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From the 1 of 78 linked papers with an AI index.

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20242026
most citedAI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot

1 citations · 1 across the 36 of their papers we have counts for

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cs.AI2026

VESTA: Visual Exploration with Statistical Tool Agents

William Rudman, Abhishek Divekar, Kanishk Jain +6

Fitting quantitative models to data is a central step in scientific workflows, yet it remains one of the least automated. Recent agent-based systems leverage language and vision-la…

cs.AI2026

AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library

Minwei Kong, Ao Qu, Xiaotong Guo +12

Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise…

cs.AI2026

FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization

Minwei Kong, Chonghe Jiang, Ao Qu +24

Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a…

cs.AI20261 cited

AI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot

Joydeep Biswas, Sheila Schoepp, Gautham Vasan +10

Scientific peer review faces mounting strain as submission volumes surge, making it increasingly difficult to sustain review quality, consistency, and timeliness. Recent advances i…

cs.AI2026

RecNet: Self-Evolving Preference Propagation for Agentic Recommender Systems

Bingqian Li, Xiaolei Wang, Junyi Li +5

Agentic recommender systems leverage Large Language Models (LLMs) to model complex user behaviors and support personalized decision-making. However, existing methods primarily mode…

cs.AI2025

Experience-Guided Reflective Co-Evolution of Prompts and Heuristics for Automatic Algorithm Design

Yihong Liu, Junyi Li, Wayne Xin Zhao +2

Combinatorial optimization problems are traditionally tackled with handcrafted heuristic algorithms, which demand extensive domain expertise and significant implementation effort.…