Agents | Rabimba Karanjai

Unanimous but Wrong: Conformity in Multi-Agent LLM Audit Deliberation

Thu, 01 Oct 2026 00:00:00 +0000

Rules That Govern: A Per-Property Analysis of Multi-Agent LLM Deliberation

Thu, 01 Jan 2026 00:00:00 +0000

HPCAgentTester: A Multi-Agent LLM Approach for Enhanced HPC Unit Test Generation

Sat, 01 Nov 2025 00:00:00 +0000

Securing Smart Contract Languages with a Unified Agentic Framework for Vulnerability Repair in Solidity and Move

Sat, 01 Nov 2025 00:00:00 +0000

Build Smarter AI Agents Faster: Introducing the Google Agent Development Kit (ADK)

Wed, 09 Apr 2025 00:00:00 +0000

Deep Dive into the Google Agent Development Kit (ADK): Features and Code Examples

Wed, 09 Apr 2025 00:00:00 +0000

LLM Reasoning & Code Generation

Wed, 01 Jan 2025 00:00:00 +0000

My research on LLMs spans reasoning evaluation, automated code/test generation, and reliability. Key contributions include:

AgentTester — A multi-agent framework for automated unit test generation that significantly improves software reliability (AIWare 2025).
HPCAgentTester — Extending multi-agent test generation to high-performance computing environments.
LogBabylon — A unified framework for cross-log file integration and analysis using LLMs.
Hallucination Mitigation — Techniques for reducing AI hallucinations in diagnostic and clinical contexts.
Evaluating LLM Rationality & Randomness — Benchmarking the quality of reasoning and entropy in LLM-supported tasks.
Synthesizing Public Opinions — Using LLMs to synthesize democratic discourse (IEEE ICEDEG 2025).

This line of research explores how multi-agent systems can produce more reliable, verifiable AI outputs, with applications from software engineering to healthcare.