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FINAL-Bench/FreeWeb-Security-SCAN

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reacted to SeaWolf-AI's post with 👍 2 days ago

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5,194

HF Post draft (English) — FreeWeb Security SCAN
🛡 FreeWeb Security SCAN — free web security & AI-visibility check

Drop in a domain and get a non-intrusive (read-only) assessment in ~10–25s. No login, no install, no credit card.

What it checks Security headers · HTTPS/HSTS · cookies · TLS certificate · CORS · exposed files; email security (SPF · DKIM · DMARC · BIMI · MTA-STS · TLS-RPT); DNS (DNSSEC · CAA · MX · NS); site-wide mixed content & external-script SRI; SEO foundations; and reachability of 9 AI/search crawlers (GPTBot · ClaudeBot · PerplexityBot · OAI-SearchBot · Googlebot…) — your AI visibility (GEO).

Why now AI agents now break in on their own. In 2026 the open-source AI penetration agent ARTEX was turned on real targets — hitting 2,250+ Microsoft 365 tenants, breaching e-commerce order databases, and automating recon-to-exfiltration against unpatched internet-facing systems worldwide. Attacks are automated — so must defense be.

3 steps ① Free instant scan → ② Domain-verified deep scan (HF login on your org-domain email) → ③ In-depth consultation (Web · AX Infra · AI Model · AI Agent). Aligned to OWASP · NIST · ISO · EU AI Act.

👉 Scan now: FINAL-Bench/FreeWeb-Security-SCAN

#websecurity #cybersecurity #AIsecurity #SEO #GEO #AIvisibility #SPF #DMARC #OWASP #LLM

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reacted to SeaWolf-AI's post with 🚀 3 days ago

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We pretrained a 325M language model from zero on an inference-only NPU

The accepted view is that inference-only NPUs cannot train, let alone pretrain a language model from scratch. We put that view to a direct test on the Rebellions ATOM-Max, a commercially available Korean NPU released in 2025 and designed for inference. We built our own training loop that runs backpropagation on the chip itself. Starting from random initialization, we pretrained VKORE-325M-NPU-Base, a 325.6M parameter model, on 5 billion byte tokens of FineWeb-Edu. The run took 76,292 steps over about four days and finished without a single divergence. No pretrained weights were used.

To make the comparison fair, we trained the same model on a B200 GPU with identical initialization, data and learning-rate schedule. On 1,024 windows of text that neither model ever saw, the NPU reached a loss of 0.7993 and the GPU 0.7994. The window-by-window difference is -0.0001, well inside the noise. The two models are indistinguishable.

This is seven times larger than our first NPU model, VKORE-46M, and the first time this hardware has carried a real pretraining run to GPU quality. Please note that the hardware alone does not make this possible: the NPU and its standard software are built for inference, and training on it depends on our own training stack and stability technique, which are developed in house and under patent application. Buying the chip does not by itself enable training. Both sets of weights and the evaluation script are public, so anyone can verify the result in minutes.

📄 Technical report: https://huggingface.co/blog/FINAL-Bench/pretraining-a-325m-language-model-from-zero-on-an
🤗 Models: FINAL-Bench/VKORE-325M-NPU-Base

#NPU #Rebellions #Pretraining #VKORE

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reacted to SeaWolf-AI's post with ❤️ 15 days ago

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🚀 We pretrained a language model from scratch on an inference-only NPU, with real backpropagation.

🤗 Model: FINAL-Bench/VKORE-46M-NPU 📝 Technical write-up: https://huggingface.co/blog/FINAL-Bench/backpropagation-on-an-inference-only-npu-pretraini

"NPUs are inference-only, you can't train on them." We built a counterexample: VKORE-46M-NPU, trained from scratch on a first-gen (2025) inference NPU, 32 chips, no pretrained weights. A 46M decoder-only model (RoPE · SwiGLU · RMSNorm · GQA), char-level, TinyStories.

📏 Why trust it: a falling-loss curve proves nothing. So we trained the same model under identical init, data, and schedule on the NPU and on a GPU reference. · Loss trajectories correlate at 0.99 · Throughput converged to a comparable level The NPU backward pass and optimizer updates do numerically the same work as the GPU, not an approximation that merely trends down.

