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MOV-AAD: A Large-Scale Multimodal Dataset for Auditory Attention Decoding During Moving Conversations
Xiaomin He, Vishal Choudhari, Tristan J. Spratt +3
Auditory attention decoding (AAD) is often evaluated on static, simplified speech scenes that poorly match everyday listening. We introduce MOV-AAD, a large-scale dataset for study…
Grammatical "grandmother neurons" are rare in LLMs
Linyang He, Nima Mesgarani
Understanding how Large Language Models (LLMs) encode linguistic structures remains a fundamental challenge in interpretability research. While diagnostic classifiers (or "probes")…
Beyond the Stability--Plasticity Frontier in Streaming Target Speaker Extraction
Yuesheng Ma, Linyang He, Nima Mesgarani
Streaming target speaker extraction must maintain a representation of whom to extract while the target may fall silent, be masked by interference, or drift acoustically away from e…
Cocktail-Talker: Multi-Speaker Dialog Modeling in Noisy Social Environments with Turn Action GRPO
Xilin Jiang, Riki Shimizu, Sukru Samet Dindar +3
Spoken dialog systems are typically designed for clean, dyadic interactions in which a single user and an assistant take turns speaking. Real-world social conversations, however, a…
Sympatheia: Emotionally Adaptive Voice Assistant with Continuous Affect Conditioning
Sukru Samet Dindar, Riki Shimizu, Xilin Jiang +1
Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or ambiguous affective cues. To ad…
Prune, Interpret, Evaluate: A Cross-Layer Transcoder-Native Framework for Efficient Circuit Discovery via Feature Attribution
Qinhao Chen, Linyang He, Nima Mesgarani
Existing feature-interpretation pipelines typically operate on uniformly sampled units or exhaustive feature sets, incurring massive costs on units irrelevant to target behaviors.…
LiveMathematicianBench: A Live Benchmark for Research-Level Mathematical Reasoning with Proof Sketches
Linyang He, Qiyao Yu, Hanze Dong +5
Mathematical reasoning is a hallmark of human intelligence, and whether large language models (LLMs) can meaningfully perform it remains a central question in artificial intelligen…
AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking
Xilin Jiang, Qiaolin Wang, Junkai Wu +30
Internet audio-visual clips convey meaning through time-varying sound and motion, which extend beyond what text alone can represent. To examine whether AI models can understand suc…
LLMs Think with DAGs: Probing the Latent Graph Structure of Reasoning
Tianjun Zhong, Linyang He, Ziyang Li +1
Large language models often externalize reasoning as linear chains, but many reasoning problems can be more naturally modeled as directed acyclic graphs (DAGs), where intermediate…