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Normal view

Received — 13 April 2026 ⏭

Establishment and characterization of an immortalized porcine gastric epithelial cell line and identification of NPC1 as a key mediator of aflatoxin B1 toxicity

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Liqi Liu · JianLin Yuan · Jinyi Bai · Yuhan Ma · Kaitao Kong · Xiaofei He · Shuangshuang Chen · Jin Chai · Keren Long · Long Jin · Mingzhou Li · Juan Deng
12 April 2026 at 18:00

Gene. 2026 Apr 9:150160. doi: 10.1016/j.gene.2026.150160. Online ahead of print.

ABSTRACT

Porcine gastric epithelial cells (PGECs) serve as a valuable model for studying the molecular and pathogenic mechanisms of the stomach. However, PGECs face limitations such as isolation challenges, short lifespan, and restricted proliferation. To address this, we established an immortalized PGECs (i-PGECs) to enable in vitro investigation of pathogen infection mechanisms. Primary PGECs were isolated from the acid-secreting glands using stepwise digestion with multiple enzymes (dispase II/collagenase I/hyaluronidase). Immortalization was achieved via lentiviral vectors expressing simian virus 40 large T antigen (SV40T) and human telomerase reverse transcriptase (hTERT), with successful expression confirmed by qRT-PCR (P < 0.05). Epithelial identity of i-PGECs was confirmed by stable expression of CK18, EpCAM, and E-cadherin, as shown by qRT-PCR and immunofluorescence. i-PGECs retained the morphological and ultrastructural features of PGECs and exhibited enhanced proliferation, as demonstrated by WST-8 assays, apoptosis and cell cycle analysis, karyotyping, and transmission electron microscopy (TEM). Telomere length analysis and scratch wound assays demonstrated stable telomere maintenance and consistent migration capacity unaffected by passaging. RNA-sequencing and differential expressed genes (DEGs) analysis revealed significantly upregulating of genes involved in cell proliferation pathways (P < 0.01). Following aflatoxin B1 (AFB1) exposure, i-PGECs significantly upregulated immune-related factors, such as NPC1 and PLAUR (P < 0.01). CRISPR/Cas9-mediated knockout of NPC1 in i-PGECs conferred increased resistance to AFB1-induced cytotoxicity, as shown by WST-8 assay. The i-PGECs remained stable after more than 50 passages, supporting their use as a reliable for in vitro model investigating the mechanisms of toxicity infection in the porcine gastric epithelium.

PMID:41966285 | DOI:10.1016/j.gene.2026.150160

Epigenome-wide Mendelian randomization with multi-omics validation identifies epigenetic drivers of idiopathic pulmonary fibrosis

✇Omics In Lung
By: Zhaoqi Li · Luna Hong · Xingyu Lv · Shuangyu Yang · Fengzhan Ye · Linjie Huang · Shanping Jiang · Huiying Zhao
11 April 2026 at 18:00

Commun Biol. 2026 Apr 11. doi: 10.1038/s42003-026-10033-1. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a complex disease without clear etiology or effective therapy. While DNA methylation has been implicated in IPF pathogenesis, the tissue-specific causal effects of the epigenetic factors on IPF remain undetermined. Here, we perform epigenome-wide Mendelian randomization using blood-based methylation quantitative trait loci of 420,509 CpG sites and genome-wide association study for IPF to elucidate the causal effects of the CpG sites on IPF. Totally, 452 CpG sites has shown putative causal effects on IPF risk after Bonferroni correction. Among them, 13 CpG sites have shown strong colocalization evidence with genetic factors associated with IPF. Specifically, DNA methylation at CpG sites within MAN2A2 and TRIM27 shows significant differences between IPF lungs and controls, correlating with altered mRNA expressions of these genes in lung tissues. The CpG site in MAN2A2 is a binding site of ZNF384 according to transcription factor databases. RNA sequencing in the TGFβ1-induced alveolar epithelia confirms significantly reduced expression of MAN2A2 and ZNF384 comparing to the controls. Collectively, our study suggests a putative causal link between DNA methylation within MAN2A2 and IPF risk, wherein lung-specific DNA methylation in MAN2A2 may perturb the interaction between ZNF384 and MAN2A2, revealing novel roles for these genes in IPF pathogenesis.

