Papers by Ran Qiu
Beyond Blind Following: Evaluating Robustness of LLM Agents under Imperfect Guidance (2026.eacl-long)
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Yao Fu, Ran Qiu, Xinhe Wang, Jacob Sansom, Sathvika Ayyappa Prabhu, Huijie Tang, Jaekyeom Kim, Sungryull Sohn, Honglak Lee
| Challenge: | Large language models (LLMs) have shown strong capabilities as task-solving agents across interactive domains, but in complex environments, auxiliary guidance may be imperfect. |
| Approach: | They propose a benchmark to measure the robustness of large language models under imperfect guidance. |
| Outcome: | The proposed benchmark compared LLM agents in navigation, cooking, and gaming in a variety of environments with auxiliary guidance and noisy or underspecified instructions extracted from demonstrations. |
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning (2021.acl-long)
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| Challenge: | Existing methods for solving geometric problems are either small in scale or not publicly available. |
| Approach: | They propose a large-scale benchmark for geometric problem solving using formal language and symbolic reasoning. |
| Outcome: | The proposed approach parses geometry problems into formal language and performs symbolic reasoning step by step. |
NumNet: Machine Reading Comprehension with Numerical Reasoning (D19-1)
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| Challenge: | Existing numerical MRC models are weak in numerical reasoning, such as addition, subtraction, sorting and counting. |
| Approach: | They propose a numerical MRC model that integrates numerical reasoning into existing MRC models and achieves an EM-score of 64.56% on the DROP dataset. |
| Outcome: | The proposed model outperforms all existing machine reading comprehension models by considering the numerical relations among numbers on the DROP dataset. |
Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing non-autoregressive neural machine translation models suffer from multi-modality problem . despite their autoregressivity, most NMT models suffer with slow decoding speed . |
| Approach: | They propose a semi-autoregressive model which generates a translation as a sequence of segments while each segment is predicted token-by-token. |
| Outcome: | The proposed model can achieve 4 times speedup while maintaining comparable performance. |
BOOKWORLD: From Novels to Interactive Agent Societies for Story Creation (2025.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have enabled social simulation through multi-agent systems. |
| Approach: | They propose a system for constructing and simulating book-based multi-agent societies that simulates established fictional worlds and characters. |
| Outcome: | The proposed system generates high-quality stories while maintaining fidelity to the source books, surpassing previous methods with a win rate of 75.36%. |