Rayna Bhattacharyya

dblp:409/9947 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Automated reasoning and model checking · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Automated reasoning and model checking › theorem proving
interactive theorem proving
1.012026
LeanTutor: Towards a Verified AI Mathematical Proof Tutor · AAAI 2026
Automated reasoning and model checking
theorem proving
1.012026
LeanTutor: Towards a Verified AI Mathematical Proof Tutor · AAAI 2026
Computing education
intelligent tutoring systems
0.312026
LeanTutor: Towards a Verified AI Mathematical Proof Tutor · AAAI 2026

Methods — techniques the papers use, named apart from their topics

lean theorem prover · 2.0large language model · 2.0autoformalization · 2.0
YearPublicationVenuePosition
2026 LeanTutor: Towards a Verified AI Mathematical Proof Tutor
abstract
This paper considers the development of an AI-based provably-correct mathematical proof tutor. While Large Language Models (LLMs) allow seamless communication in natural language, they are error prone. Theorem provers such as Lean allow for provable-correctness, but these are hard for students to learn. We present a proof-of-concept system (LeanTutor) by combining the complementary strengths of LLMs and theorem provers. LeanTutor is composed of three modules: (i) an autoformalizer/proof-checker, (ii) a next-step generator, and (iii) a natural language feedback generator. To evaluate the system, we introduce PeanoBench, a dataset of 371 Peano Arithmetic proofs in human-written natural language and formal language, derived from the Natural Numbers Game.
Manooshree Patel, Rayna Bhattacharyya, Thomas Lu, Arnav Mehta, Niels Voss, Narges Norouzi, Gireeja Ranade
AAAI2