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This article discusses possible reasons for the rapid consumption of Codex quotas, including IP quality issues, frequent account switching, etc., and provides methods to self-test if usage is normal.
The paper diagnoses three failure modes in per-field selective risk control for document extraction systems and introduces a validity ladder of fixes, demonstrating improvements through experiments on real-world data with frontier AI models.
SeqLLM is a framework that injects behavioral-sequence modeling into pretrained LLMs while preserving language ability, enabling joint analysis of text and behavior for high-stakes decisions. Deployed at WeChat Pay, it improves merchant screening precision from 92.0% to 97.5% and achieves state-of-the-art results on open recommendation benchmarks.
This paper introduces Opal (Opportunity-aware Policy Authorization for Laboratories), a framework that certifies whether adaptive experimentation should be enabled by precommitting to non-trivial adaptation, controlled target risk, and positive executed value after cost. It establishes an impossibility boundary and demonstrates the method on a Cell Painting dataset, achieving risk control and positive value.
In-depth review of risk control loopholes in Sequoia China (Hongshan)'s investment decisions, using cases like 虎头局渣打饼行 to reveal the due diligence failures and capital forcing caused by its 'fast, accurate, ruthless' strategy, and summarizing its strategic correction direction.
CP-Agent presents a calibrated risk-controlled approach for feedback-driven competitive programming using large language models, achieving significant improvements on benchmarks without parameter updates.
Introduces Conformal Selective Acting (CSA), a deployment-time wrapper for RLVR-trained LLMs that provides anytime-valid selective risk control on individual streams, enabling safe deployment in regulated settings without pooling or long-run averages.