition intelligence, app-locking, and an on-device AI sleep coach to help users fall asleep faster and wake up refreshed. Unlike passive sleep trackers, Naptick is built phone-free by design and actively helps improve sleep before the night begins.

Implementation of D4RT, Efficiently Reconstructing Dynamic Scenes, from Deepmind

Tendem is a platform where human experts and AI agents complete high-stakes tasks. Submit a task in plain language. AI agents handle the volume. Human experts level up the the final output. What comes back is complete, accurate, and ready to act on. Built by Toloka.ai, a company that has spent more than a decade building human-in-the-loop quality systems for frontier AI labs. Trusted by founders, operators, and AI-native users who need reliable results.

90% of startups die from no money, not bad products. Causo's AI agents find matching investors and email them for you while you ship. Upload your deck or website, get matched with specific partners at relevant VC funds, send your pitch. All on autopilot. Let our raccoons do the work while you ship product and talk to customers.

Notion Developer Platform lets teams build on Notion with CLI, Workers, database syncs, agent tools, webhook triggers, MCP, and External Agents APIs, so data, workflows, and agents can operate inside the same shared workspace.

Design Mode gives designers direct ownership of what ships. Point to any element in the live preview, tweak styles visually, and push straight to your codebase from Figma or Claude Design. No handoff. No translation layer. What you designed is what ships. Finally, real superpowers for designers.

Asteroid lets ops teams and engineers build computer-use agents for browser, Linux, and Windows workflows in minutes. Our meta-agent, Astro, builds the agents, writes scripts as it goes, and makes repeat runs faster and cheaper. Last month, Asteroid agents completed 150,000+ executions across EHRs, benefits portals, insurance carriers, Citrix, desktop apps, and VPN-protected environments.

Arena AI Model ELO History tracks the performance rankings of various AI models over time using ELO rating systems. The data provides insights into how different models have competed and evolved in capability benchmarks.

Learn how new ChatGPT safety updates improve context awareness in sensitive conversations, helping detect risk over time and respond more safely.

We introduce PersonalAI 2.0 (PAI-2), a novel framework, designed to enhance large language model (LLM) based systems through integration of external knowledge graphs (KG). The proposed approach addresses key limitations of existing Graph Retrieval-Augmented Generation (GraphRAG) methods by incorporating a dynamic, multistage query processing pipeline. The central point of PAI-2 design is its ability to perform adaptive, iterative information search, guided by extracted entities, matched graph ve...

Intensive care units (ICU) generate long, dense and evolving streams of clinical information, where physicians must repeatedly reassess patient states under time pressure, underscoring a clear need for reliable AI decision support. Existing ICU benchmarks typically treat historical clinician actions as ground truth. However, these actions are made under incomplete information and limited temporal context of the underlying patient state, and may therefore be suboptimal, making it difficult to ass...

The scalability of robotic manipulation is fundamentally bottlenecked by the scarcity of task-aligned physical interaction data. While vision-language models (VLMs) and video generation models (VGMs) hold promise for autonomous data synthesis, they suffer from semantic-spatial misalignment and physical hallucinations, respectively. To bridge this gap, we introduce RoboEvolve, a novel framework that couples a VLM planner and a VGM simulator into a mutually reinforcing co-evolutionary loop. Operat...

In-context learning (ICL) adapts large language models (LLMs) to new tasks by conditioning on demonstrations in the prompt without parameter updates. With long-context models, many-shot ICL can use dozens to hundreds of examples and achieve performance comparable to fine-tuning, yet current understanding of its scaling behavior is largely derived from non-reasoning tasks. We study many-shot chain-of-thought in-context learning (CoT-ICL) for reasoning and show that standard many-shot rules do not...

Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where solving the task requires chaining multiple retrieval and reasoning steps. Key challenges are that current methods represent reasoning through free-form natural language, where intermediate states are implicit, retrieval queries can drift from intended entities, and errors are detected by the same model that produces the...

Current interactive LLM agents rely on goal-conditioned stepwise planning, where environmental understanding is acquired reactively during execution rather than established beforehand. This temporal inversion leads to Delayed Environmental Perception: agents must infer environmental constraints through trial-and-error, resulting in an Epistemic Bottleneck that traps them in inefficient failure cycles. Inspired by human affordance perception and cognitive map theory, we propose the Map-then-Act P...

