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Nishanth Dikkala

Nishanth Dikkala has 3 papers in our library, in 2024–2025, most of them on Reasoning, Language models & pretraining; often with Bahare Fatemi, Gladys Tyen, Hritik Bansal.

3 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 10 · 38 works · 308 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 97 times in our library · ⭐ Paper Stars ranking: #8082

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

🎓Massachusetts Institute of TechnologyCambridge · United States2015–2021 · 12 papers🏢GoogleMountain View · United States2021–2025 · 10 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksComputer Science · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 3 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20252 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. BIG-Bench Extra Hard (2025 · 58 citations)
    BIG-Bench Hard (BBH) used to be the standard test of broad, non-mathematical reasoning in LLMs. Frontier systems now score above 90% on it, so it no longer separates strong models from each other. The authors present BIG-Bench Extra Hard (BBEH), which swaps each of the 23 BBH tasks for a new, much tougher task that…
  2. Reasoning with Latent Thoughts: On the Power of Looped Transformers (2025 · 44 citations)
    The authors, from Google Research and TTIC, test whether reasoning requires a large number of parameters. Their hypothesis is that for many such tasks, what matters most is the model's depth. To test this, they use a "looped" transformer. In it, the same block of k layers with shared weights is applied to the input L…
  3. Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles (2024 · 12 citations)
    The paper examines whether Transformers trained only with next-token prediction can learn real search and reasoning, using Sudoku and Zebra (Einstein) logic puzzles as controlled synthetic tasks.

 Co-authors here

12 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 97 times (of the 149,439 papers whose bibliographies we read); 34 citing papers with a page here →.

Updated

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