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版本更新 · v2arXiv:2608.01092

Local-Frame Rotary Attention for Antibody-Specific Epitope Prediction

Chuanliu Fan, Junjie Wu, Nan Yu, Guohong Fu

原文摘要

Antibody-specific epitope prediction aims to identify which antigen residues are recognized by a given antibody, a task that depends on the three-dimensional complementarity between antibody CDRs and the antigen surface. Existing methods usually leverage PLM embeddings and inject structure through additional graph, surface, or point-cloud encoders, where the positional mechanism inside attention remains largely tied to one-dimensional sequence order. For proteins, the analogue of a token offset is not only sequence separation, but also the three-dimensional displacement between residues after folding. This raises a question, can folded residue geometry serve as the positional mechanism of attention itself? We propose Local-Frame Rotary Attention (LFRA), which expresses inter-residue displacements in backbone-defined local frames and injects them directly into rotary attention. This design preserves continuous directional geometry while ensuring invariance to independent $\mathrm{SE}(3)$ transformations. The code and dataset are available at https://github.com/better-fcl/lfra.

cs.AI

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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2012ACL Anthology · ACL 2026

Locate and Explain: Joint Multimodal Emotion Cause Extraction and Summarization in Conversation

Jikun Wan, Chen Gong, Guohong Fu Soochow University, China,

原文摘要

Multimodal emotion cause analysis in conversation aims to identify the causes of emotions by leveraging multimodal information. Existing studies mainly formulate this problem as either utterance-level emotion cause extraction, which provides clear cause localization but limited explanation, or multimodal emotion cause generation, which offers fine-grained explanations but lacks explicit traceability to source utterances. Moreover, existing datasets rely heavily on human judgment and lack well-defined structured theoretical guidance, leading to subjective and inconsistent annotations. To address these issues, we introduce joint Multimodal Emotion Cause Extraction and Summarization in conversation (MECES), a new task that simultaneously extracts emotion cause utterances and generates cause summaries, enabling both precise localization and interpretable explanations of emotion cause. We further construct a MECES dataset guided by the Activating Events–Beliefs–Consequences theory from psychology. This dataset consists of 5,787 emotion utterances annotated with causes, comprising 12,231 emotion-cause pairs and 6,040 cause summaries. We also propose an effective end-to-end joint learning approach for MECES task, establishing strong benchmark results for this newly introduced task and dataset.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.592ACL Anthology · ACL 2026

Interleaved Tool-Call Reasoning for Protein Function Understanding

Chuanliu Fan Soochow University, Zicheng Ma, Huanran Meng Soochow University, Aijia Zhang, Wenjie Du, Jun Zhang, Ziqiang Cao Soochow University, China, Guohong Fu Soochow University, China,

原文摘要

Recent advances in large language models (LLMs) have highlighted the effectiveness of chain-of-thought reasoning in symbolic domains such as mathematics and programming. However, our study shows that directly transferring such text-based reasoning paradigms to protein function understanding is ineffective: reinforcement learning mainly amplifies superficial keyword patterns while failing to introduce new biological knowledge, resulting in limited generalization. We argue that protein function prediction is a knowledge-intensive scientific task that fundamentally relies on external biological priors and computational tools rather than purely internal reasoning. To address this gap, we propose Protein Function Understanding Agent (PFUA), a tool-augmented protein reasoning agent that unifies problem decomposition, tool invocation, and grounded answer generation. Instead of relying on long unconstrained reasoning traces, PFUA integrates domain-specific tools to produce verifiable intermediate evidence. Experiments on four benchmarks demonstrate that PFUA consistently outperforms text-only reasoning models with an average performance improvement of 103%. We believe PFUA has the potential to become a standard paradigm for agentic reasoning in knowledge-intensive life science domains.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表