The paper introduces a hybrid quantum–classical regression framework that uses a lightweight classical embedding as a learnable geometric preconditioner to improve the conditioning of a downstream variational quantum circuit. It further incorporates a curriculum optimization protocol that gradually increases circuit depth and switches from SPSA-based exploration to Adam-based fine‑tuning. Experiments on PDE‑informed and standard regression datasets show that this approach consistently outperforms pure QNN baselines, yielding more stable convergence and reduced structured errors, especially in data‑limited regimes.
By Qingyu Meng, Yangshuai Wang
arXiv:2609.12552v1 Announce Type: new
Abstract: Open-vocabulary detection accepts any class list at inference, and promptable segmentation returns regions without class names: the taxonomy has left t...
By Ma\"elic Neau
arXiv:2609.12993v1 Announce Type: cross
Abstract: We investigate the effect of pretrained T5 model scale and explicit motion features on pose-to-text Indian Sign Language Translation (SLT) for the WS...
By Manav Dhamecha, Praveen Kumar Chandaliya, Pruthwik Mishra
The paper introduces Confidence-Gated Transductive Test Generation (CoTT), a method that combines an efficient inductive approach with selective transductive generation triggered only when inductive confidence is low. This adaptive strategy enhances the reliability of test case outputs while limiting additional computational effort. Experiments on code reranking benchmarks demonstrate that CoTT surpasses previous baselines in performance metrics and reduces overall cost compared to applying transductive generation to every input.
By Sungjae Lee, Youngsik Yoon, Seockbean Song, Siwei Wang, Wei Chen, Jungseul Ok
The paper proposes a simple technique of chunking workloads into smaller parts that alternate between compute-intensive and memory-bound operations to smooth power and temperature spikes in GPU systems. By doing so, it prevents throttling, leading to faster wall-clock times and lower total energy consumption. Experiments on a DGX Spark show up to 2% performance and energy gains, while similar benefits, though smaller, are observed on multi‑GPU servers.
By Erik Schultheis, Maximilian Kleinegger, Dan Alistarh
arXiv:2609.12884v1 Announce Type: new
Abstract: A medical claim's correctness often depends not on the claim alone, but on the clinical structure around it. A claim may require a lab reference range,...
By Hasan Iqbal, Sarfraz Ahmad, Hyunjae Kim, Sihyeon Park, Junjie Liao, Qingyu Chen, Preslav Nakov, Yuxia Wang
arXiv:2609.12791v1 Announce Type: new
Abstract: Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, hetero...
By Gengxian Zhou, Jian Xu, Zichen Tang, Shiming Xiang, Haihong E, Cheng-Lin Liu
Doc2FRC introduces Fixed-Range Chunking (FRC), a dynamic programming method that partitions documents into chunks of a predefined length interval, ensuring consistent length distributions during training and inference. This approach reduces train-test length mismatch, mitigates n-gram repetition, and improves translation quality for 7B LLMs compared to direct Doc2Doc fine-tuning. Experiments on IWSLT2017 and a new 10-language test set, GlobVDoc, demonstrate that FRC outperforms existing document-level machine translation methods and enhances out-of-distribution translation performance.
By Xiaotian Wang, Youyuan Lin, Zhan Shen, Hitomi Yanaka
The paper introduces an Early Intervention (EI) framework for multimodal medical image classification that addresses two key challenges: limited exploitation of complementary multimodal information and scarcity of labeled data for Vision Foundation Models (VFMs). EI treats one modality as the target and uses high‑level semantic tokens from other modalities as intervention tokens to guide the target’s embedding early in the process. The authors also propose Mixture of varied‑rank LoRAs (MoR) for efficient VFM adaptation, and demonstrate the method’s effectiveness on retinal, skin, and knee medical image datasets.
By Qijie Wei, Hailan Lin, Xirong Li
The paper introduces ASCIL, a post‑ASR correction framework that re‑evaluates wake‑up intent by combining acoustic embeddings, linguistic cues, device context, and past misclassifications. ASCIL interprets both implicit (hesitation, disengagement, silence) and explicit (cancellation, repetition) signals as noisy indicators of misclassification, enabling online pattern updates without manual annotation. On a proprietary dataset of 3,667 interactions, ASCIL reduces errors by up to 54.27% relative on a session‑disjoint subset and 24.39% at a 0.90 threshold, while adding less than 60 ms of latency and improving intentional acceptance rates.
By Preeti Saraswat, Divya Neelagiri, Anil Yadav
The paper introduces a certification framework for assessing concept unlearning in text-to-image diffusion models, offering high‑confidence guarantees with bounded error on residual concept leakage. Unlike prior methods that rely solely on attack success rates from automated prompt searches, this approach combines statistical certification with worst‑case analysis along concept‑relevant embedding directions to derive explicit upper bounds on leakage probability. Evaluations across NSFW content, artistic styles, and celebrity identities reveal that certified leakage bounds exceed standard attack success rates by 16.2%, highlighting significant residual risks overlooked by existing protocols.
