How Do Transformers Learn to Associate Tokens: Gradient Leading Terms Bring Mechanistic Interpretability
Shawn Im · Changdae Oh · Zhen Fang · Sharon Li
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Shawn Im · Changdae Oh · Zhen Fang · Sharon Li
Yongxin Deng · Zhen Fang · Sharon Li · Ling Chen
Junfeng Liao · Qizhou Wang · Shanshan Ye · Xin Yu · Ling Chen · Zhen Fang
Bo Peng · Yuanwei Hu · Bo Liu · Ling Chen · Jie Lu · Zhen Fang
Qisheng Su · Shiting Huang · Zhen Fang · Ziyan Chen · Zehui Chen · Feng Zhao
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...
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
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...