4 citations · 4 across the 18 of their papers we have counts for
6 papers · 1 filter
Joint, Decoupled, or Agent: When Each Multimodal Alignment Paradigm Wins
Yi Fang, Gaurav Srivastava, Sirui Ding +1
Jointly pretrained multimodal encoders are a common way to combine modalities, while alternatives integrate their information during downstream training or inference. When does eac…
Cache2LoRA: Understanding and Improving Context Internalization
Yi Fang, Gaurav Srivastava, Zhenyu Bi +2
Context-to-LoRA methods encode documents as low-rank parameter updates, allowing language models to answer without retaining the source text in their decoding context. How reliably…
Mergeable Model-Side Aggregation States for Long-Context Language Models
Dachuan Song, Junyu Yin, Zechen Hu +1
A known limitation of long-context language models is their increasingly unreliable performance in non-additive, set-based aggregation as context length grows. Examples include car…
Elastic Spectral State Space Models for Train-Once Budgeted Inference
Dachuan Song, Xuan Wang, Junyu Yin +1
Modern sequence models are typically trained at a fixed computational capacity, while real-world applications require deployment across platforms with different resource constraint…
EffGen: Enabling Small Language Models as Capable Autonomous Agents
Gaurav Srivastava, Aafiya Hussain, Chi Wang +2
Most existing language model agentic systems today are built and optimized for large language models (e.g., GPT, Claude, Gemini) via API calls; while powerful, this approach faces…
MEDVISTAGYM: A Scalable Training Environment for Thinking with Medical Images via Tool-Integrated Reinforcement Learning
Meng Lu, Yuxing Lu, Yuchen Zhuang +6
Vision language models (VLMs) achieve strong performance on general image understanding but struggle to think with medical images, especially when performing multi-step reasoning t…