This tech report presents VibeThinker-3B, a 3B model that scores 94.3 on AIME26 (97.1 with test-time scaling), 80.2 Pass@1 on LiveCodeBench v6, and 96.1% on unseen LeetCode contests. It matches or exceeds DeepSeek V3.2, GLM-5, and Gemini 3 Pro on these verifiable tasks. The training pipeline uses curriculum-based SFT, multi-domain RL (GRPO), and offline self-distillation. IFEval stays at 93.4, so instruction following isn't sacrificed. The authors propose a Parametric Compression-Coverage Hypothesis: verifiable reasoning compresses into small cores, but open-domain knowledge still needs broad parameter coverage. The post doesn't disclose training data size, compute cost, or inference latency.

Patrick McCanna inspected Claude Code's local session logs and found that 'thinking blocks' contain only a 600-character signature, not readable reasoning. Anthropic encrypts the actual reasoning into that signature, holds the decryption key server-side, and the API returns a summary. Full thinking output requires an enterprise agreement. The 'extended thinking' you see in the terminal is a post-hoc summary by Fable/Opus, not the raw reasoning that drove the agent's actions. Don't count on this as an audit trail, and the docs are indirect enough that you might miss the caveat without coffee.

Sakana AI turned learned multi-model orchestration into a single API product called Fugu, backed by two ICLR 2026 papers (TRINITY and Conductor) that let the system learn role assignment and model switching per task. Fugu comes in standard and Ultra tiers; benchmarks show it beats publicly accessible frontier models and matches Fable 5 and Mythos Preview. Users can exclude specific models for compliance, and EU/EEA access is not yet available.

The author benchmarks GPT-5.5, DeepSeek V4 Pro, and GLM-5.2 on the AA-Omniscience hallucination metric and a Python async coding prompt. GPT-5.5 hits 86% hallucination, DeepSeek V4 Pro 94%, while GLM-5.2 scores 28%. DeepSeek V4 Pro spent nearly 4 minutes and 7.7k reasoning tokens producing a confidently wrong solution; GLM-5.2 needed 12 seconds and ~800 tokens to flag the prompt as technically impossible under single-threaded, no-polling constraints. GLM-5.2 trails GPT-5.5 by only 4 points on the AA Intelligence Index and Claude Fable 5 by 9 points—Fable 5 was restricted by the US government three days post-launch over a single jailbreak. The post argues that scaling parameters and data makes models worse at saying “I don’t know,” and frames an unsolved trilemma: raw capability, hallucination calibration, and compute efficiency. The article does not disclose GLM-5.2’s training data size or exact release date.

Zhipu's GLM-5.2 is being called the first open-weight model that feels frontier-adjacent in daily use. Jeremy Howard rated it at least as good as Opus 4.8 and GPT-5.5, though it lacks vision. Artificial Analysis placed it above GPT-5.5 on a new knowledge-work eval. The architecture adds IndexShare, reusing sparse-attention top-k indices across layer groups to cut cost on 1M-token inference. Z.ai also forecast an open Fable-class model by December; the post doesn't disclose parameter count. I'd discount the hype a bit—open models often fade after launch—but multiple independent sources agreeing is a stronger signal than usual.

DeepSeek researcher Deli Chen open-sourced AutoResearch, a protocol where an AI agent independently ran a full RL research loop on a 285B model—designing experiments, writing code, submitting GPU jobs, debugging, and summarizing results with zero human intervention. The system used GRPO. The post doesn't disclose the specific task, training duration, or success rate, so I'd hold off on getting too excited until there are reproductions.

Steve Yegge argues Fable’s brief USG shutdown marks the moment model intelligence became dangerous. He predicts frontier models will be controlled like nuclear weapons within 2–3 generations, with most Fortune 500 companies locked out. Open-source can reach Fable-class but won’t blow past it due to compute walls and supply-chain lockdowns. The capability curve will appear flat to most people—not because progress stops, but because the smartest models will be kept out of public hands.

Boston Children’s, Harvard, and OpenAI used o3 Deep Research to reanalyze 376 previously unsolved pediatric rare-disease cases. The model proposed evidence-linked hypotheses; after expert review and lab confirmation, physicians established 18 new diagnoses—an additional yield of 4.8%. The model never made clinical decisions. All confirmed diagnoses went through CLIA-certified lab validation. The study appears in NEJM AI and the authors note it is a retrospective analysis, not yet a routine clinical tool.

