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This work identifies structured design specifications as a practical control point for exploring alternative UI concepts while keeping downstream generation settings fixed through an inference architecture that makes design direction an explicit intermediate decision.
LLMs can now generate reliable hardware reference models with 95% accuracy thanks to a novel co-evolutionary verification mechanism that weeds out correlated hallucinations between model and testbench.
LLMs can now reliably fix decompiled code, but only if you make them *execute* it.
Achieve higher quality 3D Gaussian Splatting reconstructions of objects from mobile phone captures using fewer images, thanks to a novel object-centered capture guidance system.
SynthFix achieves up to 32% better accuracy in code vulnerability repair by intelligently combining supervised and reward-based training strategies.
Context-augmented RL lets smaller MLLMs punch *way* above their weight, rivaling much larger models on reasoning tasks while dodging reward hacking.
