Reasoning

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.

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%.