“Shines on tool use, math, and instruction following.”

“The split came out to 52% augmentation and 45% automation on Claude.ai, a slight shift from January 2025 when augmentation led 55% to 41%.”

“While generative AI and LLM-based technology options are being increasingly adopted by individuals for personal use, the same cannot be said for large enterprises.”

“According to the Wall Street Journal, citing internal financial disclosure documents, OpenAI's current equity incentive program for employees has reached a new high in the history of tech startups, with an average equity compensation of approximately $1.5 million per employee, applicable to about 4,000 employees, far exceeding the levels of previous well-known tech companies before their IPOs.”

“The paper proposes a model that outperforms two established baselines, DINO-WM and V-JEPA-2-AC, in both navigation and manipulation tasks.”

“Alibaba Tongyi Lab have released MAI-UI—a family of foundation GUI agents. It natively integrates MCP tool use, agent user interaction, device–cloud collaboration, and online RL, establishing state-of-the-art results in general GUI grounding and mobile GUI navigation, surpassing Gemini-2.5-Pro, Seed1.8, and UI-Tars-2 on AndroidWorld.”

“SeedFold outperforms AlphaFold3 on most protein-related tasks.”

“SenseNova-MARS achieves state-of-the-art performance on open-source search and fine-grained image understanding benchmarks. Specifically, on search-oriented benchmarks, SenseNova-MARS-8B scores 67.84 on MMSearch and 41.64 on HR-MMSearch, surpassing proprietary models such as Gemini-3-Flash and GPT-5.”

“CoHalLo achieves a Top-1 accuracy of 0.4253, Top-3 accuracy of 0.6149, Top-5 accuracy of 0.7356, Top-10 accuracy of 0.8333, IFA of 5.73, Recall@1% Effort of 0.052721, and Effort@20% Recall of 0.155269, which outperforms the baseline methods.”

“MedKGI improves dialogue efficiency by 30% on average while maintaining state-of-the-art accuracy.”

“LoongFlow outperforms leading baselines (e.g., OpenEvolve, ShinkaEvolve) by up to 60% in evolutionary efficiency while discovering superior solutions.”

“GCA-ResUNet achieves Dice scores of 86.11% and 92.64% on Synapse and ACDC benchmarks, respectively, outperforming a range of representative CNN and Transformer-based methods.”

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

“BOAD outperforms single-agent and manually designed multi-agent systems. On SWE-bench-Live, featuring more recent and out-of-distribution issues, our 36B system ranks second on the leaderboard at the time of evaluation, surpassing larger models such as GPT-4 and Claude.”

“CoFi-Dec substantially reduces both entity-level and semantic-level hallucinations, outperforming existing decoding strategies.”

“CEM significantly improves generation fidelity of existing acceleration models, and outperforms the original generation performance on FLUX.1-dev, PixArt-$α$, StableDiffusion1.5 and Hunyuan.”

“PEG-DRNet achieves an overall AP of 29.8%, an AP$_{50}$ of 84.3%, and a small-object AP of 25.3%, surpassing the RT-DETR-R18 baseline.”

“LLM-PeerReview is conceptually simple and empirically powerful. The two variants of the proposed approach obtain strong results across four datasets, including outperforming the recent advanced model Smoothie-Global by 6.9% and 7.3% points, respectively.”

“DKT achieves the highest discrimination performance (AUC = 0.83) and consistently outperforms the LLM across settings. LLMs exhibit substantial temporal weaknesses, including inconsistent and wrong-direction updates.”

“LENS outperforms strong baselines on standard NLP metrics and task-specific measures of symptom-severity accuracy.”

“DRMNet surpasses state-of-the-art methods, particularly in complex scenarios with high object density and severe occlusion.”

“MetaCD outperforms other baselines in both accuracy and generalization.”

“OrchANN outperforms four baselines including DiskANN, Starling, SPANN, and PipeANN in both QPS and latency while reducing SSD accesses. Furthermore, OrchANN delivers up to 17.2x higher QPS and 25.0x lower latency than competing systems without sacrificing accuracy.”

“USF-MAE outperformed the DenseNet-169 baseline on all evaluation metrics.”

“Dream-VLA achieves top-tier performance of 97.2% average success rate on LIBERO, 71.4% overall average on SimplerEnv-Bridge, and 60.5% overall average on SimplerEnv-Fractal, surpassing leading models such as $π_0$ and GR00T-N1.”

“The method achieves a state-of-the-art score of 68.10% on the ICBHI 2017 dataset, outperforming existing CNN and hybrid baselines. More importantly, it reaches a sensitivity of 68.31%, a crucial improvement for reliable clinical screening.”

“SAM 3D produces more coherent roof geometry and sharper boundaries compared to TRELLIS.”

“HiFi-RAG outperforms the parametric baseline by 57.4% in ROUGE-L and 14.9% in DeBERTaScore on Test2025.”

“The framework achieves a macro F1-score of 88.78% on the UCI-HAR dataset under strict few-shot conditions, outperforming classical approaches.”

“The framework outperforms state-of-the-art methods in both predictive accuracy and interpretability.”

“The YOLOv8s model saves 75% of training time compared to the YOLO-NAS model and outperforms YOLO-NAS in object detection accuracy.”

“incorporating structured sparsity into diffusion models is a promising direction for efficient and expressive long text generation.”

“Our algorithm is simple: the agent plays a sequence of games starting from carefully chosen states from the demonstration, and learns from them by optimizing the game score using PPO, the same reinforcement learning algorithm that underpins OpenAI Five.”