Papers by Manan Suri
Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agents (2025.emnlp-main)
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Manan Suri, Puneet Mathur, Nedim Lipka, Franck Dernoncourt, Ryan A. Rossi, Vivek Gupta, Dinesh Manocha
| Challenge: | Flowcharts are a critical tool for visualizing decision-making processes, but their non-linear structure and complex visual-textual relationships make it difficult to interpret them using LLMs. |
| Approach: | They propose a task of Fine-grained Flowchart Attribution to trace components grounding a flowchart referring LLM response. |
| Outcome: | The proposed agent mitigates visual hallucinations in LLM answers over baselines by 10–14% on a FlowExplainBench dataset. |
CodeScout: Contextual Problem Statement Enhancement for Software Agents (2026.findings-acl)
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Manan Suri, Xiangci Li, Mehdi Shojaie, Songyang Han, Chao-Chun Hsu, Shweta Garg, Aniket Anand Deshmukh, Varun Kumar
| Challenge: | Current AI-powered code assistance tools struggle with ambiguous problem statements . failures on such ambiguously requests are highly correlated with longer trajectories . |
| Approach: | They propose a contextual query refinement approach that transforms ambiguous user requests into comprehensive, actionable problem statements through lightweight pre-exploration of the target codebase. |
| Outcome: | Empirical results show that CodeScout improves resolution rates with 27 additional issues resolved compared to baseline method. |
DIAGRAMS : A Review Framework for Reasoning-Level Attribution in Diagram QA (2026.acl-demo)
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Anirudh Iyengar Kaniyar Narayana Iyengar, Tampu Ravi Kumar, Manan Suri, Raviteja Bommireddy, Dinesh Manocha, Puneet Mathur, Vivek Gupta
| Challenge: | Diagram question answering (Diagram QA) requires reasoning-level attribution that links each question-answer pair to all visual regions needed to derive the answer. |
| Approach: | They propose a diagram question-answer review framework that decouples interface logic from dataset-specific JSON structures through an internal meta-schema and dataset adapters. |
| Outcome: | The proposed framework achieves 85.39% precision and 75.30% recall across six diagram QA datasets. |
Structured Uncertainty guided Clarification for LLM Agents (2026.findings-acl)
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| Challenge: | Existing approaches to clarifying tasks fail when user instructions are ambiguous or incomplete. |
| Approach: | They propose a principled formulation of structured uncertainty that operates directly over tool parameters and their domains. |
| Outcome: | The proposed framework improves when2call accuracy and training-time sample efficiency. |
DocEdit-v2: Document Structure Editing Via Multimodal LLM Grounding (2024.emnlp-main)
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Manan Suri, Puneet Mathur, Franck Dernoncourt, Rajiv Jain, Vlad Morariu, Ramit Sawhney, Preslav Nakov, Dinesh Manocha
| Challenge: | Document structure editing involves manipulating localized textual, visual, and layout components in document images based on user’s requests. |
| Approach: | They propose a framework that performs end-to-end document editing by leveraging Large Multimodal Models (LMMs) by localizing edit regions of interest and disambiguating user edit requests into edit commands. |
| Outcome: | The proposed framework outperforms baselines on edit command generation (2-33%), RoI bounding box detection (12-31%), and overall document editing (1-12%) tasks. |
CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network (2023.emnlp-main)
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| Challenge: | Existing work on detecting explicit hate speech has focused on indirect or coded language. |
| Approach: | They propose a context synergized neural network that integrates user- and conversational-contexts for detecting implicit hate speech in online conversations. |
| Outcome: | The proposed framework outperforms baselines on 6 hate speech datasets and shows that it is highly efficient. |
VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation (2025.naacl-long)
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| Challenge: | Existing benchmarks for document QA for visually rich documents outperform unimodal and long-context LLMs by 12-20%. |
| Approach: | They propose a multimodal Retrieval Augmented Generation approach that integrates visual and textual retrieval with linguistic reasoning. |
| Outcome: | The proposed approach outperforms unimodal and long-context LLM benchmarks for document QA by 12-20%. |
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER (2023.acl-long)
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| Challenge: | Named Entity Recognition (NER) is a task of detecting linguistically complex named entities in low-context text. |
| Approach: | They propose a keyword-based augmentation approach to address the context-entity mismatch issue in complex name recognition (NER) they use selective masking to retain the named entities and certain keywords in the input sentence that provide contextually relevant additional knowledge or hints about the named entity. |
| Outcome: | The proposed approach outperforms baseline methods on monolingual, cross-lingual, and multilingual complex NER in various low-resource settings. |
ChartLens: Fine-grained Visual Attribution in Charts (2025.acl-long)
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| Challenge: | MLLMs suffer from hallucinations, where generated text fails to align with visual inputs. |
| Approach: | They propose a chart attribution algorithm that uses segmentation-based techniques to identify chart objects and employs set-of-marks prompting with MLLMs for fine-grained visual attribution. |
| Outcome: | The proposed algorithm improves fine-grained attributions by 26-66% . |