6 papers
Cost-Effective Agent Harnesses for Abstract Reasoning and Generalization on ARC-AGI-1
Kabir Moghe, Peter Chin
Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time compute over frontier models (evolutionary search, exhaustive sampling,…
Probing LLMs for Syntactic Structure Beyond Universal Dependencies: A Minimalist Phase Account in English
Yuanhao Chen, Peter Chin
We show that LLMs encode syntactic distinctions not present in the Universal Dependencies (UD) tree distances that structural probes are trained to recover. On English wh-movement…
MoDAl: Self-Supervised Neural Modality Discovery via Decorrelation for Speech Neuroprosthesis
Yuanhao Chen, Peter Chin
Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communication for individuals with speech-…
TADA: A Generative Framework for Speech Modeling via Text-Acoustic Dual Alignment
Trung Dang, Sharath Rao, Ananya Gupta +6
Modern Text-to-Speech (TTS) systems increasingly leverage Large Language Model (LLM) architectures to achieve scalable, high-fidelity, zero-shot generation. However, these systems…
MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning
Thang Nguyen, Peter Chin, Yu-Wing Tai
We present MA-RAG, a Multi-Agent framework for Retrieval-Augmented Generation (RAG) that addresses the inherent ambiguities and reasoning challenges in complex information-seeking…
Reward-RAG: Enhancing RAG with Reward Driven Supervision
Thang Nguyen, Peter Chin, Yu-Wing Tai
In this paper, we introduce Reward-RAG, a novel approach designed to enhance the Retrieval-Augmented Generation (RAG) model through Reward-Driven Supervision. Unlike previous RAG m…