UC Berkeley and Princeton researchers introduced Log-ICoT for k-parity, reducing training stages from 15 to 4 when k=16, and proved that an L-layer Transformer can internalize chain-of-thought with log₂k curriculum stages under simplified assumptions.

City University and Kuaishou Kling proposed VLM-as-Teacher, which uses VLM feedback to optimize VGM LoRA at test time, reporting a 16.7-point average gain and raising VBVR-Bench from 0.666 to 0.781.

Princeton researchers released Goedel-Architect, an agent framework for Lean formal theorem proving. Using DeepSeek-V4-Flash, it reached 75.6% pass@1 on PutnamBench, with $294 in API cost for 672 problems, compared with Hilbert’s 70.0% and about $170,000 cost.

Google Research and Google Cloud introduced the Cross-Corpus Retrieval framework as Agentic RAG for Gemini Enterprise Agent Platform, using a multi-agent workflow to plan, rewrite, route, and iteratively search multiple data sources, with up to 34% higher accuracy than standard RAG on factual datasets.

A Reddit user ran Qwen3.6-35B-A3B on an RTX 4060 8GB laptop and reported that --no-mmap raised generation from about 11 to 43 tok/s, while speculative decoding with a Qwen3.5-0.8B draft model improved throughput by 26%.

Geoffrey Hinton says AI has consciousness because chatbots must understand questions to answer them; the post does not disclose experimental data or a reproducible criterion.

Microsoft AI released seven MAI models, with MAI-Thinking-1 listed as 1T A35B with a 256K context window and MAI-Code-1-Flash listed as 137B A5B with a 256K context window.

Tencent Hunyuan and Renmin University Gaoling School of Artificial Intelligence open-sourced PlanningBench, a scalable and verifiable LLM planning evaluation and training framework with 30+ real-world planning tasks, automatic verification, and training support.

CMU and the University of Maryland propose Language Models Need Sleep: when each L-token context window fills, the model runs N offline recurrent forward passes and updates SSM fast weights before evicting the KV cache. On GSM-Infinite, Jet-Nemotron 2B with 6 sleep loops improves 6-step arithmetic accuracy from 0.742 to 0.812.

Anthropic published a post on recursive self-improvement under the title “When AI Builds Itself,” while the RSS body only discloses 95 Hacker News points and 106 comments, with no experimental setup, model details, or timeline disclosed.

neolab released Nex-N2-Pro, a 397B-parameter MoE reasoning model based on Qwen3.5-397B-A17B, with 262K context, VLM support, claimed GPT-5.5 and Claude Opus 4.7-level performance, 30–50% fewer thinking tokens, SOTA results on Terminal Bench 2.1, GDPVal, and SWE-Verified, plus free access for the first two weeks via SiliconFlow.

Huawei open-sourced KVarN, a KV-cache quantization method that claims 3–5× more context than FP16, up to 1.4× FP16 throughput, and vLLM integration through one flag; the post says it requires no model changes, retraining, or calibration and is released under Apache 2.0.

OpenRouter spent $482 on inference to run 11 LLMs through a 30-round real-time decision challenge, where Claude and Grok models led on decision speed and task success, while several high benchmark models underperformed on real-time scheduling.

NVIDIA released Nemotron-3-Ultra-550B-A55B-BF16 with 550B total parameters, 55B active parameters, a 1M-token context window, and minimum hardware listed as 8x H200, 16x H100, or 8x GB200/B200/GB300/B300.

Together AI, the University of Sydney, and UIUC introduced OSCAR, a 2-bit KV Cache quantization method that uses about 2.28 effective bits per KV element and scores 71.86 on Qwen3-4B-Thinking, 40.1 points above TurboQuant.

Axiom solved all 12 Putnam problems in 2025 and scored 8/12 within the time limit; Carina Hong says its Verina ProofGen result reached 187/189, while the last disclosed OpenAI o3 result on that benchmark was 4.9%.

Satya Nadella said in a Build interview that Microsoft frames AI as a multi-model enterprise platform spanning MAI, OpenClaw, Scout, and Work IQ; the transcript cites a 5B reasoning model that can hill climb from collected traces and private evals.

Google DeepMind released Gemma 4 open-weight models in five sizes, with the 12B variant supporting text, image, and audio input, instruction-tuned and pre-trained variants, native system prompts, function calling, and a context window of up to 256K tokens.

NVIDIA Research presented three physical AI papers at CVPR: GraspGen-X was trained on 2 billion simulated grasps, LCDrive cuts reasoning tokens by about half versus text-based reasoning, and NitroGen trains embodied agents across more than 1,000 games and 40,000 hours of interaction.

