Category: Blog
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Trust, but Verify: Proving Which AI Model Answered
Quick Take Researchers at Anthropic and several universities just open-sourced FlashProve, a system that cryptographically proves which AI model generated a response, without ever exposing the model’s weights. On a 400B-parameter model, it generates proofs (~19 MB) at ~110 tokens/sec on a single H100 GPU. Verification takes about 17 seconds on a CPU using 24…
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When References Cite Nothing
A peer-reviewed PNAS study found references that could not be located in about 1 in 435 physics, chemistry, materials science, engineering and computing papers from early 2026, up from 1 in 1,600 in 2023. That puts citation reliability in question. The study measures how common the problem is and how fast it is growing; it…
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Rethinking Peer Review in the Age of AI
Peer review is essential to research, but it is also under growing pressure. Researchers face increasing publication volumes, more complex methods and data, and significant demands on the time of qualified reviewers. Against this backdrop, AI is beginning to find a potentially valuable role: not replacing peer reviewers but helping them focus their expertise where…
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When Four AI Models Agree, They Can Still Be Wrong
New research shows why agreement among AI models is a useful signal for human review, but not a substitute for judgment. More AI models, fewer mistakes? Imagine asking an AI model whether a law protects people’s data in a particular way. The model says yes and points to a passage in the legislation. You ask…