Develops a unified quadratic framework for ownership concentration with exact row/column decompositions, benchmark-adjusted dependence, multiscale aggregation, spectral characterizations, and dynamic bounds on fire-sale vulnerability and alpha variance.
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8 Pith papers cite this work, alongside 3,978 external citations. Polarity classification is still indexing.
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On 2,520 programming tasks, matched Qwen general and coder models reliably raise Bloom cognitive demand but fail to lower it, so execution skill does not imply educational control.
Shifted Legendre polynomials turn Simple Jumper martingales into multi-moment exchangeability tests, with a mean-field variational form that avoids exponential state-space cost.
In real-world repositories, AI-assisted and human-written code differ only modestly on code-level metrics, while commit size, stability, duplication, and language-specific security show clearer patterns.
A code-owned harness enforces source, routing, trace, hygiene, and recommendation contracts for enterprise LLM agents; prompt-only fails and bolt-on guardrails over-refuse.
Backdoor-adjusted ATEs on 21,098 UK Biobank participants showed total femur BMC and BMD with the largest hip fracture risk reductions (-0.0047 per SD), and adding the top 11 phenotypes to clinical variables raised AUC to 0.842 versus FRAX 0.709.
LLM-generated feedback was associated with faster time to solution for programming students than compiler messages alone, with less-guided versions showing slightly stronger effects.
FDRS combines digit frequency tests, association metrics, entropy, KL divergence, and ML models to assign risk grades to numerical datasets, showing separation between normal and irregular simulated data with high AUC.
citing papers explorer
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A Unified Theory of Ownership Concentration, Overlap, and Dependence
Develops a unified quadratic framework for ownership concentration with exact row/column decompositions, benchmark-adjusted dependence, multiscale aggregation, spectral characterizations, and dynamic bounds on fire-sale vulnerability and alpha variance.
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From Execution to Education: A Bloom-Aligned Framework for Measuring Educational Control in LLMs
On 2,520 programming tasks, matched Qwen general and coder models reliably raise Bloom cognitive demand but fail to lower it, so execution skill does not imply educational control.
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Betting on Moments: Legendre Jumper Martingales for Online Exchangeability Testing
Shifted Legendre polynomials turn Simple Jumper martingales into multi-moment exchangeability tests, with a mean-field variational form that avoids exponential state-space cost.
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A Large-Scale Comprehensive Measurement of AI-Generated Code in Real-World Repositories
In real-world repositories, AI-assisted and human-written code differ only modestly on code-level metrics, while commit size, stability, duplication, and language-specific security show clearer patterns.
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From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents
A code-owned harness enforces source, routing, trace, hygiene, and recommendation contracts for enterprise LLM agents; prompt-only fails and bolt-on guardrails over-refuse.
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DXA-Derived Skeletal Phenotypes and Hip Fracture Risk: A Backdoor-Adjusted Causal Analysis
Backdoor-adjusted ATEs on 21,098 UK Biobank participants showed total femur BMC and BMD with the largest hip fracture risk reductions (-0.0047 per SD), and adding the top 11 phenotypes to clinical variables raised AUC to 0.842 versus FRAX 0.709.
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The Effects of Structured LLM-Generated Feedback on Programming Assignment Performance
LLM-generated feedback was associated with faster time to solution for programming students than compiler messages alone, with less-guided versions showing slightly stronger effects.
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A machine-learning-assisted progressive digit-randomness screening framework for detecting non-random patterns in raw numerical research data
FDRS combines digit frequency tests, association metrics, entropy, KL divergence, and ML models to assign risk grades to numerical datasets, showing separation between normal and irregular simulated data with high AUC.