Archives AI News

Incremental Recommendation via Causal Models

arXiv:2608.26804v1 Announce Type: cross Abstract: Recommendation impressions are a finite resource, hence delivering a recommendation to a user who would discover the content organically yields no incremental value and displaces other recommendations that could. We address this by extending an…

Diff Mining: Logit Differences Reveal Finetuning Objectives

arXiv:2608.26462v1 Announce Type: new Abstract: Finetuning has become the gold standard for refining existing behaviors and inducing new ones in language models, yet it often remains unclear exactly which behaviors emerge during this process. As models grow ever more capable,…

Active Curriculum Refinement for Reinforcement Learning

arXiv:2608.26469v1 Announce Type: new Abstract: In many reinforcement learning (RL) domains, environments are connected by prerequisite relations, such as difficulty-increasing edits or parameter increments, which induce a directed acyclic curriculum graph (DAG). Although this structure is often exploited only implicitly,…

Diff Mining: Logit Differences Reveal Finetuning Objectives

arXiv:2608.26462v1 Announce Type: new Abstract: Finetuning has become the gold standard for refining existing behaviors and inducing new ones in language models, yet it often remains unclear exactly which behaviors emerge during this process. As models grow ever more capable,…

From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems

arXiv:2605.23955v4 Announce Type: replace-cross Abstract: Deploying machine learning in regulated financial environments — credit risk, fraud detection, and anti-money laundering — exposes critical vulnerabilities in algorithmic reproducibility. While early financial ML addressed statistical challenges such as backtest overfitting, deep neural…