arXiv:2605. 28850v2 Announce Type: replace Abstract: We study behavioral alignment and representation dynamics of large language model (LLM) agents in financial decision environments.
By Weicheng Xue
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.
By Ayoub Jadouli
arXiv:2607. 12248v1 Announce Type: cross Abstract: Large pretrained time-series models such as TimesFM are attractive for financial forecasting, but raw directional accuracy is a misleading scoreboard in equity markets.
By Taizhen Cheung, SA Kwon
The paper presents a protocol‑agnostic method for detecting arbitrage in Ethereum by converting transaction traces into a canonical abstract syntax tree using a convergent rewriting system of 15 rules. This canonical form enables decidable structural equivalence of fund flows, allowing the authors to identify arbitrage cycles without relying on protocol‑specific patterns. Evaluated on 220,000 Ethereum blocks, the system confirmed 469,801 arbitrage opportunities, matching 83.5% of a production MEV platform and covering 81% of a GNN classifier, while producing no false positives in a manual sample.
By Adam Khayam, Hamid Kolli, Mohamed Iguernalala, \c{C}agdas Bozman
arXiv:2605. 30363v2 Announce Type: replace-cross Abstract: Regime shifts in financial markets reorganise the joint dynamics of asset prices and macro variables, breaking any single-regime calibration.
By Mingxuan Yi, Vidal Mehra, Jing Chen, John Cartlidge
arXiv:2606. 15474v1 Announce Type: new Abstract: Continuous evaluation of LLM products relies on a strong LLM judge treated as ground truth: a cheap monitor scores every interaction and a team is paged when the score drifts down.
By Yitao Li