2041–2060
The laboratory system followed wave-speed changes in a rat artery and produced three-dimensional scans of a rabbit eye and one human finger, while the evidence remains limited to technical demonstrations.
S²GS reported lower optimization time, storage, power and energy in benchmark tests, while quality varied by dataset and the fixed hierarchy struggled with some motion.
A mathematical model and hardware-faithful digital twin link pump-phase encoding to charge-limited computation and polynomial measurement cost; no physical device was built.
The CAMA method had the best displayed results across three long-term memory benchmarks, but the paper reports no confidence intervals or significance tests.
Hierarchical versions of two methods delivered large reported speedups while matching standard results to roundoff in a two-dimensional benchmark.
RFWM reported stronger benchmark results without target-scene RF measurements, but validation on real measured fields was not reported.
GOAG uses gripper configurations and contact points, with tests spanning three grippers, five benchmarks and a real-robot demonstration on 11 YCB objects.
FlashPrefill V2 reported up to 47.26× operator-level speedup at 128K on NVIDIA H20 GPUs while keeping scores close to full attention on RULER and LongBench.
The system outperformed tested baselines on one CT dataset and produced competitive results on MRI and CT-perfusion data, but the evaluation did not measure treatment decisions or patient outcomes.
EvEMTBench pairs synchronized voltage and current waveforms with labels for faults and operating events, but it does not yet show how machine-learning systems will perform on real grids.
In laboratory tests, the device’s resonance shifted with solution concentration and used 0.04 μL for one measurement, but it was not tested on biological samples or compared with conventional sensors.
Preprint: MOSAIC reported higher overlap scores than weakly supervised baselines when institutions had different MRI modalities missing.
A theoretical comparison links a nucleon core-and-cloud picture to dense-matter models and neutron-star observations, while leaving the high-density outcome model-dependent.
The formal study identifies allowed irreducible building blocks and exact block patterns for a narrowly defined set of finite groups.
EXIMO combines exploration, imitation and residual reinforcement learning, with reported gains across a 22-task manipulation benchmark.
The approach had no larger defined risk than a fixed threshold in theory and showed lower set-estimation error in simulations, but its vehicle example was simulator-based.
Core-KAN reported gains against ResNet-50 and selected competing methods on ImageNet-1K, COCO and ADE20K, with caveats around training comparisons and response-bank cost.
Co-3DGT posted higher novel-class and overall benchmark scores on SUN RGB-D and ScanNetV2, using co-distillation with uncertainty regularization and hierarchical alignment.
In two worked mathematical examples, the method recovered solutions with reported multiplicities of 3 and 1 and reconstructed a tensor with an apolar-norm error of about 10^-13.
In simulated airline, retail and telecom customer-service tasks, PolicyGuide recorded a mean Pass4 score of 0.62, compared with 0.42 for unguided execution.