4 papers
Streaming Sequence Transduction through Dynamic Compression
Weiting Tan, Yunmo Chen, Tongfei Chen +5
We introduce STAR (Stream Transduction with Anchor Representations), a novel Transformer-based model designed for efficient sequence-to-sequence transduction over streams. STAR dyn…
X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains
Qianchu Liu, Sheng Zhang, Guanghui Qin +9
Recent proprietary models (e.g., o3) have begun to demonstrate strong multimodal reasoning capabilities. Yet, most existing open-source research concentrates on training text-only…
KV-Distill: Nearly Lossless Learnable Context Compression for LLMs
Vivek Chari, Guanghui Qin, Benjamin Van Durme
Sequence-to-sequence tasks often benefit from long contexts, but the quadratic complexity of self-attention in standard Transformers renders this non-trivial. During generation, te…
Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning
Sheng Zhang, Qianchu Liu, Guanghui Qin +2
Reinforcement learning from verifiable rewards (RLVR) has recently gained attention for its ability to elicit self-evolved reasoning capabilitie from base language models without e…