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20222026
most citedCODiT: Conformal Out-of-Distribution Detection in Time-Series Data

3 citations · 3 across the 20 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

Consilience: Conformally Calibrated Communication Control for Hidden-Profile Multi-Agent Reasoning

Abhijith Babu, Ramneet Kaur, Vishal Pramanik +7

Multi-agent LLM systems can improve reasoning by pooling diverse perspectives, but their effectiveness depends on coordinating communication, particularly in hidden-profile setting…

cs.AI2026

Closed-Loop Neural Activation Control in Vision-Language-Action Models

Abhijith Babu, Ramneet Kaur, Nathaniel D. Bastian +5

Vision-Language-Action (VLA) models can be steered at test time by intervening on semantically meaningful internal directions, but existing methods use a fixed steering coefficient…

cs.AI2026

From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents

Trilok Padhi, Ramneet Kaur, Krishiv Agarwal +9

Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments. Despite their growing capabi…

cs.LG2026

Do Diffusion Models Dream of Electric Planes? Discrete and Continuous Simulation-Based Inference for Aircraft Design

Aurelien Ghiglino, Daniel Elenius, Anirban Roy +7

In this paper, we generate conceptual engineering designs of electric vertical take-off and landing (eVTOL) aircraft. We follow the paradigm of simulation-based inference (SBI), wh…

cs.CR2026

Trojans in Artificial Intelligence (TrojAI) Final Report

Kristopher W. Reese, Taylor Kulp-McDowall, Michael Majurski +68

The Intelligence Advanced Research Projects Activity (IARPA) launched the TrojAI program to confront an emerging vulnerability in modern artificial intelligence: the threat of AI T…

cs.LG2026

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions

Noah Schwartz, Chandra Kanth Nagesh, Sriram Sankaranarayanan +3

We present a generalized framework for the range verification of neural networks featuring non-linear activation functions. Our approach first constructs an ``optimized piecewise a…