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Ai2 Releases MolmoMotion, a Language-Guided 3D Motion Prediction Model

Ai2 releases MolmoMotion: by inputting video frames, 3D points on objects, and action descriptions, it can predict the trajectories of these points in 3D space over the next few seconds, outperforming existing prediction methods. It also open-sources the MolmoMotion-1M dataset (derived from 1.16M videos) and the PointMotionBench benchmark (2.7K video clips), and releases the model weights. In simulation, a control policy based on MolmoMotion achieved a 76.3% success rate on pick-and-place tasks, higher than the 56.0% achieved by one based on Molmo 2.

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OpenAI Team Responds to Computer Use Progress Doubts, Recaps One-Week Sprint on Decisions API

In a podcast after OpenAI DevDay, Ari Weinstein, who is in charge of the Computer Use agent, responded to Dwarkesh's doubts about Computer Use progressing slowly, saying it is now completely different from a few months ago because the model has become better at debugging and retrying, and can write JavaScript to execute multiple steps at once while combining screenshots, accessibility, DOM, and Playwright. He also mentioned that Dots provides each Dot with a cloud Linux virtual computer, and that Computer Use has entered the Agents API. Nikunj Handa, head of the OpenAI API team, introduced updates to GPT-6 such as asynchronous tool calling, mid-turn steering, WebSockets, UltraFast inference, prompt caching and pre-warming, and compaction, and said the Decisions API is based on Luna weights, with no new model trained, and took only about a week from the emergence of the idea to a prototype.

After Meta's personal agent Muse went viral, who is most likely to build a Chinese version of Muse

Meta's personal AI agent Muse launched on September 8, surpassing 2.5 million downloads in 13 days, with 642,000 daily active users on the US mobile end, nearly 3 times that of ChatGPT in its early launch period; on September 21, Meta's market value rose by more than $190 billion in a single day. The article benchmarks domestic players across four items—entry point, payments, ecosystem, and execution capability—arguing that Tencent's Xiaowei has the most similar foundation but is constrained by WeChat's boundaries, Alibaba's Qwen is closest to transactions but lacks lifestyle scenarios, and ByteDance's Doubao phone relies on simulated operations and faces platform risk controls; in the short term, it is more likely that butlers within each ecosystem will coexist.

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Anthropic's Thariq on the Next Phase of Claude Code: Claude Mods, Projects, and Agent Security

Anthropic's Thariq Shihipar introduced the next phase of Claude Code on the Latent Space podcast, including Claude Mods for customizable harnesses, Projects, Claude Tag, and recent releases such as Opus 5.5 and Sonnet 5.5. He calls prompting the core skill for using Claude Code, recommending that users first spend time filling in context and allocate effort by task—for example, high or maximum for code review and security, and low or medium for UI-type tasks. He also discussed agent security, including sandboxing, prompt injection, Auto Mode, and Anthropic's "Pacing the Frontier" position.

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When Young People No Longer Worship Silicon Valley: A Crisis of Trust in AI Leaders Among America's Younger Generation

A CNBC and Generation Lab survey of 1,088 Americans aged 18 to 34 found that distrust of nine tech leaders ranged from 65% to 81%, and 45% believed AI would harm their career prospects. Pew data shows that 66% of Americans aged 18 to 29 have already used AI chatbots, but the share of this same group who believe AI will reduce employment rose from 61% in 2024 to 73% in 2026. The author, Chen Yongwei, argues that this wariness stems from narrowing entry points into employment and negative past experiences with digitalization, and advocates letting affected people participate in negotiations over AI rules.

Generating Images and Videos with vLLM-Omni on SageMaker AI (Part 2)

On Amazon SageMaker AI, deploy two endpoints using the same AWS vLLM-Omni DLC image: a real-time endpoint running FLUX.2-klein-4B to generate images, and an asynchronous endpoint running Wan2.1-VACE-1.3B for image-conditioned video generation. A text prompt first generates a PNG, which is then sent along with a motion prompt to the video endpoint; the generated MP4 is retrieved from Amazon S3, and the example also provides a command-line workflow and an optional Streamlit interface.

Anthropic releases Claude Sonnet 5.5, 30% faster than Sonnet 5 with unchanged per-token pricing

Anthropic has released Claude Sonnet 5.5, the second model in the Claude 5.5 family. It is smarter and more efficient than Sonnet 5 and 30% faster, with unchanged per-token pricing; because it needs fewer tokens to complete the same work, costs for most tasks drop by up to 30%. The official recommendation is to use Sonnet 5.5 for well-scoped everyday tasks such as bug fixing and rapid feature iteration, while complex long-running work should still use Opus 5.5; Haiku 5.5 will join the family in the coming weeks. The model ID is claude-sonnet-5-5, and it is already available on platforms including the Claude API, Amazon Bedrock, Google Cloud, and Microsoft Foundry. Starting with v2.1.284, the sonnet alias in Claude Code points to this model, which runs at medium effort by default and natively supports a 1M context window.

