Cainew

A comparison of music video generation capabilities between Claude Fable 5 and GPT-5.6 Sol, with the project costing approximately $100 to produce.

LM Studio Bionic introduces an AI agent designed specifically to work with open source models, enhancing their usability and deployment capabilities.

Claude doesn't know what happens in GPT. Neither one really knows who you are or what your company does. Now they can. Unabyss gives Claude memories from your other AI agents and everyday apps: email, Drive, GitHub, Notion, meeting recorders, and 20+ more. It saves new memories too, so GPT and Cursor stay in sync with the exact same context - sharper than wiring each tool into Claude one by one. Finally, a real memory that follows you. Private. Portable.

The only GTM orchestration platform you will need to successfully take your products & services to market. Pebbles AI is a Go-To-Market Operating System built for B2B revenue teams. It brings strategy, lead generation, outreach, sales, & shared company knowledge into one AI-powered workspace. Using neurosymbolic AI trained on your business, it helps teams plan campaigns, personalize outreach, generate qualified leads, & execute without switching between disconnected GTM tools.

A foundational resource providing a concise overview of reinforcement learning principles, techniques, and applications for those learning the field.

Kimi K3 is the world's first open 3T-class model — frontier performance across coding, knowledge work, and reasoning, with native multimodality and 1M context.

Basedash now suggests the analysis before you ask. It studies your connected data, your past chats, and the dashboards you've built, then generates personalized suggestions — questions worth asking, dashboards worth building, automations worth scheduling. Click one and the work starts. Used ideas are replaced with fresh ones, so the well never runs dry. Every suggestion is generated per person, for growth, finance, and ops alike. No more blank page. Your analyst makes the first move.

Aye is a Chromium-based AI browser for macOS and Windows that gives web work a teachable AI intern. It reads visible pages, plans steps, and works through normal browser actions: clicking, typing, scrolling, switching tabs, and checking results. Summarize pages, research across tabs, draft replies, and automate repeatable workflows. Turn recurring tasks into reusable skills, separate accounts with profiles, and stay in control with reviewable progress and approval for sensitive steps.

A demonstration of a real-time visualization showing how bots interact with an SSH honeypot, providing insights into automated attack patterns and security threats.

A breakthrough in sandbox technology enables rapid scaling to 1 million concurrent sandboxes in seconds, dramatically improving infrastructure efficiency for code execution and testing environments.

Ring-Zero demonstrates scaling zero-shot reinforcement learning to models with a trillion parameters, achieving emergent reasoning capabilities at massive scale.

Large language models are increasingly trained as interactive agents for long-horizon tasks involving multi-turn interaction, tool use, and environment feedback. Outcome-based reinforcement learning (RL) provides a practical optimization paradigm, but its sparse trajectory-level rewards offer limited guidance on intermediate decisions, leaving a supervision gap between episode-level outcomes and token-level policy learning. We propose SEED (SElf-Evolving On-Policy Distillation), a self-evolving ...

We present Wan-Streamer v0.3, which reframes our native-streaming interaction model under a single organizing view: a video is a world plus an event stream. The world is the persistent context in which a video unfolds, including the environment, scene, subjects, ambient acoustic conditions, voice characteristics, and other relatively stable conditions. The event stream is everything that changes over time within that world, including scene or environmental changes, subject behavior, speech, and ...

Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents. However, as interaction histories grow, agents increasingly struggle to track task progress. When search attempts fail to yield useful evidence, current single- and multi-agent systems can become trapped in repetitive loops, wasting search budgets and ultimately compromising the quality and completeness of the final output. We introduce SearchOS, a system-level multi-age...

In-context learning is commonly interpreted as a form of conditional inference, in which the prompt specifies a context and the model's output is treated as an estimate of the corresponding conditional distribution. If this interpretation holds, then LLM estimates should satisfy basic probabilistic identities. In particular, the law of total probability asserts that prior-weighted conditional distributions aggregate into population-level marginals over any valid partition of the population. In t...