PMID:41965819 | DOI:10.1038/s42003-026-10033-1

Epigenome-wide Mendelian randomization with multi-omics validation identifies epigenetic drivers of idiopathic pulmonary fibrosis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zhaoqi Li · Luna Hong · Xingyu Lv · Shuangyu Yang · Fengzhan Ye · Linjie Huang · Shanping Jiang · Huiying Zhao
11 April 2026 at 18:00

Commun Biol. 2026 Apr 11. doi: 10.1038/s42003-026-10033-1. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a complex disease without clear etiology or effective therapy. While DNA methylation has been implicated in IPF pathogenesis, the tissue-specific causal effects of the epigenetic factors on IPF remain undetermined. Here, we perform epigenome-wide Mendelian randomization using blood-based methylation quantitative trait loci of 420,509 CpG sites and genome-wide association study for IPF to elucidate the causal effects of the CpG sites on IPF. Totally, 452 CpG sites has shown putative causal effects on IPF risk after Bonferroni correction. Among them, 13 CpG sites have shown strong colocalization evidence with genetic factors associated with IPF. Specifically, DNA methylation at CpG sites within MAN2A2 and TRIM27 shows significant differences between IPF lungs and controls, correlating with altered mRNA expressions of these genes in lung tissues. The CpG site in MAN2A2 is a binding site of ZNF384 according to transcription factor databases. RNA sequencing in the TGFβ1-induced alveolar epithelia confirms significantly reduced expression of MAN2A2 and ZNF384 comparing to the controls. Collectively, our study suggests a putative causal link between DNA methylation within MAN2A2 and IPF risk, wherein lung-specific DNA methylation in MAN2A2 may perturb the interaction between ZNF384 and MAN2A2, revealing novel roles for these genes in IPF pathogenesis.

PMID:41965819 | DOI:10.1038/s42003-026-10033-1

Received — 8 April 2026 ⏭

ClawArena: Benchmarking AI Agents in Evolving Information Environments

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haonian Ji · Kaiwen Xiong · Siwei Han · Peng Xia · Shi Qiu · Yiyang Zhou · Jiaqi Liu · Jinlong Li · Bingzhou Li · Zeyu Zheng · Cihang Xie · Huaxiu Yao
7 April 2026 at 12:00
arXiv:2604.04202v1 Announce Type: cross Abstract: AI agents deployed as persistent assistants must maintain correct beliefs as their information environment evolves. In practice, evidence is scattered across heterogeneous sources that often contradict one another, new information can invalidate earlier conclusions, and user preferences surface through corrections rather than explicit instructions. Existing benchmarks largely assume static, single-authority settings and do not evaluate whether agents can keep up with this complexity. We introduce ClawArena, a benchmark for evaluating AI agents in evolving information environments. Each scenario maintains a complete hidden ground truth while exposing the agent only to noisy, partial, and sometimes contradictory traces across multi-channel sessions, workspace files, and staged updates. Evaluation is organized around three coupled challenges: multi-source conflict reasoning, dynamic belief revision, and implicit personalization, whose interactions yield a 14-category question taxonomy. Two question formats, multi-choice (set-selection) and shell-based executable checks, test both reasoning and workspace grounding. The current release contains 64 scenarios across 8 professional domains, totaling 1{,}879 evaluation rounds and 365 dynamic updates. Experiments on five agent frameworks and five language models show that both model capability (15.4% range) and framework design (9.2%) substantially affect performance, that self-evolving skill frameworks can partially close model-capability gaps, and that belief revision difficulty is determined by update design strategy rather than the mere presence of updates. Code is available at https://github.com/aiming-lab/ClawArena.

