most citedDS-STAR: Data Science Agent for Solving Diverse Tasks across Heterogeneous Formats and Open-Ended Queries

2 citations · 6 across the 26 of their papers we have counts for

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13 papers · 1 filter

cs.AI2026

AIM: Agentic Idea Management for Automated Research

Hyeong Kyu Choi, Bhavana Dalvi Mishra, Jiefeng Chen +7

Frontier LLMs are increasingly used to automate scientific research through iterative search. We distinguish idea-driven search from solution-driven search and identify three core…

cs.AI2026

ScientistTwo: Pioneering the Human Knowledge Frontier with Autonomous AI

Jaehyun Nam, Jinsung Yoon, Yanzhou Pan +4

Scientific discovery is defined by the ability to identify the boundaries of existing knowledge and venture into unexplored territory. The ultimate vision for AI in science is prob…

cs.AI2026

LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks

Po-Nien Kung, Linfeng Song, Dawsen Hwang +10

Large Language Models (LLMs) exhibit strong informal mathematical reasoning but struggle to generate mechanically verifiable proofs in formal languages like Lean. We present LEAP,…

cs.AI2026

ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence

Rui Meng, Bhavana Dalvi Mishra, Jiefeng Chen +10

Autonomous research agents produce competitive solutions and professional-looking manuscripts, yet their outputs contain verifiability failures undetectable by surface-level evalua…

cs.AI2026

Inductive Deductive Synthesis: Enabling AI to Generate Formally Verified Systems

Shubham Agarwal, Alexander Krentsel, Shu Liu +10

AI agents increasingly excel at generating, testing, and refining code. However, they fall short on tasks requiring formal guarantees of full coverage that testing alone cannot pro…

cs.AI2026

Nexus : An Agentic Framework for Context-Aware Time Series Forecasting

Sarkar Snigdha Sarathi Das, Palash Goyal, Mihir Parmar +6

Language models have emerged as highly capable reasoners, yet their performance in time series forecasting remains uneven across domains and levels of contextual grounding. This is…

cs.AI2026

Co-Director: Agentic Generative Video Storytelling

Yale Song, Yiwen Song, Nick Losier +13

While diffusion models generate high-fidelity video clips, transforming them into coherent storytelling engines remains challenging. Current agentic pipelines automate this via cha…

cs.AI2026

TFRBench: A Reasoning Benchmark for Evaluating Forecasting Systems

Md Atik Ahamed, Mihir Parmar, Palash Goyal +7

We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. Traditionally, time-series forecasting has been evaluated solely…

cs.AI2026★ 2 cited

PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing

Yiwen Song, Yale Song, Tomas Pfister +1

Synthesizing unstructured research materials into manuscripts is an essential yet under-explored challenge in AI-driven scientific discovery. Existing autonomous writers are rigidl…

cs.AI2026

MARS: Modular Agent with Reflective Search for Automated AI Research

Jiefeng Chen, Bhavana Dalvi Mishra, Jaehyun Nam +3

A critical bottleneck in automating AI research is the execution of complex machine learning engineering (MLE) tasks. MLE differs from general software engineering due to computati…

cs.AI2025

CoDA: Agentic Systems for Collaborative Data Visualization

Zichen Chen, Jiefeng Chen, Sercan Ö. Arik +3

Deep research has revolutionized data analysis, yet data scientists still devote substantial time to manually crafting visualizations, highlighting the need for robust automation f…

cs.AI2025

ATLAS: Constraints-Aware Multi-Agent Collaboration for Real-World Travel Planning

Jihye Choi, Jinsung Yoon, Jiefeng Chen +2

While Large Language Models (LLMs) have shown remarkable advancements in reasoning and tool use, they often fail to generate optimal, grounded solutions under complex constraints.…

cs.AI2025★ 2 cited

DS-STAR: Data Science Agent for Solving Diverse Tasks across Heterogeneous Formats and Open-Ended Queries

Jaehyun Nam, Jinsung Yoon, Jiefeng Chen +3

While large language models (LLMs) have shown promise in automating data science, existing agents often struggle with the complexity of real-world workflows that require exploring…