A growing gap separates inference context lengths from RL post-training: inference systems are approaching million-token contexts, while post-training workloads often remain at 256K tokens or below and rely on length generalization at deployment. The gap is especially important for AI agents, whose observations, tool outputs, documents, and prior decisions accumulate over long trajectories. LongStraw is an architecture-aware execution stack for million-token RL post-training under a fixed GPU bu...

Visually impaired individuals (VIIs) encounter significant daily challenges due to limited access to visual information. Although Multimodal Large Language Models (MLLMs) have achieved impressive results on general vision and language tasks, their practical utility in real-world blind assistance still remains largely underexplored. To fill this gap, we introduce VIABench, a comprehensive video benchmark specifically designed to evaluate MLLMs in Visually Impaired Assistance scenarios using first...

Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current open-source models remain limited in several ways. They often struggle to generalize across diverse video types, making them effective only in specific domains. High computational demands further restrict their efficiency and scalability. Moreover, most models are only partially open, with key components such as train...

Recent robot foundation models operate with single-step or short-history visuomotor context. We introduce Test-Time-Training Robot Policies (RoboTTT), a robot model and training recipe that scale visuomotor context to 8K timesteps, three orders of magnitude beyond state-of-the-art policies, without growing inference latency. At this context length, we unlock new robot capabilities: one-shot in-context imitation from human video demonstrations, on-the-fly policy improvement, robustness to perturb...

CAD-to-image alignment aims to estimate an object's 9D pose (rotation, translation, and anisotropic scale) from a single RGB image, enabling applications in robotics and augmented reality. Recent zero-shot methods use visual foundation models to match image regions to CAD models, yet typically their correspondences are appearance-driven and degrade under occlusion or sim-to-real domain shift. To address these limitations, we introduce SUFLECA (Scaling Up Feature LEarning for CAD Alignment), a we...

Music generation foundation models have recently attracted significant industry attention. However, achieving efficient generation and high-fidelity long-form audio while supporting controllability remains challenging. To address these needs, we present WanSong, a simple yet powerful approach for long-form, commercial-grade song generation. Unlike autoregressive (AR) and cascaded multi-stage pipelines (\eg, AR followed by diffusion), WanSong is a pure diffusion-based model that directly generate...

MeanFlow generators achieve fast few-step sampling by predicting average velocities over time intervals, making them attractive for efficient generation. Reinforcement learning (RL) has become a powerful way to align diffusion and flow models with human preferences and task-specific objectives. In particular, DiffusionNFT offers an efficient forward-process RL framework that does not require reverse-process trajectories or likelihood estimation. However, applying such RL methods to MeanFlow rema...

World-action models (WAMs) are emerging as a promising foundation for embodied control: rather than predicting actions alone, they learn representations that couple action generation with future world prediction. This coupling is often viewed as a source of robustness, interpretability, and safety, as a robot's action can in principle be checked against its imagined future. In this paper, we show that this assumption is fragile. We introduce BadWAM, a unified framework for modeling and evaluatin...

Video models are evolving into vision foundation models, yet they still lack human-like multi-step reasoning. Streaming autoregressive diffusion models are efficient but limited in reasoning, while bidirectional diffusion enables global revision with high inference costs due to dense frame-level denoising. Both paradigms struggle to achieve logical consistency and low-latency streaming for complex reasoning tasks. We propose HDR (Hierarchical Denoising for Visual Reasoning), a unified framework ...

This article explores how artificial intelligence is being applied to cryptography and reveals insights AI discovered while analyzing OpenVM's ZkVM zero-knowledge virtual machine.

Apple has sent legal letters to dozens of OpenAI employees, likely related to intellectual property or non-compete disputes as the companies navigate competitive dynamics.

Mozilla examines the current landscape of open source AI, discussing its development, challenges, and opportunities in the evolving AI ecosystem.

Sarah Friar, CFO of OpenaAI, introduces a practical AI scorecard to measure ROI through useful work, cost per successful task, dependability, and return on compute.

The concept that human oversight is essential for AI systems is increasingly challenged as automation advances, raising questions about the practical limits and necessity of human-in-the-loop approaches.

An critical examination of a problematic feature in Claude Code, analyzing its design flaws and exploring why it may be considered a misfeature.