works on

From the 1 of 22 linked papers with an AI index.

collaborators

22 papers

cs.SD2026

Cocktail-Talker: Multi-Speaker Dialog Modeling in Noisy Social Environments with Turn Action GRPO

Xilin Jiang, Riki Shimizu, Sukru Samet Dindar +3

The paper presents Cocktail-Talker, a speech‑language model framework that lets a spoken assistant decide whether to respond, listen, or ignore in multi‑speaker, noisy social conve…

cs.SD2026

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…

cs.CL2026

A cross-species neural foundation model for end-to-end speech decoding

Yizi Zhang, Linyang He, Chaofei Fan +9

Speech brain-computer interfaces (BCIs) aim to restore communication for people with paralysis by translating neural activity into text. Most systems use cascaded frameworks that d…

cs.CL2026

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.…

cs.CL2026

From Chains to DAGs: Probing the Graph Structure of Reasoning in LLMs

Tianjun Zhong, Linyang He, Nima Mesgarani +1

Recent progress in large language models has renewed interest in how multi-step reasoning is represented internally. While prior work often treats reasoning as a linear chain, many…

cs.CL2026

LiveMathematicianBench: A Live Benchmark for Mathematician-Level 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…