“Music and audio that is generated wholly or in substantial part by AI is not permitted on Bandcamp.”

“Transfer learning with fine-tuning substantially outperforms CNNs trained from scratch for pediatric pneumonia detection, showing near-perfect accuracy.”

“The authors' method enables simulations of bosonic quantum mixtures with substantially larger bond dimensions than previous works.”

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

“LSRE attains semantic risk detection accuracy comparable to a large VLM baseline, while providing substantially earlier hazard anticipation and maintaining low computational latency.”

“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.”

“Our proposed method maintains estimation accuracy even with high proportions of protest votes, while being substantially faster than MCMC-based methods.”

“The proposed method substantially improves power when p-values are conservative, while achieving comparable performance to existing methods when p-values are exact.”

“BF-APNN substantially reduces training time compared to RT-APNN while preserving high solution accuracy.”

“For certain combinations of charge and size, the interplay between hydrodynamic and electrostatic forces creates strong radially inward particle relative velocities that substantially alter particle pair dynamics and modify the conditions required for contact.”

“Memory representation plays a central role in consolidating spatial experience, with structured memories particularly sequential and graph-based representations, substantially improving performance on structure-intensive tasks such as path planning.”

“Analyzing 1.4 million customer transactions across seven markets, our approach reduces false positive and false negative rates to 4.64% and 11.07%, substantially outperforming single-institution models. The framework prevents 79.25% of potential losses versus 49.41% under fixed-rule policies.”

“The proposed methods substantially outperform nuclear-norm-based and non-robust alternatives under heavy-tailed noise and contamination.”

“Unmanaged demand growth can substantially offset technological mitigation gains, highlighting the necessity of integrated demand- and technology-oriented strategies.”

“The prototypes exhibited excellent performance, achieving a time resolution of 25 ps and a light yield of 10^4 photoelectrons, both substantially surpassing the design requirements.”

“PointRAFT achieved a mean absolute error of 12.0 g and a root mean squared error of 17.2 g, substantially outperforming a linear regression baseline and a standard PointNet++ regression network.”

“The approach substantially improves both the representational power and the RD performance of 2DGS while maintaining over 1000 FPS decoding. Compared with the baseline GSImage, we reduce BD-rate by 43.44% on Kodak and 29.91% on DIV2K.”

“The strongest results arise when priors are combined with content embeddings (AUC~0.815), while content-only models remain above chance but substantially weaker (AUC~0.626).”

“Incorporating scale gap metadata substantially improved the predictive performance of LLMs, with Gemini Stage 2 achieving the highest accuracy, with a mean absolute error of 5.43 cm, root mean square error of 8.58 cm, and R squared of 0.84 under optimal image conditions.”

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

“ViLaCD-R1 substantially improves true semantic change recognition and localization, robustly suppresses non-semantic variations, and achieves state-of-the-art accuracy in complex real-world scenarios.”

“India's startup ecosystem raised nearly $11 billion in 2025, but investors wrote far fewer checks and grew more selective.”

“Instruction-following capabilities improve substantially (+46% to +75% in IFEval for Llama-3.2-1B and 3B models).”

“DICE achieves 85.7% agreement with human experts, substantially outperforming existing LLM-based metrics such as RAGAS.”

“OBEYED-VLA substantially improves robustness over strong VLA baselines across four challenging regimes and multiple difficulty levels: distractor objects, absent-target rejection, background appearance changes, and cluttered manipulation of unseen objects.”

“SWE-RM substantially improves SWE agents on both TTS and RL performance. For example, it increases the accuracy of Qwen3-Coder-Flash from 51.6% to 62.0%, and Qwen3-Coder-Max from 67.0% to 74.6% on SWE-Bench Verified using TTS, achieving new state-of-the-art performance among open-source models.”

“By concentrating adversarial perturbations on these positions, we achieve semantic degradation comparable to global methods while using substantially smaller budgets. More importantly, across multiple representative VLMs, such selective attacks convert 35-49% of benign outputs into harmful ones, exposing a more critical safety risk.”

“The Modified TSception achieves a comparable accuracy of 83.46% (vs. 83.15% for the original) on the SEED-VIG dataset, but with a substantially reduced confidence interval (0.24 vs. 0.36), signifying a marked improvement in performance stability.”

“Scaling compressors is substantially more effective than scaling predictors.”

“SemDAC outperforms DAC across perceptual metrics and achieves lower WER when running Whisper on reconstructed speech, all while operating at substantially lower bitrates (e.g., 0.95 kbps vs. 2.5 kbps for DAC).”

“On a five-class TACO subset (paper, plastic, bottle, can, cigarette), the strongest variant, TrashDet-l, achieves 19.5 mAP50 with 30.5M parameters, improving accuracy by up to 3.6 mAP50 over prior detectors while using substantially fewer parameters.”

“"Our findings indicate that the UDE model is much more data efficient, needing only 20% of data for a correct forecast, whereas the Neural ODE requires 90%."”

“The paper explores how basic arithmetic operations can be leveraged to improve LLM performance.”