AIShanghai AI Laboratory and Professor Xu Disheng's team say the Shusheng-S2 scientific multimodal model, working with human researchers, produced a proof of the three-dimensional Ghys conjecture.
Why it matters: The article details how humans and the Shusheng-S2 model worked iteratively on a three-dimensional Ghys conjecture result, and how Peking University verified it in Lean.
Artificial AnalysisOfficialAI score62
AIArtificial Analysis added DeepSeek V4.1 Flash to its AA-AgentPerf benchmark, which replays agentic coding sessions and measures throughput per megawatt.
Why it matters: The post gives per-GPU throughput and per-user speed figures for DeepSeek V4.1 Flash across NVIDIA and AMD hardware, useful for comparing serving costs.
AIResearchers from Huawei Noah's Ark Lab and University College London released Memento 3, an agent that cleared all 25 public games in ARC-AGI-3 using 7,518 actions, about 44% of the human total.
Why it matters: The source separates the agent design from the base model and states the public-set limits, so readers can judge what the benchmark result shows.
meng shaoXAI score79AIXiaomi's MiMo-V2.6 technical report argues that scaling reinforcement learning, not more pretraining data, is the main lever for frontier capability, along batch size, environment diversity, and grader strength.
Why it matters: The piece walks through the report's three-way RL scaling method, batch size, environments, and grading, with concrete failure modes and stabilization fixes useful to agentic RL practitioners.
AIEpoch AI finds that China spends about 2.7 times as much of its final demand on semiconductor producers as the US does, measured as chip revenue per $1,000 of spending in 2022.
Why it matters: The study adds a separated semiconductor input-output dataset, showing China's exposure to chip disruptions is larger than the US's, which helps readers judge supply-chain risk.
AIChinese AI companies earn about 10% of the combined AI-related revenue of OpenAI and Anthropic, according to Epoch AI as of September 2026.
Why it matters: The piece maps how Chinese AI firms earn revenue and why open-weight releases weaken API pricing, giving context for comparing them with US frontier labs.
AIEpoch AI estimates that high-bandwidth memory shipped in 2025–27 could eventually support about 30 to 170 million concurrent frontier-model agents once deployed and fully allocated.
Why it matters: The piece converts HBM shipment data into agent-capacity and spending scenarios, letting readers test whether AI hardware buildout outpaces likely demand.
Tencent HyOfficialAI score62
AIResearchers from Tencent Hy, Fudan University, and Tsinghua University released ExplorationBench, a benchmark that tests whether AI systems can discover hidden rules in executable Alien World sandboxes.
Why it matters: The benchmark separates feedback-driven discovery from recall by testing systems on rules that conflict with familiar knowledge, with answers checked by an interpreter or proof checker.
ModelScopeOfficialAI score62
AIShanghai AI Lab and SJTU's LUMIA Lab released NCP-ArchPreview, an 8.9B open-weight language model under Apache 2.0.
Why it matters: The post pairs an Apache 2.0 open-weight release with training-efficiency figures, showing how the concept module adapts to new domains with few trainable parameters.
AIEpoch AI's newsletter reports that Huawei is far behind Nvidia and is unlikely to catch up this decade due to export controls.
Why it matters: The newsletter bundles several analyses of AI chips, GDP measurement, and benchmarks, so it helps readers scan the research agenda behind each finding.
Meituan LongCatOfficialAI score62
AIMeituan LongCat evaluated 7 frontier models on 36 AI R&D tasks covering 756 trajectories, looking beyond final scores.
Why it matters: The paper separates final scores from reliability and novelty, showing where agent research loops succeed and where they fall short.
AIMiniMax describes Forge, its internal reinforcement learning framework for training real-world agents, which was used during the development of MiniMax M2.5.
Why it matters: The post details how the Forge framework balances throughput, stability, and agent flexibility, with concrete scheduling and prefix-merging methods for training agent RL at scale.