An agentic conversational video intelligence solution built on AWS

AWS has released an agentic video intelligence solution based on the Strands Agents SDK, in which a single AI agent orchestrates Amazon Bedrock, Amazon Rekognition, and Amazon Transcribe at runtime, allowing users to query video content by asking questions in natural language. Responses for already-analyzed content are returned within 1 second, while the first analysis of a new video takes 5–10 minutes; one media and entertainment company used it to reduce the manual review time for more than 200 multi-hour recordings by about 80%.

Biosecurity Is an AI Arms Race: Radical Numerics CEO Eric Nguyen on Genomic Language Models

Eric Nguyen, co-founder and CEO of Radical Numerics, believes that biosecurity is an AI arms race, and that models which enhance biological capabilities can equally help defenders keep pace. The company is building genomic language models (GLMs) that use chain-of-thought reasoning and multimodal perception in the language of DNA to address problems involving RNA, proteins, and more; its team previously participated in developing Evo and Evo 2 at Arc Institute. Nguyen says defenders are currently at a disadvantage in this race.

Fireworks ARCv3 Cuts RL Weight Update Size by Nearly 50%, Enables Cross-Region Training

Fireworks has released the ARCv3 compressor. Across 1,000 production RL weight update deltas, its transfer payload is nearly 50% smaller than ARCv2, with the average delta at about 0.19% of the original BF16 weight size (versus 0.36% for ARCv2), and it can losslessly reconstruct the trainer's exact BF16 weights. The smaller transfer volume lets rollout clusters catch up with the current policy faster, making it more feasible to use compute across regions than to rely on a single large co-located cluster. ARCv3 is available in the Fireworks Training API via fireworks-delta-compression, for teams running their own trainers alongside Fireworks rollouts.

Evaluating Agents Equipped with Skills Using Strands Evals and Amazon Bedrock AgentCore

The Strands Evals SDK and Amazon Bedrock AgentCore Evaluations add three new skill-oriented evaluators: Skill Selection Accuracy returns a binary result for each invoked skill, Skill Instruction Following gives a five-level rating for each prescribed step, and Strands Evals also provides a deterministic Skill Invoked check. These evaluators can run on trajectories recorded by Strands Evals or on OpenTelemetry traces, and are used to pinpoint two types of failures: selecting the wrong skill and incompletely executing instructions; all are operated through the AgentCore CLI.

TypeSafe CEO Diogo Almeida on Jev: Building a System One Model for Production, Not for God

InstructGPT paper author and TypeSafe CEO Diogo Almeida detailed his new model Jev on the Latent Space podcast, describing it as a System One large programmatic model oriented toward code consumption, with its core innovation being an unpublished RLCD (reinforcement learning for calibrated decision-making), optimized for "intelligence per dollar" rather than human preference feedback. He criticized RLHF for causing mode collapse, sycophancy, and hallucinations, arguing that safety alignment suits products like ChatGPT but fragments intelligence at the API layer, and said TypeSafe would rather be seen as a data lab, refusing to publish benchmark tests. He revealed that Jev's daily token consumption has exceeded one trillion since launch, and previewed follow-up models such as ReasoningJev.

What are agentic workflows? A practical guide to automation

Agentic workflows let AI agents reason at each step and decide the next action themselves, rather than executing along a fixed script. They consist of an LLM reasoning layer combined with RAG, memory, and guardrails; high-risk steps still go through fixed approvals, and anything beyond their authority is escalated to a human. ElevenLabs' ElevenAgents Workflows editor can explicitly map decision points, routing between specialized sub-agents or human agents.

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AIUC Completes $40 Million Series A, Launches AIUC-1 Agent Safety Standard and Insurance Program

AIUC announced the completion of a $40 million Series A funding round, led by Ribbit Capital and First Harmonic, with customers including Cursor, Harvey, Lovable, and ElevenLabs. Its AIUC-1 standard covers agent safety, security, and reliability, requiring thousands of simulated tests each quarter for jailbreaks, hallucinations, and data leaks, with a certification period of about 3 to 10 weeks. ElevenLabs has purchased the first AI agent insurance policy, underwritten with participation from Lloyd's of London, and AIUC's evaluation results are used directly for pricing.

Ant Ling Open-Sources Native Multimodal Model Ling-3.0-flash-VL

Ant Ling has released and open-sourced Ling-3.0-flash-VL, the first native multimodal large model in the Ling series. Built as an extension of the Ling-3.0-flash MoE architecture, it has 124B total parameters and 5.5B activated per inference, natively supports image, text, and video inputs, and has a 256K Token context window. The company says native multimodal joint training improved the model's score on the Artificial Analysis Intelligence Index v4.1.1 by 4 points over the text-only version, and it introduces a visual feedback loop of observe, act, verify, and correct for front-end replication, GUI Agent, and medical report interpretation. The model is now available for free trial on Ling Studio, and the BF16 and FP8 versions have been open-sourced on Hugging Face and ModelScope.