31 citations · 93 across the 30 of their papers we have counts for
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cs.LG2026
Structuring MoE Expert Selection for Agentic Reinforcement Learning
Bolian Li, Ting-Yao Hu, Cheng-Yu Hsieh +3
Long-horizon LLM agents are frequently implemented using sparse mixture-of-experts (MoE) models, yet the co-design of agentic behavior and MoE structures remains underexplored. In…
cs.AI2026
Simulate to Generalize: Scaling Stateful Supervision for API-calling Agents using LLM World Models
Seanie Lee, Sanjoy Chowdhury, Chao Jiang +5
Training agents that generalize to unseen, stateful environments requires a massive dataset of state-changing trajectories covering a vast and diverse set of APIs. However, scaling…
cs.AI2026
ASTRA-bench: Evaluating Tool-Use Agent Reasoning and Action Planning with Personal User Context
Zidi Xiu, David Q. Sun, Kevin Cheng +9
Next-generation AI must manage vast personal data, diverse tools, and multi-step reasoning, yet most benchmarks remain context-free and single-turn. We present ASTRA-bench (Assista…