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Krea's Head of Research @sleenyre will speak at the SGLang Summit

Krea announced that its Head of Research @sleenyre will give a talk at the SGLang Summit on how to scale reinforcement learning and inference for Krea 3. The summit is scheduled to take place from November 12 to 13 at Fort Mason in San Francisco. Krea released this news on October 31, 2026, and no further details about the talk's content or preparations have been disclosed yet.

Krea Head of Research to Speak at SGLang Summit

Krea announced that its Head of Research @sleenyre will give a talk at the SGLang Summit on how to scale reinforcement learning and inference for Krea 3. The summit is scheduled to take place from November 12 to 13 at Fort Mason in San Francisco. Krea released this news on October 31, 2026, and no further details about the talk's content or preparations have been disclosed yet.

View story:Krea's Head of Research @sleenyre will speak at the SGLang Summit

OpenAI Releases GPT-Rosalind-6 System Card Addendum

On October 31, 2026, OpenAI released the GPT-Rosalind-6 system card addendum. The model uses the same set of weights as GPT-6 Astra, with no new training data or post-training added, and is provided only through trusted access to vetted scientific research institutions, enterprises, and government partners. At the highest reasoning budget, MedChemBench v7 averaged a score of 49.6% (GPT-Rosalind-5.5: 28.9%), GeneBench Pro v13 achieved a single-attempt success rate of 37.7% (previous generation: 16.7%), and LifeSciBench Gold averaged 61.3% (previous generation: 44.0%). Under the Preparedness Framework, this release is classified as High capability in the biological and chemical domains, below the Critical threshold, as Critical in the cybersecurity domain, and below High for AI self-improvement. There are currently no subsequent developments.

X.PIN Announces Benchmark Test Results for Iluvatar CoreX Tiange 300

On October 30, 2026, X.PIN relayed and published benchmark test data for the Iluvatar CoreX Tiange 300. Its report claimed that in standard inference tests, the Tiange 300's throughput was 1.05 times that of the Nvidia H200; under MoE workloads, performance improved by 15% and first-token latency decreased by 30%; and when fine-tuning Qwen3-32B, its speed was 1.33 times that of the H100. There are currently no follow-up developments or official responses.

Ibiden decides to invest 1.28 trillion won to expand CPU package substrate production

Japanese semiconductor package substrate manufacturer Ibiden has decided to invest 1.28 trillion won to expand production capacity for high-performance CPU package substrates used in AI servers, with new equipment to be phased into operation starting in fiscal 2029. Ibiden expects that demand for CPU substrates will continue to rise as AI inference workloads grow and agentic AI becomes more widespread. Another report said Ibiden plans to invest 151 billion yen (approximately 6.4 billion yuan) to expand production capacity for high-performance IC package substrates for CPUs at Building 6 of its Gama plant in Gifu Prefecture, also phasing it into operation starting in fiscal 2029, but it did not disclose the scale of the added capacity. In the first half of fiscal 2026, Ibiden posted revenue of 262.546 billion yen and operating profit of 60.116 billion yen, up 34.3% and 84.6% year on year, respectively.

Mistral releases Large 4 weights on Hugging Face

On October 29, 2026, Mistral released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter native multimodal model that continues reinforcement learning on the basis of the preview version, with improved coding and defensive cybersecurity capabilities. According to data published by Mistral AI, the model scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model supports three reasoning modes, uses a modified MIT Mistral Ecosystem Open-Weight license, and is priced at $0.70 per million input tokens, $0.07 for cached input, and $2.10 for output via API. On the same day, Unsloth released a 1-bit GGUF quantized version of the model, stating that it can run locally on a Mac Studio, retains 91% top-1 accuracy after quantization, and provides a download link and usage guide.

Hainan Computing Power and Tencent Cloud Sign Strategic Cooperation Agreement

On October 30, 2026, Hainan Computing Power Technology Co., Ltd. and Tencent Cloud signed a strategic cooperation agreement to jointly create an integrated "AI + computing power" solution for the Hainan government and enterprise market. According to reports by Yicai and Securities Times that day, Tencent Cloud will provide Hainan Computing Power and its customers with resources such as high-performance computing, intelligent computing power, storage and networking, to support the large-scale training and inference needs of the provincial-level artificial intelligence public service platform. The two parties will also carry out business cooperation around large-model Token invocation, and, leveraging the institutional advantages of the free trade port's cross-border data flow regime, explore commercialization paths such as Token going global and cross-border computing power export. No subsequent progress has been disclosed so far.

