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Quantum Cryptanalysis on Real Hardware: Pushing Symmetric-Structure Key Recovery Beyond the Published Frontier
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Quantum Cryptanalysis on Real Hardware: Pushing Symmetric-Structure Key Recovery Beyond the Published Frontier reacted to SeaWolf-AI's post with π about 13 hours ago
π We ran genuine quantum key-recovery on 'real IBM quantum hardware' β and pushed the frontier well past the largest hardware demos we're aware of (which sat at N=4).
Using Simon's algorithm on `ibm_kingston`, we recovered the secret key of two symmetric-cipher structures:
β’ EvenβMansour β N=5 β N=10
β’ 3-round Feistel (DES-family) β block 6 β 8
Each verified against an 'independent control key', using error mitigation only (no QEC).
π§ Honest scope: this is not a quantum speedup (the effective difficulty tracks the classical birthday bound ~2^{n/2}), not a break of real AES/RSA, and not 16-round DES (ours is 3-round). The recovery method is reserved for a forthcoming paper; formal record status is pending peer review.
π Write-up: https://huggingface.co/blog/FINAL-Bench/quantum
πΉοΈ Try it live in your browser: https://vidraft-quantumos.hf.space/crypto
π Leaderboard: https://huggingface.co/spaces/FINAL-Bench/quantum-bench-leaderboard
#quantum #cryptography #quantumcomputing reacted to SeaWolf-AI's post with β€οΈ about 13 hours ago
π We ran genuine quantum key-recovery on 'real IBM quantum hardware' β and pushed the frontier well past the largest hardware demos we're aware of (which sat at N=4).
Using Simon's algorithm on `ibm_kingston`, we recovered the secret key of two symmetric-cipher structures:
β’ EvenβMansour β N=5 β N=10
β’ 3-round Feistel (DES-family) β block 6 β 8
Each verified against an 'independent control key', using error mitigation only (no QEC).
π§ Honest scope: this is not a quantum speedup (the effective difficulty tracks the classical birthday bound ~2^{n/2}), not a break of real AES/RSA, and not 16-round DES (ours is 3-round). The recovery method is reserved for a forthcoming paper; formal record status is pending peer review.
π Write-up: https://huggingface.co/blog/FINAL-Bench/quantum
πΉοΈ Try it live in your browser: https://vidraft-quantumos.hf.space/crypto
π Leaderboard: https://huggingface.co/spaces/FINAL-Bench/quantum-bench-leaderboard
#quantum #cryptography #quantumcomputingOrganizations
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upvoted an article about 13 hours ago
reacted to SeaWolf-AI's post with πβ€οΈ about 13 hours ago
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π We ran genuine quantum key-recovery on 'real IBM quantum hardware' β and pushed the frontier well past the largest hardware demos we're aware of (which sat at N=4).
Using Simon's algorithm on
β’ EvenβMansour β N=5 β N=10
β’ 3-round Feistel (DES-family) β block 6 β 8
Each verified against an 'independent control key', using error mitigation only (no QEC).
π§ Honest scope: this is not a quantum speedup (the effective difficulty tracks the classical birthday bound ~2^{n/2}), not a break of real AES/RSA, and not 16-round DES (ours is 3-round). The recovery method is reserved for a forthcoming paper; formal record status is pending peer review.
π Write-up: https://huggingface.co/blog/FINAL-Bench/quantum
πΉοΈ Try it live in your browser: https://vidraft-quantumos.hf.space/crypto
π Leaderboard: FINAL-Bench/quantum-bench-leaderboard
#quantum #cryptography #quantumcomputing
Using Simon's algorithm on
ibm_kingston, we recovered the secret key of two symmetric-cipher structures:β’ EvenβMansour β N=5 β N=10
β’ 3-round Feistel (DES-family) β block 6 β 8
Each verified against an 'independent control key', using error mitigation only (no QEC).