Vision-Language-Action (VLA) policies are commonly trained from dense robot demonstration trajectories, often collected through teleoperation, by sampling every recorded frame as if it provided equally useful supervision. We argue that this convention creates a temporal supervision imbalance: long low-change segments dominate the training stream, while manipulation-critical transitions such as alignment, contact, grasping, and release appear only sparsely. We introduce FrameSkip, a data-layer fr...

Voice agents, artificial intelligence systems that conduct spoken conversations to complete tasks, are increasingly deployed across enterprise applications. However, no existing benchmark jointly addresses two core evaluation challenges: generating realistic simulated conversations, and measuring quality across the full scope of voice-specific failure modes. We present EVA-Bench, an end-to-end evaluation framework that addresses both. On the simulation side, EVA-Bench orchestrates bot-to-bot aud...

Long-context modeling is becoming a core capability of modern large vision-language models (LVLMs), enabling sustained context management across long-document understanding, video analysis, and multi-turn tool use in agentic workflows. Yet practical training recipes remain insufficiently explored, particularly for designing and balancing long-context data mixtures. In this work, we present a systematic study of long-context continued pre-training for LVLMs, extending a 7B model from 32K to 128K ...

Evaluation of software engineering (SWE) agents is dominated by a binary signal: whether the final patch passes the tests. This outcome-only view treats a principled solution and a chaotic trial-and-error process as equivalent. We show that this equivalence is empirically false. We evaluate 2,614 OpenHands trajectories from eight model backends on 60 SWE-bench Verified tasks. Of these, 47 have enough passing trajectories to construct task-level process references, yielding a 1,815-trajectory eva...

Flow-based generation in high-dimensional spaces is difficult because velocity prediction requires modeling high-dimensional noise, even when data has strong low-rank structure. We present Asymmetric Flow Modeling (AsymFlow), a rank-asymmetric velocity parameterization that restricts noise prediction to a low-rank subspace while keeping data prediction full-dimensional. From this asymmetric prediction, AsymFlow analytically recovers the full-dimensional velocity without changing the network arch...

Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any-step video diffusion. This limitation arises because consistency distillation replaces the original probability-flow ODE trajectory with a consistency-sampling trajectory, weakening the desirable test-time scaling behavior of ODE sampling. To address th...

Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated abstractions distilled across many episodes into reusable, schema-like lessons. Recent agentic-memory systems pursue the consolidated form: an LLM rewrites past trajectories into a textual memory bank that it continuously updates with new interactions, promising self-improving agents without parameter updates. Yet we find that such consolidated m...

Most existing medical dialogue systems operate in a single-turn question--answering paradigm or rely on template-based datasets, limiting conversational realism and multilingual applicability. We introduce IndicMedDialog, a parallel multi-turn medical dialogue dataset spanning English and nine Indic languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Punjabi, Tamil, Telugu, and Urdu. The dataset extends MDDial with LLM-generated synthetic consultations, translated using TranslateGemma, verif...

Traditional retrieval pipelines optimize utility through stages of candidate retrieval and reranking, where ranking operates over a predefined candidate set. Large Language Models (LLMs) broaden this into a generative process: given a candidate pool, an LLM can generate a subset and order it within a single autoregressive pass. However, this flexibility introduces a new optimization challenge: the model must search a combinatorial output space while receiving utility feedback only after the full...

Sam Altman's business activities are facing scrutiny from GOP members as OpenAI prepares for a potential IPO. The examination raises questions about potential conflicts of interest ahead of the company's public offering.

Anthropic has announced a $200 million partnership with the Gates Foundation to advance AI development and research initiatives. This collaboration aims to leverage AI for addressing global challenges and societal impact.

Medicare has implemented a new payment model designed with AI capabilities in mind, but many technology companies remain largely unaware of this opportunity. The development represents significant potential for AI integration in healthcare reimbursement systems.

This discusses recent controversies and disputes involving Anthropic and its leadership or policies. The piece provides analysis of the key issues and disagreements within or surrounding the company.

May 14, 2026Policy2028: Two scenarios for global AI leadership

This piece explores often-overlooked aspects of AI safety beyond technical alignment concerns. It highlights the importance of institutional, social, and deployment-related safety considerations in AI development.