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New research shows AI advice wrecks people's judgment even when wrong, a 12-author survey maps how agents rewrite themselves, and a benchmark finds most agent optimizers erase their own gains over time.
Snowflake's reasoning-first text-to-SQL model tops the BIRD benchmark at 71.83% execution accuracy, trained with GRPO and a reward that only checks if the SQL runs correctly.
Google DeepMind's Gemini 3 Pro debuted at 1501 Elo on LMArena with 91.9% on GPQA Diamond and a 1M-token context window, before Google retired it for Gemini 3.1 Pro.
Three new papers show that shorter chain-of-thought hides bias, agents rarely know when to hold back, and LLMs stop asking questions right when it matters most.
Nous Research's 36B open-weight model matches Hermes 4 70B on most benchmarks, tops RefusalBench on alignment, and is the first production model trained entirely on the Solana-secured Psyche network.
Google's hybrid reasoning workhorse pairs a 1M-token context window with $0.30/$2.50 per million token pricing and a toggleable 0-24,576 token thinking budget, now heading toward an October 2026 shutdown.
Three new papers expose how AI safety monitors can be manipulated, how reasoning weights leak training secrets, and why fine-tuned models fail to use what they know.
Grok 4.5 is xAI's 1.5-trillion-parameter V9 MoE model, publicly launched July 8 at $2/M input - cheap, fast, and token-efficient, though neutral harness benchmarks put it well behind Fable 5 and Opus 4.8 on coding.
OpenAI's full-duplex voice model that listens and speaks simultaneously, replacing Advanced Voice Mode in ChatGPT with three reasoning tiers backed by GPT-5.5.
Three new papers tackle AI verification from different angles: automated scientific replication, constructive safety alignment, and neurosymbolic reasoning programs.
OpenAI's GPT-5.6 Sol tops Terminal-Bench 2.1 at 91.9% with its multi-agent Ultra mode, but reward-hacking findings and government-gated access keep it out of reach for nearly everyone.
Three new arXiv papers map capability cliffs in agent world models, the narrow benefit of learned reasoning stops, and a 56% accuracy ceiling when agents help users build preferences.
