1 citations · 1 across the 6 of their papers we have counts for
6 papers
Off-Context GRPO: Learning to Reason on Hard Problems using Privileged Information
Priyank Agrawal, Ankur Samanta, Shervin Ghasemlou +5
Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models. Yet, typical RLVR approaches fail on difficult problems: when a model cannot gene…
Dopamine: Brain Modes, Not Brains
Shervin Ghasemlou
Parameter-efficient fine-tuning (PEFT) methods such as \lora{} adapt large pretrained models by adding small weight-space updates. While effective, weight deltas are hard to interp…
Multimodal Generative Recommendation for Fusing Semantic and Collaborative Signals
Moritz Vandenhirtz, Kaveh Hassani, Shervin Ghasemlou +5
Sequential recommender systems rank relevant items by modeling a user's interaction history and computing the inner product between the resulting user representation and stored ite…
CRAG-MM: Multi-modal Multi-turn Comprehensive RAG Benchmark
Jiaqi Wang, Xiao Yang, Kai Sun +38
Wearable devices such as smart glasses are transforming the way people interact with their surroundings, enabling users to seek information regarding entities in their view. Multi-…
Doppelgänger's Watch: A Split Objective Approach to Large Language Models
Shervin Ghasemlou, Ashish Katiyar, Aparajita Saraf +5
In this paper, we investigate the problem of "generation supervision" in large language models, and present a novel bicameral architecture to separate supervision signals from thei…
Toward a language-theoretic foundation for planning and filtering
Fatemeh Zahra Saberifar, Shervin Ghasemlou, Dylan A. Shell +1
We address problems underlying the algorithmic question of automating the co-design of robot hardware in tandem with its apposite software. Specifically, we consider the impact tha…