5 papers
Can LLM Agents Automate Reinforcement Learning for Text-to-Speech?
Xuanjun Chen, Zixiong Su, Hao Shi +4
Although reinforcement learning (RL) post-training repairs the localized segmental errors of zero-shot text-to-speech (TTS), arriving at a working recipe still relies on tedious ma…
GrepSeek: Training Search Agents for Direct Corpus Interaction
Alireza Salemi, Chang Zeng, Atharva Nijasure +4
Large Language Model (LLM) search agents have shown strong promise on knowledge-intensive tasks through iterative reasoning and retrieval. Most existing systems rely on retrievers…
StepAudio 2.5 Technical Report
Bin Lin, Bo Zhao, Boyong Wu +98
Unified audio-language modeling has emerged as a prominent trend in modern speech systems, promising to bring the reasoning capabilities of large language models to auditory tasks.…
BEAVER: A Training-Free Hierarchical Prompt Compression Method via Structure-Aware Page Selection
Zhengpei Hu, Kai Li, Dapeng Fu +4
The exponential expansion of context windows in LLMs has unlocked capabilities for long-document understanding but introduced severe bottlenecks in inference latency and informatio…
A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation
Kai Li, Jintao Cheng, Chang Zeng +5
Query-based universal sound separation is fundamental to intelligent auditory systems, aiming to isolate specific sources from mixtures. Despite recent advances, existing methods c…