Trends

AIMiniMax has upgraded its Agent, renamed Mavis, and introduced Agent Teams that run multiple role-based Agents in parallel on desktop. The team uses Leader, Worker, and Verifier roles so complex tasks can be split, checked, and reported at key checkpoints, and it merges TokenPlan and Agent Plan into one subscription with credits shared between Agent and API. The post also discusses the added token, handoff, and retry costs of multi-Agent work, and says the Agent will be open-sourced alongside MiniMax M3.

Why it matters: The post explains why multi-Agent helps long tasks and where its verification, token, and aggregation costs come from, useful for judging when a team setup beats a single Agent.

AIOpenAI has shipped GPT-5.5 Instant to ChatGPT, rolling out to everyone over the next couple of days. The quoted post says the model focuses on factuality, reducing hacks, and improving baseline intelligence, and is significantly less likely to hallucinate.

Why it matters: The quoted post gives concrete targets for the update, factuality and hallucination reduction, useful for judging whether the default ChatGPT model changed in practice.

AIAndrej Karpathy's year-in-review lists the LLM paradigm changes he found most notable in 2025. He highlights Reinforcement Learning from Verifiable Rewards (RLVR), which drove most capability gains as labs ran longer RL training, and describes LLM intelligence as jagged, strong in verifiable domains and weak elsewhere. He also covers Cursor-style LLM apps, Claude Code running on the user's computer, vibe coding, and the case for a visual LLM GUI.

Why it matters: Karpathy ties the year's shifts to RLVR, jagged capability, and local agents, giving readers a framework for judging how LLM progress is changing.

AIARC Prize reports that the top Kaggle entry reached 24% on the ARC-AGI-2 private dataset at $0.20 per task, and that all winning solutions and papers are open source. The top verified commercial model, Opus 4.5 (Thinking, 64k), scored 37.6% at $2.20 per task, while a Poetiq refinement on Gemini 3 Pro reached 54% at $30 per task. The author argues that refinement loops are the main driver of 2025 progress, and says ARC-AGI-3 is planned for early 2026.

Why it matters: The post links 2025 competition results to a broader argument about refinement loops, showing how benchmark outcomes are being read as evidence of AI reasoning progress.