The paper proposes that self‑evolving large‑language‑model agents can only be safely reviewed if their changes are confined to the runtime harness—instruction text, tool‑call logic, and primitive composition—while the model weights remain fixed. A dual‑loop engine with an admission gate records a hash‑chained diff before deployment, and simulation experiments show that this gate admits only safe changes, preventing error increases and maintaining false‑positive rates. When the gate is removed, many harmful changes slip through and missed flags rise, illustrating the gate’s protective role.
By Ravil Akhtyamov
Self-improving LLM agents can adapt a credit pipeline to a changed rule, but an agent that rewrites itself destroys the artefact a supervisor reviews: a named change, a recorded test, an approval. We...
arXiv:2607.20491v4 Announce Type: replace-cross
Abstract: When a financial agent repeats its decision, how much does that tell us about the actions behind it? DFAH-Bench examines this question throug...
By Raffi Khatchadourian
arXiv:2608. 16852v1 Announce Type: new Abstract: Regulatory compliance monitoring in deployed language models is increasingly implemented as a legal and audit control, checking model outputs against written rules spanning data protection, healthcare, financial regulation, and platform policy.
By Saisab Sadhu, Aadit Sengupta, Vinay Kumar Sankarapu, Pratinav Seth
arXiv:2610. 32380v1 Announce Type: new Abstract: Large language models (LLMs) can read startup pitch decks, but letting them emit investment scores directly invites score inflation, run-to-run variance and rationales that are hard to audit.
By Ming Ze Tang, David Cutting, Boyd Carson, Vasiliki Carson
The paper investigates whether large language model (LLM) compliance systems truly base their verdicts on the regulatory rules they are given. By systematically deleting, swapping, or negating rules across five models and 20 regulatory domains, the authors find that verdicts and internal compliance representations change little, indicating that models often ignore the supplied rule. Even the guard model, adapted to a custom rule taxonomy, shows minimal rule sensitivity and only marginally better than chance.
The study highlights that accuracy alone does not guarantee that a compliance verdict is grounded in the supplied rule, underscoring the need for more robust auditing of regulatory rule sensitivity in LLM systems.
By Saisab Sadhu, Aadit Sengupta, Vinay kumar Sankarapu, Pratinav Seth