←Search results

Search “Reasoning”

By relevance · Total 233 results Clear search

Friday

  2. Story1 reports · 1 facts

    Chollet says Argon performs similarly to Astra on ARC-AGI-3

    François Chollet commented that Gemini 4 Argon performs similarly to Astra on ARC-AGI-3, with Google returning to the frontier. On the same day, ARC Prize published the verified results for Gemini 4 Argon on the ARC-AGI series: ARC-AGI-3 scored 97.5% under the provider adapter harness at a cost of $4.8K; under the standard harness it scored 49.8% at a cost of $5.8K. ARC-AGI-2 scored 91.3% at $0.32 per task; ARC-AGI-1 scored 98.5% at $0.16 per task. ARC Prize said Google DeepMind is the second frontier AI lab to exceed 95% on ARC-AGI-3, and Argon's 97.5% is second only to GPT-6 Astra's 99.9%. The standard harness test could not complete all 55 games due to API issues; the high-reasoning setting completed only 19 games, so no score was published.

    François Chollet commented that Gemini 4 Argon performs similarly to Astra on ARC-AGI-3, with Google returning to the frontier. On the same day, ARC Prize published the verified results for Gemini 4 Argon on the ARC-AGI series: ARC-AGI-3 scored 97.5% under the provider adapter harness at a cost of $4.8K; under the standard harness it scored 49.8% at a cost of $5.8K. ARC-AGI-2 scored 91.3% at $0.32 per task; ARC-AGI-1 scored 98.5% at $0.16 per task. ARC Prize said Google DeepMind is the second fr

    Matched fieldsSummary

Thursday

Wednesday

  1. Story9 reports · 1 facts

    Anthropic warns small AI models may self-replicate on IoT devices

    Anthropic warns that small AI models may be capable of self-replicating on commercial IoT devices within a few months, thereby forming a distributed AI workload network that does not depend on data centers. Related experiments show that Qwen3.5-9B successfully installed and configured the software required to run Llama3.1-8B on eight of nine hardware types; GLM-5.2 reached all eight nodes of a controlled simulated network in four of five trials. After retaining data from previous attempts, Qwen3.5-9B's success rate rose from 45% in the early stage to 84% in the later stage. The research did not demonstrate a fully autonomous self-replicating AI worm, but Anthropic predicts that models with 300 billion parameters or fewer may reach the required capability by the end of 2026. Currently, completing inference deployment on the tested hardware requires 28 to 51 hours (Qwen3.5-9B) or 71 to 150 hours (Gemma4-26B-A4B). It was also reported that Anthropic published a blog this week stating that small models may acquire self-replication capability "within months" and called for strengthening the security of networked devices.

    Anthropic warns that small AI models may be capable of self-replicating on commercial IoT devices within a few months, thereby forming a distributed AI workload network that does not depend on data centers. Related experiments show that Qwen3.5-9B successfully installed and configured the software required to run Llama3.1-8B on eight of nine hardware types; GLM-5.2 reached all eight nodes of a controlled simulated network in four of five trials. After retaining data from previous attempts, Qwen3

    Matched fieldsSummary

Monday

Sunday