🧩 One accelerator now covers pretraining · LoRA · inference · quantization. We call this layer VKORE: "CUDA for NPUs."

⚖️ Public: weights, model card, NPU-vs-GPU comparison. Private (patent-pending, paper to follow): the core training technique. 46M char-level is toy scale on purpose: it proves the training path, not model quality. Larger models and real data are next.

Feedback from folks working on custom autograd backends and low-precision training kernels on non-GPU accelerators is very welcome. 🙌

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reacted to SeaWolf-AI's post with 🔥❤️ 18 days ago

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⚛️ The Open Quantum Challenge is live. Both seasons open today.

Quantum computers are expensive, queued, and reachable by only a few. So we removed the barrier. With a single GPU and a public harness, anyone can contribute to core quantum-computing problems with no quantum hardware at all. That is the point of this challenge: to move quantum research from a handful of labs to everyone.

• Season 1, Quantum simulation: reproduce a target quantum system on a GPU.
• Season 2, QEC decoder: correct errors on coded quantum states.

Answers are held privately and every submission is auto-scored against a frozen ground truth, so the ranking is reproducible and hardware-independent.

🏆 Prize: 2,000 USD total (1,000 USD per season, 🥇600 / 🥈300 / 🥉100)

Getting started takes one step: copy the participation guide from the Space and paste it into Codex or Claude Code, then submit against the harness.

📄 Read the article: https://huggingface.co/blog/FINAL-Bench/the-open-quantum-challenge-quantum-simulation-and
👉 Challenge: FINAL-Bench/open-quantum-challenge

reacted to SeaWolf-AI's post with 🔥 20 days ago

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🏆 Darwin-27B-ZTC-v2 just took #1 on the System One Mosaic Benchmark (S1MB).

S1MB compares 102 models across 137 specialized benchmarks, in three task types: Noul (assess a condition), Choice (select an option), Score (rate on a scale). Ranking is by overall Borda score.

📊 Top of the board
🥇 Darwin ZTC v2 (FINAL-Bench) 89.58
🥈 OpenJev-27B 87.50
🥉 AutoJev-27B 87.07
4️⃣ Eikos 27B 85.43
5️⃣ Jev 1.13 85.05

🔎 Ranks 2 to 5 are all the JEV family (TypeSafe AI's System One model, from ex-OpenAI researchers). S1MB exists to compare these System One judges, so leading it is the headline.

⚙️ Why a zero-token judge wins here
🔹 It does not generate. It reads the input and typed questions and returns a calibrated distribution in a single forward pass.
🔹 Zero generated tokens, no decoding loop, so latency and cost stay low.
🔹 Holds up out of distribution too: General Noul 96.00, General Choice 99.34.

It is also #1 on the typed-decisions leaderboard (0.743, zero-shot). Same message from both: a deterministic, calibrated judge at one forward pass per call.

🔗 Model: FINAL-Bench/Darwin-27B-ZTC-v2
🔗 Leaderboard: hotchpotch/S1MB-leaderboard

Standings move as new models are added. Numbers reflect the board at the time of writing. 🙌

reacted to SeaWolf-AI's post with 👍 21 days ago

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4,130

🧠 We just released Darwin-27B-ZTC, a judgment engine that reaches a verdict without generating anything.

Most LLMs answer by generating, decoding one token at a time. Darwin-27B-ZTC takes a different route.

⚙️ How it works
🔹 It makes its call in a single forward pass.
🔹 Zero generated tokens, and no decoding loop.
🔹 That keeps latency and cost far below what a generative model needs.

🎯 What it judges
🔹 It handles several question types: free-form correctness (noul), multiple choice (choice), and scoring (score).
🔹 For each one it hands back a calibrated confidence, not just an answer.

📊 How well calibrated (measured)
🔹 KL 0.204, Brier 0.097, so the confidence it reports lines up with what actually happens.
🔹 0.743 accuracy (zero-shot, general split), across 2,000 judgments with zero errors.
🔹 By type: noul 0.847, choice 0.723, score 0.675.
🔹 None of the benchmark's train split went into it. It is pure zero-shot.

🚀 Where it fits
🔹 Grading at scale, model routing, safety gating, anywhere you want a fast decision without paying for generation.

