Internal-state probes enable truthfulness prediction before a large language model generates an answer. When detectors change both the layers they read and the rules used to combine them, the source of improved prediction becomes difficult to identify. We separate these choices and find that retaining more layers impro...
Zhi-Hao Guo, Zong-Han Wu, Huan Huo et al.· 0 citations
SPMC, a training framework that identifies grounding-responsive heads and selectively regularizes their text-to-image attention, demonstrates that targeted guidance of sparse and implicit visual evidence pathways can directly improve multimodal reasoning.
Jia-Qi Deng, Zong-Han Wu, Zhan Heng et al.· 0 citations
This work establishes a principled paradigm shift toward reliability-aware multi-modal learning for drug discovery by performing integrated representation learning across multiple modalities, and proposes Cross-Modal Subspace Alignment (CMSA), which treats incomplete knowledge graphs as recoverable signals, synthesizin...
Xiaodong Zhu, Longjun Song, Shi-Ying Zeng et al.· IEEE journal of biomedical a...· 0 citations
Mint-Agent, a family of finance-native agentic models designed around these two scales of financial intelligence, is presented, establishing a path toward trustworthy financial intelligence in which domain expertise, long-horizon execution, and auditable evidence are jointly engineered as a unified foundation for front...
Mint-Agent Team, Kun Wang, Gavin Zhang et al.· 0 citations
HalluTracer is introduced, a detection framework that reads and aggregates truthfulness evidence across every layer of the forward pass before the model emits any answer token, recasts hallucination detection from a layer-selection problem into a depth-aggregation problem governed by the geometric sparsity of the truth...
Zhi-Hao Guo, Zong-Han Wu, Huan Huo et al.· 0 citations
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