By Mansi, Luca Marzari, Francesco Leofante
The paper investigates whether retrieval-time signals add value to Adaptive Retrieval-Augmented Generation (RAG) routing when the query is already known. Experiments across document, audio, and video RAG compare routers that use only the query versus those that also incorporate retrieval signals, keeping all other factors constant. The results show that adding retrieval signals does not reliably improve routing decisions over a query-only baseline, suggesting that any routing benefit must be explicitly demonstrated rather than presumed.
By Qiaomu Li, Qiuyuan Zhang, Nong Ming
Physics-Informed Conformal Prediction (PI‑CP) embeds PDE residuals into the nonconformity score of split conformal prediction, yielding distribution‑free prediction intervals with provable coverage that adapt spatially to physics violations. The method demonstrates consistent 89‑91% coverage across six physics scenarios, outperforming MC Dropout and Deep Ensembles, while Fourier Neural Operators (FNO) achieve superior accuracy over CNN and DeepONet. Additionally, the authors prove that FNO’s translation equivariance limits its ability to solve PDEs with Dirichlet boundary conditions, and show that adding coordinate channels can reduce error by up to 63×.
By Michael Chin
The paper introduces MMD-FUSE, a two-sample test that remains effective on small datasets by fusing classical and quantum kernels. By combining the inductive biases of classical kernels with the expressive power of quantum kernels, the hybrid approach achieves higher test power, especially for small, high‑dimensional data. Experiments on synthetic and real clinical datasets confirm that the method consistently outperforms purely classical counterparts and adapts robustly to varied data characteristics.
By Yu Terada, Yugo Ogio, Ken Arai, Hiroyuki Tezuka, Yu Tanaka
The paper introduces a general machine learning framework called the localization method, built on localization kernels and local means that underpin self‑attention. It formally defines the framework through a local‑ized model and a localization trick, and systematically connects it to a wide array of existing models such as kernel methods, MeanShift, Hopfield networks, LLE, fuzzy inference, and denoising autoencoders. The authors also extend the framework with adaptive kernels, hierarchical local models, and non‑local models, showing that the Transformer can be constructed using hierarchical local models, thereby unifying and generalizing state‑of‑the‑art architectures.
By Congwei Song
The paper investigates nonlinear dimensionality reduction for Bayesian optimisation (BO) by transforming high‑dimensional black‑box optimisation problems into a sequence of low‑dimensional latent‑space BO (LSBO) tasks. It extends earlier linear embedding approaches by using variational autoencoders (VAEs), deep metric loss, and adaptive retraining to better capture nonlinear structure, and couples LSBO with sequential domain reduction (SDR‑LSBO) to progressively narrow search domains. Experiments on GPU‑accelerated BoTorch with Matérn‑5/2 Gaussian‑process surrogates show that VAE‑based LSBO outperforms adaptive linear embeddings, and the authors provide a theoretical analysis of latent‑space error versus representation gap under PAC‑Bayes conditions.
By Luo Long, Coralia Cartis, Paz Fink Shustin
The paper introduces EAR, an Entity‑Aware Partitioning approach that improves retrieval‑augmented generation for multiple‑choice question answering by extracting normalized surface anchors from questions, answers, and the corpus. EAR retrieves local windows around matching anchors and can attach a larger parent passage via an extractive summary, reducing retrieved words by 37.5‑40.2% compared to fixed‑size chunks. Experiments on a cleaned MMLU‑style subset with Mistral, Gemma, and DeepSeek show modest accuracy changes, none statistically significant, highlighting EAR’s methodological contribution of compact, inspectable retrieval units.
By Cenab Batu Bora, Oylum Alatl{\i}, Sebnem Bora, Oguz Dikenelli
arXiv:2609.12898v1 Announce Type: new
Abstract: Fine-grained robotic manipulation depends on understanding parts, not only whole objects. Existing 3D foundation models tend to be either generalized b...
By Xinqiang Yu, Zekun qi, Jiawei He, Wenyao Zhang, Xuchuan Chen, Guaocai Yao, Li Yi, Zhaoxiang Zhang, He Wang
The paper introduces a three‑stage training pipeline that builds compact, efficient dense retrievers without requiring ground‑truth relevance labels. Using cross‑lingual alignment, relational knowledge distillation, and contrastive fine‑tuning, the authors develop PolDense (six Polish models ranging from 17 M to 1 B parameters) and EuroDense (a 435 M‑parameter model covering nine European languages). Extensive evaluation on 41 Polish and 150 multilingual tasks shows that PolDense‑1B outperforms larger retrievers up to 9 B parameters, while EuroDense leads in task‑averaged and language‑averaged performance among models below 1 B parameters.
By S{\l}awomir Dadas, Rafa{\l} Po\'swiata, Ma{\l}gorzata Gr\k{e}bowiec, Micha{\l} Pere{\l}kiewicz
arXiv:2503.00612v2 Announce Type: replace-cross
Abstract: The basic problem of semantic compression is to minimize the length of a message while preserving its meaning. This differs from classical no...
By Tankut Can