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. Such monitoring is meaningful only if a detector's verdict depends on the s...
Tool-using agents increasingly require a common substrate for building, training, evaluating, distilling, and maintaining agentic systems, yet existing tooling typically addresses these stages in isolation. We present AgentTune, a source-available Python library that provides a unified framework for developing and opti...
R E Zera Marveen Lyngkhoi, Abhivansh Gupta, Vidushee Vats et al.· 0 citations
Large language models increasingly justify legal decisions by naming the statute or precedent behind a verdict, treated as evidence that the decision follows from it. We test this directly: holding case facts fixed, we substitute the named legal authority for an unrelated one and decode a model's evolving verdict from...
Ablate a component of a language model, and other components often appear to adjust and compensate. This phenomenon, termed self-repair, has been observed repeatedly, but its mechanism remains unclear. The most systematic study to date concluded that self-repair is noisy and unlikely to have a single explanation. We ar...
Methods for addressing safety drift in fine-tuned Large Language Models (LLMs) are scattered across incompatible implementations, lifecycle stages, and evaluation protocols, making them difficult to adopt and compare. We introduce SafeTune, a source-available library that unifies four intervention paradigms: post-hoc w...
Pratinav Seth, Saisab Sadhu, Anshul Kaushal et al.· 0 citations
Two paired audits built on one-shot neuron-row zeroing show that rank-stability proxies miss the kinds of selector failures a direct causal audit can surface, and audit selectors at the language-modeling level.
Ananth Eswar, Pratinav Seth, Utsav Avaiya et al.· 1 citation
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