π§ Honest scope: this is not a quantum speedup (the effective difficulty tracks the classical birthday bound ~2^{n/2}), not a break of real AES/RSA, and not 16-round DES (ours is 3-round). The recovery method is reserved for a forthcoming paper; formal record status is pending peer review.
π Write-up: https://huggingface.co/blog/FINAL-Bench/quantum
πΉοΈ Try it live in your browser: https://vidraft-quantumos.hf.space/crypto
π Leaderboard: FINAL-Bench/quantum-bench-leaderboard
#quantum #cryptography #quantumcomputing
reacted to ginigen-ai's post with π₯ππβ 1 day ago
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π§ Does your LLM know when it's about to be wrong?
Most leaderboards measure accuracy. We measure metacognition β whether a model catches its own errors. Benchmark + leaderboard + adapters, all open. π
The surprise: even a K-AI #1 model (JGOS-31B-Citizen) is the strongest on multiple-choice traps (trap_rate 0.005 β ~2 misses in 400) yet blind to its own free-form mistakes (self-confidence AUROC = 0.5, pure random). A tiny base-frozen adapter recovers that signal.
Two independent axes (never compared across a row): β trap_rate β does it fall for tempting trap options? (lower = stronger) β‘ adapter gain Ξ β how much a lightweight adapter catches errors the model itself misses. (higher = more adapter value)
What's open: π 300+100 trap problems (each with a hidden trap + TICOS type) π 24-model leaderboard 𧩠11 per-model adapters β adapters, NOT fine-tunes (base stays frozen; the adapter just reads the hidden state β P(wrong))
Submit any HF model β auto-scored daily at 09:00 KST and added to the board.
π Leaderboard β ginigen-ai/Metacognition-Leaderboard-Space
π Benchmark β ginigen-ai/Metacognition-Bench
𧩠Adapters β FINAL-Bench/metacognition-adapters-6a42c032e6beb803dd032961
π Article β https://huggingface.co/blog/ginigen-ai/metacognition
Benchmark by ginigen-ai Β· Adapters by FINAL-Bench (Darwin/Chimera platform + AETHER metacognition tech).
Most leaderboards measure accuracy. We measure metacognition β whether a model catches its own errors. Benchmark + leaderboard + adapters, all open. π
The surprise: even a K-AI #1 model (JGOS-31B-Citizen) is the strongest on multiple-choice traps (trap_rate 0.005 β ~2 misses in 400) yet blind to its own free-form mistakes (self-confidence AUROC = 0.5, pure random). A tiny base-frozen adapter recovers that signal.
Two independent axes (never compared across a row): β trap_rate β does it fall for tempting trap options? (lower = stronger) β‘ adapter gain Ξ β how much a lightweight adapter catches errors the model itself misses. (higher = more adapter value)
What's open: π 300+100 trap problems (each with a hidden trap + TICOS type) π 24-model leaderboard 𧩠11 per-model adapters β adapters, NOT fine-tunes (base stays frozen; the adapter just reads the hidden state β P(wrong))
Submit any HF model β auto-scored daily at 09:00 KST and added to the board.
π Leaderboard β ginigen-ai/Metacognition-Leaderboard-Space
π Benchmark β ginigen-ai/Metacognition-Bench
𧩠Adapters β FINAL-Bench/metacognition-adapters-6a42c032e6beb803dd032961
π Article β https://huggingface.co/blog/ginigen-ai/metacognition
Benchmark by ginigen-ai Β· Adapters by FINAL-Bench (Darwin/Chimera platform + AETHER metacognition tech).
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reacted to SeaWolf-AI's post with βπ₯ππ 1 day ago
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π Adding a GPU without building one
AI is usually framed as "how smart is the model / how many GPUs did you buy." The real bottleneck is elsewhere β how efficiently you use the GPUs you already have.
Training happens once; inference runs the entire time users use your product. So a service's economics come down to cost per token. Inference acceleration uses software to pull several times more out of the same GPU β the effect of plugging in one more "virtual GPU."