🏆 It currently sits at #1 on the official typed-decisions leaderboard on Hugging Face (0.743 accuracy, zero-shot).

🔗 Links
Model: FINAL-Bench/Darwin-27B-ZTC
Leaderboard: LocalLLaMA/typed-decisions

Curious to hear what you make of the single-pass, no-generation approach. 🙌

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reacted to SeaWolf-AI's post with 🤝 27 days ago

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💻 Data-center AI, now on a laptop: POCKET-Darwin-180B

We're releasing a 4-bit GGUF build of Darwin-180B-RSI, #1 on seven official Hugging Face leaderboards (self-reported), that runs without a GPU.

📦 360 GB → 111 GB (4-bit GGUF, 4 files)
🖥️ No GPU: one server CPU (16 threads) at 18.4–21.0 tokens/s
💻 RTX 5060 laptop (8 GB VRAM) + 32 GB RAM: 4.17 tokens/s
🧊 128 GB mini PC: whole model in memory, no GPU needed
🎯 MMLU-Pro, 2,000 questions, paired: original 87.65% = 4-bit 87.65%

How?
· Only ~3B of 180B parameters are active per token (10 of 512 experts)
· llama.cpp streams just the needed experts from SSD, so 32 GB RAM is enough
· Graft quantization: we took the proven Unsloth UD-Q4_K_XL base build and swapped in only the 300 tensors our RSI training changed (300/300 verified)

Under the hood is Model-level Recursive Self-Improvement. The model solves verifiable problems, keeps only its own solutions that check out as correct, and trains on them. No human-written solutions or reasoning traces.

Built for teams that can't send data to an external cloud (defense, finance, public sector) to run a top-tier model fully offline.

📝 Article: https://huggingface.co/blog/FINAL-Bench/data-center-ai-now-on-a-laptop-pocket-darwin-180b
🤗 Model: FINAL-Bench/POCKET-Darwin-180B-GGUF
🧬 Original: FINAL-Bench/Darwin-180B-RSI

#Darwin #RSI #GGUF #llamacpp #OnDevice #MoE

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reacted to SeaWolf-AI's post with 👍 about 1 month ago

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🧬 Darwin-180B-RSI — an AI that learns from itself and knows when it's right
👉 FINAL-Bench/Darwin-180B-RSI

🧬 Darwin — crossbreed and evolve the parent
Darwin diagnoses strong parent models like an MRI, inherits only their best parts, and evolves the weak spots — producing a child stronger than its parents.
Father model: Qwen3.8-Flash-Next (180B MoE).

🔧 Rewired paths
🔹 12 full-attention layers · 🔹 36 linear-attention layers · 🔹 48 shared-expert layers — precision-strengthened
🔒 512 routed experts · router · vision encoder — untouched
→ Only 0.02% of the weights changed.

🔁 RSI × 🏛️ ZTC
RSI (recursive self-improvement): solve → verify against real answers → learn only the correct reasoning → repeat.
ZTC (Zero-Token Confidence): reads the model's internal state once, before answering, and returns the probability the answer is right — zero extra tokens. Returns answer + confidence as JSON.
{"answer": "...", "confidence": 0.97, "truncated": false}

✨ Synergy: ZTC finds where the model wavers → RSI learns exactly there → confidence gets sharper. Low confidence = stop, so agents don't act on wrong answers.
⚡ Same accuracy, 11% shorter reasoning — faster and cheaper.

📄 https://arxiv.org/abs/2605.14386
🤗 FINAL-Bench/Darwin-180B-RSI
🏛️ https://huggingface.co/collections/FINAL-Bench/ztc-models-jev-ecosystems

🏆 The result — #1 on five Hugging Face official leaderboards
🥇 AIME 2026 100% (first perfect score on the board)
🥇 HMMT Feb 2026 100% (first perfect score on the board)
🥇 GPQA Diamond 94.44%
🥇 MMLU-Pro 88.12%
🥇 MMMU-Pro 79.48%

📏 131K-token thinking budget · bf16 · samples per benchmark listed on the model card. 🚀

#Darwin #RSI #ZTC #AIME #HMMT #GPQA #MMLUPro #MMMUPro #OpenSource

reacted to SeaWolf-AI's post with 🔥 about 1 month ago

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🖼️ NO GPU, Only CPU : Z-Image model

Zero graphics cards. 46 seconds. Photoreal.

