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Framework: Multi-Agent LLMs For Conversational Task-Solving (MALLM)
Research-backed methodology for multi-AI collaborative decision-making with structured debate, consensus synthesis, and bias reduction
Research paper on how agentic debate pipelines can be constructed to reduce hallucinations in LLMs with open-source and commercial models
Neurips paper code - Evaluating and enhancing Large Language Models (LLMs) using mathematical datasets through innovative Multi-Agent Debate Architecture, without traditional fine-tuning or Retrieval-Augmented Generation techniques. This project explores advanced strategies to boost LLM capabilities in mathematical reasoning.
A brutally fault-tolerant Mixture-of-Agents (MoA) pipeline built in pure Python. Designed to orchestrate chaotic, round-robin LLM proxy endpoints through a rigorous 4-stage Agentic Workflow (Generate ➔ Cross-Critique ➔ Rebuttal ➔ Judge). Built to eradicate hallucination and guarantee absolute accuracy in complex, multi-step reasoning tasks.
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