MetaRAG introduces a belief-action aligned policy optimization framework for agentic retrieval-augmented generation (RAG). It incorporates Verify-first Action Generation and Internal Belief Probing to assess whether the current evidence is sufficient before taking an action, and uses a consistency reward gated by answer correctness to guide training. Experiments on seven public QA benchmarks demonstrate that MetaRAG improves the accuracy-efficiency trade-off over existing RL-based agentic RAG baselines, with benefits that transfer across research settings, optimizers, and model backbones.
By Qiuyi Qi, Tian Liang, Jiamu Wang, Jinjian Zhang, Wei Zhou, Pengcheng Zhu, Linjian Mo, Ming Kong, Jie Liu, Qiang Zhu
arXiv:2604.09057v3 Announce Type: replace
Abstract: Audio-video (AV) generation has recently made strong progress in perceptual quality and multimodal coherence, yet generating content with plausible...
By Junchao Liao, Zhenghao Zhang, Xiangyu Meng, Litao Li, Ziying Zhang, Siyu Zhu, Long Qin, Weizhi Wang
The paper "Ghaib in Translation" investigates how large language models (LLMs) handle Urdu, a widely spoken language that is largely absent from safety evaluations. Five prominent LLMs—GPT‑4o, Claude Sonnet 4.5, Gemini 2.5 Flash, Qwen‑2.5, and Llama‑3.1—were tested on six datasets covering Nastaliq Urdu, Roman Urdu, English, and code‑switched Urdu‑English. The study found significant label instability between original‑script and English‑translation classifications, with missed‑in‑Urdu rates ranging from 2.4% to 9.9% (median 4.3%). A review of 205 papers across nine ALW/WOAH editions revealed no dedicated Urdu research, underscoring the language’s neglect in current safety research.
By Fawzia Zehra (Fuzzy), Kara-Isitt, Sonal Khosla, Stephen Swift
The paper introduces Variance‑Guided Spatial Attention Fusion (VG‑SAF), a method for robust end‑to‑end driving that fuses camera and LiDAR data while handling asymmetric sensor degradation. VG‑SAF uses a physically grounded augmentor to generate dense reliability masks, modality‑specific experts to predict per‑pixel reliability scales, and a hybrid attention mechanism that gates unreliable cells and balances modalities. The approach also includes a Laplace uncertainty head to signal severe or combined sensor failures, and demonstrates improved closed‑loop robustness on the CARLA Longest6 benchmark across various degradation scenarios.
By Weizhi Tao, Zengwang Jin, Xiao Wang, Hailong Huang
O3N is a novel framework that performs open‑vocabulary occupancy prediction from a single omnidirectional RGB image. It introduces a polar‑spiral voxel embedding (PsM) for continuous 360° spatial representation, an Occupancy Cost Aggregation (OCA) module that unifies geometric and semantic supervision, and a Natural Modality Alignment (NMA) pathway that aligns visual, voxel, and text features. Experiments show state‑of‑the‑art results on QuadOcc and Human360Occ benchmarks, with strong cross‑scene generalization and semantic scalability.
By Mengfei Duan, Hao Shi, Fei Teng, Guoqiang Zhao, Yuheng Zhang, Zhiyong Li, Kailun Yang
The paper investigates the blending ratio used in few‑shot adaptation of vision‑language models, which combines a zero‑shot text prototype with the mean of labeled image features. It shows that the theoretically optimal ratio—derived from a closed‑form mean‑squared error minimizer—does not align with the ratio that actually maximizes performance, falling short by an average of 8.5 points. Moreover, a leave‑one‑out estimate on the support set achieves near‑oracle performance, and validation‑free linear probes outperform even oracle‑tuned blends, indicating that the hyperparameter can be set near‑optimally without external validation data.
By Liangzhi Li, Bowen Wang, Yiming Qian, Thorsten Neumann, Xia Xie, Guangshun Li
The paper introduces a circuit‑grounded framework that links training‑dynamics‑based data valuation with mechanistic interpretability. It defines data quality along learnability, challenge, and alignment, identifies internal model circuits that control these utilities, and uses them as controllable interfaces for data generation. The authors present SAMS, a stage‑aware scheduling method that steers circuit‑guided data to match the model’s evolving optimization needs, achieving more diverse and effective data than prompt‑based baselines on multiple‑choice QA tasks.
By Nakyung Lee, Sangwoo Hong, Jungwoo Lee
The paper introduces NAIMA, a guided depth super‑resolution framework that leverages global contextual semantic priors from pretrained vision transformer token embeddings. Its Guided Token Attention (GTA) module uses depth encodings as queries to attend over semantic tokens, with a zero‑initialized gate controlling the influence of semantic evidence. NAIMA achieves competitive in‑distribution performance while delivering superior cross‑dataset generalization without relying on decoded priors or auxiliary objectives.
By Tayyab Nasir, Daochang Liu, Ajmal Mian
CoMMa is a decentralized large language model (LLM) agent framework designed for oncology decision support. It allows specialists to work on partitioned clinical data streams, enforcing data decentralization and enhancing role specialization through agent-specific fine‑tuning. The framework introduces a contribution‑aware aggregation mechanism that replaces stochastic reasoning with deterministic embedding projections, enabling explicit credit assignment and a stable, interpretable decision pathway.
By Yichen Wu, Kailong Fan, Sangjoon Park, Yuhan Liu, Zhiyi Shi, Sekeun Kim, Dania Daye, Hana Farzaneh, Xiang Li, Raul Uppot, Yujin Oh, Quanzheng Li
arXiv:2608.24631v1 Announce Type: cross
Abstract: Similarity in many decision systems is governed not by distance alone but by interactions among variables. In fraud and anomaly detection, small loca...
By Hanqiu Peng, Jianlong Lu, Ying Chen
OpenAI shares findings from the Hugging Face security incident and the steps we’re taking to strengthen AI model security, monitoring, and alignment.
The development of federated learning (FL) techniques has helped improve the privacy preservation of users' data and extended the applications of machine learning models. However, the involvement of a...
Whole-heart segmentation from CT and MRI is essential for quantitative cardiac image analysis, but remains challenging under multi-center and multi-modality distribution shift. In the CARE whole-heart...
Action-conditioned world models are increasingly used as learned simulators for policy evaluation and improvement, yet their effectiveness rests on an unverified assumption: generated futures faithful...
Outcome-supervised search agents learn when and how to retrieve evidence, but terminal rewards neither localize intermediate errors nor redirect an ongoing trajectory before those errors compound. Tre...
Discovering stable neuron behavior across entire domains remains a challenge in mechanistic interpretability. Existing methods often rely on instance-level point estimates or computationally expensive...
Existing linear program (LP) and semidefinite program (SDP) relaxations for rectified linear unit (ReLU) neural network (NN) verification yield overly-conservative safety guarantees due to significant...
Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent representations and provide limited interpretabilit...
While text-based hallucination detection has been extensively studied, spoken hallucination detection remains largely unexplored, particularly for low-resource languages. We present the first multilin...
Markov models are established tools for symbolic music, including non-homogeneous formulations. The narrower contribution examined here is an observation-driven estimation mechanism: overlapping slidi...