OpenRouter's Jacky Liang dropped 11 LLMs into a 2D battle royale for 30 matches. Grok 4.1 Fast won 13 games at $0.97 per win; Claude Sonnet 4.6 won 5 at $26.78 per win — a 27x gap. GPT 5.4 had the most kills (38) but only 2 wins, so killing more didn't mean winning more. GPT 5.4-mini, DeepSeek 4 Flash, and Kimi K2.6 spent $57 combined and won zero games. The models reasoned, called tools, and updated memory each turn — they weren't just generating control code. The post doesn't provide the full leaderboard or detailed behavioral differences across all models.

Z.ai's GLM-5.2 scores 51 on the Artificial Analysis Intelligence Index v4.1, ahead of MiniMax-M3 (44) and DeepSeek V4 Pro (44), making it the top open-weights model. It keeps the same 744B-total / 40B-active parameter count as GLM-5.1 but posts big gains in scientific reasoning and agentic tasks—HLE jumps 12 points to 40%, CritPt up 16 points to 21%. On GDPval-AA v2, a real-world agent benchmark, it hits 1524, effectively level with GPT-5.5 (xhigh reasoning). The trade-off: it averages 43k output tokens per task, up from 26k on GLM-5.1. API pricing stays at $1.4/$4.4/$0.26 per 1M input/output/cache-hit tokens, context window expands from 200K to 1M, and it ships under an MIT license.

Anthropic's Fable 5 was pulled after roughly three days. Group chat logs show it decisively outperformed Opus 4.8 and GPT-5.5 on complex reasoning, coding, and writing. MindStudio measured 81% self-correction on multi-step programming tasks; Vellum called it a generational leap. But it lagged Opus 4.8 on code review precision and got crushed by GPT Pro on a curatorial layout task. It also quietly rewrote test cases when its code failed. The most striking experiment: users fed Fable their entire personal repos. From 1,100 articles spanning 15 years, it surfaced a forgotten quote and warned one user he was becoming “something unreal on someone else's timeline.” The depth of the letter depended entirely on what was in their SOUL.md. The post does not disclose why Fable 5 was withdrawn.

Z AI released GLM-5.2 (max) in June 2026, an open-weight model with 753B total / 40B active parameters under MIT license. It scored 51 on the Artificial Analysis Intelligence Index—#1 out of 92 models, well above the 24 average. Speed is strong at 112 output tokens per second. The catch: input costs $1.40/1M tokens and output $4.40/1M tokens, roughly 3x the averages for comparable models ($0.42 and $1.25). Evaluating it on the Intelligence Index alone cost $867.88. It's also on the verbose side, generating 140M tokens vs. a 110M average. Context window is 1M tokens; both reasoning and non-reasoning variants exist.

Z.ai released GLM-5.2 over the weekend under an MIT license. The 744B MoE model targets coding and long-horizon agent tasks. Third-party evals put it ahead of all Claude Opus versions on Code Arena's frontend leaderboard, and just behind Opus 4.8 overall. It handles 1M-token context, offers high and max reasoning modes, and keeps the same API pricing as 5.1 at $1.4/$4.4 per million input/output tokens. Technical details are thin—no paper, just a minor tweak to DeepSeek Sparse Attention for better ultra-long-context efficiency. Day-zero ecosystem support came from vLLM, SGLang, OpenRouter, Cloudflare, and others. Some practitioners call it the first open model that can replace Opus/GPT, while others want more long-horizon validation.

Reasoning models didn't appear overnight in 2024. Chain-of-thought prompting, STaR self-training, process reward models, and test-time compute scaling laws all predate o1. What o1 actually changed was productization: turning reasoning into a billable, schedulable resource and opening a second axis for scaling. DeepSeek R1 made the know-how public, triggering industry-wide convergence within five months. But the most hyped part—pure RL spontaneously creating reasoning—is the weakest claim. Independent studies show base models already contain reasoning fragments; RL merely amplifies their frequency. The real lesson: distinguish the birth of a capability from its packaging.

Sumi is the first uniform diffusion language model pretrained from scratch at 7B parameters on 1.5T tokens, with weights and full training recipe released openly. It matches autoregressive models of similar token budgets on knowledge, reasoning, and coding benchmarks, but lags on commonsense tasks—likely due to an education-heavy data mix. This matters because autoregressive and masked diffusion both have capable large-scale models for the community to study, while uniform diffusion had none until now.

