The paper introduces computable error bounds and a certified early‑stopping criterion for regularized inverse problems by exploiting an exact Fenchel–Young duality‑gap identity. The total duality gap splits into a data‑fidelity loss and a regularizer loss, both expressed as Fenchel–Young losses that are oracle‑free and vanish exactly at Mirror Alignment. Using a constructive Brønsted–Rockafellar approach, the authors build a dual‑feasible proxy via a proximal step in the fidelity geometry, enabling an early‑stopping rule based on the regularizer loss.
By Pierre-Cyril Aubin-Frankowski (CERMICS UMR 9032, ENPC), Yohann de Castro (ICJ, ECL, IUF, PSPM)
arXiv:2609.17544v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly being explored for clinical applications, yet their assessment for real-world traditional Chinese medicin...
By Jiacheng Xie, Xiaoting Tang, Yang Yu, Jinpu Li, Shouli Li, Congcong Jing, Yantao Yang, Zhiyong Zhao, Ziyang Zhang, Qilin Song, Guanghui An, Dong Xu
arXiv:2609.17552v1 Announce Type: new
Abstract: Moral-reward RL can make language-model agents more cooperative, but whether that alignment survives adversarial persona pressure is unknown. Such atta...
By Arth Singh
arXiv:2609.18677v1 Announce Type: new
Abstract: Speech language models enable richer spoken interactions between humans and machines than cascaded systems, allowing access to paralinguistic informati...
By Timoth\'ee Weisselberger, Edouard Graves, Alexandre D\'efossez
arXiv:2609.18729v1 Announce Type: cross
Abstract: Consumers increasingly use AI chatbots for advice on what to buy. With companies like OpenAI and Google monetising their AI through advertising, this...
By Lucas G. Uberti-Bona Marin, Thales Bertaglia, Giovanni Astante, Bram Rijsbosch, Gijs van Dijck, Anik\'o Hann\'ak, Gerasimos Spanakis, Konrad Kollnig
arXiv:2609.18697v1 Announce Type: new
Abstract: Historians have reconstructed the twentieth-century transformation of general relativity and gravitation (GRG) at the field level and through individua...
By Raphael Schlattmann, Malte Vogl
arXiv:2609.18961v1 Announce Type: new
Abstract: Mechanistic interpretability identifies sparse subsets of heads and MLP blocks that carry specific behaviors. We ask whether such causal signals can gu...
By Son Ha Xuan, Phat T. Tran-Truong, Xuan-Bach Le
arXiv:2609.18009v1 Announce Type: cross
Abstract: Lightweight audio-visual speech enhancement (AVSE) models face a critical trade-off between computational efficiency and cross-modal alignment accura...
By Guo-Ruei Tseng, Hung-Shin Lee, Hsin-Min Wang, Berlin Chen
arXiv:2609.17800v1 Announce Type: new
Abstract: Vision-language models (VLMs) remain largely unreliable on panoramic dental radiographs and can rely on learned anatomical priors rather than evidence...
By Ahmed Rafid, Fariya Ahmed, Rumman Adib, Mehedi Ahamed, Ajwad Abrar, Tareque Mohmud Chowdhury
arXiv:2405.06818v2 Announce Type: replace
Abstract: Natural Language Processing (NLP) for Ghana's 73 living indigenous languages remains deeply fragmented, under-resourced, and heavily skewed toward...
By Sheriff Issaka, Erick Rosas Gonzalez, Colene Agbo, Evans Kofi Agyei, Shruti Tyagi, John Emeka Eze, Enock Appiah Tieku, Junlin Fang, Thanh Do Nguyen, Juliet Arthur, Zhaoyi Zhang, Mihir Heda, Keyi Wang, Yinka Ajibola, Rebecca Akpanglo-Nartey, Frank Lawrence Nii Adoquaye Acquaye, Dennis Owusu, Jerry John Kponyo, Stephen Moore, Isaac Wiafe, Sean Du
arXiv:2505.24427v2 Announce Type: replace
Abstract: Accurate modeling of subjective phenomena such as emotion expression requires data annotated with authors' intentions. Commonly such data is collec...
