4 citations · 9 across the 31 of their papers we have counts for
33 papers
Self-Healing Harness for Runtime Oversight of Agent Self-Modification
Sina Tayebati, Divake Kumar, Nastaran Darabi +2
LLM agents can change their own future behavior, raising a basic control question of which self-generated changes should be allowed to persist. We formulate this as admission contr…
CURA: Certified Runtime Alarms for Computer-Use Agents
Divake Kumar, Sina Tayebati, Devashri Naik +5
Self-report is the cheapest oversight channel a deployer has, and on capable computer-use agents (CUAs) it fails precisely where oversight matters. On 361 OSWorld tasks our pipelin…
From Generation to Discovery: Diffusion Mutation Kernels for Circuit and Physical Design
Dinithi Jayasuriya, Aravind Saravanan, Nilesh Ahuja +2
Generation and discovery are different problems. A generative model trained on valid artifacts reproduces a distribution, whereas discovery must produce artifacts that lie outside…
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…
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…
When Robots Sleep: Offline Skill Consolidation for Shared-Policy Robot Learning
Nethmi Jayasinghe, Diana Gontero, Amit Ranjan Trivedi
Robots that learn over long deployments must add new skills without losing the shared policy structure that makes earlier skills reusable. We study sequential robot skill learning,…
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…
Structural Verification for Reliable EDA Code Generation without Tool-in-the-Loop Debugging
Dinithi Jayasuriya, Aravind Saravanan, Nilesh Ahuja +2
Large language models (LLMs) have enabled natural-language-driven automation of electronic design automation (EDA) workflows, but reliable execution of generated scripts remains a…
ProGAL-VLA: Grounded Alignment through Prospective Reasoning in Vision-Language-Action Models
Nastaran Darabi, Amit Ranjan Trivedi
Vision language action (VLA) models enable generalist robotic agents but often exhibit language ignorance, relying on visual shortcuts and remaining insensitive to instruction chan…
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…
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…
Stability-Aligned Residual Adaptation for Rapid Recovery from Dynamics Shifts
Nethmi Jayasinghe, Diana Gontero, Francesco Migliarba +4
Robotic systems deployed beyond controlled environments encounter abrupt and unobserved dynamics shifts that induce substantial transient performance loss, even when the nominal cl…
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…
TRACER: Trajectory Risk Aggregation for Critical Episodes in Agentic Reasoning
Sina Tayebati, Divake Kumar, Nastaran Darabi +3
Estimating uncertainty for AI agents in real-world multi-turn tool-using interaction with humans is difficult because failures are often triggered by sparse critical episodes (e.g.…
Cerebellar-Inspired Residual Control for Fault Recovery: From Inference-Time Adaptation to Structural Consolidation
Nethmi Jayasinghe, Diana Gontero, Spencer T. Brown +3
Robotic policies deployed in real-world environments often encounter post-training faults, where retraining, exploration, or system identification are impractical. We introduce an…
Calibrated Decomposition of Aleatoric and Epistemic Uncertainty in Deep Features for Inference-Time Adaptation
Divake Kumar, Patrick Poggi, Sina Tayebati +3
Most estimators collapse all uncertainty modes into a single confidence score, preventing reliable reasoning about when to allocate more compute or adjust inference. We introduce U…
Causal-Guided Dimension Reduction for Efficient Pareto Optimization
Dinithi Jayasuriya, Divake Kumar, Sureshkumar Senthilkumar +3
Multi-objective optimization of analog circuits is hindered by high-dimensional parameter spaces, strong feedback couplings, and expensive transistor-level simulations. Evolutionar…
Learnable Conformal Prediction with Context-Aware Nonconformity Functions for Robotic Planning and Perception
Divake Kumar, Sina Tayebati, Francesco Migliarba +2
Deep learning models in robotics often output point estimates with poorly calibrated confidences, offering no native mechanism to quantify predictive reliability under novel, noisy…
EigenTrack: Spectral Activation Feature Tracking for Hallucination and Out-of-Distribution Detection in LLMs and VLMs
Davide Ettori, Nastaran Darabi, Sina Tayebati +4
Large language models (LLMs) offer broad utility but remain prone to hallucination and out-of-distribution (OOD) errors. We propose EigenTrack, an interpretable real-time detector…
RMT-KD: Random Matrix Theoretic Causal Knowledge Distillation
Davide Ettori, Nastaran Darabi, Sureshkumar Senthilkumar +1
Large deep learning models such as BERT and ResNet achieve state-of-the-art performance but are costly to deploy at the edge due to their size and compute demands. We present RMT-K…
EigenShield: Causal Subspace Filtering via Random Matrix Theory for Adversarially Robust Vision-Language Models
Nastaran Darabi, Devashri Naik, Sina Tayebati +3
Vision-Language Models (VLMs) inherit adversarial vulnerabilities of Large Language Models (LLMs), which are further exacerbated by their multimodal nature. Existing defenses, incl…
Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perception
Divake Kumar, Nastaran Darabi, Sina Tayebati +1
Safety-critical perception systems require both reliable uncertainty quantification and principled abstention mechanisms to maintain safety under diverse operational conditions. We…
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models
Sina Tayebati, Divake Kumar, Nastaran Darabi +3
Large Language and Vision-Language Models (LLMs/VLMs) are increasingly used in safety-critical applications, yet their opaque decision-making complicates risk assessment and reliab…
SPARC: Subspace-Aware Prompt Adaptation for Robust Continual Learning in LLMs
Dinithi Jayasuriya, Sina Tayebati, Davide Ettori +2
We propose SPARC, a lightweight continual learning framework for large language models (LLMs) that enables efficient task adaptation through prompt tuning in a lower-dimensional sp…