Official verification of Qwen3-235b Instruct: it gets 11% on ARC-AGI-1 and 1.3% on ARC-AGI-2 (semi-private sets). These numbers are in line with other SotA base models. Qwen3 stands out by being the cheapest base model we tested to score above 10% on ARC-AGI-1.
MACHINE LEARNING
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Training a Strong Model with Trust and Morale Support
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Had a super fun time training this model. A big yolo run that resulted in a super strong model. Most important thing is to trust your model and give it morale support. Was also a big eye opener to see how prep for IMO is done. Before this I knew absolutely zero about this
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Scaling Time Series Integration for Industrial Predictive Maintenance
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Scaling #TimeSeries integration across complex plants is challenging but essential for achieving true predictive maintenance at scale. Here’s how to tackle it. https://
buff.ly/A7DAFWt #sponsored #influxdata_iiot #InfluxDB #IndustrialData #IIoT @YvesMulkers @Lago72 via @fogoros -
AI Benchmarks Correlation Despite Known Limitations and Issues
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The mitigating factor for the problem with AI benchmarks (errors, saturation, contamination) is that, despite issues, they are all still fairly heavily correlated. So if your AI does well on GPQA or MMLU or HLE it also tends to do well on other benchmarks & on vibes & real work.
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Elevating 3D Models: High-Quality Texture and Geometry Refinement
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Elevating 3D Models High-Quality Texture and Geometry Refinement from a Low-Quality Model
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QA Testing Edge Cases: When Real World Surprises Engineering Plans
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I just spit out my water, this is so funny.
— Allie K. Miller (@alliekmiller) 24 juillet 2025
Fully reminded of the QA process we had at IBM to attempt every single edge case we could think of, and then STILL being shocked by real world issues (like pigeons standing in front of CCTV cameras).
source: @howtokillanhour on… pic.twitter.com/F3zyOmrzZwI just spit out my water, this is so funny. Fully reminded of the QA process we had at IBM to attempt every single edge case we could think of, and then STILL being shocked by real world issues (like pigeons standing in front of CCTV cameras). source: @howtokillanhour on
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Multi-Domain Reasoning Through Reinforcement Learning: A Data-Centric Study
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Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learning
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RLVR Models Sacrifice Diversity for Base Model Preferences
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RLVR'd model would fail to answer questions that its base model previously could. "The Invisible Leash: Why RLVR May Not Escape Its Origin" This research shows that RLVR just boosts the base model’s favorite answers (raising pass@1) and would sacrifice diversity.
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Databricks Free Edition: Learn AI, Data, and ML Technologies
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Learn and explore on the latest data and AI technologies for free with Databricks Free Edition. Users can experiment with the full range of use cases — from building AI agents to collaborating on DS/ML projects to analyzing data with SQL — on the same platform used by