ATLAS: Adaptive Test-time Learning System Achieves 74.6% Code Benchmark Performance with Frozen 14B Model
By
yogthos
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Summary
ATLAS (Adaptive Test-time Learning and Autonomous Specialization) is a system that wraps a frozen smaller language model (14B parameters) with intelligent infrastructure to achieve 74.6% LiveCodeBench pass@1-v(k=3) performance on a single consumer GPU, up from 36-41% in previous versions. The approach uses constraint-driven generation, energy-based verification, and self-verified iterative refinement without fine-tuning, API calls, or cloud dependencies. The system is fully self-hosted, ensuring no data leaves the machine, and aims to compete with frontier API models at a fraction of the cost.
Key quotes
· 3 pulledA.T.L.A.S achieves 74.6% LiveCodeBench pass@1-v(k=3) with a frozen 14B model on a single consumer GPU -- up from 36-41% in V2
The premise: wrap a frozen smaller model in intelligent infrastructure -- structured generation, energy-based verification, self-verified repair -- and it can compete with frontier API models at a fraction of the cost
No fine-tuning, no API calls, no cloud. Fully self-hosted -- no data leaves the machine
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