Papers by Jinfeng Jiang

5 papers
Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (2023.emnlp-demo)

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Challenge: Reaction Miner is a system designed to extract chemical reactions from raw scientific PDFs.
Approach: They propose a system that extracts chemical reactions directly from raw scientific PDFs.
Outcome: The proposed system can extract chemical reactions from raw scientific PDFs.
s3: You Don’t Need That Much Data to Train a Search Agent via RL (2025.emnlp-main)

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Challenge: Existing approaches to optimize retrieval using search-only metrics ignore downstream utility and fine-tune entire LLM to jointly reason and retrieve limit retrieval utility and compatibility with frozen or proprietary models.
Approach: They propose a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the search user using a Gain Beyond RAG reward.
Outcome: The proposed framework outperforms baselines trained on over 70 more data with 2.4k training samples.
SeCuRepair: Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework (2026.acl-long)

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Challenge: Existing methods for automating vulnerability repair suffer from syntactic overfitting . nvd published 49,230 Common Vulnerabilities and Exposures (CVE) records in 2025 alone .
Approach: They propose a semantic-aware reward framework that optimizes for code semantic equivalence rather than lexical mimicry.
Outcome: The proposed framework outperforms state-of-the-art frameworks on repository-level splits . it incorporates expert-aligned reasoning mechanism that grounds patch generation in structured diagnosis.
QDMR-based Planning-and-Solving Prompting for Complex Reasoning Tasks (2024.lrec-main)

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Challenge: Existing Plan-and-Solve prompting methods are difficult to implement for complex questions.
Approach: They propose a plan-and-solve prompting method based on Question Decomposition Meaning Representation (QDMR) it allows LLM to generate a QDMR graph to represent problem-solving logic .
Outcome: The proposed method can represent and execute the problem-solving logic of complex questions more accurately than existing methods.
Zero-Shot Open-Schema Entity Structure Discovery (2026.eacl-long)

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Challenge: Existing methods based on large language models (LLMs) rely heavily on predefined entity attribute schemas or annotated datasets, often leading to incomplete extraction results.
Approach: They propose a novel approach to entity structure extraction that does not require any schema or annotated datasets.
Outcome: Experiments show that ZOES improves LLMs’ ability to extract more complete entity structures across three different domains, showcasing both the effectiveness and generalizability of the method.

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