Publications — Som Sagar | Failure Diagnosis and Evaluation of Foundation Models

Papers by Som Sagar on failure diagnosis, red teaming, and evaluation of foundation models, vision-language models, and robot manipulation policies. See also Google Scholar and the CV.

Conference papers

RoboMD: Uncovering Robot Vulnerabilities through Semantic Potential Fields

Som Sagar, Jiafei Duan, Sreevishakh Vasudevan, Yifan Zhou, Heni Ben Amor, Dieter Fox, Ransalu Senanayake
International Conference on Learning Representations (ICLR), 2026

RoboMD trains a deep reinforcement learning policy to navigate a vision-language embedding space, structured as a potential field of successes and failures, to discover vulnerabilities in robot manipulation policies without real-world trials — up to 23% more unique vulnerabilities than vision-language baselines.

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PAC Bench: Do Foundation Models Understand Prerequisites for Executing Manipulation Policies?

Atharva Gundawar*, Som Sagar*, Ransalu Senanayake
Conference on Neural Information Processing Systems (NeurIPS), 2025

PAC Bench is a benchmark of over 30,000 annotations that tests whether vision-language models understand the object Properties, action Affordances, and physical Constraints required for reliable robot manipulation.

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Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models

Som Sagar, Aditya Taparia, Ransalu Senanayake
International Conference on Machine Learning (ICML), 2024 — Spotlight (top 3.5%)

A deep reinforcement learning method that explores and maps the failure landscape of large-scale vision and language models, then restructures that landscape with limited human feedback to mitigate accuracy failures, social biases, and misalignment.

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Trustworthy Explanations for Robot Behaviors

Som Sagar*, Aditya Taparia*, Harsh Mankodiya, Pranav Bidare, Yifan Zhou, Ransalu Senanayake
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025

BaTCAVe is a Bayesian concept-based explainability method that produces human-interpretable explanations of robot decisions together with calibrated uncertainty scores, enabling trustworthy post-hoc diagnosis of neural network policies.

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Explainable Concept Generation through Vision-Language Preference Learning for Understanding Neural Networks' Internal Representations

Aditya Taparia, Som Sagar, Ransalu Senanayake
International Conference on Machine Learning (ICML), 2025

RLPO frames concept-based explanation as an image generation problem and uses reinforcement learning preference optimization to fine-tune a vision-language generative model, automatically producing concept images that reveal what a neural network has internally learned.

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ExpressivityArena: Can LLMs Express Information Implicitly?

Joshua Tint, Som Sagar, Aditya Taparia, Caleb Liu, Kelly Raines, Bimsara Pathiraja, Ransalu Senanayake
Findings of the Association for Computational Linguistics: EACL, 2026

ExpressivityArena is an information-theoretic framework for evaluating how well large language models communicate tone, emotion, identity, and intent implicitly, across nine tasks — revealing that models handle affective content well but lag behind human baselines on sociolinguistic signals.

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Strategic Vantage Selection for Learning Viewpoint-Agnostic Manipulation Policies

Sreevishakh Vasudevan, Som Sagar, Ransalu Senanayake
IEEE International Conference on Robotics and Automation (ICRA), 2026

Vantage selects a small, strategic set of camera poses for fine-tuning, formulating camera placement as an information-gain optimization so that pre-trained manipulation policies become robust to viewpoint shifts at deployment — raising task success by 25% for diffusion policies.

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Workshop papers

From Mystery to Mastery: Failure Diagnosis for Improving Manipulation Policies

Som Sagar, Jiafei Duan, Sreevishakh Vasudevan, Yifan Zhou, Heni Ben Amor, Dieter Fox, Ransalu Senanayake
Robotics: Science and Systems (RSS) Workshop on Out-of-Distribution Generalization in Robotics, 2025

A deep reinforcement learning framework that systematically diagnoses failure modes of robot manipulation policies under unseen environmental variations, and uses the discovered vulnerabilities to fine-tune and improve policy robustness.

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LLM-Assisted Red Teaming of Diffusion Models through “Failures Are Fated, But Can Be Faded”

Som Sagar, Aditya Taparia, Ransalu Senanayake
NeurIPS Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?, 2024

An extension of the Failures Are Fated framework to text-to-image diffusion models with LLM-generated rewards and states, action screening inspired by design of experiments, and a comparison of DQN, PPO, and A2C for red teaming.

article Details picture_as_pdf arXiv

About the author. Som Sagar is a computer science PhD student at Arizona State University, advised by Ransalu Senanayake in the LENS Lab. He works on reinforcement learning for post-training and agentic LLM systems, and on failure diagnosis, red teaming, and safety evaluation of foundation models, vision-language models, and robot manipulation policies.

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