7 citations · 7 across the 9 of their papers we have counts for
9 papers
WhiteMatter: All-to-All Cross-Layer Connections via KV Source Mixing
Wenbo Zhang, Xiang Ren
When generating text, a Transformer produces representations of past tokens at every layer, but each layer can normally use only representations from the same depth. This restricti…
OpaqueToolsBench: Learning Nuances of Tool Behavior Through Interaction
Skyler Hallinan, Thejas Venkatesh, Xiang Ren +4
Tool-calling is essential for Large Language Model (LLM) agents to complete real-world tasks. While most existing benchmarks assume simple, perfectly documented tools, real-world t…
Scalpel-SAM: A Semi-Supervised Paradigm for Adapting SAM to Infrared Small Object Detection
Zihan Liu, Xiangning Ren, Dezhang Kong +2
Infrared small object detection urgently requires semi-supervised paradigms due to the high cost of annotation. However, existing methods like SAM face significant challenges of do…
OSGym: Scalable OS Infra for Computer Use Agents
Zengyi Qin, Jinyuan Chen, Yunze Man +25
Training computer use agents requires full-featured OS sandboxes with GUI environments, which consume substantial hardware resources as the number of sandboxes scales. Stochastic e…
Attributing Culture-Conditioned Generations to Pretraining Corpora
Huihan Li, Arnav Goel, Keyu He +1
In open-ended generative tasks like narrative writing or dialogue, large language models often exhibit cultural biases, showing limited knowledge and generating templated outputs f…
Demystifying Language Model Forgetting with Low-rank Example Associations
Xisen Jin, Xiang Ren
Large language models (LLMs) suffer from forgetting of upstream knowledge when fine-tuned. Despite efforts on mitigating forgetting, few have investigated how forgotten upstream ex…
What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement
Xisen Jin, Xiang Ren
Language models deployed in the wild make errors. However, simply updating the model with the corrected error instances causes catastrophic forgetting -- the updated model makes er…
Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance
Jesse Zhang, Jiahui Zhang, Karl Pertsch +5
We propose BOSS, an approach that automatically learns to solve new long-horizon, complex, and meaningful tasks by growing a learned skill library with minimal supervision. Prior w…
DOMINO: A Dual-System for Multi-step Visual Language Reasoning
Peifang Wang, Olga Golovneva, Armen Aghajanyan +4
Visual language reasoning requires a system to extract text or numbers from information-dense images like charts or plots and perform logical or arithmetic reasoning to arrive at a…