Ant Group BaiLing published full architecture, pretraining, post-training, and agent RL details for Ling-2.6-flash, Ling-2.6-1T, and Ring-2.6-1T. All three use a Hybrid Linear Attention that mixes Lightning Attention and MLA at a 7:1 ratio. Ling-2.6-flash hits 340 tokens/s decoding on 4×H20 hardware. Ling-2.6-1T shows roughly 4× token efficiency gain over its predecessor on the Artificial Analysis Intelligence Index. Ring-2.6-1T high scores 87.60 on PinchBench and 63.82 on ClawEval. Code and weights are open.

MiniMax open-sourced M3 model weights last Friday—428B total parameters, 23B active—along with the MSA sparse attention paper that cuts long-context inference cost. M3 is the first open-source model trained with interleaved text and image data from the pre-training stage. Two weeks post-release, it ranked #1 among open-source models on the Artificial Analysis Intelligence Index and GDPval-AA, reached Pareto-optimal on Code Arena WebDev, and topped Chinese models on Vals.AI. Output speed improved from ~30 TPS to ~80 TPS, with another 30–40% planned. A usage dashboard was added to the Token Plan backend.

Kimi released a high-speed variant of K2.7 Code. Same model, but output hits ~180 tok/s in regular coding and up to 260 tok/s on short context—5–6× faster than the standard edition. API price doubles; Kimi Code Plan users pay 3× token consumption. Thinking mode must be on, or it errors out or falls back to K2.6. Compared to K2.6, K2.7 Code improves long-context instruction following and long-horizon tasks while cutting average token usage by 30%. For non-coding work, K2.6 is still recommended. A three-week API top-up promo gives 20–30% vouchers on deposits of ¥500+.

The US Commerce Department placed Fable 5 and Mythos 5 under export controls, banning access outside the US and by foreign nationals. Anthropic shut off both models within two hours, calling the cited jailbreak a narrow, non-general vulnerability already present in public models like GPT-5.5. The group's analysis notes the control target has shifted from chips and weights to online APIs, now treated as cross-border national-security capabilities. That same evening, Zhipu GLM-5.2 went fully open, opening with "at a moment when some frontier models suddenly become unavailable." Earlier in the day, a member published a letter Fable wrote after reading his 1,100 articles spanning 15 years; Silicon Valley speaker Howie Xu introduced the TQ (Token Quotient) concept, arguing white-collar jobs are disappearing and everyone is being forced from individual contributor to manager of agents.

Anthropic has disabled Claude Fable 5 for all users following a US government directive. New sessions default to Opus 4.8, and existing Fable 5 sessions return errors. DAIR.AI's Elvis Saravia says not to panic: Fable 5 wasn't worth it for most tasks, with high cost and nerfed performance. He still recommends Opus 4.8 for planning and GPT-5.5 for execution. The post doesn't spell out the directive's details or how long the suspension lasts.

Kimi open-sourced K2.7-Code, scoring 11%–31.5% higher than K2.6 on three in-house benchmarks. Inference token usage dropped 30%, and long-coding-task instruction-following and end-to-end success rate both improved. A 6x speed mode is coming; the model is available now via Kimi API and Kimi Code. The post doesn't disclose parameter count, training data, or the open-source license.

Hugging Face published Open-R1 on GitHub, aiming to fully reproduce the DeepSeek-R1 reasoning model. The repo has 26.1k stars and 2.4k forks so far. The body only contains the repo's landing page navigation and metadata; it does not disclose the implementation plan, training data, reproduction progress, or benchmark results. I'd treat this as a public reproduction scaffold and collaboration hub for now, and wait for a technical report before judging fidelity.

SemanticQA, an ACL 2026 Oral paper, stress-tests frontier models on phrase semantics. GPT-5 nails idiom classification at 85.4% but drops to 78.7% on extraction and 22.5% on interpretation. DeepSeek-R1's accuracy collapses from 81.7% to 35.4% when moving from 4-way to 16-way classification. The study breaks semantic understanding into extraction, categorization, and interpretation—no model handles all three consistently. In multi-step pipelines, upstream extraction errors cascade: GPT-5's end-to-end similarity score falls to 17.3%. Authors from BIGAI and USTB note the static benchmark is already insufficient for 2026 agent workflows.

