· 6 min read
Meta's first frontier model, Anthropic vs. distillation, and research taste doubling every 3 months
Meta ships Muse Wonder, Anthropic cuts Claude distillation 10–100×, research taste doubles every ~3 months, and GPT-6 Astra reads scenes far worse than humans.
Today in 30 seconds
- Meta Superintelligence Lab released Muse Wonder and Muse Wonder Full, its first openly frontier models.
- Anthropic says new defenses cut harvested Claude reasoning tokens roughly 10–100×.
- P-Zero's TasteVal finds frontier experimental taste doubling every 3.0 months since December 2025.
- On Scale's HSS benchmark humans score 93.1% and the best model, GPT-6 Astra, 53.6%.
- Perplexity's open-weights pplx-decider-v1.1-27b scores 61.56 on Decision Index, 3.5+ points above Jev.
Tuesday tea after a dense Monday. Meta finally ships a frontier model, Anthropic explains how it cut Claude distillation by an order of magnitude, P-Zero measures models’ “research taste”, and Scale shows GPT-6 Astra still understands a scene only about half as well as a human.
Top news
Muse Wonder: Meta’s first frontier model after the MSL rebuild
Meta Superintelligence Lab released Muse Wonder and Muse Wonder Full, the first models Meta openly calls frontier since Alexandr Wang took over AI. Wonder is in the Meta Model API and Muse Code; Wonder Full is for Muse Code subscribers only. API price (per TechCrunch): $4 / $20 per million input/output tokens. Headline features are Active Memory and Active Focus for long-running goals; Meta claims a MemoryBench score above Fable, Opus and Astra. Wang admitted launch-day bugs on X.
Why it matters: Meta is now competing for the same shelf as OpenAI and Anthropic, with an emphasis on memory and long tasks — exactly what personal agents need. Benchmarks are Meta’s own claims.
Sources: Meta · TechCrunch · The Verge
Anthropic: 90–99% less data for Claude distillers
Anthropic described a new defense layer against industrial extraction of Claude’s reasoning to train other models. Early measurements: harvested reasoning tokens fell roughly 10–100×. Three layers: preserved thinking (reasoning no longer flows to older, weaker models), a two-stage classifier with FPR under 0.1%, and higher costs for repeat offenders (limits, bans, and charging for blocked requests). Anthropic admits legitimate API users felt friction.
Why it matters: Distillation is the main way labs catch up with closed frontier models. Some “strange” refusals and charges for blocked calls are this fight, not model whims.
Sources: Anthropic · @AnthropicAI
TasteVal: model “research taste” doubles every ~3 months
P-Zero Research measures how well a model designs AI R&D experiments and interprets results, with a fixed coding agent on one H100 doing the implementation. Since December 2025, frontier experimental taste has doubled every 3.0 months (95% CI 1.7–5.0). Best: Opus 5.5 with a 2.30× compute multiplier vs. human experts at ~1/30 of their cost. Jack Clark moved full RSI from “more likely 2028” to “more likely 2027”.
Why it matters: A harder metric than “the model wrote a paper”. If taste grows faster than coding, choosing and interpreting experiments becomes the bottleneck.
Sources: P-Zero · arXiv:2610.06824 · Import AI 476
How well do models read a scene? Humanity’s Sixth Sense: humans 93%, Astra 54%
Scale Labs and Elorian published HSS: 522 open-ended tasks on 288 images and 234 video clips. Humans score 93.1%; the best model, GPT-6 Astra at max effort, 53.6%; median model 30.9%. 94% of failures are perception or latent inference, only 5% bad logic. More thinking doesn’t close the gap; agents with crop/zoom tools reach 59.3%.
Why it matters: Frontier models read text and charts but can’t “read the room” — a real limit for agents in homes, cars and offices.
Is Gemini 4 Argon coming? Traces in Antigravity, no launch yet
TLDR AI collected signs that Google is preparing a public Gemini 4 Argon, with a Carbon checkpoint visible in the Antigravity model picker (256K/512K/900K context options). No official Google post confirms names, dates or benchmarks.
Why it matters: If Argon lands now, October becomes a three-way frontier sprint. Don’t migrate production on rumors.
