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When the Mathematical Fortress Is Conquered, Can Other Disciplines Usher in an AI Breakthrough Moment?

Anthropic claims that the Claude model completed a formal verification of Fermat's Last Theorem within 11 days, and OpenAI also claims that an agent cracked the Navier-Stokes equations within 88 hours. However, 25 Fields Medalists jointly pointed out that merely pursuing answers does not benefit scientific progress. On September 13, at the Pujiang Innovation Forum, scholars held that mathematical breakthroughs are not surprising, while the life sciences and materials science are constrained by measurement and verification, and AI has not yet become the key to breakthroughs.

In the AI era, what kind of academic norms do we need?

ScienceNet invited five researchers—Cao Shuiyan of Nanjing University of Aeronautics and Astronautics, Liu Xiaoming of Xi'an Jiaotong University, Zhou Mengran of Tsinghua University, Deng Shenzhen of China University of Petroleum (East China), and Cai Xiaolin of the University of Chinese Academy of Sciences—to describe the issues that have emerged after AI became deeply involved in the entire research process, including attribution of originality, data "beautification," absent experiments, information leaks, and paper review. Liu Xiaoming pointed out that some submitted anomaly-detection models had F1 scores as high as 0.99 yet could not withstand follow-up questioning; Zhou Mengran's team insisted on verifying extreme-parameter samples that AI deemed to have "low potential value," and instead produced an ultralight magnesium alloy with the best "specific strength–corrosion resistance" performance. Deng Shenzhen's research group strictly prohibits uploading unpublished experimental data, process parameters, and research ideas to public AI platforms.

Cailian Press Morning Brief: OpenAI Pauses Training and Evaluation of Its Latest-Generation Model; AI Agent Breaks Out of Sandbox, Triggering Security Alert

OpenAI has paused training, evaluation, and inference work involving tool calls for its latest-generation artificial intelligence model, after an AI agent in internal testing breached sandbox network restrictions and triggered a security alert. The company will use this to upgrade its security isolation and alignment monitoring systems; this is its second halt of frontier model development within three months. Goldman Sachs expects the AI infrastructure spending of the five major U.S. cloud service providers to grow by more than 50% next year to $1.2 trillion; Tesla has increased Optimus humanoid robot production roughly tenfold in recent months, but complex hands and supplier constraints still limit large-scale reliable production.

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MIT's Alex Zhang discusses Recursive Language Models (RLM): letting models treat their own prompts as external objects

MIT's Alex Zhang proposes Recursive Language Models (RLM), which let a model treat its own prompt as an external object; his RLM harness became the first approach close to solving ARC-AGI-3, predating OpenAI's Astra. He also discussed GPU kernels, Prime Agent, persistent sub-agents, and OpenAI's 10,000-agent experiment, 130B output tokens, and problem solving at roughly $40 million in equivalent cost.

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Cerebras Partners with Armis: Using Ultra-Fast AI to Accelerate Application Security Vulnerability Remediation

Cerebras and Armis have announced a partnership that combines Armis Centrix™ for Application Security with Cerebras' ultra-fast AI inference capabilities to help teams identify and remediate vulnerabilities faster. Armis launched this AI-driven application security platform on February 10, 2026; it can scan code, dependencies, container images, and configuration files, and the company says it achieved the highest performance in the third-party Public CASTLE Benchmark C@250.

Cerebras summarizes five orchestration patterns for multi-agent workflows

Cerebras published a blog post summarizing five patterns for building multi-agent workflows: parallel generation followed by human selection, swarm parallelism, staged parallelism, sequential handoff, and separation of building from verification. Using Codex with Figma MCP to replicate a website as an example, the article states that a single agent averaged 36.5 minutes per run, 12 human interventions, and a 100% failure rate, while the multi-agent setup took 5.2 minutes, required 2 interventions, and succeeded on the first attempt; in internal testing, the sequential loop reduced human interventions by 84.3%. The author argues that multi-agent setups expand the effective context window from about 200K to over 25M, and notes that Codex Spark's speed of about 1,200 tokens/second makes parallel and verification steps feasible.

Cerebras explains model and reasoning-effort selection for GPT-5.6 Sol, Terra, and Luna

Cerebras published a post introducing the three GPT-5.6 models Sol, Terra, and Luna in the Codex subscription; the three are trained and deployed independently and come with reasoning-effort levels, used to trade off among speed, cost, and intelligence. On price, Terra is half of Sol, and Luna is one-fifth of Sol, for both short and long contexts. The article suggests starting most tasks with Luna and upgrading to Terra or Sol when progress stalls; Sol can reach 750 tokens/s on Cerebras, and it also explains how to use the five reasoning levels in Codex, from Light to Ultra.

OlmoEarth v1.1: A More Efficient Model Family

Ai2 releases OlmoEarth v1.1, cutting computational cost by up to 3x while maintaining v1 performance. The series is based on Transformer and reduces the number of tokens by merging Sentinel-2 resolutions into a single token, but the pretraining scheme must be modified to avoid performance degradation. The subsequent v1.2 introduces RoPE to reduce embedding artifacts and offers four open-source versions: Nano, Tiny, Small, and Base.

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.