The paper introduces Muslim, an Arabic voice AI platform that delivers grounded Islamic knowledge to users in real time. It combines a NeMo Arabic ASR, an OpenAI-compatible LLM endpoint, and a self-hosted TTS system, supported by a deterministic multi-source retrieval layer across six Model Context Protocol servers. The authors release fine‑tuned Arabic Islamic model artifacts, describe an account‑based metering layer that prevents abuse, and present a three‑layer observability stack that monitors GPU‑bound agent hosts, reporting latency, accuracy, and engineering trade‑offs for production deployment.
By Yahya Mohamed Elnawasany
The Epstein Files Engine is an AI agent developed by the New York Times to help journalists investigate a massive mixed‑media collection released by the U.S. Department of Justice on January 30, 2026, which contains about three million pages of PDFs related to Jeffrey Epstein. The Engine translates reporter questions into Google BigQuery SQL queries across three corpora—Epstein‑related releases, the Times’s archive, and external Epstein‑related news headlines—using an LLM to plan queries and return citation‑rich answers that reporters can verify. Over 100 journalists used the Engine, contributing to at least 20 published stories, and the system includes a Diff method for text‑and‑visual duplicate matching to surface genuinely new information.
By Duy K. Nguyen, Teresa Mondr\'ia Terol, Dylan Freedman, Zach Seward
The paper introduces a prompt‑injection detection framework for email assistants that models attacks as a chain of stages. It combines a text detector, stage‑specific verifiers, rule‑based risk signals, user intent consistency checks, and a logistic decision policy. Experiments on five benchmarks show the framework outperforms pretrained detectors, achieving a mean F1 of 0.406 versus 0.216, and demonstrate that training on benign emails resembling attacks reduces false alarms.
By Ahmad Hashmi, Dhyey Patel, Yunting Yin
The paper tackles the problem of automatically generating dictionary definitions for learner’s dictionaries, focusing on simplicity and clarity. It introduces a new evaluation framework that uses large language models as judges, validated against human annotators with comparable agreement levels. The authors also present an iterative simplification approach that produces definitions scoring highly on their criteria and exhibiting lexical simplicity.
By Yusuke Ide, Adam Nohejl, Joshua Tanner, Hitomi Yanaka, Christopher Lindsay, Taro Watanabe
Large language models (LLMs) are increasingly used for legal research, but their fixed training cutoffs and reliance on static knowledge clash with the evolving nature of statutory law. This study introduces a benchmark of 312 expert‑validated, time‑sensitive German statutory QA pairs that examine two temporal failure modes: post‑cutoff staleness and recency bias. Five LLMs were evaluated under four inference settings, and the results show that retrieval‑augmented approaches that enforce temporal validity significantly improve performance, while web search yields unstable gains and a pronounced recency bias.
By Max Prior, Andreas Schultz, Matthias Grabmair
Audio large language models (Audio LLMs) often fail to transcribe English‑Mandarin code‑switching speech, exhibiting language omission, translation‑instead‑of‑transcription, and hallucination. By applying Direct Preference Optimization (DPO) with 100 K preference pairs, the models learn to preserve mixed‑language content rather than translate, leading to significant reductions in mixed‑error rates (up to 89.6% in‑distribution). The study demonstrates that DPO can effectively align multilingual Audio LLMs for accurate code‑switching transcription.
By Trung Nguyen Quang, Cheng Yi Lewis Won, Minh Duc Pham, Yingxu He, Shuo Sun, Ai Ti Aw
The paper introduces an automated pipeline that converts court decisions into legal commentaries for specific German Civil Code sections, using paragraph extraction, summarization, keyword clustering, and large language models to generate headings and citation-rich sections. The system processes 4,555 decisions from the German Federal Court of Justice, evaluates the output on relevance, heading match, citation faithfulness, cluster distinction, and logical ordering, and demonstrates that rapid, low-cost commentary generation is feasible while noting limitations due to source restrictions and legal reasoning norms.
