Predict any LLM's score on any benchmark. Use the code to reproduce the paper, or download the score matrix behind the predictor. Report benchmark scores for a model. Include the model, benchmark, score, evaluation setting, effort, and source; we will review provenance before adding it to the matrix.

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DeepSWE delivers four advances over existing public benchmarks: Contamination free: Tasks are written from scratch, not adapted from existing commits or PRs, so no model has seen the solution during pretraining. High diversity: Tasks span a broad pool of 91 repositories across 5 languages. Real-world complexity: Prompts are ~half the length of SWE-bench Pro's, yet solutions require 5.5x more code and ~2x more output tokens. Reliable verification: Verifiers are hand-written to test software behavior rather than implementation details. The result is a benchmark that reflects how today's frontier coding agents actually perform in software engineering work. https://preview.redd.it/lacvagyr159h1.png?width=1373&format=png&auto=webp&s=6514340a15d51d7f03da733f08fb3f6a302cac75 It's open-source: https://github.com/datacurve-ai/deep-swe submitted by /u/we_are_mammals [link] [Kommentare]

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Have you read Alex Karp's Palantir manifesto? Well, I have! Do you want to know what he says?

A woman with severe Alzheimer's disease who hadn't spoken more than monosyllables in years began initiating conversation after a single dose of psilocybin

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