The Labeling Problem in Hallucination Detection Benchmarks: An Empirical Evaluation

Jorma Valjakka, Juhani Kivimäki, Juha Mylläri, Jukka K Nurminen

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

Filter Banks: from Low-Rank Representations to Deep Models for Efficient Time Series Forecasting

Ashutosh Vaishnav, Mohsen Amidzadeh, Teemu Kämäräinen, Matti Siekkinen and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Avoiding Feature Collapse in Graph ODEs via Hysteretic Topology Evolution

Qinhan Hou, Jing Tang

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

Function graph transformers universally approximate operators between function spaces

Function graph transformers lift functions to graph measures to universally approximate nonlinear operators between function spaces via standard attention and MLPs.

Takashi Furuya, S D Mis, Ivan Dokmanić, Maarten V. de Hoop and 1 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting

Njord is a probabilistic graph neural network that produces efficient ensemble ocean forecasts via single-pass sampling, achieving lowest average upper-ocean errors globally and in the Baltic Sea.

Daniel Holmberg, Joel Oskarsson, Erik Wikingsson, Fredrik Lindsten and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Learning Energy-Based Models from Stochastic Interpolants using Spatiotemporal Differences

Spatiotemporal Noise-Contrastive Estimation learns energy-based models via joint spatiotemporal differences to avoid failure modes of spatial or temporal methods alone, matching state-of-the-art density estimation.

Hanlin Yu, RuiKang OuYang, Partha Kaushik, Arto Klami and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps

SCALLOP introduces a Hutchinson-free likelihood distillation objective for few-step Boltzmann generators, reducing training variance and time while achieving up to 10x inference speedup.

RuiKang OuYang, Hanlin Yu, Xinyue Ai, Yutong He and 6 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026