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Sathwik Tejaswi Madhusudhan

Sathwik Tejaswi Madhusudhan has 3 papers in our library, in 2025–2026, most of them on Language models & pretraining, Question answering, Reasoning; often with Vikas Yadav, Masoud Hashemi, Shiva Krishna Reddy Malay.

3 papers in our library · 2025–2026

OpenAlex · h-index 4 · 38 works · 34 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 25 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8928

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢ServiceNowSanta Clara · United States2024–2026 · 7 paperscurrent🎓University of Illinois ChicagoChicago · United States2024–2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksQuestion answering · 1 workReasoning · 1 work

 Works by year

2020s

20262 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models (2025 · 22 citations)
    The paper examines whether large language models can recognise when they should decline to answer, which the authors call Abstention Ability (AA).
  2. Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA (2026)
    Iterative retrieval-augmented generation helps with multi-hop questions, but each retrieval round adds more documents to the prompt.
  3. Grammar Search for Multi-Agent Systems (2026)
    Automatically designing multi-agent systems (MASes) built from LLMs usually means letting an LLM write free-form code for each candidate.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language ModelsInternational Conference on Computational Linguistics (COLING)Nishanth Madhusudhan, Sathwik Tejaswi Madhusudhan, Vikas Yadav, et al.2025 · 22 citations
  2. BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning ↗arXiv (Cornell University) · no summary yetAhmed Masry et al.2025 · 3 citations
  3. Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QALanguage Resources and Evaluation Conference (LREC)Rishabh Maheshwary, Masoud Hashemi, Khyati Mahajan, et al.2026
  4. Grammar Search for Multi-Agent SystemsAnnual Meeting of the Association for Computational Linguistics (ACL)Mayank Singh, Vikas Yadav, Shiva Krishna Reddy Malay, et al.2026

 Co-authors here

9 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 25 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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