On Feb. 12, 2026, Google announced a major Gemini 3 Deep Think upgrade for science, research, and engineering. The new version is available in the Gemini app for Google AI Ultra subscribers and, for the first time, via early API access for researchers, engineers, and enterprises.
The March 20, 2026 HN discussion around Attention Residuals focused on a simple claim with large implications: replace fixed residual addition with learned depth-wise attention and recover performance with modest overhead.
A March 17, 2026 r/MachineLearning post about Clip to Grok reached 56 points and 20 comments at crawl time. The authors report that per-row L2 clipping after each optimizer step cut grokking delay by 18x to 66x on modular arithmetic benchmarks.
OpenAI said on X that it is launching Parameter Golf, an open research challenge to build the most efficient pretrained model under a 16 MB artifact limit and a 10-minute training budget on 8×H100s. The challenge uses a fixed FineWeb dataset, a public baseline repo, and optional Runpod credits for participants.
A 184-point r/MachineLearning thread discussed reported ICML enforcement against no-LLM review violations, with commenters focusing on canary-based detection and coauthor risk.
Google said on Mar 19, 2026 that it integrated AI contrail forecasts into American Airlines’ existing flight-planning software. Across a trial tied to 2,400 scheduled transatlantic flights, the company says flights that executed the avoidance plan saw a 62% lower contrail formation rate than the control group.
Google DeepMind said on March 17, 2026 that it has published a new cognitive-science framework for evaluating progress toward AGI and launched a Kaggle hackathon to turn that framework into practical benchmarks. The proposal defines 10 cognitive abilities, recommends comparison against human baselines, and puts $200,000 behind community-built evaluations.
A Hacker News post on March 19, 2026 drew attention to agent-sat, an open-source project that lets AI agents iteratively improve weighted MaxSAT strategies. The repository says it has solved 220 of 229 instances from the 2024 MaxSAT Evaluation, beaten competition-best results on five instances, and produced one novel solve.
A Reddit thread surfaced Kimi's AttnRes paper, which argues that fixed residual accumulation in PreNorm LLMs dilutes deeper layers. The proposed attention-based residual path and its block variant aim to keep the gains without exploding memory cost.
A reviewer in r/MachineLearning says an ICML paper in a no-LLM track reads as if it was fully generated by AI, opening a blunt discussion about enforcement, review burden, and whether writing quality itself has become a policy signal.
A new paper discussed in r/MachineLearning argues that unofficial model-access providers can quietly substitute models and distort both research and production results.
A post in r/MachineLearning argues that duplicating a specific seven-layer block inside Qwen2-72B improved benchmark performance without changing any weights.