7 papers
Certified Access: Test-Time Compute on Conservative Physics-Structured AI Memory
Pratik Jawahar, Riccardo Maggioni, Jonas Petersen +5
Test-time compute mechanisms, such as retries, confidence-based escalation and non-local routing, are conventionally deployed as black-box additions to trained models. They typical…
KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting
Jonas Petersen, Pratik Jawahar, Riccardo Maggioni +6
Forecasting rare, high-cost events in multivariate time-series systems is hard because their precursors are seldom observed, so a purely data-driven model has little to learn from.…
HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series
Jonas Petersen, Gian-Alessandro Lombardi, Riccardo Maggioni +3
Critical events in multivariate time series, from turbine failures to cardiac arrhythmias, demand accurate prediction, yet labeled data is scarce because such events are rare and c…
FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models
Karim Othman, Pratik Jawahar, Jonas Petersen +14
Industrial machines are actuated systems, yet most open time-series corpora record sensor outcomes alone; the few that do log commanded signals expose them in vendor-specific forms…
FactoryBench: Evaluating Industrial Machine Understanding
Yanis Merzouki, Coral Izquierdo, Matei Ignuta-Ciuncanu +8
We introduce FactoryBench, a benchmark for evaluating time-series models and LLMs on machine understanding over industrial robotic telemetry. Q&A pairs are organized along four cau…
What Structural Inductive Bias Helps Transformers Reason Over Knowledge Graphs? A Study with Tabula RASA
Jonas Petersen, Camilla Mazzoleni, Gian-Alessandro Lombardi +2
What structural inductive bias helps transformers reason over knowledge graphs? Through controlled ablations of a minimal transformer modification with four independently removable…
Distributed Time-Varying Gaussian Regression via Kalman Filtering
Nicola Taddei, Riccardo Maggioni, Jaap Eising +2
We consider the problem of learning time-varying functions in a distributed fashion, where agents collect local information to collaboratively achieve a shared estimate. This task…