Claims Browser

In realistic conditions with self-selection and drift effects, compositional online learning achieves approximately 8x improvement on LLM query costs

"Self-selection at the cascade boundary, sample-budget shrinkage, and selectivity-estimation drift reduce it to a realistic figure near $8\times$."

The high latency of LLM calls inverts a classical constraint in adaptive query processing where online learners had to stay lightweight

"The latency window inverts a design constraint of classical adaptive query processing, where online learners had to stay lightweight to avoid dominating the predicates they optimize."

Vision generative AI has emerged as one of the most rapidly advancing areas of deep learning

"Vision generative artificial intelligence (AI) has emerged as one of the most rapidly advancing areas of deep learning."

Event-based object classification using Spiking Neural Networks on neuromorphic devices can solve issues of size, weight, power consumption, security, latency, and connectivity present in conventional frame-based computer vision approaches

"The use of Spiking Neural Networks (SNNs) hosted on neuromorphic devices attempts to solve several issues present in this conventional approach."

Enterprise AI deployment is fundamentally a coordination problem across multiple organizational functions including business units, application teams, AI teams, testing, platform engineering, infrastructure, security, operations, and data governance.

"Enterprise AI deployment is a coordination problem across business units, application and AI teams, testing, platform engineering, infrastructure, security, operations, and data governance."

Current use-case benchmarks are insufficient because they only measure whether one agent completes one task, but fail to capture how changing capabilities, models, runtime mechanisms, capacity, and enterprise data should be managed organizationally.

"Use-case benchmarks show whether one agent completes one task, but not how changing capabilities, models, runtime mechanisms, capacity, and enterprise data should be owned, changed, admitted, or evidenced together."

A runtime architecture based on four responsibility objects (Skill, Harness, Scaffold, and external data substrate) can serve as shared organizational contracts for enterprise AI deployment.

"We present four responsibility objects as shared organizational contracts: Skill (reusable, versioned capability and workflow asset), Harness (runtime compiler and governor), Scaffold (execution/control boundary and NFR owner), and a stack-external data substrate under independent CIO-governed semantics and telemetry."

Within a declared operating region, it is possible to achieve cost-aware capability-capacity separability where changing activated capability preserves capacity-response interaction within a preregistered equivalence margin, and changing compatible Scaffold capacity preserves capability semantics up to a non-inferiority margin.

"within a declared operating region, changing activated capability preserves the capacity-response interaction within a preregistered equivalence margin, while changing compatible Scaffold capacity preserves capability semantics up to a non-inferiority margin, and the required controls stay within a declared enforcement budget."

A cluster-period randomized crossover experiment with balanced order, reset/washout, repeated seeds and failure regimes can produce a four-state verdict (supported, falsified, conditional-engineering, or inconclusive) for testing the cost-aware capability-capacity separability hypothesis.

"We propose a cluster-period randomized crossover experiment (balanced order, reset/washout, repeated seeds and failure regimes, cluster-aware uncertainty) with a four-state verdict: supported, falsified, conditional-engineering, or inconclusive."

The proposed framework has no completed implementation, experiment, dataset, or measured results yet.

"It reports no completed implementation, experiment, dataset, or measured result."

GASHE (Gradient-Aware Selective Homomorphic Encryption) dynamically encrypts only gradient components exceeding a DP-calibrated sensitivity threshold, improving efficiency over uniform encryption schemes

"GASHE (Gradient-Aware Selective Homomorphic Encryption), a novel selective encryption strategy that dynamically identifies and encrypts only the gradient components exceeding a DP-calibrated sensitivity threshold, rather than encrypting all parameters uniformly as in static layer-based or full-parameter CKKS schemes"

SecureDrive-FL creates the first closed-loop DP+HE privacy pipeline by coupling DP-SGD with GASHE, unifying training-time privacy and communication-time confidentiality

"SecureDrive-FL, a federated driver monitoring framework that couples DP-SGD with GASHE to create the first closed-loop DP+HE privacy pipeline: DP-SGD calibration parameters directly derive the GASHE encryption mask, unifying training-time privacy and communication-time confidentiality"

SecureDrive-FL matches DP-SGD's poisoning resistance while additionally protecting against MitM attacks with only 8-10% runtime overhead

"SecureDrive-FL matches DP-SGD alone's poisoning resistance (73.6% vs. 74.0% accuracy, 3.9% Attack Success Rate for both) while additionally withstanding MitM interception, where DP-SGD alone collapses to near-random accuracy (78.2% vs. 10.4%), all under only approx. 8--10% additional runtime overhead relative to DP-SGD alone"

Gradient updates in federated learning remain vulnerable to Man-in-the-Middle interception and model poisoning attacks

"gradient updates remain exploitable: Man-in-the-Middle (MitM) interception exposes updates in transit, while model poisoning corrupts global convergence"

Llama-3.2-1B shows better TwinKV improvements on RULER benchmark than Qwen3-4B across all evaluated configurations

"On RULER with Llama-3.2-1B, however, that fourth policy improves in every evaluated cell because its Alone score leaves substantial room to improve."

Modern deep learning-based CSI predictors often provide only point predictions and lack calibrated uncertainty estimates

"Modern deep learning-based CSI predictors, however, often provide only point predictions and lack calibrated uncertainty estimates."

TRACE-CRC achieves reliable trajectory-level coverage with substantially smaller uncertainty balls than conservative multi-step corrections

"Empirically, TRACE-CRC achieves reliable trajectory-level coverage with substantially smaller uncertainty balls than conservative multi-step corrections, while avoiding the trajectory undercoverage of compact stepwise and adaptive conformal baselines."

TRACE-CRC avoids the trajectory undercoverage problems of compact stepwise and adaptive conformal baselines

"TRACE-CRC achieves reliable trajectory-level coverage with substantially smaller uncertainty balls than conservative multi-step corrections, while avoiding the trajectory undercoverage of compact stepwise and adaptive conformal baselines."