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Jieun Kim

ACL 2026×2ICLR 2026多模态推理可解释性效率与系统评测与基准
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1 一作 · 3 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

3
一作ACL 2026Poster· Natural Language Processing

Injecting Context via Situation Working Memory for Logical Reasoning with LLM s

Jieun Kim · Seoha Lim · YoungHae Choi · Sung-Bae Cho

摘要

Recent advances in large language models (LLMs) have improved logical reasoning by injecting formal logic or explicit structured representations. However, such methods often lose track of what is true now in multi-step reasoning, failing to maintain a coherent global state and its logical consequ...

推理可解释性效率与系统OpenReview 项目页
作者位次 #2/4ACL 2026Poster· Natural Language Processing

Diagnosing Spatial Consistency across Perspectives and Viewpoints in Large Vision-Language Models

Yoonji Kim · Jieun Kim · Yujin Jeong · Sung-Bae Cho

摘要

Consistent reasoning about 3D spatial relations across changing viewpoints is fundamental for Embodied AI agents operating in dynamic environments. While Large Vision-Language Models (LVLMs) have advanced multimodal perception, their ability to maintain spatial consistency across diverse perspect...

多模态评测与基准推理OpenReview 项目页

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