13 stories tagged with #stochastic, in publish-time order across the WeSearch catalog. Tag pages update as new stories ingest.
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Why Gradient Descent Became Stochastic
A step-by-step journey from calculus-based optimization to Stochastic Gradient Descent…
Formalizing a Divergence Monitor: Stochastic Implementation of Phase Divergence and Invisible Moves
Modeling Agentic Technical Debt and Stochastic Tax: A Standalone Framework for Measurement, Simulation, and Dashboarding
Agentic AI systems combine probabilistic reasoning with delegated action through tools, context, memory, orchestration, and external workflow integration. This note develops a form…
Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling
Test-time scaling improves language model reasoning by spending additional compute to explore multiple solution trajectories. The key challenge is to maximize accuracy while minimi…
Boosting Inference with Guided Reasoning: Stochastic Exploration for Recursive Models
Recent work on recursive architectures has shown that tiny neural networks can be surprisingly powerful on structured reasoning tasks. The trick is to model reasoning trajectories …
# Mitigating Market Inefficiency in eSports: A Stochastic Approach to EA Sports FC25 Modeling
### By Bettrails Data Lab *Technical Classification: Data Science / Predictive Modeling / Sports...…
SFQ: Simple, Stateless, Stochastic Fairness
Correcting Stochastic Update Bias in Preconditioned Language Model Optimizers
Preconditioned optimizers are central to language model training, but their stochastic update rules are usually treated as direct approximations to population preconditioned descen…
Benders’ Decomposition 101: How to Crack Open a Stochastic Program That’s Too Big to Swallow Whole
Whenever you can rewrite an optimization problem so that fixing some variables makes the rest separable, you could try Benders.…
Not all uncertainty is alike: volatility, stochasticity, and exploration
Adaptive decision-making in biological and artificial intelligence requires balancing the exploitation of known outcomes with the exploration of uncertain alternatives. Although pr…
LAST-RAG: Literature-Anchored Stochastic Trajectory Retrieval-Augmented Generation for Knowledge-Conditioned Degradation Model Selection
Stochastic-process-based degradation modeling is a core approach for estimating the distribution of remaining useful life (RUL); however, the selection of an appropriate stochastic…
Residual Reinforcement Learning for Robot Teleoperation under Stochastic Delays
Stochastic communication delays in teleoperation introduce signal discontinuities that undermine control stability and degrade control performance. Consequently, the conventional r…
Stochastic Flocks and the Critical Problem of 'Useful' AI
Acknowledging that AI systems are advancing does not buy into hype, it sharpens the precision of critical thinking about their impacts, says Eryk Salvaggio.…