ANNEAL
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Related Articles from SNS
Sensor Quality Control and Annealing Studies of HGCAL Silicon Sensors
arXiv:2606.07278v1 Announce Type: new Abstract: We summarise Sensor Quality Control (SQC) results of non-irradiated silicon sensors for the CMS HGCAL detector, as well as the first detailed annealing campaign with a wafer-scale 120\,\textmu m (Epitaxial) sensor exposed to \(2\times10^{15}\)\,\si{n_{eq}/cm^2} at the Rhode Island Nuclear Science Center (RINSC). For the non-irradiated sensors, we present an overview of the QC workflow developed for HGCAL, including automated handling of vendor...
Two-Phase Simulated Annealing for Equitable Team Formation: Eliminating Complaints in Large Engineering Cohorts
Announce Type: new Abstract: Contribution: This paper presents a novel two-phase algorithmic approach that decouples preference satisfaction from fairness optimization in student team formation, achieving both objectives without compromise. The method applies simulated annealing -- a core materials science technique -- to an educational challenge, demonstrating pedagogical integration of administrative processes. Background: Forming effective teams in large engineering cohorts (100+...
Towards Efficient LLMs Annealing with Principled Sample Selection
arXiv:2605.31175v1 Announce Type: new Abstract: The annealing phase is a pivotal convergence stage in LLM pre-training that ultimately determines final model quality. However, effectively selecting training data during this phase remains a key challenge. Current strategies rely on empirical heuristics, such as domain filtering or context extension, which lack a principled grounding in optimization theory.
Qubit-Efficient Quantum Annealing for Stochastic Unit Commitment
arXiv:2502.15917v3 Announce Type: replace-cross Abstract: Stochastic Unit Commitment (SUC) has been proposed to manage the uncertainties driven by renewable integration, but it leads to significant computational complexity. When accelerated by Benders Decomposition (BD), the master problem becomes binary integer programming, which is still NP-hard and computationally demanding for classical methods. Quantum Annealing (QA), known for efficiently solving Quadratic Unconstrained Binary...
Ferroelectricity in dipolar liquids: the role of annealed positional disorder
arXiv:2512.05758v3 Announce Type: replace Abstract: Ferroelectric ordering in polar liquids has been observed in numerical simulations and liquid-crystal experiments. Within mean-field framework, this behaviour remains associated with sample-shape dependent, surface contribution to the free energy, which does not vanish in the thermodynamic limit due to the long-range nature of dipolar interaction. Yet, numerical simulations performed under conducting periodic boundary conditions, for which...
ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning
arXiv:2605.16309v2 Announce Type: replace Abstract: LLM-based agents can recover from individual execution errors, yet they repeatedly fail on the same fault when the underlying process knowledge--operator schemas, preconditions, and constraints--remains unrepaired. Existing self-evolving approaches address this gap by updating prompts, memory, or model weights, but none directly repair the symbolic structures that encode how tasks are executed, and few provide the governance guarantees...
Scalable Inference-Time Annealing with Surrogate Likelihood Estimators
Announce Type: new Abstract: A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generative modeling have been proposed to address the limitations of conventional sampling techniques by eliminating the computational cost of simulation. A promising direction is iteratively finetuning diffusion models along a temperature ladder whereby training data is generated via importance sampling during...
Towards interpretable AI with quantum annealing feature selection
arXiv:2604.25649v2 Announce Type: replace Abstract: Deep learning models are used in critical applications, in which mistakes can have serious consequences. Therefore, it is crucial to understand how and why models generate predictions. This understanding provides useful information to check whether the model is learning the right patterns, detect biases in the data, improve model design, and build systems that can be trusted.
Scalable Inference-Time Annealing with Surrogate Likelihood Estimators
Announce Type: replace Abstract: A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generative modeling have been proposed to address the limitations of conventional sampling techniques by eliminating the computational cost of simulation. A promising direction is iteratively finetuning diffusion models along a temperature ladder whereby training data is generated via importance sampling during...
Annealed Softmax Greedy in Many-Armed Bayesian Bandits
Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) and group-based policy optimization methods such as GRPO update a stochastic policy by sampling multiple completions per prompt and increasing the policy's probability on those with higher reward, regularized by a KL penalty toward a reference policy. These updates do not include explicit mechanisms that track epistemic uncertainty. This paper studies a stylized explanation for why such uncertainty-agnostic...