6 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…
SPARK: Stepwise Process-Aware Rewards for Reference-Free Reinforcement Learning
Salman Rahman, Sruthi Gorantla, Arpit Gupta +3
Process reward models (PRMs) that provide dense, step-level feedback have shown promise for reinforcement learning, yet their adoption remains limited by the need for expensive ste…
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-…
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.…
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…
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…