That laptop you're reading this on. No graphics card, right? It generates images.

No CUDA install. No Python environment. No driver changes. One binary, three model files. Done.

📊 Measured — GPU count used: zero

512×512 : 46.4 s
Korean prompt : 45.3 s (faster than English)
1024×1024 : 192.7 s
Peak RAM : 6.42 GB
GPUs used : 0

(Intel Xeon Gold 6526Y ×2, 48 threads, Q4_0, 3 steps)

⚡ From 244 seconds to 46 — 5.3×

Run it on defaults and it takes 244 s. Switch to 3 steps and it's 48.6 s. Add VAE tiling and it's 46.4 s.

The biggest culprit was the default. Z-Image Turbo is distilled to paint in few strokes, but the tool's default is 20. We were throwing away 5× for no reason. So were we, at first.

3 is the floor. Put 4 and 3 side by side and you cannot tell them apart. At 2 it collapses — water droplets and wood grain vanish, and the surface turns cloth-like.

🔗 Links

Model
FINAL-Bench/POCKET-Zimage-CPU

Live demo Space (runs on CPU)
FINAL-Bench/POCKET-Zimage-CPU

POCKET collection
FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6

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reacted to SeaWolf-AI's post with 🚀 about 1 month ago

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3,420

The cost of a judging gate is usually quoted as a number. This puts it on a Tetris board.

Three boards get the same piece order, and on every move the same proposal and the same noise — a paired comparison. The gate decides one thing: keep this move, or draw again. Each board gets the same 60 seconds of gate time.

The text-writing gates get through 15–22 moves. The generation-free gate gets through 40–50. The boards that stop simply run out of clock.

It does not win on accuracy: on the same 2,018-question LODO set, JEV scores AUC 0.7350 against ZTC-Judge-27B's 0.7289. The separation is elsewhere. Clock — 2.1 s vs 0.0615 s per call, and on a 200-candidate agent screen one judging call measured 3.206 s generative vs 0.033 s readout, same server. Calibration — a gate is a threshold, and at ECE 0.4985 (vs ZTC 0.0245) a threshold stops carrying information. Mechanism — a text judge can name option 42 when there is no option 42; a scoring readout cannot. Not a lower error rate. No path.

The curve in the ZTC panel is real online fitting, scored prequentially — predict first, learn after — with base weights untouched. Not recursive self-improvement.

Limits, also stated on the page: Laya's AUC and latency are not our measurements and are set equal to JEV's, so calibration is the only measured axis it differs on. The page is a simulation driven by measured constants.

KO / EN / ZH.

FINAL-Bench/Tetris-JEV-LAYA-ZTC
FINAL-Bench/ZTC-Judge-27B

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reacted to SeaWolf-AI's post with 🔥 about 1 month ago

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Ask a language model how confident it is and you get an AUC of 0.5000. Exactly a coin flip. We measured it across 2,018 items.

FINAL-Bench/gate-tetris

https://huggingface.co/blog/FINAL-Bench/ztc

Collection: https://huggingface.co/collections/FINAL-Bench/ztc-models-jev-ecosystems

Zero-Token Confidence (ZTC) reads it. One forward pass over the model's hidden state returns a calibrated probability that the answer is correct. Zero generated tokens.

It sits at the top of the shared board. Same 2,018 items, same harness for every entry: ZTC on Darwin-397B 0.7394, JEV 0.7335, ZTC-Judge-27B 0.7255, a surface baseline that reads only answer length and formatting 0.7036, Lynx 8B 0.5157, the model's own self-reported confidence 0.5000, HHEM 0.4852. First and third place both emit nothing at all.

The number worth staring at is 0.7036. That is a baseline reading no content whatsoever, just how long the answer is and how it is formatted. Any verifier scoring below it is not reading content either.

On speed, one gate call costs 0.0615 seconds, measured on four B200s across 2,000 items. Generating a single candidate answer takes 1.631 seconds, so the gate is 26 times cheaper than the work it guards. A verifier that generates competes with your agent for the same budget. A verifier that only reads can be attached to every action instead of a sampled few.

