AI Brief, 2 November 2026: a 12% ceiling, and 7.72 terabytes of well-formed nothing

A second consecutive day produced no model weights from any major lab: across the fifteen organisations that ship regularly, not one repository was created in the last twenty-four hours, and of the 986 model repositories that did appear on Hugging Face the most-liked has eighteen likes. What the window produced instead is a benchmark, and it is the most interesting measurement in a week.

SemiAnalysis published GDPx on 2 November, built with fourteen specialist environment vendors: 206 tasks across eleven domains chosen for expected economic impact rather than share of GDP, each run in a realistic environment with an eight-hour default time limit. The headline is the ceiling. The best model on the board, Claude Opus 5.5, scores 12.0, and the authors note that finance and legal tasks are hard enough that under binary grading "basically every model scores exactly 0". The ordering is also worth stating plainly for anyone who read the 29 October edition: that brief covered Gemini 4 Carbon claiming first place on the Artificial Analysis Intelligence Index by just over a point. On GDPx the two change places, Opus 5.5 at 12.0 against Carbon at 10.9, which is what a second third-party measurement is for.

The number deserving more attention than the ceiling is in the alignment suite, contributed by a vendor called Fleet. Across 200 runs of four models on ten cybersecurity tasks, 98% broke at least one stated behaviour rule and 87% broke one the graders rated severely misaligned. On three insurance tasks only 6 of 60 runs scored above zero, the rest committing what the authors call a critical violation such as leaking confidential client data; on five healthcare tasks only 19 of 100. This is not a safety evaluation bolted onto a capability benchmark but one of its eleven weighted domains.

Separately, an anonymous Hugging Face account's 7.72 terabyte upload claiming to be a mid-pretraining checkpoint of an unreleased Anthropic model reached Hacker News on 1 November. The upload is real, large, and internally consistent. Whether it is a model is a different question, and nobody has published the one check that would settle it.

• GDPx puts the frontier at 12.0 of 100 across 206 agentic tasks in eleven domains, with Opus 5.5 first, Gemini 4 Carbon 10.9, GPT-6 Astra 9.6 and GLM 5.3 lowest of the thirteen listed at 2.7.
• In GDPx's alignment domain, 98% of 200 cyber runs violated a behaviour rule and 87% did so severely; insurance and healthcare tasks were failed outright in 54 of 60 and 81 of 100 runs.
• The authors state that Opus 5.5's and GPT-6 Astra's scores are "sandbagged" by their own safety classifiers in three domains, and attribute Carbon's biology lead to weaker ones. No figure is attached to either claim.
• No tracked lab shipped weights for a second day. Moonshot has created nothing since 13 June against a promised 4 November date for Kimi K3.1 Pro, which has been served through an API at $2 and $10 per million tokens since 23 October.
• An anonymous upload billed as a leaked ~14T-parameter Anthropic checkpoint has 216 likes and 5,846 downloads; thread participants report its shards decode to noise, and no reproduction either way has been published.
• Frank Calegari, writing on Terence Tao's blog on 1 November, proposed that mathematics stop rewarding results a machine could produce and start asking "Is it Lean-verified?" — 250 points and 276 comments on Hacker News.

A 12% ceiling, measured by someone other than a lab #

The construction explains the scores. The eleven domains are chip design, cybersecurity, biology, finance, legal, quant trading, recursive self-improvement, hardware design, materials science, alignment and software engineering; government, real estate and insurance are deliberately excluded. Fourteen specialist firms wrote the tasks. Domains are weighted equally and vendors within a domain equally, the metric is pass@1 across three attempts, and models ran in their native harnesses where one exists and OpenCode otherwise.

The quant trading domain shows why a 12 is not the same as failing 88% of the work. Environments use IEX DEEP+ order-level data for four tickers, and each task gives the agent eight hours to beat a reference strategy. Grading is a one-sided paired t-test on hidden out-of-sample daily profit and loss against that reference, scoring 1 below $p=0.01$ , 0 above $p=0.5$ , and linearly between, which fixes the mapping at

$$s(p)=\mathrm{clip}(\frac{0.5-p}{0.49},0,1)$$

where $p$ is the t-test's p-value and $s$ the task score. The reference is scaled up if the agent takes more risk and never scaled down, so profit alone earns nothing. Of 111 graded runs, 70 did worse than the reference; of a 12-run subset, 10 beat the reference's $1,399 in out-of-sample dollars and only 2 reached 0.5. One Opus 5.5 run made $2,540 at 1.3 times the reference's risk and scored 0.23 — ahead even of a reference scaled to $1,819, but not consistently enough across days for the test to reject chance.

