“”

“By reducing propagation steps in LLM deployments, MetaJuLS contributes to Green AI by directly reducing inference carbon footprint.”

“DeepSeek solved the instability by constraining the learnable matrices to be "Double Stochastic" (all elements ≧ 0, rows/cols sum to 1). Mathematically, this forces the operation to act as a weighted average (convex combination). It guarantees that signals are never amplified beyond control, regardless of network depth.”

“B-Trans effectively leverage the wisdom of crowds, yielding superior semantic diversity while achieving better task performance compared to deterministic baselines.”

“R$^2$CCL is highly robust to NIC failures, incurring less than 1% training and less than 3% inference overheads.”

“TG consistently improves efficiency over matched GPT-2 runs, among other baselines, with scaling fits indicating GPT-2 requires ~5-8% more data and ~33-42% more parameters to match TG's loss.”

“ProDM significantly improves CAC scoring accuracy, spatial lesion fidelity, and risk stratification performance compared with several baselines.”

“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.”

“ADOPT explicitly models the dependency between each LLM step and the final task outcome, enabling precise text-gradient estimation analogous to computing analytical derivatives.”

“BEDA consistently outperforms strong baselines: on CKBG it improves success rate by at least 5.0 points across backbones and by 20.6 points with GPT-4.1-nano; on Mutual Friends it achieves an average improvement of 9.3 points; and on CaSiNo it achieves the optimal deal relative to all baselines.”

“ArtiSG significantly outperforms baselines in functional element recall and articulation estimation precision.”

“The model achieves 12% median relative error using discovered semantic features from multimodal listing data, substantially outperforming a GPT-5 baseline (38% error).”

“Tests on Arabic, Bangla, English, and Spanish datasets show that our approach consistently beats strong baselines.”

“The paper introduces a learnable feature embedding for segmentation in Gaussian primitives and a novel 'Embedding-to-Label' process.”

“Utilizing 2:4 sparsity combined with quantization on $4096 imes 4096$ matrices, our approach achieves a reduction of up to $4\times$ in weight storage and a $1.71\times$ speedup in matrix multiplication, yielding a $1.29\times$ end-to-end latency reduction compared to dense GPU baselines.”

“EVOL-SAM3 not only substantially outperforms static baselines but also significantly surpasses fully supervised state-of-the-art methods on the challenging ReasonSeg benchmark in a zero-shot setting.”

“R-Debater achieves higher single-turn and multi-turn scores compared with strong LLM baselines, and human evaluation confirms its consistency and evidence use.”

“HeteroHBA consistently achieves higher attack success than prior backdoor baselines with comparable or smaller impact on clean accuracy.”

“DynaFix repairs 186 single-function bugs, a 10% improvement over state-of-the-art baselines, including 38 bugs previously unrepaired.”

“The framework delivers a 3x increase in task processing speed over single-agent baselines, 98.7% structural/style consistency in writing, and a 74.6% test pass rate in coding.”

“DARFT suppresses strong distractors and sharpens decision boundaries without additional supervision.”

“BC-TAS achieves orders-of-magnitude improvement in outage probability and significant gains in energy efficiency compared to conventional MU-MIMO baselines.”

“DISF reduces CoM misalignment while maintaining geometric compatibility, translating into higher grasp success in both simulation and real-world execution compared to baselines.”

“The two-stage approach decomposes spatial reasoning into atomic building blocks and their composition.”

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

“USF-MAE achieved the highest performance across all evaluation metrics, with 90.57% accuracy, 91.15% precision, 90.57% recall, and 90.71% F1-score.”

“The approach achieves a 20.15% reduction in Mean Spectral Information Divergence (MSID), up to 1.09% PSNR improvement, and a 1.62% log transformed MS-SSIM gain over strong learned baselines.”

“The paper formulates a unified taxonomy for pre-training paradigms, ranging from single-modality baselines to sophisticated unified frameworks.”

“TASIF integrates three synergistic components: (1) a simple, plug-and-play time span partitioning mechanism to capture global temporal patterns; (2) an adaptive frequency filter that leverages a learnable gate to denoise feature sequences adaptively; and (3) an efficient adaptive side information fusion layer, this layer employs a "guide-not-mix" architecture.”

“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.”

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

“HY-MT1.5-1.8B demonstrates remarkable parameter efficiency, comprehensively outperforming significantly larger open-source baselines and mainstream commercial APIs.”

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

“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.”

“The method produces higher-quality images at novel extrapolated viewpoints compared with baselines.”

“CEC-Zero outperforms supervised baselines by 10--13 F$_1$ points and strong LLM fine-tunes by 5--8 points across 9 benchmarks.”

“CRMS reduces latency by over 14% and improves energy efficiency compared with heuristic and search-based baselines.”

“The proposed method demonstrates superiority over baseline schemes in terms of average sum rate, robustness to CSI imperfection, user mobility, and scalability.”

“DreamTacVLA outperforms state-of-the-art VLA baselines, achieving up to 95% success, highlighting the importance of understanding physical contact for robust, touch-aware robotic agents.”

“Yggdrasil achieves up to $3.98\times$ speedup over state-of-the-art baselines.”

“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.”

“Achieving an accuracy (< 1.25) of 98.1% and reducing Squared Relative Error by over 17% compared to established baselines.”

“CreativeDC achieves significantly higher diversity and novelty compared to baselines while maintaining high utility.”

“The distilled model matches the visual quality of full-step, bidirectional baselines with 20x less inference cost and latency.”

“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.”

“MATP surpasses prompting-based baselines by over 42 percentage points in reasoning step verification.”

“ACT achieves an F1 score of 0.91, with superior Recall (0.89) and Precision (0.94).”

“Experimental outcomes indicate better detection accuracy, shorter mitigation latency and reasonable build-time overhead than rule-based, provenance only and RL only baselines.”

“The paper claims that Dual-approx Bridge demonstrates consistent and superior performance in terms of image quality and faithfulness to ground truth compared to both stochastic and deterministic baselines.”