Sina Tayebati — accepted AI-conference papers

2026

EigenShield: Inference-Time, Model-Agnostic Jailbreaking Defense via Causal Subspace Filtering

Nastaran Darabi, Devashri Naik, Sina Tayebati, Dinithi Jayasuriya, Ranganath Krishnan, Amit Ranjan Trivedi

AAAI 2026technical

Large Language Models (LLMs) and Vision-Language Models (VLMs) remain highly vulnerable to adversarial attacks despite widespread adoption. Existing defenses typically require retraining, rely on heuristics, or fail under adaptive and out-of-distribution (OOD) conditions. We introduce EigenShield, a

Cited by 0 Source ↗PDF ↗Scholar ↗

2026

Learnable Conformal Prediction for Safe and Efficient Robotics under Perception and Planning Uncertainties

Divake Kumar, Sina Tayebati, Francesco Migliarba, Ranganath Krishnan, Amit Ranjan Trivedi

ICRA 2026poster

Deep learning models in robotics often output point estimates with poorly calibrated confidences, offering no native mechanism to quantify predictive reliability under novel, noisy, or out-of-distribution inputs. Conformal prediction (CP) addresses this gap by providing distribution-free coverage gu…

Cited by 0 Scholar ↗

2026

Resilience in Ambient Multi-Agent LLMs via Decentralized Bio-Autonomic Control and Immune-Inspired Anomaly Detection

Nastaran Darabi, Devashri Naik, Sina Tayebati, Dinithi Jayasuriya, Amit Ranjan Trivedi

AAAI 2026technical

Large Language Model (LLM) agents are now widely deployed in Ambient Intelligence (AmI) environments, where autonomous agents must sense, act, and coordinate at scale. As agent capabilities and interdependence increase, traditional reliability strategies such as isolated adaptive control, anomaly de

Cited by 0 Source ↗PDF ↗Scholar ↗

2026

TRACER: Trajectory Risk Aggregation for Critical Episodes in Agentic Reasoning

Sina Tayebati, Divake Kumar, Nastaran Darabi, Davide Ettori, Ranganath Krishnan, Amit Trivedi

ICML 2026poster

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., looping, incoherent tool use, or user-agent miscoordination) even when local generation appears confident. Existing un…

Cited by 0 Source ↗Scholar ↗

2025

Generative Sensing: Pre-training LiDAR with Masked Autoencoders for Ultra-Frugal Perception

Sina Tayebati, Theja Tulabandhula, Amit Ranjan Trivedi

ICASSP 2025accepted

We propose a disruptively frugal generative sensing approach for LiDAR that generates, rather than senses, parts of the environment that are either predictable based on extensive training or have limited impact on overall prediction accuracy. Our generative pre-training strategy for this purpose, ra…

Cited by 0 Source ↗Scholar ↗