Peilin Han | Searcharxiv

arXiv2026

Point-in-Time Audit Before Alpha: Public-Archive Availability and a Negative Matched-Budget Study on BTC Perpetual Futures

Public cryptocurrency archives may appear usable when files exist, although factor research requires observations available and executable at each decision time. We audit public Binance BTCUSDT USD-M perpetual-futures data using event, publication, and availability times and separate proposal from deterministic auditing, evaluation, and holdout access. An initial gapless five-minute requirement for trade, mark, index, and open interest failed: the longest unrepaired intersection was 304.5729166666667 days. A disclosed revision made trade, mark, index, and realized funding the core streams and made open interest optional because its publication time was unverified. The revised mask retained 727 complete UTC days and supported a 436/145/146-day train, validation, and historical-holdout split. On 80 frozen known-rule templates, the auditor detected 40/40 violations and rejected 0/40 legal templates. Across ten null-signal paths, full auditing reduced mean false passes from 0.2910 to 0.0625. Under matched valid-candidate budgets, the audited adaptive agent tied random search and did not establish superiority. In the one-time historical holdout, all evaluated runs had positive IC but negative net Sharpe under primary costs. We therefore report a scoped negative result rather than a profitability or agent-superiority claim.

cs.SE↗

arXiv2026

Open Models, Open Risks: Measuring Unsafe Generation in Text-to-Image Models In the Wild

Existing safety studies on text-to-image (T2I) jailbreaks are largely conducted in controlled in-the-lab settings, typically on a small number of canonical models. As a result, the current safety status of the rapidly growing in-the-wild T2I ecosystem remains unclear. This uncertainty is amplified by two factors: existing detector-based metrics are designed for controlled evaluation, and in-the-wild risks may arise not only from adversarial prompting, but also from unsafe release practices and unsafe model derivatives. In this paper, we present a large-scale empirical study of in-the-wild T2I safety through the lens of jailbreak. We first show that detector-only jailbreak metrics substantially overestimate practical risk over in the wild due to semantic drift and generation artifacts, and we introduce Advanced ASR to better capture semantically valid and visually plausible unsafe generation. Using this refined metric, we evaluate 200+ in-the-wild T2I models from Hugging Face under three representative jailbreak attacks. Our results show that many downstream models retain a non-trivial degree of safety even without explicit post-hoc safeguards, indicating that safety degradation in the wild is neither universal nor uniform. At the same time, we identify a set of high-risk models, including explicitly NSFW-oriented releases as well as seemingly benign models whose unsafe behavior is only exposed through systematic evaluation. We further trace these models to their release context and report high-risk cases to Hugging Face.

cs.CR↗