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20242026
most citedThought of Search: Planning with Language Models Through The Lens of Efficiency

4 citations · 7 across the 18 of their papers we have counts for

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cs.AI2025★ 1 cited

Can AI autonomously build, operate, and use the entire data stack?

Arvind Agarwal, Lisa Amini, Sameep Mehta +2

Enterprise data management is a monumental task. It spans data architecture and systems, integration, quality, governance, and continuous improvement. While AI assistants can help…

cs.DB2025

Evaluating Joinable Column Discovery Approaches for Context-Aware Search

Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati +4

Joinable Column Discovery is a critical challenge in automating enterprise data analysis. While existing approaches focus on syntactic overlap and semantic similarity, there remain…

cs.DB2025

QueryGym: Step-by-Step Interaction with Relational Databases

Haritha Ananthakrishnan, Harsha Kokel, Kelsey Sikes +4

We introduce QueryGym, an interactive environment for building, testing, and evaluating LLM-based query planning agents. Existing frameworks often tie agents to specific query lang…

cs.LG2025

MiGrATe: Mixed-Policy GRPO for Adaptation at Test-Time

Peter Phan, Dhruv Agarwal, Kavitha Srinivas +3

Large language models (LLMs) are increasingly being applied to black-box optimization tasks, from program synthesis to molecule design. Prior work typically leverages in-context le…

cs.DB2025

TOPJoin: A Context-Aware Multi-Criteria Approach for Joinable Column Search

Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati +4

One of the major challenges in enterprise data analysis is the task of finding joinable tables that are conceptually related and provide meaningful insights. Traditionally, joinabl…

cs.AI2025

ACPBench Hard: Unrestrained Reasoning about Action, Change, and Planning

Harsha Kokel, Michael Katz, Kavitha Srinivas +1

The ACPBench dataset provides atomic reasoning tasks required for efficient planning. The dataset is aimed at distilling the complex plan generation task into separate atomic reaso…

cs.CL2025

Can Cross Encoders Produce Useful Sentence Embeddings?

Haritha Ananthakrishnan, Julian Dolby, Harsha Kokel +2

Cross encoders (CEs) are trained with sentence pairs to detect relatedness. As CEs require sentence pairs at inference, the prevailing view is that they can only be used as re-rank…