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This work forms this missing dimension of harness optimization as an automated curriculum learning problem and introduces ActiveSaddler, which establishes automated curriculum learning as a new crucial optimization dimension for harness optimization.
The Prefix-Aware Internal Reward (PAIR), a two-stage model with a frozen hidden-state probe estimating belief-consistency and a lightweight attention-based head correcting it toward grounded correctness, is proposed, enabling dense step-level reward signals for GRPO training without external model calls, ground-truth d...
PQA, a page-level quality anchoring method that guides simulators using a personalized anchor reflecting each user's intrinsic preference standard, is proposed, and experiments show PQA mitigates trait interference and improves the reliability of LLM-based simulator evaluation under activity shifts.
To address the sequential and evolving nature of time series, the Online Time Series Forecasting (OTSF) task has been extensively studied in multiple domains. Existing research focuses on adapting to non-stationary environments by employing memory buffer-based retrieval strategies. However, we observe that such framewo...
The proposed AutoSaddler, an automatic harness optimization framework that formulates harness improvement as an offline learning problem and iteratively updates the harness using failure signals from mini-batches, suggests that automatic harness optimization is a promising path toward more performant and reliable agent...
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