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Dipankar Sarkar

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#artificial intelligence Preprint Sep 2026

Evaluating Bounded Autonomy in Regulated Agentic AI: A Diagnostic Harness with Constitutional Rewards, Escalation Labels, and Runtime Governance

We propose RegLLM, a diagnostic harness for bounded autonomy in regulated agentic workflows. It instruments six trustworthiness signals: citation validity, source grounding, schema compliance, escalation correctness, constitutional alignment, and unsafe-action rate. Signals are distinguished by their source of supervis...

Dipankar Sarkar · 0 citations

#machine learning Preprint Sep 2026

When Privacy Moves ML-Mediated Decisions On Device: Information and Incentive Misalignment in Auctions

Moving ML-mediated decision making onto privacy-preserving clients decentralises the economic decision along with the inference. Shared budget constraints then depend on information that cannot be globally current, creating an information-structure failure that conventional pacing is not designed to solve. We study thi...

Dipankar Sarkar · 0 citations

#artificial intelligence Preprint Sep 2026

How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM-Inferred Prompt Structure

Evaluations of LLM systems routinely average over small prompt sets and report models as a ranked table. We ask how much confidence such a table deserves, using LLM-based prompt-structure inference as the case study: eight open model variants across five families and 8B to 675B parameters, caching disabled, 293 raw int...

Dipankar Sarkar · 0 citations

Preprint Aug 2026

From Errors to Proofs: Minimal-Core-Guided Repair for Neuro-Symbolic Constraint Solving

On a new benchmark of 77 problems with an exact oracle, translation to Answer Set Programming is faithful on six of seven domains and fails only on aggregate coverage scheduling, which concentrates the translation tax in one diagnosable pattern.

Dipankar Sarkar · 0 citations