Honglin Guo

Experience-rich memory boosts agent performance in office workflows but can also lead to misleading recall, challenging traditional evaluation methods.

IACM-RL reduces infinite loops and stale context errors by proactively managing dynamic user intents, setting a new standard for robust tool invocation.

Even state-of-the-art language models struggle significantly in real-world tasks, exposing critical shortcomings in their deployment readiness.

Even the top-performing language models struggle with archive-grounded reasoning, achieving only 59.4% accuracy on a benchmark designed to test their agentic capabilities across diverse workplace documents.

Multi-turn reinforcement learning gets a boost: weighting trajectories by semantic similarity dramatically improves baseline estimation and agent performance in long-document visual QA.

Achieve anatomically consistent and controllable counterfactual CXR synthesis by steering diffusion model attention with organ masks and pathology guidance.