ARC-AGI-3 - Wealth Engine

Nvidia AVO — Agentic Variation Operators — scored 100.00 RHAE on the ARC-AGI-3 public set on August 21, 2026, clearing all 183 levels across 25 environments in 6,624 actions, roughly 12% fewer than the VISTA baseline. The same base model, Claude Opus 5, scores 30.2% on its own. The harness did the work, and that changes where agent money goes.

What is Nvidia AVO?

Nvidia AVO stands for Agentic Variation Operators. It is not a model. It is a general-purpose coding-agent system that wraps an existing frontier model in a loop — inspect, plan, implement, evaluate — plus persistent memory and a supervisor that intervenes when progress stalls. Nvidia published the results on August 21, 2026.

The base model inside the winning run was Anthropic’s Claude Opus 5. Nvidia also ran limited experiments with GPT-5.6 Sol on a subset of games, and labeled those findings preliminary.

That detail is the whole story. Nvidia did not train a better reasoner. It built better scaffolding around someone else’s reasoner.

How the AVO loop works

AVO runs a four-step cycle: inspect the current context, plan a change, implement it, then evaluate the result against the environment.

Two additions separate it from a standard agent loop, according to Nvidia’s technical blog:

Persistent memory that carries forward prior implementations, evaluation results and reasoning across the whole run, not just the current context window.
A supervision mechanism that watches the trajectory and redirects the agent when it detects the run has stopped making progress.
Variation operators that generate structured alternatives rather than retrying the same failed approach.

Why the supervisor is the expensive part

Long-horizon agent failure is rarely a single wrong answer. It is a slow drift — the agent loops on a dead approach and burns tokens without noticing.

A supervisor that detects stagnation is cheap to describe and hard to build. It is also the component least likely to transfer cleanly to another benchmark.

What is ARC-AGI-3 and why does a 100% score matter?

ARC-AGI-3 is ARC Prize’s interactive reasoning benchmark: 25 pixel-art puzzle environments containing 183 public levels. Agents get no instructions, no rules and no goal labels. They must infer the mechanics purely by playing. When the benchmark launched, humans cleared 100% of environments and the best AI managed 0.37%.

That 0.37% figure is why this result registered. ARC-AGI-3 was designed as the benchmark models could not touch.

How RHAE scoring works

The metric is RHAE — Relative Human Action Efficiency. It combines task completion with how many actions the agent needed per level, measured against initial human performance, then aggregates across every level and environment.

So a 100.00 does not just mean “finished everything.” It means finishing everything at roughly human action efficiency. ARC Prize published its human performance dataset specifically so this number would have a floor to sit on.

How much did the harness add versus the raw model?

The gap is 30.2% to 100.00 — the same model class, wrapped differently. ARC Prize reported Claude Opus 5 at 30.2% on ARC-AGI-3 in July 2026, which it called a genuine reasoning leap at the time. Nvidia’s harness took that model to a clean sweep of the public set.

System	ARC-AGI-3 result	Actions used	Reported by
Best AI at benchmark launch	0.37%	—	ARC Prize
Claude Opus 5 (bare model)	30.2%	—	ARC Prize, July 2026
VISTA baseline agent	Cleared same level sets	7,542	Nvidia
Nvidia AVO (Claude Opus 5 inside)	100.00 RHAE, all 183 levels	6,624	Nvidia, Aug 21 2026

For context on the base model’s ceiling elsewhere: Claude Opus 5 at maximum reasoning effort scores 97.5% on ARC-AGI-1 and 90.4% on ARC-AGI-2, per The New Stack. ARC-AGI-3 was the one that stayed hard.

Nvidia’s own framing, from the blog post: “The model matters, but the model is not the entire agent.”

Is the 100% score real, or is it benchmark theater?

It is real, and it is narrower than the headline suggests. The score covers the ARC-AGI-3 public set only — not the semi-private or private competition sets that ARC Prize maintains precisely to catch overfitting. Nvidia says so in its own post.

The public set is not the hidden exam

Benchmark designers split datasets for a reason. A public set is a practice test with the answers eventually leaking into the ecosystem around it.

One commenter on Nvidia’s announcement put it bluntly, as flagged in explainX’s write-up: “I would not file this as solved AGI… if you post 100 like it is the hidden exam.” Until AVO posts a semi-private number, that objection stands.

These are not controlled ablations

Nvidia explicitly labels its comparisons as not controlled ablations. That matters more than it sounds.

The AVO-versus-VISTA action count — 6,624 against 7,542 — varies agent backends, observation formats, memory systems and reasoning settings all at once. The 100.00-versus-30.2% comparison swaps the entire system architecture and the reasoning-effort setting simultaneously.

Neither number isolates how much the harness itself contributed. The honest reading is “a well-built harness closed a very large gap,” not “the harness is worth exactly 70 points.”

