5 papers
Does Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, Matters
Wenlong Wang, Fergal Reid
Chain-of-thought (CoT) prompting improves LLM reasoning, but the source is contested: do the intermediate steps help because they carry useful semantic content, or because conditio…
MGA: Memory-Driven GUI Agent for Observation-Centric Interaction
Weihua Cheng, Junming Liu, Yifei Sun +3
Multimodal Large Language Models (MLLMs) have significantly advanced GUI agents, yet long-horizon automation remains constrained by two critical bottlenecks: context overload from…
Tiny Recursive Reasoning with Mamba-2 Attention Hybrid
Wenlong Wang, Fergal Reid
Recent work on recursive reasoning models like TRM demonstrates that tiny networks (7M parameters) can achieve strong performance on abstract reasoning tasks through latent recursi…
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient
Wenlong Wang, Ivana Dusparic, Yucheng Shi +2
Model-based reinforcement learning (RL) offers a solution to the data inefficiency that plagues most model-free RL algorithms. However, learning a robust world model often requires…
Applying Neural Monte Carlo Tree Search to Unsignalized Multi-intersection Scheduling for Autonomous Vehicles
Yucheng Shi, Wenlong Wang, Xiaowen Tao +2
Dynamic scheduling of access to shared resources by autonomous systems is a challenging problem, characterized as being NP-hard. The complexity of this task leads to a combinatoria…