Representation learning to advance multi-institutional studies with electronic health record data from US and France

✇cs.AI, q-bio.NC updates on arXiv.org
By: Doudou Zhou · Han Tong · Linshanshan Wang · Suqi Liu · Xin Xiong · Ziming Gan · Romain Griffier · Boris Hejblum · Yun-Chung Liu · Chuan Hong · Clara-Lea Bonzel · Tianrun Cai · Kevin Pan · Yuk-Lam Ho · Lauren Costa · Vidul A. Panickan · J. Michael Gaziano · Kenneth Mandl · Vianney Jouhet · Rodolphe Thiebaut · Zongqi Xia · Kelly Cho · Katherine Liao · Tianxi Cai
7 April 2026 at 12:00
arXiv:2502.08547v2 Announce Type: replace Abstract: The widespread adoption of electronic health records has created new opportunities for translational clinical research, yet this promise remains constrained by fragmented data across privacy-siloed institutions and substantial heterogeneity in local coding practices. While privacy-preserving collaborative learning allows institutions to work together without sharing patient-level data, it does not address inconsistencies in how clinical concepts are represented across sites. We introduce a graph-based framework that addresses this gap by treating data harmonization as a scalable representation learning problem. Rather than relying on fixed standards or manual mappings, the framework integrates institution-specific summary statistics from health records, curated biomedical knowledge graphs, and semantic information derived from large language models to learn a shared semantic space. This joint learning approach aligns diverse, site-specific vocabularies while preserving patient privacy. Evaluated across seven institutions and two languages, the framework provides a robust, data-centric foundation for training and deploying clinical models across heterogeneous healthcare systems.

A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mengqi Li · Lei Zhao · Anthony Man-Cho So · Ruoyu Sun · Xiao Li
7 April 2026 at 12:00
arXiv:2510.18814v2 Announce Type: replace-cross Abstract: Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training? We show that they can. We propose Self-evolving Post-Training (SePT), a simple post-training method that alternates between self-generation and training on self-generated responses. It repeatedly samples questions, uses the model itself to generate low-temperature responses, and then finetunes the model on the self-generated data. In this self-training loop, we use an online data refresh mechanism, where each new batch is generated by the most recently updated model. Across six math reasoning benchmarks, SePT improves a strong no-training baseline, defined as the untuned base model evaluated at its best swept decoding temperature, on several tested models. In some settings, SePT can even approach the performance of Reinforcement Learning with Verifiable Rewards (RLVR). Additional ablations demonstrate the importance of online data refresh and temperature decoupling. Overall, our results identify a practical regime in which reasoning can be improved using self-generated supervision alone. Our code is available at https://github.com/ElementQi/SePT.
Received — 4 April 2026 ⏭

Hierarchical Memory Orchestration for Personalized Persistent Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junming Liu · Yifei Sun · Weihua Cheng · Haodong Lei · Yuqi Li · Yirong Chen · Ding Wang
3 April 2026 at 12:00
arXiv:2604.01670v1 Announce Type: new Abstract: While long-term memory is essential for intelligent agents to maintain consistent historical awareness, the accumulation of extensive interaction data often leads to performance bottlenecks. Naive storage expansion increases retrieval noise and computational latency, overwhelming the reasoning capacity of models deployed on constrained personal devices. To address this, we propose Hierarchical Memory Orchestration (HMO), a framework that organizes interaction history into a three-tiered directory driven by user-centric contextual relevance. Our system maintains a compact primary cache, coupling recent and pivotal memories with an evolving user profile to ensure agent reasoning remains aligned with individual behavioral traits. This primary cache is complemented by a high-priority secondary layer, both of which are managed within a global archive of the full interaction history. Crucially, the user persona dictates memory redistribution across this hierarchy, promoting records mapped to long-term patterns toward more active tiers while relegating less relevant information. This targeted orchestration surfaces historical knowledge precisely when needed while maintaining a lean and efficient active search space. Evaluations on multiple benchmarks achieve state-of-the-art performance. Real-world deployments in ecosystems like OpenClaw demonstrate that HMO significantly enhances agent fluidity and personalization.

DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jinliang Xu · Bingqi Li
3 April 2026 at 12:00
arXiv:2604.01236v1 Announce Type: cross Abstract: Traditional network architectures suffer from severe protocol ossification and structural fragility due to their reliance on static, human-defined rules that fail to adapt to the emergent edge cases and probabilistic reasoning of modern autonomous agents. To address these limitations, this paper proposes DarwinNet, a bio-inspired, self-evolving network architecture that transitions communication protocols from a \textit{design-time} static paradigm to a \textit{runtime} growth paradigm. DarwinNet utilizes a tri-layered framework-comprising an immutable physical anchor (L0), a WebAssembly-based fluid cortex (L1), and an LLM-driven Darwin cortex (L2)-to synthesize high-level business intents into executable bytecode through a dual-loop \textit{Intent-to-Bytecode} (I2B) mechanism. We introduce the Protocol Solidification Index (PSI) to quantify the evolutionary maturity of the system as it collapses from high-latency intelligent reasoning (Slow Thinking) toward near-native execution (Fast Thinking). Validated through a reliability growth framework based on the Crow-AMSAA model, experimental results demonstrate that DarwinNet achieves anti-fragility by treating environmental anomalies as catalysts for autonomous evolution. Our findings confirm that DarwinNet can effectively converge toward physical performance limits while ensuring endogenous security through zero-trust sandboxing, providing a viable path for the next generation of intelligent, self-optimizing networks.

Omni-SimpleMem: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaqi Liu · Zipeng Ling · Shi Qiu · Yanqing Liu · Siwei Han · Peng Xia · Haoqin Tu · Zeyu Zheng · Cihang Xie · Charles Fleming · Mingyu Ding · Huaxiu Yao
3 April 2026 at 12:00
arXiv:2604.01007v2 Announce Type: replace Abstract: AI agents increasingly operate over extended time horizons, yet their ability to retain, organize, and recall multimodal experiences remains a critical bottleneck. Building effective lifelong memory requires navigating a vast design space spanning architecture, retrieval strategies, prompt engineering, and data pipelines; this space is too large and interconnected for manual exploration or traditional AutoML to explore effectively. We deploy an autonomous research pipeline to discover Omni-SimpleMem, a unified multimodal memory framework for lifelong AI agents. Starting from a na\"ive baseline (F1=0.117 on LoCoMo), the pipeline autonomously executes ${\sim}50$ experiments across two benchmarks, diagnosing failure modes, proposing architectural modifications, and repairing data pipeline bugs, all without human intervention in the inner loop. The resulting system achieves state-of-the-art on both benchmarks, improving F1 by +411% on LoCoMo (0.117$\to$0.598) and +214% on Mem-Gallery (0.254$\to$0.797) relative to the initial configurations. Critically, the most impactful discoveries are not hyperparameter adjustments: bug fixes (+175%), architectural changes (+44%), and prompt engineering (+188% on specific categories) each individually exceed the cumulative contribution of all hyperparameter tuning, demonstrating capabilities fundamentally beyond the reach of traditional AutoML. We provide a taxonomy of six discovery types and identify four properties that make multimodal memory particularly suited for autoresearch, offering guidance for applying autonomous research pipelines to other AI system domains. Code is available at this https://github.com/aiming-lab/SimpleMem.