Sources: TLDR AI
pplx-decider-v1.1 beats Jev by 3.5+ points
Perplexity’s open-weights pplx-decider-v1.1-27b (Apache-2.0) raises Decision Index from 56.4 to 61.56, more than 3.5 points above Jev. It joins Microsoft-Decision-1 in the race for cheap “System 1” models for routing, guardrails and scoring. No independent head-to-head yet.
Why it matters: Narrow, cheap models keep beating general frontier models on structured tasks.
Sources: Hugging Face · TLDR AI
DOE federated training across El Capitan, Aurora and Frontier
Import AI summarized two US DOE projects that train models without moving sensitive data: a Sandia/LLNL/LANL tri-lab setup (1B–70B LLaMA-based models) and a cross-facility fine-tune on ~1,700 GPUs across Aurora, Polaris, Frontier and Perlmutter. Still ~100× below private frontier labs.
Why it matters: Federation of existing supercomputers with data isolation is a realistic pattern for distributed public compute, including in the EU.
Sources: Import AI 476
Will the AI race leave Earth sooner than we think? An AI Frontiers essay
An AI Frontiers essay argues that the 2030s space economy (orbital energy, compute, manufacturing) creates a new axis of advantage that AI both accelerates and may dominate, via autonomous craft and orbital assembly.
Why it matters: Same logic as chips and energy: whoever first combines compute and manufacturing where humans are the bottleneck gains asymmetry.
Sources: Import AI 476
S-Space: a spatial “workspace” inside multimodal models
The S-Space paper finds a linear subspace where object tokens encode continuous coordinates. Perception writes locations, reasoning transforms them; models build good spatial representations but struggle to transform them when the viewpoint changes — consistent with HSS.
Why it matters: A mechanistic handle for debugging screen- and scene-reading agents.
google/skills: official Agent Skills catalog for Cloud and Gemini
google/skills (~21.2k★) is an official set of Agent Skills for Google products — GKE, BigQuery, SecOps, Ads, Gemini Agent Platform — with marketplaces for Claude Code, Codex and Antigravity CLI. Install: npx skills add google/skills.
Why it matters: The skills standard is becoming a shared language across harnesses; runbooks written as skills move between agents.
Sources: github.com/google/skills
GitHub repos
- View repo →
p-e-w/heretic
Automatic removal of safety alignment from transformers via directional ablation + Optuna. Use only on models and in jurisdictions that allow it.
- View repo →
google/skills
Official Google Cloud / Gemini / SecOps Agent Skills (see item 10).
- View repo →
MadsLorentzen/ai-job-search
A Claude Code job-application framework: job scoring, CV, cover letter, interview prep, all local.
Research papers
- Read paper →
S-Space
Internal spatial workspace in MLLMs.
- Read paper →
DiffusionART
RL for diffusion without fragile EMA; matches late-stage reward with ~12× fewer GPU-hours.
- Read paper →
TerminalHorizon
Long terminal-agent trajectory synthesis; 1.5k environments, gains on 12 benchmarks.
- Read paper →
MSAR — Multi-Stream Attention Residuals
1.58× iso-loss speedup vs. standard residuals at 18B/28B MoE.
- Read paper →
Thinking Inertia
“no-think” models still emit inference.
AI agent use cases
- Test memory agents yourself: compare Muse Wonder with Opus/Astra on your own mini MemoryBench — a few facts from a week-old conversation plus a task that needs them.
- Cheap routing: if you route requests through a small model “because it’s cheap”, compare it with Decision-1 or pplx-decider on 100 labeled tickets; measure accuracy, calibration and p50 latency.
- Skills before custom prompts: run
npx skills add google/skillsand pick 2–3 skills for your stack before writing your own.
AI workflows
1. BooksAndBotsAI: month-end close with Claude + NetSuite
- Replaces: the repetitive part of month-end close: tying out reconciliations, writing flux explanations, chasing the same accruals every month.
- What to connect: Claude Cowork + NetSuite (~50 skills for the close), with a human sign-off on every number. Cost depends on the Claude plan.
2. edwardross01: scanning building permits with Grok Bot
- Replaces: manually searching construction and renovation permits for trucking leads, “tens of hours a week”.
- What to connect: Grok Bot; no extra connectors named in the post.
3. itmeig: three scheduled agents instead of morning scrolling
- Replaces: about 3 hours of morning feed scrolling (world and markets, watchlist, X).
- What to connect: Grok Bot with three routines (News 6:30, Delta 7:00, X Radar 8:00), each ending in a short brief.