The study examines how different harness configurations affect a fixed large language model’s compliance with banking conduct rules. Four open‑weight models were tested across five harnesses on 156 pre‑registered retail‑banking scenarios, yielding 3,120 two‑turn episodes scored for rule violations and over‑refusals. Results show that while overall compliance rates varied modestly (87.3%–92.1%), the impact of a harness differed markedly between models, with some models’ best and worst harnesses differing by over 10 percentage points and a same‑model compliance reviewer reducing violations from 9.6% to 6.4%.
whyItMatters":"The findings suggest that validating a model’s compliance requires testing the specific model‑harness pair rather than the model alone, highlighting the importance of action logs over conversation transcripts for accurate conduct assessment."
By Mehmet Murat Huyut
The paper investigates the safety of Code World Models, where a language model generates executable world models that a planner uses. It shows that accepting a model based on sampled transitions only guarantees sample consistency, not full safety, because the probability of missing critical events decays as (1‑r)^N. Experiments on hybrid instruments reveal that omitted mode‑boundaries can severely limit planner performance, and that even sophisticated LLMs (GPT‑5.x) struggle to repair such omissions in higher‑dimensional settings.
By Javier Aguilar Mart\'in
arXiv:2609.38266v1 Announce Type: cross
Abstract: Agentic large language models (LLMs) now move money through tools, yet the record of what they did is usually a trace their own process emits beside...
By Mustafa Arslan
arXiv:2608.22389v1 Announce Type: cross
Abstract: Regulation (EU) 2024/886 obliges European payment service providers to settle euro credit transfers in under ten seconds, around the clock. This remo...
By Ahmed Abolfadl
FraudSociety is a novel LLM‑agent‑based simulator that jointly models informational and financial dynamics in the financial‑fraud economy. It features a dynamic social environment, persistent financial ledgers, and a Need‑Intent‑Action architecture, allowing fraudsters to adapt their actions based on interaction histories and ledger states. Validation shows macro‑level outcomes match real‑world patterns, micro‑level loss concentration aligns with Canadian statistics, and personalized interventions can dramatically reduce repeat‑payment rates.
By Keyang Chen, Mingxuan Jiang, Zeping Li, Yiwen Zhao, Yifan Wang, Qiuyang Zhang, Shuaicheng Qi, Yicun Liu, Zhixin Li, Sen Liu, Yinan Jing, Guangnan Ye, Hongfeng Chai
arXiv:2607. 21910v2 Announce Type: replace Abstract: World models let agents plan against predicted physical state, but that state drifts; re-observation is costly and delayed, and repair can fail.
By Edward Y. Chang