3 citations · 4 across the 6 of their papers we have counts for
7 papers
Reward Engineering for Software Tasks: A Survey of Reinforcement Learning Approaches
Md Rayhanul Masud, Azmine Toushik Wasi, Salman Rahman +1
Reinforcement learning is increasingly used for code-centric software engineering tasks, including code generation, understanding, repair, testing, and optimization, especially wit…
ScalePRM: Training Process Reward Models by Scaling Verification Compute Without Ground Truth
Salman Rahman, Sruthi Gorantla, Arpit Gupta +3
Training process reward models (PRMs) requires step-level correctness labels, obtained either through expensive human annotation or by relying on ground-truth answers, limiting the…
Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team
Md Tanzib Hosain, Salman Rahman, Md Kishor Morol +1
Despite impressive progress on complex reasoning, current large language models (LLMs) typically operate in isolation - treating each problem as an independent attempt, without acc…
AI Debate Aids Assessment of Controversial Claims
Salman Rahman, Sheriff Issaka, Ashima Suvarna +11
As AI grows more powerful, it will increasingly shape how we understand the world. But with this influence comes the risk of amplifying misinformation and deepening social divides-…
X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents
Salman Rahman, Liwei Jiang, James Shiffer +7
Multi-turn interactions with language models (LMs) pose critical safety risks, as harmful intent can be strategically spread across exchanges. Yet, the vast majority of prior work…
MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations
Genglin Liu, Vivian Le, Salman Rahman +3
We present a novel, open-source social network simulation framework, MOSAIC, where generative language agents predict user behaviors such as liking, sharing, and flagging content.…
Out of Distribution Performance of State of Art Vision Model
Salman Rahman, Wonkwon Lee
The vision transformer (ViT) has advanced to the cutting edge in the visual recognition task. Transformers are more robust than CNN, according to the latest research. ViT's self-at…