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Hao Liu

4 papers hereh-index 325 citations8 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cond-mat.mtrl-sci1
  • cs.AI1
  • cs.LG1
  • physics.comp-ph1
same name
  • Hao Liu — 13 papers, h 5
  • Hao Liu — 12 papers, h 8
  • Hao Liu — 11 papers, h 8
  • Hao Liu — 10 papers, h 10
  • Hao Liu — 9 papers, h 4
  • Hao Liu — 8 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

4 papers

physics.comp-ph2026

SimulCost: A Cost-Aware Benchmark and Toolkit for Automating Physics Simulations with LLMs

Yadi Cao, Sicheng Lai, Jiahe Huang +12

Evaluating LLM agents for scientific tasks has focused on token costs while ignoring tool-use costs like simulation time and experimental resources. As a result, metrics like pass@…

cond-mat.mtrl-sci2026

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

Aritra Roy, Kevin Shen, Andrew MacBride +350

Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…

cs.AI2026

Programmatic Context Augmentation for LLM-based Symbolic Regression

Hao Liu, Xiao-Wen Yang, Atharva Sehgal +4

Symbolic regression (SR), the task of discovering mathematical expressions that best describe a given dataset, remains a fundamental challenge in scientific discovery. Traditional…

cs.LG2025

Beyond Numeric Rewards: In-Context Dueling Bandits with LLM Agents

Fanzeng Xia, Hao Liu, Yisong Yue +1

In-Context Reinforcement Learning (ICRL) is a frontier paradigm to solve Reinforcement Learning (RL) problems in the foundation model era. While ICRL capabilities have been demonst…

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