Claims Browser

Successive Capacity Growth (SCG) achieves 20.3% improvement in prediction loss over fixed small baseline with 56 times greater parameter efficiency on multi-object dynamics tasks

"On a 60-dimensional multi-object dynamics task, SCG naturally triggers depth expansion, improving prediction loss by 20.3% over the fixed small baseline with 56 times greater parameter efficiency than scaling to the fixed large model"

SCG achieves 23% improvement over fixed large models on 2D navigation tasks with a single width expansion

"on a 2D navigation task, a single width expansion yields even an 23% improvement over the fixed large model"

JEPA world model encoders can grow successively as tasks demand rather than being pre-allocated at maximum capacity, achieving significant compute and data efficiency

"JEPA world model encoders need not be pre-allocated at maximum capacity - they can grow successively as the task demands, achieving significant compute and data efficiency while maintaining representation quality"

The Sketched Isotropic Gaussian Regularizer (SIGReg) prevents representation collapse by ensuring all learned semantic dimensions remain statistically independent and aligned with the predictive objective

"The Sketched Isotropic Gaussian Regularizer (SIGReg) ensures that all learned semantic dimensions remain statistically independent and aligned with the predictive objective, preventing collapse even as the architecture grows"

SCG's adaptive encoder matches or exceeds fixed small baseline across all tested environments with zero false-positive expansions and bit-exact function preservation

"Across all three tested environments of increasing complexity, the adaptive encoder matches or exceeds the fixed small baseline, with zero false-positive expansions and bit-exact function preservation (ratio = 1.0, absolute difference = 0.0)"

Generative AI is transforming how people access information and challenging traditional advertising mechanisms

"Generative AI is transforming how people access information, challenging traditional advertising mechanisms built around predefined slots."

LAMA improves platform welfare and revenue while maintaining user-facing response quality in commercial search applications

"Proof-of-concept experiments on real-world commercial-search query splits show that LAMA improves platform welfare and revenue while maintaining user-facing response quality"

Generation-native advertising through token-level mechanisms is feasible

"providing initial evidence for the feasibility of generation-native advertising"

QuantumBoostNet, a hybrid classical-quantum architecture with a 10-qubit quantum circuit, consistently outperforms state-of-the-art classical and hybrid classical-quantum models in cardiac ultrasound view identification

"Extensive experiments indicate that, despite the limited number of qubits that can be simulated, QuantumBoostNet consistently outperforms state-of-the-art classical and hybrid classical-quantum models in cardiac ultrasound view identification, achieving a relative improvement over the best competitor."

QuantumBoostNet demonstrates superior performance on established image classification benchmarks and exhibits robustness to noise

"QuantumBoostNet also demonstrates superior performance on established image classification benchmarks and exhibits robustness to noise."

The findings support the continued development of hybrid classical-quantum models for specialized medical imaging applications

"These findings support the continued development of hybrid classical-quantum models for specialized medical imaging applications."

A smartphone-based safety-check system using image capture and drug reference matching can provide allergy alerts in resource-constrained hospital settings

"This paper proposes and outlines the evaluation of a lightweight, smartphone-based safety-check system for this setting. At registration a soft identifier (a phone number) is recorded; after the physician writes a prescription, its image is captured, the brand names are resolved to active ingredients using national drug references, and the ingredients are matched against the patient's recorded severe reaction history."

Retrieval-based systems that are silent by default and only flag high-risk matches are more appropriate than predictive systems in high-volume clinical settings due to alert fatigue concerns

"The system is retrieval-based rather than predictive, and is silent by default, raising a flag only for high-risk matches a design grounded in the alert-fatigue literature."

The low base rate of severe adverse drug events places clinical-outcome effects beyond the scope of a single-site feasibility study

"We explicitly do not claim a clinical-outcome effect, which the low base rate of severe events places beyond a single-site feasibility study."

Neural operators have demonstrated broad empirical success at approximating solution operators of PDEs from data

"Neural operators have demonstrated broad empirical success at approximating such maps from data."

Most existing neural operator architectures enforce boundary conditions indirectly through training from data even though the boundary condition is often known exactly

"most existing neural operator architectures enforce boundary conditions indirectly through training from data even though the boundary condition is often known exactly"

Existing modifications that enforce boundary conditions explicitly suffer from impractical restrictions including boundary smoothness, uniform grids, and separable box-like domains

"existing modifications and approaches that do enforce boundary conditions explicitly suffer from impractical restrictions, including boundary smoothness, uniform grids, and separable, box-like domains"

The proposed architecture satisfies homogeneous Dirichlet boundary conditions independently of training whilst retaining expressivity of existing kernel-integral neural operators

"we propose an architecture which, independently of training, satisfies homogeneous Dirichlet boundary conditions, whilst simultaneously retaining the expressivity of existing kernel-integral neural operator architectures"

The proposed method is applicable to arbitrary mesh data and general geometries by requiring only that the output domain be bounded with Lipschitz boundary

"The method requires only that the output domain be bounded with Lipschitz boundary and places no restriction on the choice of discretization, making it applicable to arbitrary mesh data and general geometries."