most citedAdvancing Human-Machine Teaming: Concepts, Challenges, and Applications

2 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL2026

TPBench: A Turning-Point Benchmark for Dialogue Compression

Minji Park, Seunghyun Yoon, Hyuk Lim

A compressor can keep the facts of a dialogue and still drop the turn that changed them. A user corrects a price, reverses a choice, or adds a constraint. We call this failure turn…

cs.AI2026

Transfiver: Human-AI Co-Inference through a Shared Editable State

Minji Park, Seunghyun Yoon, Hyuk Lim

Long-term human-AI interaction is difficult because the information that guides inference is updated implicitly by the model and is not directly inspectable or controllable by the…

cs.AI2026

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory

Hyuk Lim, Seunghyun Yoon

Long-sequence memory tracking places two opposing demands on a recurrent state: near-lossless retention of stored bindings over long horizons, and active overwriting of stale ones.…

cs.AI2026

Risk-Aware Human-in-the-Loop Framework with Adaptive Intrusion Response for Autonomous Vehicles

Dawood Wasif, Terrence J. Moore, Seunghyun Yoon +4

Autonomous vehicles must remain safe and effective when encountering rare long-tailed scenarios or cyber-physical intrusions during driving. We present RAIL, a risk-aware human-in-…

cs.HC2025

DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System

Zelin Wan, Han Jun Yoon, Nithin Alluru +6

We present DASH (Deception-Augmented Shared mental model for Human-machine teaming), a novel framework that enhances mission resilience by embedding proactive deception into Shared…

cs.RO2025

DriveMind: A Dual Visual Language Model-based Reinforcement Learning Framework for Autonomous Driving

Dawood Wasif, Terrence J. Moore, Chandan K. Reddy +5

End-to-end autonomous driving systems map sensor data directly to control commands, but remain opaque, lack interpretability, and offer no formal safety guarantees. While recent vi…

cs.HC2025★ 2 cited

Advancing Human-Machine Teaming: Concepts, Challenges, and Applications

Dian Chen, Han Jun Yoon, Zelin Wan +9

Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust cali…