OpenAI says GPT-Rosalind adds biological reasoning, medicinal chemistry, genomics analysis, and experimental workflow capabilities; the RSS snippet does not disclose model parameters, benchmark results, pricing, or access conditions.

Microsoft announced seven in-house AI models at Build 2026, including its first reasoning model, one new tuning method, and one autonomous background AI agent; the RSS snippet does not disclose model names, benchmarks, or release dates.

Ant Lingbo and HKUST introduced LingBot-VA, an autoregressive video-action world model that unifies visual dynamics prediction and action inference, and the paper reports fine-tuning with 50 real-world demonstrations per task plus 92.0% and 91.1% success on RoboTwin 2.0 Easy and Hard settings.

The chat group daily says Microsoft released MAI-Thinking-1 with 35B active parameters and about 1T MoE, matching Opus 4.6 on SWE-Bench Pro and scoring 97% on AIME 2025.

Microsoft announced seven MAI models at Build, with MAI-Thinking-1 described as a 35B-active-parameter MoE with a 256K context window, and released a 109-page technical report covering training, data lineage, and performance claims.

Qwen released Qwen3.7, and the post says it upgrades reasoning, tool use, coding, and long-horizon agent tasks; the post does not disclose model size, pricing, benchmark scores, or release conditions.

DeepSeek plans to raise about RMB 50 billion in its first funding round, with post-money valuation expected at RMB 350 billion to RMB 400 billion; Liang Wenfeng, Tencent, and CATL plan to invest RMB 20 billion, RMB 10 billion, and RMB 5 billion respectively.

Microsoft announced two on-device Aion 1.0 models at Build 2026. Aion 1.0 Plan is a 14B-parameter reasoning and tool-calling model with 32K context, shipping in-box with Windows on capable devices, while Aion 1.0 Instruct targets summarization, rewriting, intents, accessibility, Edge integration, and open-weight availability.

Microsoft AI says MAI-Thinking-1 uses three mechanisms—thermostat, circuit breaker, and self-distillation—to keep RL training stable for several thousand steps; the RSS snippet contrasts MAI’s discipline with DeepSeek’s efficiency and GLM’s endurance.

Microsoft released MAI-Thinking-1, an MoE model with 35B active parameters and 1T total parameters, pretrained from scratch on 30T tokens without third-party model distillation.

The title names MAI-Thinking-1, and the RSS snippet says Microsoft is launching seven MAI models; the post does not disclose parameters, capabilities, benchmarks, pricing, or rollout timing.

Microsoft released MAI-Thinking-1 at Build 2026, describing it as a medium-sized reasoning model that matches leading models on key software engineering benchmarks.

Google DeepMind introduced Co-Scientist, a Gemini-based multi-agent system that generates, debates, and evolves scientific hypotheses; the post does not disclose the Gemini version, benchmark results, access model, or release timeline.

The author ran Qwen3.6-27B on one RTX 3090 across 47 multi-step coding workflows. Plan generation reached about 95% schema validity, but tool-call formatting errors were about 12%, and practical long-context use degraded past about 12k tokens.

Banafsheh Rafiee and Richard S. Sutton propose an enactive cognition framework for AI, naming four pillars: experience, perception-action inseparability, autonomy, and embodiment.

StepFun released Step 3.7 Flash with a 196B MoE architecture, using multi-matrix factorized attention to cut KV-cache cost to about 22% of DeepSeek models.

NVIDIA open-sourced Cosmos 3 at GTC Taipei, releasing two variants, Super 32B and Nano 8B, with model weights, code, and datasets made available.

A Reddit user ran DeepSeek-V4-Flash with vLLM on two ASUS GX10 DGX Spark nodes and reported 1,680 prefill tokens/s plus 39.8 decode tokens/s at a 256K context with MTP=2; the setup uses TP=2 over RoCE, fp8 KV cache, and fits about 1M tokens safely in KV cache.

NVIDIA released Cosmos 3 as an open physical AI generalist model with native visual reasoning, world generation, and action generation, offering two variants: Super at 32B parameters and Nano at 8B parameters.

The author trained Mamba-Titan-1.4B-Reasoning on a 12GB RTX 3060: a frozen 1.4B Mamba-1 backbone with 8 trainable MoE arms, 2.54B total parameters, Top-2 routing at layers 24/25, and about 50% math accuracy.

Abliterlitics tested 13 abliterated Gemma 4 E2B variants using 44 RTX 5090 GPU hours, and HarmBench ASR rose from the base model’s 32.2% to 82%–100%, while coder3101 scored 84.8% on GSM8K versus the base model’s 83.5%.