Papers by Sumitra Ganesh
SlideAgent: Hierarchical Agentic Framework for Multi-Page Visual Document Understanding (2026.acl-long)
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| Challenge: | Multimodal large language models (MLLMs) are a promising tool for document understanding, but they are not able to handle complex multi-page visual documents. |
| Approach: | They propose a flexible agentic framework for understanding multi-modal, multi-page, and multi-layout documents . SlideAgent employs specialized agents and decomposes reasoning into three specialized levels . |
| Outcome: | a new agentic framework improves accuracy over open-source and proprietary models . it decomposes reasoning into three levels to capture themes and visual cues . the framework is based on a multimodal large language model and a MLLM . |
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering (2026.acl-long)
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| Challenge: | Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts. |
| Approach: | They propose a novel agentic framework that explicitly performs visual reasoning directly within the chart’s spatial domain. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy on the ChartBench and ChartX benchmarks surpassing prior methods by up to 16.07% absolute gain overall and 17.31% on numerically intensive queries. |
What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) are often treated as defects of the model or its decoding strategy. |
| Approach: | They construct a 22-dimension query feature vector covering clause complexity, lexical rarity, anaphora, negation, answerability, and intention grounding. |
| Outcome: | The proposed model covers clause complexity, lexical rarity, anaphora, negation, answerability, and intention grounding, all known to affect human comprehension. |
TASER: Table Agents for Schema-guided Extraction and Recommendation (2026.eacl-industry)
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| Challenge: | Real-world financial filings report critical information about an entity’s investment holdings, but they are often buried in messy, multi-page, fragmented tables that are difficult to parse. |
| Approach: | They propose to train a system that converts highly unstructured, multi-page, heterogeneous tables into normalized, schema-conforming outputs. |
| Outcome: | The proposed system outperforms vision-based table detection models by 10.1% and can generate more useful recommendations by 10%. |