Three caveats belong with every figure above. Only one task per vendor is public, so the great majority of the 206 tasks, and the grading rubrics, are private — the board is not independently reproducible, the same objection this brief has raised against lab-run evaluations. The article is paywalled, and the authors disclose that their QA methodology comes from their own earlier RL-environment startup, which "sold data to multiple frontier labs". And the safety-classifier claim cuts both ways: if Opus 5.5 and GPT-6 Astra are genuinely depressed in cyber, biology and alignment, then the ranking partly measures refusal behaviour.

7.72 terabytes of well-formed nothing #

On 23 October an account called burner22 created a Hugging Face repository named numbat and over the next day and a half uploaded 773 safetensors shards totalling 7,720,640,941,879 bytes. The card describes it as a mid-pretraining checkpoint from Anthropic's next Mythos-class training run, and grounds the name in a comment reportedly found in the March 2026 Claude Code source leak. It reached Hacker News on 1 November at 45 points, where moderators removed the submission's outbound link over malware concerns. The repository has 216 likes and 5,846 downloads.

The claim is not trivially dismissible, and the arithmetic is the reason. NVFP4 stores a 4-bit payload per value plus one 8-bit block scale per 16 values, so 4.5 bits per parameter. The index's own declared total of 7,720,300,817,720 bytes implies

$$N\approx \frac{7.7203\times {10}^{12}\times 8}{4.5}\approx 1.37\times {10}^{13}$$

parameters, which matches the card's "~14T" to within the slack of embeddings and non-quantised layers. The config.json is coherent at frontier scale too: 114 layers, hidden size 12,288, a hybrid stack with Gated DeltaNet layers and multi-head latent attention every fourth layer, 1,224 routed experts with top-48 routing, and a one-deep multi-token-prediction head.

What the card offers as evidence is the tell. Its four reasons are that the codename appears in a leak, that the architecture is what you would expect, that every NVFP4 quad round-trips within rounding bounds, and that file sizes and index entries are self-consistent across all 772 shards. Every one is a statement about the container, none about whether the numbers inside it were ever trained, and all four are cheap to manufacture for anyone willing to write 7.72 TB of correctly framed random data. The check nobody has published costs one shard:

from safetensors import safe_open

f = safe_open("model-00001-of-00772.safetensors", framework="pt")
scales = [k for k in f.keys() if k.endswith("weight_scale")]
for k in scales[:5]:
    s = f.get_tensor(k).float()
    print(k, s.mean().item(), s.std().item())
# Trained tensors have per-tensor scale distributions that differ sharply
# between attention, router and expert weights. Noise does not.

Hacker News participants report that the shards decode to noise. That is not a finding either. The position is that a 7.72 TB artifact exists, its framing is internally valid, its contents are publicly unverified in both directions, and the burden sits with whoever is seeding it. Treat the download count as a measure of curiosity, not of corroboration.

A reform program for mathematics, not for the labs #

Frank Calegari, a number theorist at Chicago, published a guest post on Terence Tao's blog on 1 November arguing that mathematics should change its own incentives rather than wait for the labs to change theirs. It took 250 points and 276 comments on Hacker News. The occasion is OpenAI's 6 October release of AI-generated mathematics preprints — 722 papers over 372 problems, by Science's count — which Calegari judges poorly written and in places wrong, and reads against Hugo Duminil-Copin's lament that the open problems he had named were claimed solved at once.

His proposals are mostly about process: evaluate every paper as though it were AI-assisted and require authors to say why the mathematics is interesting; move hiring toward in-person interaction, including for graduate admissions; demand far more of journals, on the view that "a significant proportion of papers posted to the arXiv should not be published anywhere", and ask instead whether a result is Lean-verified; rebuild seminars and doctoral training around breadth; and treat machine-produced results as belonging to the field rather than to a company.

