early-prediction

EarlyEval introduces a lightweight framework that predicts LLM agent outcomes from intermediate behavior to reduce evaluation costs by halting runs early with high accuracy. It eliminates 13%-26% of agent steps and up to 44% input tokens across benchmarks like SWE-bench Verified, TerminalBench, and Toolathlon.

The paper proposes BatteryMFormer, a multi-level Transformer for early battery degradation trajectory forecasting that integrates aging-condition-aware decoding, meta degradation pattern memory, and dual-view encoding to capture multi-level degradation structures and SOC-localized variations, consistently outperforming state-of-the-art baselines across four battery domains.