ARC Prize says scores on three ARC-AGI benchmarks are strongly correlated

On October 29, 2026, ARC Prize proposed a judgment that there is a strong correlation among model scores on the three ARC-AGI benchmarks. After plotting ARC-AGI model scores, the organization found that the power curve v2 = v1^4.53 could explain about 98% of the variation in ARC-AGI-2 scores; models that were flat on ARC-AGI-1 could still differ by more than 20 percentage points on ARC-AGI-2. A similar trend also appeared on ARC-AGI-3, but the low-reasoning tier of Gemini 4 Argon was a clear exception: its ARC-AGI-2 score was only 42.1%, while its ARC-AGI-3 score reached 97.5%. There have been no subsequent developments so far.

ARC Prize says models scoring high on ARC-AGI-2 also tend to score high on ARC-AGI-3

ARC Prize proposes a judgment: models that score higher on ARC-AGI-2 tend to also score higher on ARC-AGI-3. ARC Prize also points out an obvious exception—Gemini 4 Argon at the low reasoning tier scores only 42.1% on ARC-AGI-2, but reaches 97.5% on ARC-AGI-3 under the provider adapter harness. Currently this judgment is released by ARC Prize alone, and there are no other developments or facts yet.

ARC Prize announces the evaluation results of Muse Spark Open on ARC-AGI

On October 29, 2026, ARC Prize announced the verified results of Meta's Muse Spark Open on ARC-AGI: 89.0% on ARC-AGI-1 at $0.29 per task, 57.5% on ARC-AGI-2 at $0.73 per task, and 0.1% on ARC-AGI-3 under the standard harness at $3.9K per task. ARC Prize stated that its v1/v2 scores are close to those of Haiku 5.5 and GPT-6 Luna, but at roughly 9 times the cost of the latter; the model is priced on OpenRouter at $1.25 per million input tokens and $4.25 per million output tokens. As the reasoning tier increases, ARC-AGI-2 rises from 29.4% at medium (an average of 55k tokens per task) to 57.2% at xhigh (180k tokens per task).

Jensen Huang proposes that doubling model speed could double AI lab revenue

On October 29, 2026, Jensen Huang put forward a judgment: under the premise that demand exceeds capacity, doubling model speed (for example, through software or kernel optimization) could double AI lab revenue. The logic is that inference revenue equals the number of tokens served multiplied by the price per token; a faster kernel can increase tokens per second under the same GPUs, power, and data center conditions, and if all the additional tokens can be sold at the current price, a 2x speedup could double revenue with zero additional capital expenditure. Currently, this claim is presented in a report by Rohan Paul, with no other developments or official response yet.

AI/ML API releases Jev Router for automatic model selection

AI/ML API releases Jev Router, an MCP connector for agent tools. According to Testing Catalog, the tool uses TypeSafe's Jev to assess the difficulty of each request and then automatically selects models and tools: simple requests go to lightweight models such as GPT-6 Luna, while high-difficulty reasoning requests go to Claude Opus 5.5, avoiding paying flagship-model rates for simple tasks. Users only need to provide the task; Jev Router first selects the model and tools such as web search and page extraction, then returns the result. No follow-up developments have been reported yet.

Aditya Agarwal explains the five dimensions by which SPC evaluates founders

On October 29, 2026, Aditya Agarwal published an article explaining the five core dimensions by which South Park Commons evaluates founders who have not yet succeeded: curiosity, clarity, depth, magnetism, and engine, rather than a vague "spike" criterion. The article states that this framework has been used for projects such as Baseten (a 2019 bet on inference), Goodfire (2024 interpretability), Gamma, Profound, Density, and Gradient Robotics. SPC emphasizes observing founders' growth slope through long-term in-person community involvement, rather than performance across two or three pitch sessions. There are currently no subsequent developments.

OpenRouter launches a new version of its provider router

On October 30, 2026, OpenRouter launched a new version of its provider router. According to information released by OpenRouter, this is the largest improvement to its provider router since the company was founded. The new router no longer primarily sorts by price; instead, it rewards the highest-quality reasoning based on the actual cost of the request, keeping requests low-cost while also taking speed and reliability into account. OpenRouter stated that providers offering higher value automatically receive more traffic, giving them an incentive to keep improving, and that as providers improve, users' reasoning experience on OpenRouter improves in tandem. The router is currently live, and there is no follow-up development information at this time.