VIDRAFT's VKAE, measured (B200, same-harness, no quality loss):
Qwen3.5-35B-A3B (MoE): 25.7 β 601 tok/s (23.4Γ)
Darwin-36B-Opus (in-house MoE): 25.0 β 280.8 (11.2Γ)
10,000+ tok/s peak aggregate under concurrency
The key: it's reproducible β model + serving shipped as one container.
docker pull vidraft/qwen35-vkae:601
Don't take our word for it β run it yourself. The mechanism will be released as a paper.
π Leaderboard & demo π VIDraft/vkae
Articles π https://huggingface.co/blog/FINAL-Bench/vkae-leaderboard
AI is usually framed as "how smart is the model / how many GPUs did you buy." The real bottleneck is elsewhere β how efficiently you use the GPUs you already have.
Training happens once; inference runs the entire time users use your product. So a service's economics come down to cost per token. Inference acceleration uses software to pull several times more out of the same GPU β the effect of plugging in one more "virtual GPU."
VIDRAFT's VKAE, measured (B200, same-harness, no quality loss):
Qwen3.5-35B-A3B (MoE): 25.7 β 601 tok/s (23.4Γ)
Darwin-36B-Opus (in-house MoE): 25.0 β 280.8 (11.2Γ)
10,000+ tok/s peak aggregate under concurrency
The key: it's reproducible β model + serving shipped as one container.
docker pull vidraft/qwen35-vkae:601
Don't take our word for it β run it yourself. The mechanism will be released as a paper.
π Leaderboard & demo π VIDraft/vkae
Articles π https://huggingface.co/blog/FINAL-Bench/vkae-leaderboard
upvoted a collection 2 days ago
upvoted an article 2 days ago
Article
Adding a GPU Without Building One
FINAL-Bench
β’ β’ 16 reacted to SeaWolf-AI's post with β€οΈ 2 days ago
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2922
π Adding a GPU without building one
AI is usually framed as "how smart is the model / how many GPUs did you buy." The real bottleneck is elsewhere β how efficiently you use the GPUs you already have.
Training happens once; inference runs the entire time users use your product. So a service's economics come down to cost per token. Inference acceleration uses software to pull several times more out of the same GPU β the effect of plugging in one more "virtual GPU."
VIDRAFT's VKAE, measured (B200, same-harness, no quality loss):
Qwen3.5-35B-A3B (MoE): 25.7 β 601 tok/s (23.4Γ)
Darwin-36B-Opus (in-house MoE): 25.0 β 280.8 (11.2Γ)
10,000+ tok/s peak aggregate under concurrency
The key: it's reproducible β model + serving shipped as one container.
docker pull vidraft/qwen35-vkae:601
Don't take our word for it β run it yourself. The mechanism will be released as a paper.
π Leaderboard & demo π VIDraft/vkae
Articles π https://huggingface.co/blog/FINAL-Bench/vkae-leaderboard
AI is usually framed as "how smart is the model / how many GPUs did you buy." The real bottleneck is elsewhere β how efficiently you use the GPUs you already have.
Training happens once; inference runs the entire time users use your product. So a service's economics come down to cost per token. Inference acceleration uses software to pull several times more out of the same GPU β the effect of plugging in one more "virtual GPU."
VIDRAFT's VKAE, measured (B200, same-harness, no quality loss):
Qwen3.5-35B-A3B (MoE): 25.7 β 601 tok/s (23.4Γ)
Darwin-36B-Opus (in-house MoE): 25.0 β 280.8 (11.2Γ)
10,000+ tok/s peak aggregate under concurrency
The key: it's reproducible β model + serving shipped as one container.
docker pull vidraft/qwen35-vkae:601
Don't take our word for it β run it yourself. The mechanism will be released as a paper.
π Leaderboard & demo π VIDraft/vkae
Articles π https://huggingface.co/blog/FINAL-Bench/vkae-leaderboard
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Does Your LLM Know *When It's About to Be Wrong*?
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