collaborators
Showing cs.AIShow all

7 papers · 1 filter

cs.AI2026

Compiling Expert Procedures into Verified Programs: When a Small Model Is Enough

Vishvesh Bhat, Asad Aali, Muhammad Ahmed Mohsin

In procedural tool-use domains, how much of the gap between base models is procedure rather than capability? We build an explicit program from a teacher's traces and run it in fron…

cs.AI2026

AgentPersonaBench: Benchmarking Persona-Driven User Simulation

Jintao Huang, Yifan Wang, Hongyu Shen +43

We introduce AgentPersonaBench (APB), a benchmark evaluating whether persona conditioning faithfully steers downstream agent behavior. While language models are increasingly deploy…

cs.AI2026

Teaching Agents to Code Reliably

Muhammad Ahmed Mohsin, Myeongsoo Kim, Kangrui Ruan +3

Autonomous coding agents solve repository issues by reading code, running commands, editing files, and submitting patches. Extra inference-time compute yields gains only when it pr…

cs.AI2026

Refining Over Resampling: Test-Time Self-Correction for LLM Reasoning

Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer +4

Test-time scaling improves LLM reasoning by using additional inference compute, but wider sampling alone can suffer from diminishing returns: new rollouts often repeat existing ans…

cs.AI2026

MatrAIx: Simulating the World with 8.3 Billion Persona Agents

Xiaomin Li, Yuexing Hao, Jianheng Hou +90

Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and inter…

cs.AI2026

MAVEN: Improving Generalization in Agentic Tool Calling

Omkar Ghugarkar, Vishvesh Bhat, Muhammad Ahmed Mohsin +1

Generalization across agentic tool-calling environments remains a central challenge for reliable agentic reasoning systems. Although large language models achieve strong results on…

cs.AI2026

Pressure, What Pressure? Sycophancy Disentanglement in Language Models via Reward Decomposition

Muhammad Ahmed Mohsin, Ahsan Bilal, Muhammad Umer +1

Large language models exhibit sycophancy, the tendency to shift their stated positions toward perceived user preferences or authority cues regardless of evidence. Standard alignmen…