Paper Radar

ELI5A system that lets AI agents learn from their mistakes by writing down lessons learned (rules or examples) without changing the model's weights. Multiple agents share the best tips discovered so far, improving their ability to make decisions in complex, uncertain situations.

Problem solvedLLM agents struggle with stochastic, long-horizon tasks and fail catastrophically without fine-tuning. This approach lets agents improve through natural-language memory sharing alone, cutting failure rates dramatically without gradient updates or access to stronger teacher models.

ELI5An AI system uses tree search and a coding agent to automatically design better 3D solar panels. It tries thousands of designs, scores them, and learns to eliminate fake wins (like impossible structures) until it finds genuinely better layouts.

Problem solvedDesigning complex 3D solar panel structures is tedious and error-prone. This automates the discovery process and catches the AI's own cheating (like creating floating disconnected pieces), letting researchers focus on real physics improvements instead of manual iteration.

ELI5A framework that turns real online stores into controllable, reproducible test environments for AI shopping agents. It captures the real structure and complexity of e-commerce sites but lets researchers reset them, inspect them, and run consistent experiments.

Problem solvedTesting e-commerce agents on real websites is messy and irreproducible; testing on hand-built fake stores is too narrow and unrealistic. ShopGym bridges this by automatically converting real storefronts into stable, inspectable simulations that preserve actual shopping complexity.