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FinalityBench is an executable benchmark that tests how agents decide on shipping, re‑capturing, refunding, or waiting when a merchant’s payment processor, ledger, ERP, and bank feed receive delayed, duplicated, dropped, or reordered messages, causing contradictory beliefs about an order. The benchmark uses a hidden canonical event log and faulted delivery streams to generate system views, scoring each episode by the merchant’s terminal economic position relative to a privileged reference. It contains 321 tasks, including 45 twin pairs where all four views are identical yet the correct disposition differs, and evaluates nine programmatic policies, revealing that a ship‑on‑first‑sign policy performs best by accuracy but worst by paired loss, while a runtime‑gated irreversible‑action policy achieves 85.4% accuracy without losing money.
The paper audits the impact of temporal leakage on financial-news direction prediction across 49,799 articles and 16 feature-model combinations, including TF‑IDF, MiniLM, FinBERT, and fine‑tuned RoBERTa‑large / DeBERTa‑v3‑large, as well as zero/few‑shot and LoRA probes of Llama‑3 and Qwen2.5. Random train‑test splits inflate MCC scores by 1.1× to 6.5×, with larger models and richer features showing greater gains, while end‑to‑end FinBERT fine‑tuning actually increases the gap. Only the mergers and acquisitions (M&A) category shows a positive locked‑test signal under near‑temporal chronological evaluation, with the signal localized to 2024‑2025 European‑tilted M&A semantics and not transferring to a 2009‑2020 U.S. corpus.
arXiv:2607. 18867v1 Announce Type: new Abstract: Large language models leak parametric knowledge of realized outcomes into historical financial decision tasks.
The paper presents a deployed system that scores blockchain addresses using their position in a massive multi‑chain transaction graph instead of relying on sanctions lists. The system operates on a single graph of 835 million addresses and 15.8 billion edges across five EVM chains, employing a shared inductive encoder with per‑chain normalization and two scoring heads. It demonstrates label‑free transfer, achieving high recall on held‑out positives for Base, Arbitrum, and Gnosis at a very low alert rate, and shows significant lead time over external registry events, while maintaining fast, reproducible serving performance.
arXiv:2607. 19453v1 Announce Type: cross Abstract: We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance Spot paper policies after assumed costs.
The paper presents a compliance screening system that evaluates blockchain addresses by their position in a large multi‑chain transaction graph instead of relying on sanctions lists. Using a single graph of 835 million addresses and 15.8 billion edges across five EVM chains, the system employs a shared inductive encoder with per‑chain normalization and two scoring heads, with decision thresholds set as exact quantiles of the score distribution. The authors demonstrate label‑free transfer, achieving high recall on held‑out positives for Base, Arbitrum, and Gnosis, and report significant lead‑time in flagging external registry events, efficient serving latency, and robustness checks against adversarial behavior.