activity
20242026
collaborators

15 papers

cs.LG2026

Uncertainty Quantification for Computer-Use Agents: A Benchmark across Vision-Language Models and GUI Grounding Datasets

Divake Kumar, Sina Tayebati, Devashri Naik +5

Computer-use agents turn vision-language model (VLM) predictions into executable GUI clicks, so reliable uncertainty estimates are essential for rejection, calibration, miss-severi…

cs.RO2026

GroundControl: Anticipating Navigation Failures in Vision-Language Agents via Trajectory-Consistent Uncertainty Estimates

Nastaran Darabi, Divake Kumar, Sina Tayebati +2

Vision-language navigation agents achieve competitive average success on benchmark tasks, yet failures often arise through predictable trajectory-level breakdowns such as oscillati…

cs.LG2026

VLM Judges Can Rank but Cannot Score: Task-Dependent Uncertainty in Multimodal Evaluation

Divake Kumar, Sina Tayebati, Devashri Naik +2

Vision-language models (VLMs) are increasingly used as automated judges for multimodal systems, yet their scores provide no indication of reliability. We study this problem through…

cs.RO2026

Belief Dynamics for Detecting Behavioral Shifts in Safe Collaborative Manipulation

Devashri Naik, Divake Kumar, Nastaran Darabi +1

Robots operating in shared workspaces must maintain safe coordination with other agents whose behavior may change during task execution. When a collaborating agent switches strateg…

cs.RO2026

TRIAGE: Type-Routed Interventions via Aleatoric-Epistemic Gated Estimation in Robotic Manipulation and Adaptive Perception -- Don't Treat All Uncertainty the Same

Divake Kumar, Sina Tayebati, Devashri Naik +4

Most uncertainty-aware robotic systems collapse prediction uncertainty into a single scalar score and use it to trigger uniform corrective responses. This aggregation obscures whet…

cs.CV2026

Uncertainty-Guided Inference-Time Depth Adaptation for Transformer-Based Visual Tracking

Patrick Poggi, Divake Kumar, Theja Tulabandhula +1

Transformer-based single-object trackers achieve state-of-the-art accuracy but rely on fixed-depth inference, executing the full encoder--decoder stack for every frame regardless o…