Nvidia also disclosed no compute cost, no token usage and no wall-clock runtime for the 6,624 actions. For anyone pricing an agent product, that is the number that actually matters — and it is missing.

Has AVO done anything useful outside a puzzle benchmark?

Yes, and this is the part investors should read twice. Nvidia ran AVO continuously for seven days on GPU-kernel optimization, exploring more than 500 optimization directions. The system produced kernels that beat FlashAttention-4 by up to 10.5% on NVIDIA DGX B200 hardware.

FlashAttention is not a soft target. It is hand-tuned infrastructure that the entire industry’s inference economics rest on.

A 10.5% kernel improvement compounds across every token served on that hardware. If it holds in production, it is worth more to Nvidia than the benchmark headline — and it lands in the same week the company has been buying capability outright elsewhere.

Who wins and who loses financially?

The winner is whoever owns the orchestration layer. If a 30% model becomes a 100% agent through harness design, then value is accruing above the weights, not inside them. That is bad news for anyone whose entire moat is a checkpoint.

Nvidia is climbing the stack

AVO did not appear in isolation. On the same day, Nvidia paid $6 billion to license Poolside’s model-development software and invested $1 billion more in the startup, according to PYMNTS.

A chip company publishing frontier agent architecture and licensing a model factory in the same 24 hours is not a coincidence. It is a company that has watched its customers capture the margin its silicon creates — the same dynamic behind its recalculated OpenAI data center guarantee.

Model labs keep pricing power, for now

Note who supplied the brain: Anthropic. AVO’s best run needed Claude Opus 5, and the harness could not manufacture reasoning that was not already there — the 0.37% launch-day figure is proof that scaffolding alone does nothing on a weak model.

So frontier labs still sell the scarce input. What they lose is the claim that the model is the product, which shows up quickly in cheaper models closing capability gaps.

Agent startups just got a harder question

Three practical implications for anyone building or funding an agent company:

Harness design has not hit diminishing returns. A 30-to-100 jump says the scaffolding layer is still under-engineered — which is opportunity and commoditization risk in the same sentence.
Your differentiator may be a blog post away from replication. Persistent memory plus a stagnation supervisor is a describable architecture, not a trade secret.
Nvidia is now a potential competitor, not just a supplier. It has the hardware, the capital, and as of August 21, published frontier agent research.

The cost question decides all three. Running a supervised, memory-heavy loop for 6,624 actions is not free, and the economics look very different depending on whether the underlying tokens cost $2 or $60 per million — the same math that drives coding-agent unit costs and inference vendor selection.

Frequently asked questions about Nvidia AVO

Is Nvidia AVO a new AI model?

No. AVO is an agent system — a harness — that runs on top of existing frontier models. The reported 100.00 RHAE run used Claude Opus 5 as its base model.

Did Nvidia AVO solve AGI?

No. The score covers ARC-AGI-3’s public set of 183 levels across 25 environments. ARC Prize also maintains semi-private and private sets, and AVO has not posted a result on those.

What does RHAE mean?

Relative Human Action Efficiency. It scores both whether an agent completes a level and how many actions it needed relative to initial human performance, aggregated across the benchmark.

Can developers use AVO today?

Nvidia’s August 21 post describes the architecture and results. It does not announce a code or weights release, so treat AVO as published research rather than a shippable dependency.

How much does an AVO run cost?

Nvidia did not disclose compute cost, token usage or wall-clock time for the benchmark run. Without those figures, the result cannot be compared on a cost-per-task basis against cheaper agent harnesses.

What was the FlashAttention-4 result?

Running for seven days across 500-plus optimization directions, AVO produced GPU kernels that outperformed FlashAttention-4 by up to 10.5% on NVIDIA DGX B200 hardware.

Does this make Claude Opus 5 look better or worse?

Both. The model was capable enough to be driven to 100.00 RHAE, and weak enough on its own to score 30.2%. The delta belongs to the harness, not the checkpoint.

The bottom line

Nvidia AVO is the most important agent result of the month, and the headline number is the least interesting part of it.

A 100.00 on a public set with no controlled ablations and no disclosed cost is a demonstration, not a benchmark victory. Anyone treating it as “ARC-AGI-3 is solved” is reading a press release as a result.

What survives scrutiny is the gap: 30.2% to 100.00, same model, different scaffolding. That gap is the clearest evidence yet that in 2026 the agent layer, not the model layer, is where the remaining engineering leverage sits.

And the FlashAttention-4 kernels are the tell. Nvidia did not build AVO to win a puzzle leaderboard. It built AVO to make its own hardware faster — and, at a moment when record sums are being raised to finance AI chip capacity, to stop being only the company that sells the machines.

Tag: ARC-AGI-3

Nvidia AVO Hits 100% on ARC-AGI-3. The Model Alone Scored 30.2%.