As embodied agents perform object-goal navigation across residential environments, they accumulate successful experiences whose semantic structure recurs across scenes even when geometric trajectories do not. Reusing such experience is challenging because differences in region naming and observation granularity produce...
Xiaoran Xu, Yupeng Wu, Tianyu Xue et al.· 0 citations
Post-training updates change what large language models (LLMs) can answer, but may leave their answerability judgments misaligned. We study this knowledge-judgment desynchronization through functional feeling of knowing (FoK), which supports selective generation, retrieval triggering, and model routing without implying...
Zichuan Fu, Xian Wu, Jingtong Gao et al.· 0 citations
Payment fraud causes financial losses and undermines users' trust in web payment services. Although fraud detection is a critical defense for payment security, complex and diverse fraudulent behaviors can still evade detection. Generating fraud samples for data augmentation can broaden detectors' coverage of different...
Huilin He, Weiyu Han, Linbo Shao et al.· 0 citations
This paper proposes a novel text-based perturbation method, Natural Textual Unlearnable Examples (NTUE), which can effectively mislead LVLMs by subtly manipulating the image label, establishing a shortcut between an identity of interest and a separate target, without introducing factual misinformation or affecting read...
Chen-Yu Yi, Jia-Wen Zong, Sami Hormi et al.· 0 citations
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Large reasoning models achieve strong performance on challenging tasks, but their long chain-of-thought often incurs substantial inference costs by continuing to deliberate after sufficient information has been obtained. Existing reasoning compression methods primarily operate on reasoning trajectories, paying less att...
Mu-Yao Yuan, Li-Yan Xu, Wei-Zhan Zhang et al.· 0 citations
Financial agents are becoming an important class of real-world AI systems. Financial tasks vary widely in type and difficulty, and many are open-ended with no ground-truth answers. However, existing benchmarks neither capture this breadth and open-endedness nor separate grounding from correctness. To bridge this gap, w...
Shu-Wen Zhou, Zi-Yang He, Yu-Xin Rao et al.· 0 citations
RAHC, a rule-augmented HKG framework for single-slot and joint multi-slot synthesis-condition completion, builds a traceable rule catalog from training records and retrieved literature, retrieves rules for each query-candidate pair, and separately encodes graph structure and rule evidence.
Jia-Wei Sun, Si-Jia Xu, Zi-Han Chen et al.· 0 citations
LLM agents often give conflicting answers to the same question, and many deployed agents are variants of one base model, so their errors are correlated. We study truth discovery among such agents: inferring the true answer and each agent's reliability without labels. Classical truth-discovery and crowdsourcing estimato...
Large language models are candidates for collecting and analyzing records in criminal investigations and mass surveillance. Their capability and accuracy in this work cannot currently be measured. Real deployments lack the ground truth needed to score a model's conclusions, and no benchmark evaluates these capabilities...
This work proposes a new research mission for NLP: causally grounded PII suppression through circuit-level localization and causal mediation analysis, indicating that existing evaluations cannot verify whether interventions target causal pathways of PII leakage.
Chaerin Lee, Wonho Lee, Sohee Park et al.· 0 citations
A deployed LLM coaching agent answers requests whose correct response depends on player state and game version, so advice that is right for one player can be wrong for the next. We describe TactiNavi, the serving system behind the LLM coach of a commercial open-world game, which makes strategy applicability an explicit...
Qi Liu, Xiaoyang Yuan, Zhuomeng Zhang et al.· 0 citations
An activation steering edit should depend on the input exactly when the destination varies \emph{inside} the class a query requests, and that is measurable before any rule is fitted or applied. Contrastive Activation Addition (CAA) adds one fixed direction to every input, the difference of the class means. Conditioning...
