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VIDraft/ai-world
reacted to SeaWolf-AI's post with 👀 3 days ago
🧪 Open Discovery Challenge — Season 4 is open: non-opioid pain WHO titled its 2023 report "Left behind in pain." The same drug kills by excess in one part of the world and, by its absence, lets people die in agony elsewhere. About 80% of the ~600,000 drug-related deaths WHO estimated for 2019 involved opioids. The same report records a 5-fold to 63-fold gap in morphine consumption between rich and poor countries: the richest 10% use 90% of what circulates. Everyone else endures surgery, and terminal cancer, without it. Both problems have one answer: a painkiller that does not create dependence. Nav1.7 has come closest. People born without a working copy of this channel feel no pain while every other sensation stays normal — validated not in animals but in humans. There is still no drug, and the difficulty is not the target but the discrimination. The body carries several similar sodium channels, and blocking the heart's hERG channel alongside causes fatal arrhythmia. Several candidates were discontinued for exactly that. Season 4 asks one question: can you block the pain channel alone? Target — Nav1.7 VSD4, the domain IV voltage sensor where this inhibitor class binds Anti-target — hERG pore, computed as the tetramer: four subunits together form the space a drug enters, and a monomer misses the binders that matter. Closes 2027-01-31 · Prize USD 1,000 to the season's #1 Any model, any harness. However you found the candidate, it meets the same rubric. 14 days, 9,886 candidates, 108 participants ODC opened on 2026-08-15. In the fourteen days since, 9,886 candidate molecules have come from 108 participants across four seasons — malaria, tuberculosis, Chagas disease, and now non-opioid pain. About 700 a day, from people who mostly do not know each other. The candidates are the point. The leaderboard is only how we keep score. 👉 https://huggingface.co/spaces/FINAL-Bench/open-discovery-challenge
reacted to SeaWolf-AI's post with 🔥 6 days ago
🔍 The attention mask stopped being an audit. An autoregressive model must not let position t depend on anything after t. Everyone checks this by inspecting the causal mask — but hybrid stacks now mix attention with state-space scans, and a scan has no mask. Every mask can be correct while information leaks through scans, aggregations, or normalization. ⚙️ So we test the property directly. Two inputs identical except at the last position, two forward passes, compare each layer's prefix, report the first layer that moves. No training, no gradients, no accelerator — seconds on CPU. 📊 Across 192 injected faults on eight checkpoints, mask inspection detected 0. The per-layer audit localized 192/192 to the exact layer. 🎯 Then we read the source before running anything. In transformers 5.7.0, the reference chunked scan reduces the inter-chunk recurrence over the input chunk axis; zamba2 and nemotron_h reduce over the output chunk axis. One axis. The dynamic audit confirmed the prediction exactly: Zamba2-1.2B leaks from length 256, its declared chunk size, and Nemotron-H-8B from 128, its declared chunk size. Bamba, Falcon-H1, Granite-4.0-H, Mamba2 and RecurrentGemma came back clean. ⚠️ Scope: the defect is on the PyTorch chunked-scan path, which runs whenever the fused kernels are absent — CPU, CI, stock installs. We could not build those kernels, so the fast path is untested and open. That caveat cuts both ways: a model can pass every fused-kernel test and still leak the moment it runs without them. 🧪 AX-RAY now carries this as its own axis. 39 models scored across causal, white-box and behavioral axes: 21 A, 3 B, 1 C, 14 F — with exactly 2 Causal-LEAK verdicts, the two the paper predicted. Badges separate a weights-level audit from an API-only one, so the two never get read as the same claim. 📄 https://arxiv.org/abs/2608.22876 🔬 https://huggingface.co/spaces/FINAL-Bench/AX-RAY 🤗 https://huggingface.co/papers/2608.22876
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