We built it so you can watch it decide. Three lanes receive the same stream of proposed actions and the same time budget. One has no gate and must execute everything. One uses a text-reading verifier. One uses ZTC. Right action plus one, wrong action minus one, hold zero. Over 400 matches: no gate minus 3.9, text verifier plus 13.0, ZTC plus 29.1, with ZTC taking 98 percent of matches. Gating lifts executed accuracy from 49 percent to 65 percent.

reacted to mayafree's post with 🤗👀🚀🔥 about 1 month ago

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JEV Ecosystems — every answer-verification vendor publishes a benchmark, and every one of them wins it. So we ran 13 of them on one test set: 2,018 items, identical labels, same grading code.

🎯 Leaderboard
mayafree/typed-decision-leaderboard

📄 Full write-up (method, mechanism, limits)
https://huggingface.co/blog/mayafree/jve-ecosystems

🧪 Try it — ZTC, JEV and Laya on the same input, side by side
mayafree/verifier-playground

Three results

1️⃣ Only three systems clear 0.70 — ZTC (397B) 0.7364 · JEV 0.7350 · ZTC (27B) 0.7282. First and second differ by 0.0014, so no rank is assigned.

2️⃣ A baseline that reads nothing but answer length and formatting scores 0.7036. Eight of the thirteen fall below it. A leaderboard without that line is flattering its entrants.

3️⃣ Bigger does not win. On scientific reasoning, 27B 0.7410 beats 397B 0.6287 — a model fourteen times larger scoring 0.11 lower.

And AUC is not the number you deploy on.

Same 20% retry budget, wired into an agent loop, against a 74.83% no-gate baseline:
ZTC +1.34 pp · JEV −0.07 pp · random −0.25 pp.

The mechanism is the interesting part. Re-answering is double-edged: 38% of wrong answers get fixed, and 30% of right answers get broken. So a gate is paid for by precision, not recall. Of the 403 items JEV routed for a retry, 216 were already correct.

0.0014 AUC apart; 1.4 points of end-to-end agent accuracy apart.

Scores, labels and grading code are published in full. Four public reproductions that would not run from their released artefacts are listed too, with the failure and a link, and no score.

Don't take the table's word for it — paste your own case into the playground and watch all three answer at once. Want a system added? Open a discussion on the Space.

reacted to their post with 🤗👍❤️👀 about 1 month ago

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A local edge VLM you can run on a phone — with a calibration readout attached.
ginigen-ai/Edge-4B-TELL
Image in, answer out, nothing leaving the device. Google's Gemma 4 E4B QAT checkpoint carried unmodified, with the vision and audio projector, plus one thing that is ours: GINIGEN TELL, a 10 KB readout that estimates whether the answer it just gave is likely to be wrong.
On a Galaxy S25: zero network calls, 3.6 GB resident, a 12.6 MB inference binary.
Calibration matters more here than on a server: nothing downstream catches a bad answer. No retrieval, no second opinion, no reviewer. The model is alone with the user.
And its own confidence is unusable. Prompted for it, this checkpoint averages 0.863 over 665 Korean disaster-procedure questions — ranking answers by it gives AUROC 0.441, below a coin flip. It sounds more certain when it is wrong.
TELL reads the last-layer hidden state instead of asking. Same questions, 0.759. Surface cues (length, formatting) already reach 0.736, so the readout clears that baseline by +0.023 ± 0.009 (2.6σ). We publish the baseline because without it, "the hidden state carries the signal" is unfalsifiable.
Same job as JEV: a confidence number you can act on instead of the model's own. Different structure, and on a device that splits three ways.
No second model — JEV is a separate judge reading the answer as text; we fill that slot with a 10 KB vector.
Zero generated tokens — a judge writes its verdict, TELL re-reads a finished computation (3.8 s on an S25).
No network — a verdict fetched over an API stops when the signal does.
The trade is real: a readout is fitted per checkpoint, so on a server the judge wins. On a phone there is no second model to run.
TELL never says what the right answer is. It says whether the answer wobbled, and a low score falls back to source text bundled with the app. Shipping today in HeliGO, an offline disaster-response app.

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