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Related Articles from SNS
Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning
Announce Type: replace Abstract: Diffusion models generate samples through an iterative denoising process guided by a pretrained neural network. Once the denoiser is fixed, the sampling algorithm itself (noise schedules, guidance scales, stochasticity profiles) still requires careful tuning, a process typically carried out through costly empirical grid search. In this work, we introduce an inverse reinforcement learning framework for learning sampling strategies without retraining the denoiser.
Predictable Scaling Laws of Optimal Hyperparameters for LLM Continued Pre-training
arXiv:2606.05610v1 Announce Type: new Abstract: The efficacy of continued pre-training for Large Language Models (LLMs) hinges upon hyperparameter configurations, such as learning rate and batch size. However, current practices often rely on heuristics or grid searches, leading to training instability and excessive costs. In this work, we first empirically discover that optimal hyperparameters follow stable and predictable scaling laws throughout the continued pre-training process.
ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization
arXiv:2603.21180v4 Announce Type: replace Abstract: Sequential experimental design under expensive, gradient-free objectives is a central challenge in computational statistics: evaluation budgets are tightly constrained and information must be extracted efficiently from each observation. We propose \textbf{ALMAB-DC}, a GP-based sequential design framework combining active learning, multi-armed bandits (MAB), and distributed asynchronous computing for expensive black-box experimentation. A...
Hybrid Metaheuristic Combining the Dragonfly Algorithm and Tabu Search for the Traveling Salesman Problem
Announce Type: new Abstract: The Traveling Salesman Problem (TSP) is a classical NP-hard combinatorial optimization problem that aims to find the shortest Hamiltonian cycle visiting each city exactly once and returning to the starting point. This paper proposes a hybrid metaheuristic for the TSP by combining the Dragonfly Algorithm (DA), a swarm-intelligence-based global search method, with Tabu Search (TS), a memory-based local search technique. The proposed method follows a High-Level...
Police 'open' to learning from volunteers who found remains in renewed search
Remains were found yesterday during a renewed search for missing Scottsdale man Peter Willoughby. The group of volunteer searchers that made the discovery also found the remains of missing Belgian tourist Celine Cremer earlier this year. Tasmania Police has defended the initial search effort, and Inspector Aleena Crack says officers are open to discussions with the volunteer group "as to what their methods were".
PF$\Delta$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations
Announce Type: replace Abstract: Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under outages) and topology optimization (which involves PF-based searches over combinatorially large action spaces). Running these calculations at operational timescales or across large evaluation spaces remains a major computational bottleneck. Additionally, growing uncertainty in power...
Multi-feature Classification to Improve Colorimetric Loop-Mediated Isothermal Amplification Fidelity
Loop-mediated isothermal amplification (LAMP) is a cost-effective and portable assay technique for performing nucleic acid-based diagnostics in the field whose adoption is hindered by design and reproducibility issues. This is due to a complex primer design process that fine-tunes parameters across 6-8 binding regions. The likelihood of assay success depends on satisfying thermodynamic and secondary structure constraints while maintaining target specificity and avoiding overlaps between...
Short-Term Synaptic Plasticity Stabilizes Goal-Conditioned Dynamics in a PFC-Inspired Reservoir Model for Multistep Goal-Directed Action Planning
arXiv:2606.03481v1 Announce Type: cross Abstract: The prefrontal cortex (PFC) maintains goal information for action planning, but how recurrent circuits preserve it in an action-usable form over behavioral timescales remains unclear. Here we ask whether short-term synaptic plasticity (STP) can stabilize goal information as action-usable, goal-conditioned dynamics. We incorporated STP into a PFC-inspired reservoir computing model with basal-ganglia-inspired temporal-difference readout...
Optimization of EUV output by experimentally validated radiation-hydrodynamic simulations across a broad laser parameter space
arXiv:2606.05948v1 Announce Type: new Abstract: Practical requirements such as improving wall-plug efficiency and reducing system footprint have become increasingly important with the introduction of extreme ultraviolet (EUV) lithography into high-volume semiconductor manufacturing. These demands motivate the development of solid-state mid-infrared lasers as alternatives to current CO2 lasers. Systematic exploration of laser-to-EUV conversion efficiency (EUV-CE) over a broad parameter space...
Reinforcement Learning-Enabled Agent for Transmitter Optimization in Digital-Analog Radio-over-Fiber Fronthaul
arXiv:2606.04840v1 Announce Type: new Abstract: Digital-analog radio-over-fiber (DA-RoF) has emerged as a promising fronthaul solution that combines the high spectral efficiency of analog transmission with the robustness of digital transmission. However, the performance of DA-RoF critically depends on several tightly coupled parameters, including the rounding factor (RF), scaling factor (SF), geometric shaping (GS) factor, and pre-equalization taps coefficients, which jointly affect...