Author

Deepak Parashar

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Preprint Sep 2026

Instability Floors: Separating Bias from Noise in Fairness Audits of Clinical LLM Agents with FairMedAgent

Counterfactual fairness audits of clinical language-model agents report a flip rate: how often an action changes when only the patient's demographic descriptor changes. Part of that rate is not demographic. A stochastic agent also changes its own action when nothing changes, and a flip rate cannot be interpreted withou...

Rohith Reddy Bellibatlu, Manpreet Singh, Deepak Parashar et al. · 0 citations

Open access 2026

FMFT: A Memory-Augmented Multi-Feature Fusion Transformer Enhanced With Retrieval Augmented Graph-Based Few-Shot Learning

Indic text is syntactically compact, semantically ambiguous, and highly context-dependent, making English translation particularly challenging. Traditional neural machine translation (NMT) methods, even when enhanced with pre-trained language models (PLMs) and linguistic features such as part-of-speech (POS) tags, synt...

S. Chauhan, Shefali Saxena, Shweta V. Jain et al. · IEEE Access · 0 citations

#machine learning Preprint Sep 2026

Counterfactual Fairness Audits of Multi-Step Clinical LLM Agents Require a Measured Per-Action Instability Floor

Counterfactual audits are the standard tool for checking whether a clinical agent treats demographically distinct but clinically identical patients differently. They report a flip rate: how often an action changes when only the patient descriptor changes. We show that this quantity is uninterpretable on its own. Re-run...

Rohith Reddy Bellibatlu, Manpreet Singh, Deepak Parashar et al. · 0 citations