8 papers · 1 filter
Perception Before Supervision: Self-Contained Visual Distillation from Counterfactual Blind Spots
Shravan Venkatraman, Omkar Thawakar, Ritesh Thawkar +2
Self-improvement for multimodal large language models (MLLMs) is typically driven by reward-based methods that provide only coarse scalar feedback. Distillation offers a richer alt…
Ask, Solve, Generate: Self-Evolving Unified Multimodal Understanding and Generation via Self-Consistency Rewards
Ritesh Thawkar, Shravan Venkatraman, Omkar Thawakar +5
Most unified large multimodal models (LMMs) that support both visual understanding and image generation still rely on curated post-training supervision, such as human annotations,…
Paying More Attention to Visual Tokens in Self-Evolving Large Multimodal Models
Shravan Venkatraman, Ritesh Thawkar, Omkar Thawakar +4
Recently, self-evolving large multimodal models (LMMs) have received attention for improving visual reasoning in a purely unsupervised setting. However, multi-role self-play and se…
AgriChain Visually Grounded Expert Verified Reasoning for Interpretable Agricultural Vision Language Models
Hazza Mahmood, Yongqiang Yu, Rao Anwer
Accurate and interpretable plant disease diagnosis remains a major challenge for vision-language models (VLMs) in real-world agriculture. We introduce AgriChain, a dataset of appro…
MediX-R1: Open Ended Medical Reinforcement Learning
Sahal Shaji Mullappilly, Mohammed Irfan Kurpath, Omair Mohamed +5
We introduce MediX-R1, an open-ended Reinforcement Learning (RL) framework for medical multimodal large language models (MLLMs) that enables clinically grounded, free-form answers…
Thinking Beyond Labels: Vocabulary-Free Fine-Grained Recognition using Reasoning-Augmented LMMs
Dmitry Demidov, Zaigham Zaheer, Zongyan Han +2
Vocabulary-free fine-grained image recognition aims to distinguish visually similar categories within a meta-class without a fixed, human-defined label set. Existing solutions for…