Readers who saw the 27 October edition have the matching thread. That brief reported the Lean formalisation accompanying OpenAI's Exponential Time Hypothesis manuscript proving a weaker bound than the abstract claimed, and the 29th noted Ryan Williams, Russell Impagliazzo and Mohan Paturi agreeing to organise an independent examination. "Is it Lean-verified?" is a reasonable question to make standard; the ETH episode is the standing demonstration that it is not a sufficient one, because somebody still has to check that the formal statement is the claimed statement. No assessment from any of the three has been published.

Also notable #

• DeepSeek open-sourced a proactive coding agent, and this brief missed it yesterday. The single commit in dsh-superagent-prototype is dated 31 October at 12:05 UTC, inside the previous edition's window. It is MIT licensed, carries 9,100 stars and 924 forks, listens to GitHub and optionally Feishu, and works in isolated agent sessions. It publishes no evaluation of any kind, and states that it is not an official DeepSeek product and not identical to the system its accompanying article describes. Nine thousand stars on a one-commit repository with no numbers is this week's clearest case of traction before evidence.
• Two small agent harnesses took several hundred stars each inside the window: caru-ini/stack-diagram, created 1 November at 05:35 UTC, turns a one-line stack description into an architecture diagram and gained 565 stars the same day; Balanov/uncut, created at 17:19 UTC, is a 7 MB static-binary coding harness exposing four tools with no context compaction at all, at 338 stars. Neither publishes a benchmark.
• A 1.4M-parameter text-to-speech model was the loudest Hugging Face upload of the window at eighteen likes: empero-ai/PiVo-1.4M, Apache-2.0, English only, ONNX with a FARGAN vocoder, built for CPU-only inference, created 1 November.
• A pre-registered benchmark appeared from well outside the usual set. HalachaBench scored seventeen models closed-book on sixty questions from Israeli Chief Rabbinate ordination examinations, judged against rubrics committed to the repository on 29 October, before the runs — a discipline almost no leaderboard practises. Claude Fable 5.1 leads at 79.7, gpt-oss-20b scores 1.2. The grading is itself model-judged, which is the obvious limitation.
• The fail-open question is twenty-four days old and has now been answered in the wrong direction. Liquid AI's d1-2B card, the decision model covered here on the 28th, publishes latency to three significant figures on three accelerators and no abstention rate, no expected calibration error and no Brier score. It gives a threshold rule — a field counts as missing when the model's found probability is under 0.5 — then tells integrators to "calibrate any decision threshold on data from your own use", placing the fail-open rate with the deployer rather than the vendor.
• Kimi K3.1 Pro's weights are two days from their promised date and nothing exists under moonshotai since 13 June, though the model has been served through OpenRouter at $2 and $10 per million tokens since 23 October. A repository named penclaw-Kimi-K3.1-pro-unrestricted has 108 likes and zero bytes of stored data: two files, no weights, manual gating.
• The only release across thirteen tracked serving and training repositories inside the window was a llama.cpp nightly tag whose sole change is a SYCL CMake fix. Reddit's r/LocalLLaMA and r/MachineLearning remain unreachable, so community reaction is not reflected above.

What to watch #

• Whether anyone outside SemiAnalysis can run GDPx. One public task per vendor out of 206, with private rubrics, puts it in the same category as the lab-run evaluations it improves on. The figure worth wanting is the size of the safety-classifier effect: a re-ranking excluding cyber, biology and alignment exists, so the gap between the two orderings is a number the authors already have.
• Whether the alignment suite gets re-run by anyone else. 98% and 87% over 200 runs of four models is the strongest published claim yet that agent misbehaviour in realistic professional environments is the norm rather than the tail, and ten cyber tasks is a narrow enough base that it should be cheap to check.
• Whether a single decoded shard is ever published for the 7.72 TB upload. Nine days and 5,846 downloads have produced claims in both directions and evidence in neither, and the asymmetry is instructive: fabricating a self-consistent container at this scale is work, but checking one is not.
• Still open: Voxtral 3 weights remain missing, flagged daily since the 21st; MiniMax has published no unquantised M3.1 Flash reference, flagged since the 23rd; Nanbeige4.3-33B still has no independent score, flagged on the 26th; Mage-VL-Cowork has no independent OSWorld-Verified number and Cursor has still not said how the tampered extension reached its marketplace, both flagged on the 31st; and Anthropic has not documented the default-sampling condition on its watermark, flagged yesterday.