Public articles linked to the same research event.
arXiv EvoCast is a fully autonomous research-agent system for iterative time-series forecasting architecture evolution: it first establishes and diagnoses a task-specific baseline, then generates evidence-grounded research directions from dataset characteristics, diagnostic results, prior rounds, and failure records, with LLM agents handling hypothesis generation and code implementation while deterministic program authorities control source-edit boundaries, canonical evaluation, and model promotion; it reaches 96.7% success on 30 repository-level implementation tasks and the lowest MSE on three real forecasting cases (Air Quality, Melbourne Pedestrian T33, Steel Industry) while using fewer tokens and lower API cost than R&D-Agent.
EvoCast is a fully autonomous research-agent system for iterative time-series forecasting architecture evolution: it first establishes and diagnoses a task-specific baseline, then generates evidence-grounded research directions from dataset characteristics, diagnostic results, prior rounds, and failure records, with LLM agents handling hypothesis generation and code implementation while deterministic program authorities control source-edit boundaries, canonical evaluation, and model promotion; it reaches 96.7% success on 30 repository-level implementation tasks and the lowest MSE on three real forecasting cases (Air Quality, Melbourne Pedestrian T33, Steel Industry) while using fewer tokens and lower API cost than R&D-Agent.
EvoCast is a fully autonomous research-agent system for iterative time-series forecasting architecture evolution: it first establishes and diagnoses a task-specific baseline, then generates evidence-grounded research directions from dataset characteristics, diagnostic results, prior rounds, and failure records, with LLM agents handling hypothesis generation and code implementation while deterministic program authorities control source-edit boundaries, canonical evaluation, and model promotion; it reaches 96.7% success on 30 repository-level implementation tasks and the lowest MSE on three real forecasting cases (Air Quality, Melbourne Pedestrian T33, Steel Industry) while using fewer tokens and lower API cost than R&D-Agent.
EvoCast is a fully autonomous research-agent system for iterative time-series forecasting architecture evolution: it first establishes and diagnoses a task-specific baseline, then generates evidence-grounded research directions from dataset characteristics, diagnostic results, prior rounds, and failure records, with LLM agents handling hypothesis generation and code implementation while deterministic program authorities control source-edit boundaries, canonical evaluation, and model promotion; it reaches 96.7% success on 30 repository-level implementation tasks and the lowest MSE on three real forecasting cases (Air Quality, Melbourne Pedestrian T33, Steel Industry) while using fewer tokens and lower API cost than R&D-Agent.