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Related Articles from SNS
Multi-Agent Temporal Logic Planning via Penalty Functions and Block-Coordinate Optimization
arXiv:2602.17434v2 Announce Type: replace Abstract: Multi-agent planning under Signal Temporal Logic (STL) is often hindered by collaborative tasks that lead to computational challenges due to the inherent high dimensionality of the problem, preventing scalable synthesis with satisfaction guarantees. To address this, we formulate STL planning as an optimization program under multi-agent STL constraints and introduce a penalty-based unconstrained relaxation that can be efficiently solved via...
pacSTL: PAC-Bounded Signal Temporal Logic from Data-Driven Reachability Analysis
Announce Type: replace Abstract: Signal Temporal Logic (STL) is an expressive language for specifying behaviors of dynamical systems from continuous signals. However, a limitation of standard STL is its inherently deterministic semantics, which prevents it from accommodating uncertainty. Existing approaches to overcome this limitation are computationally costly and limit real-time capability, requiring repeated trajectory sampling or the redesign of probability distributions over atomic...
On the Stability and Realizability of Recurrent Polynomial Surrogate Ternary Logic Gate Networks
arXiv:2605.24649v1 Announce Type: cross Abstract: Recurrent Neural Networks (RNNs) can learn to predict Signal Temporal Logic (STL) verdicts online from partial trajectories, but deploying them as runtime monitors in safety-critical systems demands more than predictive accuracy. Standard RNN architectures offer no structural guarantee that outputs degrade gracefully under sensor degradation; a dropped input can silently flip a verdict from safe to unsafe. We introduce the Recurrent...
Fantasy Baseball Forecaster for Week 10: June 1-7
Week 10 marks the turn of the calendar to June. The first week of the month brings a relatively quiet 93-game week, beginning with nine evening affairs on Monday. Tuesday features 15 games under the lights, with the first matinee set for Wednesday.
Central Description Length (CDL) Clustering Validation Index
arXiv:2606.05230v1 Announce Type: cross Abstract: Selecting a clustering algorithm and its hyperparameters without labels is a common difficulty in engineering machine learning pipelines that work with unsupervised analysis of sensor, image, or process data. Clustering validation indices (CVIs) provide internal scores for ranking candidate clusterings, but most popular CVIs are built from Euclidean compactness and separation terms and so tend to favour compact, convex partitions.
PECKER: A Precisely Efficient Critical Knowledge Erasure Recipe For Machine Unlearning in Diffusion Models
Announce Type: replace Abstract: Machine unlearning (MU) has become a critical technique for GenAI models' safe and compliant operation. While existing MU methods are effective, most impose prohibitive training time and computational overhead. Our analysis suggests the root cause lies in poorly directed gradient updates, which reduce training efficiency and destabilize convergence.
Compatibility and Accuracy Verification of CADmesh-Based Complex Geometry Modeling in Geant4
arXiv:2606.06508v1 Announce Type: new Abstract: Geant4 Monte Carlo simulation relies on the Constructive Solid Geometry (CSG) method for complex geometric modeling. This method has low efficiency and a high application threshold. Importing triangular facet formats such as STL/OBJ via CADmesh is a promising alternative, but systematic evaluations of format compatibility, geometric accuracy, and physical simulation deviations are lacking.
Concepts in Practice: C++ MPI Bindings for the HPC Ecosystem. From a Standardizable Core to a Composable Interface
arXiv:2606.09102v1 Announce Type: new Abstract: The official C++ MPI bindings were removed from the standard in 2008, leaving a gap that numerous third-party libraries have attempted to fill. However, existing wrappers typically cover only a limited subset of MPI or target specific use cases, falling short of a general-purpose solution.
Can Local Learning Match Self-Supervised Backpropagation?
arXiv:2601.21683v2 Announce Type: replace Abstract: While end-to-end self-supervised learning with backpropagation (global BP-SSL) has become central for training modern AI systems, theories of local self-supervised learning (local-SSL) have struggled to build functional representations in deep neural networks. To establish a link between global and local rules, we first develop a theory for deep linear networks: we identify conditions for local-SSL algorithms (like Forward-forward or CLAPP)...
C++: The Documentary
C++: The Documentary premiered today on YouTube, and it was great to be on the live chat with Bjarne and many other key folks who participated in C++’s history. I’m honored to have been one of hundreds of people who have played a part in advancing Bjarne’s wonderful project over the years. If you haven’t watched this yet, make it a weekend goal.