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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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0111 · Jun 201819922001200920182026
9 results for Mastery

Neural nets learn and forget tasks sequentially, showing promising scalability.

problem Learning and forgetting of multiple visual tasks in a sequential setting.
method Simulated sequential learning of ten related visual tasks.
result Neural nets show forward facilitation and backward interference, which are key phenomena.

Novel method diagnoses large language models' reasoning abilities.

problem Fine-grained evaluation of large language models' reasoning abilities.
method Adapting cognitive diagnosis models to LLMs, estimating mastery profiles and Q-matrix, incorporating textual information.
result Accurate parameter recovery and insights into LLMs' capabilities.

The ERI is a new index for measuring exam readiness.

problem Measuring exam readiness in a clear and actionable way.
method The ERI combines six signals derived from practice and mock tests, formalizing axioms for component maps and the composite.
result The ERI is a composite score interpretable and actionable for exam readiness.

Bayesian model identifies skill difficulties and student subgroups in engineering education.

problem Identifying and supporting diverse student needs in entry-level university engineering modules.
method Hierarchical Bayesian modeling of student response data.
result Clear patterns of skill mastery and distinct student subgroups identified.

Analyzes how diffusion models learn, revealing a spectral bias in structure mastery.

problem Understanding the learning dynamics and bias in diffusion models.
method Developed an analytical framework using a Gaussian-equivalence principle to solve gradient-flow dynamics and integrate probability-flow ODEs.
result Exposes a universal inverse-variance spectral law: high-variance structure is mastered faster than low-variance detail.