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Zhen Fang

ICLR 2026×4ACL 2026×2社会与人本可解释性安全推理多模态
新星分资深
46
一作贡献30
核心/参与26
方向广度13
合作网络12
1 一作 · 6 篇 · ICLR 2026 / ACL 2026

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2026 顶会论文

6
作者位次 #3/4ICLR 2026Oral· Social Aspects / Accountability, Transparency and Interpretability

How Do Transformers Learn to Associate Tokens: Gradient Leading Terms Bring Mechanistic Interpretability

Shawn Im · Changdae Oh · Zhen Fang · Sharon Li

可解释性社会与人本理论与学习动力学OpenReview
作者位次 #2/4ICLR 2026Poster· Social Aspects / Trustworthy Machine Learning

Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detection

Yongxin Deng · Zhen Fang · Sharon Li · Ling Chen

安全社会与人本OpenReview
作者位次 #6/6ICLR 2026Poster· General Machine Learning / Everything Else

Delving into Spectral Clustering with Vision-Language Representations

Bo Peng · Yuanwei Hu · Bo Liu · Ling Chen · Jie Lu · Zhen Fang

可解释性多模态OpenReview
作者位次 #3/6ACL 2026Poster· Natural Language Processing

Beyond Accuracy: Unveiling Inefficiency Patterns in Tool-Integrated Reasoning

Qisheng Su · Shiting Huang · Zhen Fang · Ziyan Chen · Zehui Chen · Feng Zhao

摘要

In real-world Tool-Integrated Reasoning (TIR) scenarios, a major source of inefficiency is that the toolcalls create pauses between LLM requests and cause KV-cache eviction. Also, the long, unfiltered response returned by external tools inflates the KV-cache, so each decode step spends more time...

效率与系统推理智能体与工具OpenReview 项目页
一作ACL 2026Poster· Natural Language Processing

U ni C orn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision

Zhen Fang · Ruiyan Han · XinYu Sun · Yuchen Ma · Ziheng Wang · Yu Zeng · Zehui Chen · Lin Chen · Wenxuan Huang · Wei-Jie Xu · Yi Cao · Feng Zhao

摘要

While Unified Multimodal Models (UMMs) have achieved remarkable success in cross-modal comprehension, a significant gap persists in their ability to leverage such internal knowledge for high-quality generation. We formalize this discrepancy as Conduction Aphasia, a phenomenon where models accurat...

多模态强化学习与后训练评测与基准OpenReview 项目页

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