Just Now, Zuckerberg's $14.3 Billion "Avocado" Is Here: Challenging GPT-5.4 Head-On, Silicon Valley's Most Expensive Chinese Debut

But in the medical health赛道, Muse Spark is fighting fiercely.

HealthBench Hard open-ended health Q&A 42.8, Gemini 3.1 Pro only 20.6, GPT 5.4 is 40.1.

MedXpertQA multimodal medical 78.4, also not far behind Gemini's 81.3 (Gemini slightly higher here), but far exceeding Opus 4.6's 64.8.

The data cleaning and筛选 involving over 1000 clinicians during training确实 brought tangible results.

The agent赛道 is also noteworthy.

DeepSearchQA search agent scored 74.8, the highest among the five.

τ2-Bench tool use 91.5, tied with GPT 5.4.

GDPval-AA Elo office agent reached 1444, surpassing Gemini's 1320 but lower than Opus 4.6's 1606.

Significant gap in SWE-Bench, Verified 77.4 vs Opus 80.8 vs GPT 82.9 (reportedly 78.2), Pro 52.4 vs GPT 57.7.

In summary of the benchmarks: won in multimodal and health,持平 in reasoning, slightly behind in code and agent.

Alexandr Wang: Llama 4's mistakes won't be repeated, Avocado didn't cheat on scores

Independent testing by Artificial Analysis also revealed an important detail: Token efficiency.

Running the entire Intelligence Index test suite, Muse Spark used 58 million output Tokens, comparable to Gemini 3.1 Pro (57 million), but far lower than Opus 4.6 (157 million) and GPT-5.4 (120 million).

The same level of intelligence, consuming half to two-thirds fewer Tokens.

Furthermore, on FrontierMath with problems set by math experts, Muse Spark crushed Gemini 3.1 Pro on levels 1-3, but ranked last on level 4.

More notably, on the Vals index leaderboard, Muse Spark强势 seized third place, with specific indicators as follows.

One year after the release of Llama 4, Meta has returned to the AGI first tier.

Multi-agent parallel thinking, scores 58% on "Humanity's Last Exam"

The "Contemplating Mode" is Muse Spark's killer feature.

Traditional thinking mode is one agent thinking for a longer time; contemplating mode is multiple agents thinking simultaneously, then汇总 the answer.

Humanity's Last Exam (no tools), Muse Spark contemplating mode scored 50.2, Gemini Deep Think 48.4, GPT 5.4 Pro 43.9.

Humanity's Last Exam (with tools), 58.4, Gemini 53.4, GPT 5.4 Pro 58.7, almost tied.

FrontierScience Research scientific frontier research 38.3, Gemini Deep Think only 23.3, GPT 5.4 Pro is 36.7.

However, on the IPhO 2025 theoretical physics Olympiad problem, Muse Spark contemplating mode 82.6, GPT 5.4 Pro scored 93.5, a significant gap.

Overall, the contemplating mode确实 allows Muse Spark to reach the threshold of the first tier on the most difficult comprehensive reasoning tasks.

Aiming for "Personal Superintelligence," take a photo to become a personal nutritionist

Meta's defined direction for Muse Spark is clear: personal superintelligence.

Translated into plain language, it's an AI assistant that understands you and the world around you.

In terms of multimodality, Muse Spark is designed from the ground up for cross-domain integration of visual information.

Official demos showed several scenarios.

Take a photo of a Sudoku puzzle, Muse Spark can turn it into an interactive game you can play on the web.

Photograph a coffee machine and grinder, it first labels all core components, then generates an interactive web-based latte tutorial.

When hovering over a step, the bounding box for the corresponding part in the photo highlights automatically, visual guidance and操作 steps correspond one-to-one.

Health scenarios have even more imagination space.

Photograph a table of food, tell it "I have high cholesterol, I'm a pescatarian," Muse Spark will mark recommended foods with a green dot, not recommended with a red dot.

Prompt control is very granular, directly specifying the UI interaction logic.

The health score number is displayed directly above the dot without hovering; hovering pops up detailed calorie, carb, protein, and fat data, and the pop-up is required to "always be on top,不能被其他点挡住".

Photographing yoga poses follows the same idea.

It identifies which muscle groups each pose stretches, labels difficulty level, and gives posture correction suggestions on hover. Two people's images are拼在一起 side by side, scored from 1 to 10 respectively.

The underlying support for these demos is the combination of visual STEM Q&A, entity recognition, and object localization.

Individually, none are particularly novel, but串联 into scenarios, one can indeed see the product intent behind the term "personal superintelligence."

Another new feature worth mentioning separately is "Shopping Mode."

