5 papers
Similar Models Learn Differently: Final-Window Pretraining Shapes Post-Training Beyond SFT
Cen Lu, Yung-Chen Tang, Andrea Cavallaro
Developers judge a model checkpoint by how it behaves. After supervised fine-tuning (SFT), two checkpoints that perform about the same across relevant benchmarks are treated as int…
Sparse Neuron Ablation Triggers Catastrophic Collapse of the Language Core in Large Vision-Language Models
Cen Lu, Yung-Chen Tang, Andrea Cavallaro
Large Vision-Language Models (LVLMs) have shown impressive multimodal understanding capabilities, yet the structures that sustain their functionality remain poorly understood from…
Geometric Latent Reasoning Induces Shorter Generations in LLMs
Shashi Kumar, Yacouba Kaloga, Petr Motlicek +2
Large language models solve complex problems by generating lengthy chains of explicit reasoning tokens. While effective, this makes reasoning expensive, length-sensitive, and const…
FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection
Yao Wei, Andrea Cavallaro, Changjae Oh
Open-vocabulary object detection (OVD) has achieved remarkable progress through large-scale vision-language pre-training. Existing methods, however, typically formulate OVD as a di…
CarBoN: Calibrated Best-of-N Sampling Improves Test-time Reasoning
Yung-Chen Tang, Pin-Yu Chen, Andrea Cavallaro
Allocating more computation during inference time (test-time scaling) improves language model performance, especially for reasoning tasks. However, popular methods like Best-of-…