By Max Prior, Niklas Wais, Matthias Grabmair
The paper investigates prompt injection attacks on 14 open‑source and 3 closed‑source large language models (LLMs), introducing a new metric called Attack Success Probability (ASP) that accounts for uncertainty in model responses. It demonstrates that a simple hypnotism attack can trigger objectionable behavior in models such as StableLM2, Mistral, Openchat, and Vicuna, achieving roughly 90% ASP. The study highlights that moderately well‑known LLMs are particularly vulnerable, underscoring the importance of public awareness and effective mitigation strategies.
By Jiawen Wang, Pritha Gupta, Eyke H\"ullermeier, Xiaoxue Gao, Nancy F. Chen
ProCAP introduces a probabilistic cross-attentive prompt learning framework for vision-language models like CLIP, enabling improved cross-modal interaction without updating the backbone. It jointly learns visual and textual prompt tokens, linking them via stacked bidirectional multi-head cross-attention to refine each branch across prompt depth. The method incorporates Gaussian parameterization of prompt tokens, lightweight KL and L2 regularization, and a compact symmetric InfoNCE head to align image features with class-level text representations, achieving strong few-shot base-to-novel performance and competitive transfer results across multiple datasets and benchmarks.
By Hiwa Azeez Abbas, Fatemeh Daneshfar, Moloud Abdar
The paper introduces Conditional Predictive Sufficient Statistics (CPSS) as a formal way to capture useful visual representations that preserve latent factors shared with future data while discarding noise. It shows that predicting the next image patch embedding with a cosine loss approximates maximum likelihood under a von Mises-Fisher model, and that stop‑gradient alone does not enforce sufficiency. Experiments on MNIST and CIFAR‑10 with small causal Transformers demonstrate that CPSS readouts outperform intermediate blocks, while removing stop‑gradient collapses embedding rank even when the pretext loss appears perfect.
By Yuzhou Hong
The paper introduces LLPR, a framework that integrates location-aware learning and physics-based reconstruction to remove raindrops from single images. It replaces costly preprocessing masks with a learnable branch that can be discarded during inference, and reconstructs the background by first estimating a transparency matrix and raindrop layer using a physical model. The authors also present a new real-world dataset and show that LLPR outperforms existing state‑of‑the‑art methods.
By Zewei He, Xingyu Liu, Xing Luo, Guizhong Fu, Zixuan Chen, Yu Chen, Jinlei Li, Zhe-Ming Lu
ManiVid introduces a unified forensic analysis framework for manipulated videos, combining forgery detection, artifact grounding, and anomaly explanation. The authors release ManiVid-38K, a large dataset of 19K real‑fake video pairs with authenticity labels, forgery masks, and explanations, and a benchmark ManiVidBench with 1K balanced pairs. ManiVidLens, the proposed model, outperforms existing methods in artifact grounding and anomaly explanation while matching state‑of‑the‑art detection accuracy.
By Hengrui Kang, Zhonghao Yan, Yuxuan Yang, Ruoyan Jing, Yuncheng Guo, Hao Chen, Kongming Liang, Zhanyu Ma, Conghui He, Weijia Li
The paper introduces CytoCRF, a conditional random field framework tailored for cytology images. It adapts pairwise terms to focus on chromatin and cytology-specific staining and enriches neighborhood information by combining multiple backbone models. Across ten cytology datasets, CytoCRF surpasses existing CRF methods at all annotation budgets, achieving up to +13.6 percentage points over the best baseline and +33.7 over zero‑shot performance with only 50 annotations.
