GAMBLe decomposes ADRS into four parameters and an effective landscape, with experiments on 760+ runs across NP-hard problems showing no universal best generator or mechanism and potential gains of 13-67% from component choice.
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EMO-STA evolves a shared program archive across task families then adapts candidates to targets, outperforming matched-compute single-task evolution in most of eight families while reducing overfitting on low-data tasks like ARC.
LIMEN discovers effective RL interfaces by using LLMs to evolve observation and reward programs together from raw state, guided by policy training success, outperforming single-component optimization.
EvoPolicyGym is a new benchmark suite of 16 compact RL environments that evaluates autonomous policy evolution, with GPT-5.5 achieving the top aggregate rank and top-two performance on all tasks.
OPT-BENCH and OPT-Agent evaluate LLM self-optimization in large search spaces, showing stronger models improve via feedback but stay constrained by base capacity and below human performance.
Denoising Recursion Models train multi-step noise reversal in looped transformers and outperform the prior Tiny Recursion Model on ARC-AGI.
citing papers explorer
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Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems
GAMBLe decomposes ADRS into four parameters and an effective landscape, with experiments on 760+ runs across NP-hard problems showing no universal best generator or mechanism and potential gains of 13-67% from component choice.
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EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments
EvoPolicyGym is a new benchmark suite of 16 compact RL environments that evaluates autonomous policy evolution, with GPT-5.5 achieving the top aggregate rank and top-two performance on all tasks.
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OPT-BENCH: Evaluating the Iterative Self-Optimization of LLM Agents in Large-Scale Search Spaces
OPT-BENCH and OPT-Agent evaluate LLM self-optimization in large search spaces, showing stronger models improve via feedback but stay constrained by base capacity and below human performance.