By Christopher Bagdon, Aidan Combs, Carina Silberer, Roman Klinger
The paper argues that hallucinations by legal language models should be judged as failures of legal warrant rather than mere factual or citation errors. It defines claim-authority warrant as a context-sensitive relationship between a legal claim and applicable, current authority, and proposes that evaluating warrant can uncover failures missed by traditional accuracy or citation metrics. The authors outline a pilot study, benchmark specifications, and a research agenda to assess whether legal AI systems’ claims are properly licensed by law.
By Maksym Taranukhin, Vered Shwartz
The paper investigates whether large language models (LLMs) can generate synthetic cyberbullying conversations that replicate the social dynamics of real interactions. Using a comprehensive framework, the authors compare authentic dialogues with synthetic ones from GPT, Grok, and LLaMA across structural, linguistic, affective, and temporal dimensions, and conduct human evaluations of realism. Results show that while LLMs preserve high‑level interaction patterns, they systematically distort finer‑grained social phenomena, with model‑specific biases such as GPT’s suppression of harmful content and Grok’s amplification of aggression.
By Arefeh Kazemi, Hamza Qadeer, Sinan Asci, Joachim Wagner, Brian Davis
arXiv:2605.19309v4 Announce Type: replace
Abstract: Document Layout Analysis (DLA) pipelines provide structured page representations for retrieval-augmented generation, long-document question answeri...
By Yue Chen, Yihao Wang, Ziyi Tang, Yongsen Zheng, Keze Wang
RankGround is a two‑stage framework for GUI grounding that uses a single Vision‑Language Model call per query. It introduces GroundRanker, a lightweight multimodal reranker that selects the most promising crop from a dense candidate set, trained with a two‑stage curriculum on ranking supervision data derived from existing grounding datasets. Experiments show RankGround outperforms strong baselines, achieving 1.4× faster inference and a 5.5% average improvement in localization accuracy over the second‑best method across all backbones and screen scales.
By Liyang Fan, Xinping Bi, Yitai Li, Shuaimin Li, Hui Li, Min Yang
The paper introduces Code Consistency Preference Optimization Verification (CCPO), a method that generates computationally sound solutions with dependency graphs to improve execution-consistent preference optimization for language models. By building a scientific reasoning dataset and extracting reasoning steps, prerequisites, and derivability relationships, the authors compute execution consistency scores that are used to fine‑tune models such as Llama‑3‑8B and DeepSeekMath‑7B, achieving significant performance gains on MATH (+17.0%) and GSM8K (+15.1%). The extended Scientific Feasibility Control framework further boosts accuracy on PhyX physics reasoning to 50.1%, surpassing existing models while maintaining high scientific validity and reducing law violations.
By Yunlong Tan, Mingqiao Mo, Hao Zhang
arXiv:2609.18323v1 Announce Type: new
Abstract: Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H...
By Haoyu Zhao, Zihao Zhao, Tianyu Deng, Ziqin Xu, Zihao Zhang, Xudong Wang, Jinxiang Guo, Chen Gao, Ziyi Ye, Yeying Jin, Jiaxi Gu, Zuxuan Wu, Shuicheng Yan
arXiv:2609.17856v1 Announce Type: new
Abstract: Heterogeneous cooperative perception (CP) enables connected vehicles with diverse sensor setups to share spatial awareness via compact feature maps, wh...
By Chenyi Wang, Yutong Liu, Qingzhao Zhang, Ming F. Li
arXiv:2609.18133v1 Announce Type: new
Abstract: Visible-infrared object detectors are used for robust perception under challenging illumination and weather conditions. Current physical attacks apply...
By Yueqi Zhu, Qi Ming, Guo Cheng, Yongkang Zhang, Feiran Liu, Juan Fang, Jiahuan Zhou, Jiangmeng Li, Yuhan Zhang
arXiv:2609.18302v1 Announce Type: new
Abstract: RAW-to-sRGB image signal processing (ISP) must recover perceptually faithful colors and fine details from sensor measurements, often under imperfect sp...
By Tailai Chen, Xiaotong Luo, Yuan Gao, Xin Jin, Wenjun Zeng