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20242026
most citedInvestigating Implicit Bias in Large Language Models: A Large-Scale Study of Over 50 LLMs

5 citations · 8 across the 13 of their papers we have counts for

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cs.AI2026

The Agent Incident Registry: Toward Preventing Repeated AI Agent Failures

Divyanshu Kumar, Rohith HN, Nitin Aravind Birur +2

AI agents increasingly act through tools and delegated authority, but general incident repositories rarely capture the mechanisms needed to compare public failures with agent-secur…

cs.AI2026

Black-Box Red Teaming of Agentic AI: A Taxonomy-Driven Framework for Automated Risk Discovery

Divyanshu Kumar, Nitin Aravind Birur, Tanay Baswa +2

Agentic systems are rapidly moving to production, where they read untrusted inputs, call tools with real permissions, and act autonomously, expanding the security surface beyond ch…

cs.CL2026

Redirected, Not Removed: Task-Dependent Stereotyping Reveals the Limits of LLM Alignments

Divyanshu Kumar, Ishita Gupta, Nitin Aravind Birur +3

How biased is a language model? The answer depends on how you ask. A model that refuses to choose between castes for a leadership role will, in a fill-in-the-blank task, reliably a…

cs.CL2026

SocioEval: A Template-Based Framework for Evaluating Socioeconomic Status Bias in Foundation Models

Divyanshu Kumar, Ishita Gupta, Nitin Aravind Birur +3

As Large Language Models (LLMs) increasingly power decision-making systems across critical domains, understanding and mitigating their biases becomes essential for responsible AI d…

cs.SE2026

Contract2Plan: Verified Contract-Grounded Retrieval-Augmented Optimization for BOM-Aware Procurement and Multi-Echelon Inventory Planning

Sahil Agarwal

Procurement and inventory planning is governed not only by demand forecasts and bills of materials (BOMs), but also by operational terms in contracts and supplier documents (e.g.,…