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This paper presents a detailed case study demonstrating how AI-assisted paired engineering combined with two key safeguards— a precise executable YAML specification as a single source of truth and …
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M3Exam addresses the lack of realistic benchmarks for evaluating multimodal conversational memory in user-agent interactions where heterogeneous multimodal data accumulate over multiple sessions
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This paper tackles the critical problem of poor generalization in deepfake speech detection when models trained on proxy resynthesized data (CoRS) encounter real-world synthesized speech (CodecFake…
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This paper addresses the fundamental problem of recovering hidden influence networks from observed cascade data sequences, such as disease spread, product adoption, or information diffusion
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This paper addresses the computational bottleneck in robot co-design, the joint optimization of robot morphology and control policies for locomotion tasks
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This paper addresses the challenging NLP problem of detecting Turkish idiomatic light verb constructions (LVCs), which are multiword expressions with ambiguous surface forms that can have either id…
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This paper identifies and rigorously studies sycophantic praise—a distinct alignment issue in large language models (LLMs) where models produce excessive or unwarranted praise towards users
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This paper addresses the growing challenges in designing high-performance computing (HPC) workflows that integrate artificial intelligence (AI) components, especially foundation models, which intro…
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This paper addresses the problem of unsupervised continual clustering (UCC), where a neural network must cluster data from a sequence of distinct, unlabeled tasks without storing past data or labels
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This paper addresses the dual challenge of performing deep neural network (DNN) inference on low-end edge devices while simultaneously protecting model confidentiality and enabling verifiable infer…
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This comprehensive survey paper addresses the evolving challenges and methods in video understanding driven by multimodal large language models (MLLMs)
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This paper addresses the challenge of inferring the sign (positive or negative) of social relationships from user interactions in online settings
