Papers by Jianyuan Zhong
Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier (2026.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have enhanced capabilities in complex reasoning through step-by-step trace generation. |
| Approach: | They propose a generative verifier that dynamically allocates compute between rapid fast thinking and deliberative slow thinking. |
| Outcome: | The proposed solution outperforms GenPRM-32B on ProcessBench while requiring 2.3x fewer TFLOPS and 15x less training data. |
Dyve: Thinking Fast and Slow for Dynamic Process Verification (2025.emnlp-main)
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| Challenge: | Existing process verification methods struggle with reliably assessing incomplete reasoning traces and are limited by the cost of high-quality human annotations or the inherent noise in automatically generated labels. |
| Approach: | They propose a dynamic process verifier that integrates fast and slow thinking to enhance reasoning error detection in large language models. |
| Outcome: | The proposed system outperforms existing process-based verifiers and maintains computational efficiency while maintaining high performance. |
Guideline Compliance in Task-Oriented Dialogue: The Chained Prior Approach (2025.findings-naacl)
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| Challenge: | Existing solutions based on large language models cannot achieve strict guideline compliance . a novel TOD system is being developed to improve guideline adherence . |
| Approach: | They propose a task-oriented dialogue system that explicitly considers domain-specific guidelines by integrating a policy module. |
| Outcome: | The proposed system achieves 20% better guideline compliance than state-of-the-art solutions. |
UR-FUNNY: A Multimodal Language Dataset for Understanding Humor (D19-1)
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Md Kamrul Hasan, Wasifur Rahman, AmirAli Bagher Zadeh, Jianyuan Zhong, Md Iftekhar Tanveer, Louis-Philippe Morency, Mohammed (Ehsan) Hoque
| Challenge: | Humor is a unique and creative communicative behavior often displayed during social interactions. |
| Approach: | They present a dataset that allows to model multimodal language used in expressing humor using text, visual and acoustic communication. |
| Outcome: | The proposed framework opens the door to understanding multimodal language used in expressing humor. |