Papers by Wenrui Liao

3 papers
NL ⇒ Schedule: Evaluate Multitask Scheduling Capability of Large Language Models (2026.acl-long)

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Challenge: Existing methods for scheduling from natural language descriptions rely on experts with limited scheduling skills and domain knowledge.
Approach: They propose a model to generate a feasible schedule from natural language descriptions.
Outcome: The proposed framework achieves more robust performance than six state-of-the-art LLM+solver methods.
BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks (2025.findings-acl)

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Challenge: Existing studies focus on forward reasoning based planning, but this paradigm doesn't work well for complex tasks.
Approach: They propose to decompose a task into easily executed steps by planning and use a backward reasoning based agent to make the planning starting from the terminal state.
Outcome: The proposed model outperforms existing methods and the proposed modules in a virtual environment that simulates complex tasks based on real-world scenarios.
PAGED: A Benchmark for Procedural Graphs Extraction from Documents (2024.acl-long)

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Challenge: Existing methods for extraction of procedural graphs from documents are not solving the task well.
Approach: They propose a benchmark to test automatic extraction of procedural graphs from documents . they involve three advanced large language models and enhance them with a novel self-refine strategy .
Outcome: The proposed benchmark systematically examines the progress of current methods and explores the potential of emerging large language models (LLMs) on this task.

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