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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

A finite-window expected excess-debit bound under conditional charge caps is derived and positive paired slack in every bounded-value cell and a second, incentive misalignment arises when the ML/pacing score transformation is allowed to change payment units.

Dipankar Sarkar · 0 citations

#artificial intelligence Preprint Sep 2026

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

Small-sample LLM evaluations can look far more definitive than their evidence supports, and it is recommended to report rank stability, per-cell provenance, executed sensitivity comparisons, raw per-run outputs, and a measurement date alongside any ranking.

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