Papers by Zhiqiang Yin

5 papers
RoleCDE: Benchmarking and Mitigating Role–Alignment Trade-offs in Role-Playing Agents (2026.findings-acl)

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Challenge: Existing benchmarks for role-playing agents only evaluate surface-level fidelity and provide limited insight into decision making under role–alignment value conflicts.
Approach: They propose a benchmark to evaluate RPAs under role–alignment value conflicts . they use 8k diverse role profiles and 240k dilemma instances to evaluate role-aware decision making .
Outcome: The proposed benchmark covers 8k diverse role profiles and scenarios and nearly 240k dilemma instances across three difficulty levels and eight role categories.
MaPPER: Multimodal Prior-guided Parameter Efficient Tuning for Referring Expression Comprehension (2024.emnlp-main)

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Challenge: Existing methods for Referring Expression Comprehension (REC) lack specific domain abilities for precise local visual perception and visual-language alignment.
Approach: They propose a framework for Parameter-Efficient Transfer Learning to localize a visual region via natural language using a prior-guided prior.
Outcome: The proposed framework achieves the best accuracy compared to the current methods with only 1.41% tunable backbone parameters.
SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs (2025.findings-emnlp)

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Challenge: Existing evaluations for Structured Knowledge (SK) understanding are non-rigorous and focus on a single type of SK.
Approach: They propose a structured knowledge understanding benchmark that includes four widely used structured knowledge forms.
Outcome: The proposed benchmark is based on four widely used structured knowledge forms . it includes a question, an answer, positive knowledge units, and noisy knowledge units .
Nested Browser-Use Learning for Agentic Information Seeking (2026.acl-long)

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Challenge: Existing information-seeking (IS) agents rely on the web for their information acquisition.
Approach: They propose a browser-action framework that decouples interaction control from page exploration through a nested structure.
Outcome: Empirical results show that NestBrowse offers clear benefits in practice.
Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)

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Challenge: Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks.
Approach: They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration.
Outcome: The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks.

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