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Apurva Narayan

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6 papers

1 author row

AAMAS Conference 2026 Conference Paper

Agents of Diffusion: Enhancing Diffusion Language Models with Multi-Agent Reinforcement Learning for Structured Data Generation

Aja Khanal
Kaushik T. Ranade
Rishabh Agrawal
Kalyan S. Basu
Apurva Narayan

Generating high-quality structured data such as JSON records, remains a fundamental challenge for large language models (LLMs), particularlywhensemanticrichnessmustcoexistwithstrictschema adherence. While autoregressive LLMs offer strong structural consistency, they often struggle with semantic variation and output diversity. In contrast, diffusion language models (DLMs) introduce powerful mechanisms for semantic richness and bidirectional decoding, yet lack the inductive biases needed for reliable structure preservation. We present Agents of Diffusion (AoD), a novel framework that unifies the generative flexibility of DLMs with the reasoning capabilities of autoregressive models through languagemediated reinforcement learning. AoD frames structured text generation as a multi-agent alignment process, where a prompt optimization agent collaborates with a judge agent to iteratively guide a DLM using natural language feedback. This approach enables controllable, schema-consistent generation without modifying model parameters or relying on handcrafted constraints. AoD advances the state of controllable generation by demonstrating that diffusion models, when supervised by cooperative agents, can achieve both high semantic novelty and structural fidelity. Across multiple structured data benchmarks, AoD consistently outperforms diffusion and autoregressive baselines, establishing a new path forward for structure-aware, diversity-enhanced text synthesis. Code: https: //github. com/Idsl-group/AgentsOfDiffusion. Extended Version: https: //arxiv. org/abs/2601. 07152

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AAMAS Conference 2026 Conference Paper

SCMRAG 2. 0: Efficient and Scalable Multi-hop Graph RAG with Multimodal Knowledge-Graphs and Agentic Self-Correction

Rishabh Agrawal
Uday Devulapalli
Apurva Narayan

We present SCMRAG 2. 0, a next-generation retrieval-augmented generation framework that unifies text, image, and structured data into a Multimodal Knowledge Graph. Unlike traditional graph RAG systems, SCMRAG 2. 0 introduces dual linkages via symbolic relations and cross-modal embeddings, an optimized graph-retrieval algorithm, and a multimodal agentic self-correction loop. By aligning language-level structure with vector-space signals and enabling agentic critique and repair, SCMRAG 2. 0 mitigates outdated context, incomplete reasoning chains, and hallucinations common in text-only graph RAG systems. Experiments on MMLU and MRAG- Bench demonstrate that SCMRAG 2. 0 significantly outperforms strong baselines like LightRAG in retrieval precision and factuality while maintaining computational efficiency.

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AAMAS Conference 2025 Conference Paper

SCMRAG: Self-Corrective Multihop Retrieval Augmented Generation System for LLM Agents

Rishabh Agrawal
Murtaza Asrani
Hadi Youssef
Apurva Narayan

Existing Retrieval-Augmented Generation (RAG) systems primarily depend on static knowledge vectorstores which combine semantic similarity algorithms with reranking. This often leads to outdated information and retrieval errors. In this paper, we propose SCMRAG, a Self-Corrective Multihop Retrieval Augmented Generation system for LLM agents. We introduce an LLM-assisted dynamic knowledge graph creation step to enhance information retrieval and mitigate hallucinations. Unlike traditional RAG systems, SCMRAG includes a self-corrective agent driven mechanism that autonomously identifies and retrieves missing information from external web sources. Furthermore, SCMRAG’s internal reasoning agent determines whether the knowledge graph provides sufficient information or if a corrective step is needed. It further improves retrieval accuracy and efficiency. We benchmark the effectiveness of SCMRAG on five datasets - MultiHop-RAG, ARC AI2, PopQA, PubHealth, and WikiBio; showing significant improvements in retrieval precision and hallucination reduction across diverse tasks. Our results highlight SCMRAG’s potential to redefine how LLM agents interact with knowledge bases, offering a more adaptable and reliable solution for a wide range of applications.

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NeurIPS Conference 2025 Conference Paper

SpecMAS: A Multi-Agent System for Self-Verifying System Generation via Formal Model Checking

Rishabh Agrawal
Kaushik Ranade
Aja Khanal
Kalyan Basu
Apurva Narayan

We present SpecMAS, a novel multi-agent system that autonomously constructs and formally verifies executable system models from natural language specifications. Given a Standard Operating Procedure (SOP) describing a target system, SpecMAS parses the specification, identifies relevant operational modes, variables, transitions, and properties, and generates a formal model in NuSMV code syntax, an industry-standard symbolic model checker. A dedicated reasoning agent extracts both explicit and implicit properties from the SOP, and verification is performed via temporal logic model checking. If any properties fail to verify, an autonomous debugging agent analyzes counterexamples and iteratively corrects the model until all properties are satisfied. This closed-loop system design guarantees provable correctness by construction and advances the state of the art in automated, interpretable, and deployable verification pipelines. We demonstrate the generality, correctness, and practical feasibility of SpecMAS across a set of representative case studies and propose a new benchmark dataset for the evaluation and comparison of model checking performance.

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AAAI Conference 2024 Short Paper

Attacking CNNs in Histopathology with SNAP: Sporadic and Naturalistic Adversarial Patches (Student Abstract)

Daya Kumar
Abhijith Sharma
Apurva Narayan

Convolutional neural networks (CNNs) are being increasingly adopted in medical imaging. However, in the race for developing accurate models, their robustness is often overlooked. This elicits a significant concern given the safety-critical nature of the healthcare system. Here, we highlight the vulnerability of CNNs against a sporadic and naturalistic adversarial patch attack (SNAP). We train SNAP to mislead the ResNet50 model predicting metastasis in histopathological scans of lymph node sections, lowering the accuracy by 27%. This work emphasizes the need for defense strategies before deploying CNNs in critical healthcare settings.

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AAAI Conference 2024 Conference Paper

Generating Universal Adversarial Perturbations for Quantum Classifiers

Gautham Anil
Vishnu Vinod
Apurva Narayan

Quantum Machine Learning (QML) has emerged as a promising field of research, aiming to leverage the capabilities of quantum computing to enhance existing machine learning methodologies. Recent studies have revealed that, like their classical counterparts, QML models based on Parametrized Quantum Circuits (PQCs) are also vulnerable to adversarial attacks. Moreover, the existence of Universal Adversarial Perturbations (UAPs) in the quantum domain has been demonstrated theoretically in the context of quantum classifiers. In this work, we introduce QuGAP: a novel framework for generating UAPs for quantum classifiers. We conceptualize the notion of additive UAPs for PQC-based classifiers and theoretically demonstrate their existence. We then utilize generative models (QuGAP-A) to craft additive UAPs and experimentally show that quantum classifiers are susceptible to such attacks. Moreover, we formulate a new method for generating unitary UAPs (QuGAP-U) using quantum generative models and a novel loss function based on fidelity constraints. We evaluate the performance of the proposed framework and show that our method achieves state-of-the-art misclassification rates, while maintaining high fidelity between legitimate and adversarial samples.

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