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Kavitha Srinivas

Kavitha Srinivas has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning; often with Michael Katz, Joshua B. Tenenbaum, Julian Dolby.

2 papers in our library · 2023–2026

OpenAlex · ORCID · Wikidata · h-index 30 · 162 works · 3,078 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 36 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8129

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

🏢IBMArmonk · United States2004–2026 · 73 paperscurrent🔬IBM Research - Thomas J. Watson Research CenterYorktown Heights · United States2004–2026 · 42 paperscurrent🎓Texas A&M UniversityCollege Station · United States2026 · 1 papercurrent🎓University of OregonEugene · United States2026 · 1 papercurrent🎓Boston CollegeBoston · United States1992–2001 · 14 papers🎓Rice UniversityHouston · United States1990 · 2 papers
🏛️IBM ResearchYorktown Heights · United StatesEmployment

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Generalized Planning in PDDL Domains with Pretrained Large Language Models (2023 · 112 citations)
    The paper asks whether a pretrained LLM can be a generalized planner rather than a per-task planner. Given a PDDL domain and a few training tasks, the model should produce a program that quickly outputs valid plans for new, usually much larger tasks in that domain. GPT-4 receives the domain and two abbreviated…
  2. Generalization in Graph Reasoning: A Systematic Comparison of LLM Training Approaches (2026)
    The paper asks which training strategy actually helps LLMs reason over graphs in a way that transfers beyond the training setting.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Generalized Planning in PDDL Domains with Pretrained Large Language ModelsProceedings of the AAAI Conference on Artificial IntelligenceTom Silver, Soham Dan, Kavitha Srinivas, et al.2023 · 112 citations
  2. Exploring Code Style Transfer with Neural Networks ↗arXiv:2209.06273 · no summary yetKarl Munson et al.2022 · 1 citation
  3. Generalization in Graph Reasoning: A Systematic Comparison of LLM Training ApproachesWorkshop on Structured Understanding, Retrieval, and Generation in the LLM EraSola Shirai, Kavitha Srinivas, Julian Dolby, et al.2026

 Co-authors here

5 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 36 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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