Well-Being and Cognitive Factors Influencing Health Care Workers’ Adherence to Internet-Based Stress Management: Mixed Methods Analysis of a Nonrandomized Controlled Study

✇Journal of Medical Internet Research
By: Miqi Li · Quan Wang · Danli Zheng · Danni Feng · Xiaorong Lang
2 April 2026 at 23:00
Background: High stress levels are common among health care workers (HCWs), threatening their health and workforce stability. Internet-based mobile stress management (MSM) is a promising intervention for reducing work-related stress; however, poor adherence limits effectiveness. Exploring factors influencing HCWs’ adherence may thus aid in developing optimal interventions. Objective: The research aimed to investigate (1) how HCWs’ well-being and cognitive factors influenced MSM treatment adherence and (2) what HCWs’ specific needs for MSM were. Methods: This study was a convergent mixed methods secondary analysis of a nonrandomized controlled trial. HCWs who were currently employed, had internet access, had no serious medical problems, and were willing to participate were recruited by convenience sampling through an MSM project in a large Chinese general hospital from August 11, 2021, to January 31, 2022. Those intending to leave the hospital or with insufficient medical condition for follow-up were excluded. Quantitative data were collected from 157 HCWs (n=135, 86% female participants; mean age of 33.7, SD 4.9 y) electronically via Research Electronic Data Capture (REDCap). Measures included sociodemographic characteristics, the Fatigue Assessment Scale, the 14-item Perceived Stress Scale, a user experience questionnaire, an attitudes scale (perceived usefulness, feasibility, and enjoyment), and self-reported practice frequency. Qualitative data were collected via an open-ended question answered by 96 participants. Quantitative data were analyzed using hierarchical regression and structural equation modeling. Qualitative data were analyzed using reflexive-thematic analysis in NVivo (QSR International). Results: In the quantitative study (n=157), hierarchical regression analyses showed that fatigue was a significant negative predictor of adherence (b=−0.050, 95% CI −0.086 to −0.015; =−2.859; 2-tailed =.005), while user experience (b=0.074, 95% CI 0.042-0.106; =4.569; 2-tailed

Efficient amyloid-β degradation in Alzheimer’s disease using SPYTACs

✇Cell
By: Fei Teng · Jing Liu · Tongtong Cui · Xiangtian Tan · Kailun Liu · Zongren Hou · Li Zhou · Yuanzhi Xie · Rongqi Li · Da Li · Bojin Li · Dongmei Wang · Qi Zhou · Baoyang Hu · Wei Li
4 March 2026 at 08:00
SPYTAC is a synthetic peptide-programmed targeted protein degradation platform harnessing LRP1 to drive lysosomal degradation of extracellular amyloid-β in the brain and periphery. In 5×FAD mice, SPYTAC treatment efficiently degrades amyloid-β, preserves neurons, and improves cognition with reduced neuroinflammation and microhemorrhage when compared with antibody therapy.

Restoring circadian rhythms in the hypothalamic paraventricular nucleus reverses aging biomarkers and extends lifespan in male mice

✇Cell
By: Haijiao Zhao · Meimei Liao · Ran Huo · Ting He · Hongni Tian · Zeqi Li · Chen Chen · Ziqing Yu · Juan Chai · Xiaocui Song · Ruichao Shao · Shuhua Ying · Wen Gao · Ling Liu · Di Sang · Qi Li · Haohong Li · Fengchao Wang · Dapeng Ju · Eric Erquan Zhang
4 March 2026 at 08:00
Enhancing circadian amplitude in mouse hypothalamic paraventricular nucleus neurons by 3′-deoxyadenosine treatment alleviates age-related pathologies and extends lifespan.
Received — 2 April 2026 ⏭

VLA Models Are More Generalizable Than You Think: Revisiting Physical and Spatial Modeling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Weiqi Li · Quande Zhang · Ruifeng Zhai · Liang Lin · Guangrun Wang
1 April 2026 at 12:00
arXiv:2512.02902v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models achieve strong in-distribution performance but degrade sharply under novel camera viewpoints and visual perturbations. We show that this brittleness primarily arises from misalignment in Spatial Modeling, rather than Physical Modeling. To address this, we propose a one-shot adaptation framework that recalibrates visual representations through lightweight, learnable updates. Our first method, Feature Token Modulation (FTM), applies a global affine transformation to visual tokens and improves Libero viewpoint accuracy from 48.5% to 87.1% with only 4K parameters. Building on this, Feature Linear Adaptation (FLA) introduces low-rank updates to the ViT encoder, achieving 90.8% success with 4.7M parameters -- matching LoRA-scale finetuning at far lower cost. Together, these results reveal substantial untapped robustness in pretrained VLA models and demonstrate that targeted, minimal visual adaptation is sufficient to restore viewpoint generalization.
Received — 26 March 2026 ⏭

