43 citations · 56 across the 17 of their papers we have counts for
12 papers · 1 filter
TokenEconomizer: Experience-Driven Black-Box Cost Control for Coding Agents
Aaron J. Li, Weijia Xu, Chandan Singh +8
Advances in frontier coding agents have sharply increased the token costs of AI-assisted software engineering. Their token efficiency is typically evaluated on isolated tasks, wher…
Learning User Representations to Improve Simulation Fidelity
Shuhaib Mehri, Philippe Laban, Michel Galley +3
User simulators enable interactive training and evaluation of AI assistants at scale. Yet current simulators struggle to reproduce the diverse behaviors of real users, leading to a…
EAIR: Interaction Risks in Enterprise LLM Multi-Agent Systems
Yuchen Ma, Swadheen Shukla, Paulo Salem +5
LLM agents increasingly divide enterprise work across roles with different goals, authority, access, and information. Because each agent often sees only a local snapshot of the ent…
Reward Hacking Challenges Oversight of Autonomous Research Agents
Yue Huang, Zhangchen Xu, Yuchen Ma +12
Autonomous research agents can design experiments, evaluate results, and write reports, giving them control over both a scientific result and the evidence used to support it. This…
StudentSim: Training LLM-based Student Simulators
Ke Yang, Chenglong Wang, Michel Galley +4
AI tutors are most useful when they adapt to each student's strengths, weaknesses, and preferred guidance, but evidence about which guidance works for which student is sparse, slow…
VGI-Bench: Probing Visual Intelligence in Video Generation Models
Xuan He, Cong Wei, Yuhao Cheng +20
Recent studies suggest that video generation models can exhibit certain forms of zero-shot visual reasoning through generated frames. Yet reliable evaluation remains challenging: b…
Do Proactive Agents Need an LLM to Decide When to Act?
Xiaoze Liu, Ruowang Zhang, Amir H. Abdi +5
Proactive assistants continuously decide when to intervene and what context should support the intervention. Large language model (LLM) pipelines repeatedly interpret activity hist…
SkillGen: Verified Inference-Time Agent Skill Synthesis
Yuchen Ma, Yue Huang, Han Bao +5
Skills are a promising way to improve LLM agent capabilities without retraining, while keeping the added procedure reusable and controllable. However, high-quality skills are still…
Measuring and Mitigating the Distributional Gap Between Real and Simulated User Behaviors
Shuhaib Mehri, Philippe Laban, Sumuk Shashidhar +4
As user simulators are increasingly used for interactive training and evaluation of AI assistants, it is essential that they represent the diverse behaviors of real users. While ex…
Agentic-imodels: Evolving agentic interpretability tools via autoresearch
Chandan Singh, Yan Shuo Tan, Weijia Xu +4
Agentic data science (ADS) systems are rapidly improving their capability to autonomously analyze, fit, and interpret data, potentially moving towards a future where agents conduct…
Test-Time Learning with an Evolving Library
Weijia Xu, Alessandro Sordoni, Chandan Singh +4
We introduce EvoLib, a test-time learning framework that enables large language models to accumulate, reuse, and evolve knowledge across problem instances without parameter updates…
PlugMem: A Task-Agnostic Plugin Memory Module for LLM Agents
Ke Yang, Zixi Chen, Xuan He +6
Long-term memory is essential for large language model (LLM) agents operating in complex environments, yet existing memory designs are either task-specific and non-transferable, or…