15 papers
Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering
Sheldon Yu, Tong Yu, Xunyi Jiang +6
Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable. Existing methods for shap…
Reasoning-Based Personalized Generation for Users with Sparse Data
Bo Ni, Branislav Kveton, Samyadeep Basu +14
Large Language Model (LLM) personalization holds great promise for tailoring responses by leveraging personal context and history. However, real-world users usually possess sparse…
Multi-Agent Collaborative Filtering: Orchestrating Users and Items for Agentic Recommendations
Yu Xia, Sungchul Kim, Tong Yu +2
Agentic recommendations cast recommenders as large language model (LLM) agents that can plan, reason, use tools, and interact with users of varying preferences in web applications.…
Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent
Junda Wu, Yuxin Xiong, Xintong Li +9
Recent MLLMs have shown emerging visual understanding and reasoning abilities after being pre-trained on large-scale multimodal datasets. Unlike pre-training, where MLLMs receive r…
GUI Agents: A Survey
Dang Nguyen, Jian Chen, Yu Wang +27
Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction. These agents autonomo…
SAND: Boosting LLM Agents with Self-Taught Action Deliberation
Yu Xia, Yiran Shen, Junda Wu +5
Large Language Model (LLM) agents are commonly tuned with supervised finetuning on ReAct-style expert trajectories or preference optimization over pairwise rollouts. Most of these…