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Madhav S. Baidya

AI Researcher | LLM Systems | Multimodal Reasoning | Knowledge Graphs

Madhav Banner


About

Final-year undergraduate at IIT (BHU), Varanasi working at the intersection of LLMs, multimodal reasoning, and knowledge-grounded systems. My work focuses on building epistemically reliable AI systems, combining structured representations (knowledge graphs) with retrieval and generation pipelines.

I am particularly interested in:

  • Decision-aware retrieval and generation (Decision RAG)
  • Multi-agent LLM systems for reasoning and control
  • Multimodal information extraction and grounding
  • Robustness, evaluation, and failure analysis of LLM systems

Research Interests

  • Large Language Models (LLMs) and Agentic Systems
  • Multimodal Learning (Text, Vision, Audio)
  • Knowledge Graphs and Neuro-Symbolic AI
  • Retrieval-Augmented Generation (RAG) and Decision RAG
  • AI Robustness, Hallucination Mitigation, and Evaluation

Research & Publications

  • PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning
    arXiv

    • Introduced a planner-driven multi-agent system with ASK / ANSWER / ABSTAIN routing
    • Designed a decision RAG mechanism over a knowledge graph with query-guided edge weighting
    • Incorporated explicit variable injection (?var) to model missing information and enable structured multi-hop reasoning
    • Fine-tuned a Mistral-7B planner (LoRA) on a graph-grounded dataset for improved abstention and reduced hallucination
  • Detecting the Machine: A Comprehensive Benchmark of AI-Generated Text Detectors Across Architectures, Domains, and Adversarial Conditions
    arXiv

    • Built a large-scale evaluation framework across transformers, CNNs, stylometric models, and LLM-based detectors
    • Evaluated under domain shift, cross-LLM generalization, and adversarial humanization
    • Identified systemic limitations such as detector–generator coupling and robustness failure

Selected Work

  • Multimodal Knowledge Graph System
    RDF-based graph construction (RDFlib + SPARQL) from unstructured documents with hybrid retrieval (symbolic + vector) for multi-hop QA

  • Agentic Research Assistant (LangGraph)
    Multi-agent pipeline for automated paper retrieval, parsing, and synthesis across scientific sources

  • SPAWN: Spoken Environment World Modeling
    Benchmark framework for evaluating spatial reasoning in multimodal LLMs from spoken/textual inputs, including tasks such as map reconstruction, relational inference, and navigation planning under noisy and ambiguous conditions

  • SQLPilot (NL → SQL Compiler)
    Schema-aware query generation with validation and execution across MySQL + SQLite backends

  • Multimodal PDF RAG System
    CLIP-based image embeddings + LLM summarization for joint text-image retrieval


Technical Stack

AI / ML PyTorch • TensorFlow • Representation Learning • Multimodal Learning

LLMs & Agents LangChain • LangGraph • OpenAI • Mistral • Tool-Augmented Agents

Retrieval & Knowledge Systems Qdrant • Knowledge Graphs (RDF, SPARQL, RDFlib) • Sentence Transformers

Systems FastAPI • Docker • Async Systems • Microservices • DVC


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Focused on building systems that know when they know, and when they do not.

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