3 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
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.…