By Manon Dausort, Tiffanie Godelaine, Karim El Khoury, Maxime Zanella, Christophe De Vleeschouwer, Beno\^it Macq
The paper introduces SlideTIM, a transductive few‑shot classification method tailored for whole‑slide images (WSIs). SlideTIM extends the LC‑TIM approach by adding a spatial‑latent regularizer and a class‑distribution prior, ensuring that spatially and semantically similar patches receive consistent predictions and that predicted class proportions are calibrated. Experiments on four histology datasets show that SlideTIM outperforms existing TIM variants, boosting macro‑F1 scores by up to 8.1 percentage points over the best baseline and 19.4 percentage points over zero‑shot predictions at one shot.
By Tiffanie Godelaine, Manon Dausort, Karim El Khoury, Beno\^it G\'erin, Beno\^it Macq, Christophe De Vleeschouwer
The paper introduces HetA-DiT, a heterogeneous attention mechanism for video diffusion models that allocates computation based on token difficulty. A lightweight uncertainty branch predicts denoising difficulty, routing uncertain tokens through dense global attention while applying efficient local attention to reliable tokens. This adaptive routing retains global context where needed, offers a single parameter to balance quality and efficiency, and achieves competitive generation quality while only about 20% of tokens use dense attention.
By Olga Zatsarynna, Denis Korzhenkov, Juergen Gall, Amir Habibian, Mohsen Ghafoorian
The paper evaluates pseudo‑labeling to adapt pretrained ASR models (Whisper and Qwen3‑ASR) for noisy Broadcast Police Communication (BPC) from Baltimore and Chicago. It finds that internal confidence metrics cannot reliably separate high‑ and low‑quality pseudo‑labels, and proposes an external LLM‑as‑a‑judge filtering approach that more aggressively removes implausible transcripts, reducing WER. Additionally, a cross‑model pseudo‑labeling strategy is introduced, where one model is fine‑tuned with pseudo‑labels from the other, showing promise for future work.
By Kaavya Chaparala, Su Huang, Stephen L. Miller, Rhiannon N. Miller, Anjalie Field
The paper investigates whether audio large language models (Audio LLMs) can detect when their own transcriptions are unreliable. It finds that the models are poor at self-assessment and that existing methods offer limited detection. By leveraging audio-encoder representations, the authors develop a lightweight predictor that accurately flags unreliable transcriptions and can prompt user clarification without altering the underlying model.
By Amirhosein Javadi, Richa Dixit, Mehrdad Farajtabar, Minsik Cho, Devang Naik, Mohammad Samragh
The paper titled "Cheap, open agents make LLM pollution harder to mitigate" reports that open-weight language models combined with open-source agentic frameworks can produce synthetic survey responses that are competitive with commercial agents and harder to detect. The authors compared nine agent configurations, finding that fully open agents run locally without usage fees and that no single detection check reliably identifies all agents. Open-text responses were the most effective at distinguishing agents from humans, highlighting the need for multilayered detection strategies.
By Raluca Rilla, Anne-Marie Nussberger, Rui Mata, Dirk U. Wulff
Scepsy is a serving system designed to efficiently schedule arbitrary multi‑LLM agentic workflows on GPU clusters. It leverages the observation that each LLM’s share of execution time remains relatively stable across requests, profiling LLMs under various parallelism levels to build an Aggregate LLM Pipeline that predicts throughput and latency. Using this predictor, Scepsy searches for optimal GPU allocations—balancing fractional GPU shares, tensor parallelism, and replica counts—and then heuristically places them on the cluster to reduce fragmentation and honor network topology, achieving up to 2.5× higher throughput and 1.0–3.3× lower latency compared to baseline approaches.
By Otto White, Marcel Wagenl\"ander, Britannio Jarrett, Xijin Zhao, Yanda Tao, Pedro Silvestre, Guo Li, Huanzhou Zhu, Llu\'is Vilanova, Peter Pietzuch
arXiv:2609.31170v1 Announce Type: new
Abstract: Task-driven image restoration aims to improve both image quality and downstream task performance. However, existing methods predominantly focus on sing...
By Yanjie Tu, Qingsen Yan, Axi Niu, Wenxuan Cai, Tao Hu, Wei Dong, Haokui Zhang