Wang said in a tweet that shopping mode can "recognize creators, brands, and style content you follow on Instagram, Facebook, and Threads, and turn it into personalized recommendations."

This is Meta's unique data advantage: 3 billion daily active users' social behavior data + AI shopping assistant, huge commercial imagination space.

Three Scaling curves, compute slashed by 90%, thoughts can self-compress

The highlight of the tech blog isn't the benchmarks, it's Scaling.

Meta explains Muse Spark's performance来源 by breaking it down into three axes: pre-training, reinforcement learning, and test-time computation. Each has corresponding scaling curves for support.

Pre-training: Same capability, compute cut to 1/10

Over the past nine months, Meta overhauled the pre-training tech stack: architecture, optimization algorithms, data strategy—all redone.

To measure the effect, Meta fitted Scaling Law on a series of small-scale versions, then compared the training FLOPs needed to reach the same performance level.

The conclusion is solid: for the same capability level, Muse Spark requires less than one-tenth the compute of Llama 4 Maverick.

This curve说明 one thing: Meta isn't just throwing more GPUs at the problem, but has fundamentally improved the output per unit of compute from the ground up.

University of Washington's Yuchen Jin's evaluation on X was spot on: "I still believe infrastructure is the real moat for AI labs. Because you can train faster, researchers can experiment with more ideas faster."

Reinforcement Learning: Log-linear growth, generalizes to unseen problems

Large-scale RL is notoriously unstable, but Meta says the new tech stack's RL curves are exceptionally smooth.

The left graph shows performance on the training set. Both pass@1 and pass@16 (at least 1 correct in 16 attempts) show log-linear growth.

This indicates that RL improves reliability without sacrificing solution diversity; Muse Spark doesn't "go down one path blindly," it maintains the flexibility to explore different solutions.

The right graph is more important: accuracy on the held-out evaluation set.

The curve also rises steadily, showing that the progress from RL isn't rote memorization, but can generalize to completely new, unseen problems.

Test-time reasoning: Thought first expands, then compresses, then expands again

This is the most technical and interesting part of the entire article.

RL taught Muse Spark to "simulate in its mind first" before answering—this is test-time reasoning.

But the problem is, providing this service to billions of users, the Token cost is unsustainable.

Meta's solution is two-fold.

First, add "thinking time penalty" to RL training. You can think longer, but thinking too long will cost you points.

This constraint triggered an interesting "phase transition" phenomenon.

Performance on the AIME subset is like this: early in training, Muse Spark improves accuracy by thinking longer, the curve extends to the right.

Then, the length penalty triggers "thought compression." Muse Spark learns to solve the same problem using far fewer Tokens, the curve bends back left.

After compression is complete, it once again lengthens its problem-solving process to tackle harder problems.

The entire trajectory is a three-stage evolutionary path: first拐 right, then left, then right again.

The second step is solving the latency problem.

A single agent thinking longer increases latency linearly.

Meta's approach is to scale the number of parallel agents: 1, 2, 4, 16 agents thinking simultaneously.

From the graph, 16 agents at a similar latency level jump accuracy from about 54% to about 58%.

Traditional test-time scaling trades time for quality; multi-agent scaling trades parallelism for quality, with latency几乎不变.

Silicon Valley's "Most Expensive Chinese" team submits its first paper

Behind Muse Spark is Zuckerberg's complete overhaul of the Meta AI system last year.

In June 2025, Meta acquired 49% of Scale AI for $14.3 billion, bringing its founder Alexandr Wang onboard as Meta's first Chief AI Officer to form the Meta Superintelligence Lab (MSL).

Joining at the same time were former GitHub CEO Nat Friedman (co-leading product and applied research), SSI co-founder Daniel Gross, and 11 researchers poached from OpenAI, DeepMind, and Anthropic.

Now, the release of Muse Spark proves one thing: the nine-month重构 by Meta's Superintelligence Lab has yielded results.

Pre-training efficiency increased by an order of magnitude, RL scaling curves are smooth and predictable, multimodal and medical tracks have reached the first tier.

But the gaps in code and agent are there, the contemplating mode isn't fully open yet, and the open-source timeline is still a "hope".

More immediate pressure: Anthropic released the reportedly "too powerful to release" Mythos the same week, and OpenAI's codenamed Spud is also on the way.

$14.3 billion bought an entry ticket. The real exam is yet to come.

References:

https://ai.meta.com/blog/introducing-muse-spark-msl/

https://ai.meta.com/blog/scaling-how-we-build-test-advanced-ai/

https://ai.meta.com/static-resource/muse-spark-eval-methodology

https://x.com/alexandr_wang/status/2041909376508985381

This article is from the WeChat public account "新智元" (New Wisdom Element), author: 新智元