“By implementing L2-norm-based synaptic scaling and setting the number of neurons in both excitatory and inhibitory layers to 400, the network achieved classification accuracies of 88.84 % on the MNIST dataset and 68.01 % on the Fashion-MNIST dataset after one epoch of training.”

“Unlike prior single-paradigm approaches, which achieve <75% accuracy on out-of-distribution datasets, our method maintains 86.8% average accuracy across seven diverse test sets...”

“OmniNeuro is decoder-agnostic, acting as an essential interpretability layer for any state-of-the-art architecture.”

“I’m now achieving 30x real-time speeds on an i7-12700KF. To put that in perspective: it processes one minute of audio in just 2 seconds.”

“Leveraging the LLM-as-a-judge paradigm, Pat-DEVAL introduces Chain-of-Legal-Thought (CoLT), a legally-constrained reasoning mechanism that enforces sequential patent-law-specific analysis.”

“The router achieved 100% accuracy in distinguishing between coding prompts (e.g., import torch) and literary prompts (e.g., To be or not to be).”

“FoundationSLAM achieves superior trajectory accuracy and dense reconstruction quality across multiple challenging datasets, while running in real-time at 18 FPS.”

“The paper proposes using the gradient cosine similarity of low-confidence examples to predict data efficiency based on a small number of labeled samples.”

“The paper demonstrates that 'extreme-wave phenomena, often treated as deleterious fluctuations, can be harnessed as structural nonlinearity for scalable, energy-efficient neuromorphic photonic inference.'”

“CPJ significantly improves performance: using GPT-5-mini captions, GPT-5-Nano achieves +22.7 pp in disease classification and +19.5 points in QA score over no-caption baselines.”

“Achieved a classification accuracy of 96.25% on the HCPTask dataset.”

“OFL-SAM2 achieves state-of-the-art performance with limited training data.”

“Each BPS monopole state is naturally labeled by a weight of the relevant $W$-boson representation of $(H^{\vee})^{s}$.”

“DGGT's biggest breakthrough is that it gets rid of the dependence on scene-by-scene optimization, camera calibration, and short frame windows of traditional solutions.”

“Tree-based ensemble methods, including Random Forest and XGBoost, proved inherently robust to this violation, achieving an R-squared of 0.765 and RMSE of 0.731 logP units on the test set.”

“AutoFed consistently achieves superior performance across diverse scenarios.”

“Youtu-LLM sets a new state-of-the-art for sub-2B LLMs...demonstrating that lightweight models can possess strong intrinsic agentic capabilities.”

“Experiments demonstrate that Youtu-Agent achieves state-of-the-art performance on WebWalkerQA (71.47%) and GAIA (72.8%) using open-weight models.”

“The model improves multi-hop reasoning accuracy by 16.8 percent on HotpotQA, 14.3 percent on 2WikiMultihopQA, and 19.2 percent on MeetingBank, while improving consistency by 21.5 percent.”

“The model achieves 25.96 dB PSNR and 0.8375 SSIM on the test set, demonstrating its effectiveness in compressing low-resolution video while maintaining good perceptual quality.”

“WM-SAR consistently outperforms existing deep learning and LLM-based methods.”

“DRL-TH outperforms existing methods in various crowded environments. We also implemented DRL-TH control policy on a real UGV and showed that it performed well in real world scenarios.”

“DATAMASK achieves significant improvements of 3.2% on a 1.5B dense model and 1.9% on a 7B MoE model.”

“The distance-aware approach achieves higher accuracy on coarse grids (max. 60%p accuracy gain) with only a moderate verification overhead (approximately 1.4x), making zone encoding the key lever for efficient zero-knowledge spatial checks.”

“MotivNet achieves competitive performance across datasets without cross-domain training.”

“LightningDiT-XL/1+IG achieves FID=1.34 which achieves a large margin between all of these methods. Combined with CFG, LightningDiT-XL/1+IG achieves the current state-of-the-art FID of 1.19.”

“The paper demonstrates significant performance gains on planning datasets in the Blocksworld domain through intrinsic self-critique, without external source such as a verifier.”

“TPI-AI outperforms standalone LightGBM and Bi-LSTM baselines, achieving macro-F1 of 0.9562, 0.9124, 0.8345 on highD and 0.9247, 0.8197, 0.7605 on exiD at T = 1, 2, 3 s, respectively.”

“NIRNL achieves state-of-the-art performance, exhibiting remarkable robustness, especially under high noise rates.”

“The AHA framework, leveraging counterfactual hard negative mining, constructs a high-quality preference dataset that forces models to distinguish strict acoustic evidence from linguistically plausible fabrications.”

“The Hilbert-VLM model achieves a Dice score of 82.35 percent on the BraTS2021 segmentation benchmark, with a diagnostic classification accuracy (ACC) of 78.85 percent.”

“MeLeMaD outperforms state-of-the-art approaches, achieving accuracies of 98.04% on CIC-AndMal2020 and 99.97% on BODMAS.”

“DUALFloodGNN achieves substantial improvements in predicting multiple hydrologic variables while maintaining high computational efficiency.”

“The multimodal Transformer achieves RMSE = 0.90 mm and R^2 = 0.97 on the test set on the eastern Ireland tile (E32N34).”

“Bridge-TS reaches a new record of imputation accuracy in terms of mean square error and mean absolute error, demonstrating the superiority of improving prior for generative time series imputation.”

“The VBSF architecture achieves an accuracy of more than 98%.”

“OmniAgent achieves state-of-the-art performance, surpassing leading open-source and proprietary models by substantial margins of 10% - 20% accuracy.”

“HAT consistently improves 3D temporal detectors and trackers across diverse baselines. It achieves state-of-the-art tracking results with 46.0% AMOTA on the test set when paired with the DETR3D detector.”

“InfTool transforms a base 32B model from 19.8% to 70.9% accuracy (+258%), surpassing models 10x larger and rivaling Claude-Opus, and entirely from synthetic data without human annotation.”

“ThinkGen employs a decoupled architecture comprising a pretrained MLLM and a Diffusion Transformer (DiT), wherein the MLLM generates tailored instructions based on user intent, and DiT produces high-quality images guided by these instructions.”

“EGDA achieves robust cross-session performance, obtaining accuracies of 81.22%, 80.15%, and 83.27% across three transfer tasks, and surpassing several baseline methods.”

“AKG kernel agent achieves an average speedup of 1.46x over PyTorch Eager baselines implementations.”

“CoLog achieves a mean precision of 99.63%, a mean recall of 99.59%, and a mean F1 score of 99.61% across seven benchmark datasets.”

“INT8 quantization consistently preserves over 90% of the FP16 baseline accuracy and achieves a 1.5x prefill speedup on the Atlas A2.”

“ECG-RAMBA achieves a macro ROC-AUC ≈ 0.85 on the Chapman--Shaoxing dataset and attains PR-AUC = 0.708 for atrial fibrillation detection on the external CPSC-2021 dataset in zero-shot transfer.”

“Claude 3.5 Haiku achieves 99.9% parse success and 95.8% overall task accuracy across 100 benchmark problems.”

“LIMO achieves superior solution quality and faster time-to-solution on instances up to 85,900 cities compared to prior hardware annealers.”

“Our approach directly aligns textual information with the 3D scene by embedding rich linguistic features into each Gaussian primitive, thereby achieving early modality alignment.”

“EquaCode achieves an average success rate of 91.19% on the GPT series and 98.65% across 3 state-of-the-art LLMs, all with only a single query.”

“SPIRAL achieves 83.6% overall accuracy on DailyLifeAPIs, an improvement of over 16 percentage points against the next-best search framework.”