Papers by Peilin Liu

6 papers
Self-Awareness before Action: Mitigating Logical Inertia via Proactive Cognitive Awareness (2026.findings-acl)

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Challenge: Existing work on abductive and long-context reasoning reports that current models still lack self-awareness of missing premises.
Approach: They propose a reasoning framework that introduces self-awareness of missing premises before making the final decision.
Outcome: SABA achieves best performance on all three difficulty splits of detective puzzle benchmark . a small early mistake can remain uncorrected and can guide later reasoning .
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction (P19-1)

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Challenge: Existing methods for labeling relational facts require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly.
Approach: They propose a neural pattern diagnosis framework that can summarize and refine relation-specific patterns with human experts in the loop.
Outcome: The proposed framework can summarize and refine high-quality relational patterns from noise data with human experts in the loop.
Gardener: An Agentic AI System for Single-Cell RNA Sequence Analysis (2026.acl-demo)

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Challenge: Existing large language models encode workflow progress as conversational state and rely on cloud-centric execution, which hinders traceability and auditability.
Approach: Gardener is an open-source desktop application for macOS and windows under the Apache License 2.0.
Outcome: Gardener is released as an open-source desktop application for macOS and Windows under the Apache License 2.0.
TIGER: Text-Informed Generalized Enzyme-Reaction Retrieval (2026.acl-long)

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Challenge: Existing approaches to enzyme–reaction retrieval suffer from poor generalization across tasks and distributions . TIGER is a text-informed generalized enzyme-reaction retrieval framework that bridges enzymes and biochemical reactions.
Approach: They propose a text-informed generalized enzyme-reaction retrieval framework that leverages protein-to-text generation models to distill textual knowledge from enzyme sequences.
Outcome: The proposed framework outperforms state-of-the-art methods in enzyme–reaction retrieval tasks and distributions.
AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)

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Challenge: AEGIS examines whether current models can effectively audit AI-generated images in academic papers.
Approach: They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics.
Outcome: AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis.
WebDP: Understanding Discourse Structures in Semi-Structured Web Documents (2023.findings-acl)

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Challenge: Web documents are one of the most primary and biggest data resources in current era, and understanding their discourse structure will benefit various downstream document processing applications.
Approach: They propose a web document discourse structure representation schema by extending classical discourse theories and adding special features to well represent discourse characteristics of web documents.
Outcome: The proposed task is feasible but challenging for current models.

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