Chain-of-Authorization: Internalizing Authorization into Large Language Models via Reasoning Trajectories

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yang Li · Yule Liu · Xinlei He · Youjian Zhao · Qi Li · Ke Xu
25 March 2026 at 12:00
arXiv:2603.22869v1 Announce Type: new Abstract: Large Language Models (LLMs) have become core cognitive components in modern artificial intelligence (AI) systems, combining internal knowledge with external context to perform complex tasks. However, LLMs typically treat all accessible data indiscriminately, lacking inherent awareness of knowledge ownership and access boundaries. This deficiency heightens risks of sensitive data leakage and adversarial manipulation, potentially enabling unauthorized system access and severe security crises. Existing protection strategies rely on rigid, uniform defense that prevent dynamic authorization. Structural isolation methods faces scalability bottlenecks, while prompt guidance methods struggle with fine-grained permissions distinctions. Here, we propose the Chain-of-Authorization (CoA) framework, a secure training and reasoning paradigm that internalizes authorization logic into LLMs' core capabilities. Unlike passive external defneses, CoA restructures the model's information flow: it embeds permission context at input and requires generating explicit authorization reasoning trajectory that includes resource review, identity resolution, and decision-making stages before final response. Through supervised fine-tuning on data covering various authorization status, CoA integrates policy execution with task responses, making authorization a causal prerequisite for substantive responses. Extensive evaluations show that CoA not only maintains comparable utility in authorized scenarios but also overcomes the cognitive confusion when permissions mismatches. It exhibits high rejection rates against various unauthorized and adversarial access. This mechanism leverages LLMs' reasoning capability to perform dynamic authorization, using natural language understanding as a proactive security mechanism for deploying reliable LLMs in modern AI systems.
Received — 17 March 2026 ⏭

IGASA: Integrated Geometry-Aware and Skip-Attention Modules for Enhanced Point Cloud Registration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dongxu Zhang · Jihua Zhu · Shiqi Li · Wenbiao Yan · Haoran Xu · Peilin Fan · Huimin Lu
16 March 2026 at 12:00
arXiv:2603.12719v1 Announce Type: cross Abstract: Point cloud registration (PCR) is a fundamental task in 3D vision and provides essential support for applications such as autonomous driving, robotics, and environmental modeling. Despite its widespread use, existing methods often fail when facing real-world challenges like heavy noise, significant occlusions, and large-scale transformations. These limitations frequently result in compromised registration accuracy and insufficient robustness in complex environments. In this paper, we propose IGASA as a novel registration framework constructed upon a Hierarchical Pyramid Architecture (HPA) designed for robust multi-scale feature extraction and fusion. The framework integrates two pivotal components consisting of the Hierarchical Cross-Layer Attention (HCLA) module and the Iterative Geometry-Aware Refinement (IGAR) module. The HCLA module utilizes skip attention mechanisms to align multi-resolution features and enhance local geometric consistency. Simultaneously, the IGAR module is designed for the fine matching phase by leveraging reliable correspondences established during coarse matching. This synergistic integration within the architecture allows IGASA to adapt effectively to diverse point cloud structures and intricate transformations. We evaluate the performance of IGASA on four widely recognized benchmark datasets including 3D(Lo)Match, KITTI, and nuScenes. Our extensive experiments consistently demonstrate that IGASA significantly surpasses state-of-the-art methods and achieves notable improvements in registration accuracy. This work provides a robust foundation for advancing point cloud registration techniques while offering valuable insights for practical 3D vision applications. The code for IGASA is available in \href{https://github.com/DongXu-Zhang/IGASA}{https://github.com/DongXu-Zhang/IGASA}.

