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ARC Prize publishes evaluation results for DeepSeek V4.1 Flash 1017
On November 3, 2026, ARC Prize published the verified ARC-AGI results for DeepSeek V4.1 Flash 1017: 78.1% on ARC-AGI-2 at $0.16 per task, and 94.5% on ARC-AGI-1 at $0.06 per task; on ARC-AGI-3 it scored 5.8% at a cost of $1.2K under the provider adapter harness, and 0.2% at $1.0K under the standard harness. ARC Prize stated that the model leads open-weight models on ARC-AGI-3 and ARC-AGI-2, and matches the best open-weight model results on ARC-AGI-1. Compared with the original V4.1 Flash of September 10, 2026, 1017's ARC-AGI-3 score rose from 2.68% to 5.81% on the semi-private set while falling from 11.05% to 8.97% on the public set; at maximum reasoning effort, the reasoning tokens consumed by its first action decreased by an average of 36% across 25 public games and by 48.6% across 55 semi-private games.
On November 3, 2026, ARC Prize published the verified ARC-AGI results for DeepSeek V4.1 Flash 1017: 78.1% on ARC-AGI-2 at $0.16 per task, and 94.5% on ARC-AGI-1 at $0.06 per task; on ARC-AGI-3 it scored 5.8% at a cost of $1.2K under the provider adapter harness, and 0.2% at $1.0K under the standard harness. ARC Prize stated that the model leads open-weight models on ARC-AGI-3 and ARC-AGI-2, and matches the best open-weight model results on ARC-AGI-1. Compared with the original V4.1 Flash of Sept
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AWS releases a tutorial on distilling a shopping agent with SeqKD on SageMaker AI
On November 3, 2026, the official AWS Machine Learning blog published a tutorial on how to build an efficient shopping agent on Amazon SageMaker AI using sequence-level knowledge distillation (SeqKD). The tutorial uses GLM-5.3 on Amazon Bedrock as the teacher model to generate trajectories, uses the ShoppingBench scorer to retain only trajectories that solve the task, and then performs serverless SFT on the Qwen3.5-4B student model. On 324 held-out test questions, the average absolute success rate of the distilled 4B student rose from 0.297 to 0.541, essentially on par with the teacher model and mid-sized frontier models; when the teacher's reasoning process was removed and only tool calls were learned, the average success rate dropped to 0.415. The tutorial is now publicly available alongside the official blog post.
On November 3, 2026, the official AWS Machine Learning blog published a tutorial on how to build an efficient shopping agent on Amazon SageMaker AI using sequence-level knowledge distillation (SeqKD). The tutorial uses GLM-5.3 on Amazon Bedrock as the teacher model to generate trajectories, uses the ShoppingBench scorer to retain only trajectories that solve the task, and then performs serverless SFT on the Qwen3.5-4B student model. On 324 held-out test questions, the average absolute success ra
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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).
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
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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.
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 un
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Elorian and Mercor Release DecoyCount Benchmark
On October 30, 2026, Elorian and Mercor jointly released the DecoyCount benchmark to test whether models are truly counting objects or relying on visual shortcuts. According to Latent Space, frontier models all scored below 50% accuracy on this benchmark, with the highest being GPT-6 Astra at 43.17%. Elorian said its visual reasoning model Glimpse scored 50.48% in a single run on 1,660 questions, making it the only model to exceed 50%, but that result came from internal testing and did not use an agent or tool framework, whereas the six models in the paper each took the average of three runs. Elorian CEO Andrew Dai said the benchmark examines both vision and reasoning, and can distinguish whether a model failed to see the objects or miscounted them.
On October 30, 2026, Elorian and Mercor jointly released the DecoyCount benchmark to test whether models are truly counting objects or relying on visual shortcuts. According to Latent Space, frontier models all scored below 50% accuracy on this benchmark, with the highest being GPT-6 Astra at 43.17%. Elorian said its visual reasoning model Glimpse scored 50.48% in a single run on 1,660 questions, making it the only model to exceed 50%, but that result came from internal testing and did not use a
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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.
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 cont
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