Papers by Aditya Kalyanpur
From Generating Answers to Building Explanations: Integrating Multi-Round RAG and Causal Modeling for Scientific QA (2025.naacl-industry)
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Victor Barres, Clifton James McFate, Aditya Kalyanpur, Kailash Karthik Saravanakumar, Lori Moon, Natnael Seifu, Abraham Bautista-Castillo
| Challenge: | Application of Large Language Models to complex causal question answering can be stymied by their opacity and propensity for hallucination. |
| Approach: | They propose a causal QA approach that combines iterative RAG with a formal model of causation. |
| Outcome: | The proposed approach is implemented into a Collaborative Research Assistant (Cora) and evaluated in the life sciences domain. |
GLUCOSE: GeneraLized and COntextualized Story Explanations (2020.emnlp-main)
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Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, Jennifer Chu-Carroll
| Challenge: | Existing knowledge resources and pretrained language models do not include or readily predict GLUCOSE’s rich inferential content. |
| Approach: | They propose a platform for crowdsourcing GLUCOSE data at scale that uses semi-structured templates to elicit causal explanations. |
| Outcome: | The proposed model can be trained on human-readable stories and build similar models on unseen stories. |
DREAM: Deep Research Evaluation with Agentic Metrics (2026.acl-long)
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Elad Ben Avraham, ChangHao Li, Ron Dorfman, Roy Ganz, Oren Nuriel, Amir Dudai, Aviad Aberdam, Noah Flynn, Elman Mansimov, Aditya Kalyanpur, Ron Litman
| Challenge: | Recent benchmarks propose distinct methodologies, yet they suffer from the Mirage of Synthesis . static evaluators lack the tool-use capabilities required to assess temporal validity and factual correctness . |
| Approach: | They propose a framework that instantiates the principle of capability parity by making evaluation agentic. |
| Outcome: | The proposed framework is more sensitive to factual decay than existing benchmarks . large language models increasingly support autonomous, tool-using agents . |