Anthropic trains Claude to flag potential abuse in conversations

Anthropic has disclosed Claude's "self-evaluation" hidden mechanism: after answering a user, the model appends about 10 tokens in the background to generate a risk rating and report it. According to Phoenix Technology, the mechanism has been enabled by default since the release of Claude Fable 5 in June this year, adding less than 2% to inference overhead. In a security blog post, Anthropic explained that the mechanism reuses existing context and the KV cache; on Fable 5 it captured 78% of violating conversations that a heavy-duty classifier could flag, achieved a 94% detection rate for high-risk abuse, and had a 1% false-positive rate. The evaluation results are not shown to users and do not enter subsequent conversations; they are mainly used for offline aggregate screening, while real-time blocking remains the responsibility of other safety classifiers. The company says 21 of 24 out-of-bounds test trajectories were identified in the background, and users must sign an enterprise-grade zero data retention agreement to turn off this review.

Mistral AI launches public preview of Responses API

On October 28, 2026, Mistral AI launched the public preview of the Responses API, aimed at developers building agents on the Mistral platform. According to information posted by Mistral AI on X, the API returns the model's answer, reasoning process, and tool calls as separate entries with each response, making it easier for applications to process them step by step than with Chat Completions. The API is available for all chat models hosted by Mistral, including Large 4. Currently, this event has only this single piece of evidence, with no subsequent developments yet.

Addy discusses how to use Claude Code in the era of Claude Opus 5.5

Addy Osmani, a member of the Anthropic Claude Code team, published a post on October 30, 2026, discussing how to use Claude Code in the era of Claude Opus 5.5. He proposed that starting with the Claude 5 series, users should reduce piling up prompts and hand-holding task breakdowns, and instead use a single prompt to clearly state the goal and acceptance criteria, letting the model run to completion on its own and then stop; reasoning effort can start from /effort medium, and when the goal is unclear, first have Claude interview you to produce a spec, then open a new session to implement it. He mentioned that Claude Code once removed more than 80% of its system prompts for models such as Claude Opus 5 and Claude Fable 5, with no measurable decline in coding benchmarks, and recommended using /doctor prompt-audit to find prompts written for older models. He also said that the Claude Code interactive terminal recently defaulted to auto mode, but this is not a safety guarantee; subagents should be split manually as little as possible, and a single agent plus skills often yields better quality.

StepFun and Together hold Step 5 preview launch night

StepFun and Together Compute co-hosted the Step 5 preview launch night. The event discussed long-horizon agents, tool-calling reliability, inference costs, and what AI breakthroughs mean for the next generation of startups. The panelists included Dan Fu (Together Compute), Mar Hershenson (Pear VC), Saoud Rizwan (Cline), and Joey Wang (Harvey). There is currently no information on subsequent developments.

Oracle announces the introduction of Cerebras AI chips into its cloud services

Oracle announced that it will introduce Cerebras Systems' AI chips into its cloud infrastructure, giving customers more computing power options beyond Nvidia GPUs. According to Data Center Knowledge, Oracle also plans to deploy Google TPUs and inference chips from OpenAI, Cerebras, SambaNova and Positron AI on OCI, with the TPU and Cerebras wafer-scale engine scheduled to come online in 2027, and Positron Atlas already available. CNBC Technology reported the same day that Alphabet announced Oracle will offer Google's TPUs, marking the first time customers can use TPUs outside of Google's infrastructure. No further developments have been disclosed at this time.

Google releases world model Genie 4

On October 27, 2026, Google DeepMind released the world model Genie 4, focused on real-time interactive simulation and introducing native audio to the series for the first time. According to the official introduction, the model is built on capabilities from the Gemini family: Gemini Omni is responsible for making the simulation match the appearance and behavior of the real world, while other Gemini models provide the complex reasoning needed to maintain the simulation's coherence; users can control scenes in real time using natural language, and each world also instantly generates ambient sound, music, and interactive dialogue. The next day, Google DeepMind further explained that Gemini 3.8 Flash breaks down goals in a loop and checks frames to maintain causal coherence across multi-step events, and that Gemini 4 Argon has also been used to jointly simulate tasks with Gemini Robotics; the company said it remains an early experimental model, that object permanence and complex physical interactions still drift during long runs, that all generated content carries a SynthID watermark, and that it is currently being previewed to trusted testers first. On October 28, Ethan Mollick posted that Genie 4 can run DOOM, and tested it generating a game-like environment as it goes, saying the experience is not perfect. On October 29, Peter Yang used Genie 4 to build 5 worlds in real time, arguing that world models are the next big direction for AI after LLMs.