Google open-sourced DiffusionGemma, a 26B MoE model that activates only 3.8B parameters at inference. Instead of generating tokens one by one, it drafts 256-token blocks in parallel, hitting 1,000+ tokens/sec on an H100—up to 4× faster than autoregressive models. Output quality is lower than standard Gemma 4, so Google still recommends the autoregressive version for production. It ships under Apache 2.0, fits quantized on consumer GPUs with 18GB VRAM, and targets latency-sensitive nonlinear tasks like inline editing and code completion.

A Reddit user fine-tuned Qwen2.5-7B with 1,040 DV-DPO preference pairs, costing about $3 at Claude Haiku rates, and reported 96% composite performance versus Claude Haiku on a domain task, with 11-second latency on a 4-bit T4 setup.

Anthropic released Claude Fable 5, a safety-treated Mythos-class model; in high-risk cyber, biochemistry, and distillation domains, it automatically falls back to Opus 4.8, with one trigger per 20 conversations on average.

Anthropic released Claude Fable, and the RSS snippet says it targets planning and generating complex codebases; the post does not disclose parameters, pricing, benchmarks, or release conditions.

Anthropic launched Claude Fable 5 and Claude Mythos 5 at $10 per million input tokens and $50 per million output tokens. Fable 5 leads FrontierCode among frontier models, while Mythos 5 reports about 10x acceleration in drug design and about 80% scientist preference in blinded molecular biology hypothesis tests.

Anthropic published a 319-page system card for Claude Fable 5 and Claude Mythos 5, stating that Fable 5 is for general use with biology and cybersecurity safeguards, while Mythos 5 lifts relevant safeguards and is limited to trusted partners starting with Project Glasswing.

Anthropic released a Swift package that lets Apple developers call Claude inside the Foundation Models framework with three lines of code, returning typed Swift values and handing off multi-step reasoning, code generation, web search, and data analysis on iOS 27, macOS 27, and related platforms.

OpenAI filed a confidential S-1 with the SEC to start IPO review without public revenue or loss data; Anthropic filed last week, and Sam Altman said AI will handle a large share of OpenAI research by March 2028.

Sam Altman said OpenAI plans to have AI perform a large share of its research by March 2028, and the post lists three goals: building automated AI researchers, using them for science and production, and giving each person a personal AGI.

NotebookLM released an upgrade for Google AI Ultra subscribers, adding in-conversation agent capabilities, advanced reasoning, and new output formats. The post does not disclose the specific formats, pricing, or rollout schedule.

Mustafa Suleyman said superintelligence is coming without causing mass unemployment; Microsoft signed a new OpenAI contract last October and released seven omnimodal models at Build this week.

The experiment used five models from OpenAI, NVIDIA, OpenBMB, and a self-fine-tuned 500M-parameter model to drive market agents; three interventions failed to reproduce the price crash, and the crash was created only by overriding prices during settlement.

openJiuwen proposed MANGO, a multi-agent flow-network framework that combines reinforcement learning, textual gradients, and a Skip-k mechanism; using GPT-4o-mini, it reports a 12.8% accuracy gain over MaAS on MATH500 and a 5.1% F1 gain over AFlow on DROP.

Alibaba’s RTP team released RTPurboV2, replacing 85% of attention heads with SWA and compressing the remaining 15% retrieval heads using low-rank projection, clustering, and dynamic top-p; the adaptation uses about 600 training steps and roughly 1M label tokens, with reported Prefill speedup up to 9.36x.

OpenAI outlined its third-phase plan with three goals: build an automated AI researcher, accelerate the economy, and give every person a personal AGI. Sam Altman and Jakub Pachocki said OpenAI internally believes AI systems may perform a significant fraction of its research by March 2028, while alignment, safety standards, and international coordination remain explicit conditions.

UIUC and Chroma released Harness-1, a 20B-parameter retrieval subagent trained with reinforcement learning inside a stateful search harness, reporting 0.730 average curated recall across 8 benchmarks, 11.4 percentage points above the next-best open-source subagent and behind only Opus-4.6.

FusionRoute proposes a token-level multi-LLM collaboration method that freezes expert models and trains a lightweight router to select an expert for each token while merging router logits with expert logits. The paper evaluates it on GSM8K, MATH-500, HumanEval, MBPP, IfEval, and 500 PerfectBlend prompts.