Purify Once, Edit Freely: Breaking Image Protections under Model Mismatch

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qichen Zhao · Shengfang Zhai · Xinjian Bai · Qingni Shen · Qiqi Lin · Yansong Gao · Zhonghai Wu
16 March 2026 at 12:00
arXiv:2603.13028v1 Announce Type: cross Abstract: Diffusion models enable high-fidelity image editing but can also be misused for unauthorized style imitation and harmful content generation. To mitigate these risks, proactive image protection methods embed small, often imperceptible adversarial perturbations into images before sharing to disrupt downstream editing or fine-tuning. However, in realistic post-release scenarios, content owners cannot control downstream processing pipelines, and protections optimized for a surrogate model may fail when attackers use mismatched diffusion pipelines. Existing purification methods can weaken protections but often sacrifice image quality and rarely examine architectural mismatch. We introduce a unified post-release purification framework to evaluate protection survivability under model mismatch. We propose two practical purifiers: VAE-Trans, which corrects protected images via latent-space projection, and EditorClean, which performs instruction-guided reconstruction with a Diffusion Transformer to exploit architectural heterogeneity. Both operate without access to protected images or defense internals. Across 2,100 editing tasks and six representative protection methods, EditorClean consistently restores editability. Compared to protected inputs, it improves PSNR by 3-6 dB and reduces FID by 50-70 percent on downstream edits, while outperforming prior purification baselines by about 2 dB PSNR and 30 percent lower FID. Our results reveal a purify-once, edit-freely failure mode: once purification succeeds, the protective signal is largely removed, enabling unrestricted editing. This highlights the need to evaluate protections under model mismatch and design defenses robust to heterogeneous attackers.

MalURLBench: A Benchmark Evaluating Agents' Vulnerabilities When Processing Web URLs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dezhang Kong · Zhuxi Wu · Shiqi Liu · Zhicheng Tan · Kuichen Lu · Minghao Li · Qichen Liu · Shengyu Chu · Zhenhua Xu · Xuan Liu · Meng Han
16 March 2026 at 12:00
arXiv:2601.18113v3 Announce Type: replace-cross Abstract: LLM-based web agents have become increasingly popular for their utility in daily life and work. However, they exhibit critical vulnerabilities when processing malicious URLs: accepting a disguised malicious URL enables subsequent access to unsafe webpages, which can cause severe damage to service providers and users. Despite this risk, no benchmark currently targets this emerging threat. To address this gap, we propose MalURLBench, the first benchmark for evaluating LLMs' vulnerabilities to malicious URLs. MalURLBench contains 61,845 attack instances spanning 10 real-world scenarios and 7 categories of real malicious websites. Experiments with 12 popular LLMs reveal that existing models struggle to detect elaborately disguised malicious URLs. We further identify and analyze key factors that impact attack success rates and propose URLGuard, a lightweight defense module. We believe this work will provide a foundational resource for advancing the security of web agents. Our code is available at https://github.com/JiangYingEr/MalURLBench.

VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenqi Liu · Yunxiao Wang · Shijie Ma · Meng Liu · Qile Su · Tianke Zhang · Haonan Fan · Changyi Liu · Kaiyu Jiang · Jiankang Chen · Kaiyu Tang · Bin Wen · Fan Yang · Tingting Gao · Han Li · Yinwei Wei · Xuemeng Song
16 March 2026 at 12:00
arXiv:2602.07801v3 Announce Type: replace-cross Abstract: In long-video understanding, conventional uniform frame sampling often fails to capture key visual evidence, leading to degraded performance and increased hallucinations. To address this, recent agentic thinking-with-videos paradigms have emerged, adopting a localize-clip-answer pipeline in which the model actively identifies relevant video segments, performs dense sampling within those clips, and then produces answers. However, existing methods remain inefficient, suffer from weak localization, and adhere to rigid workflows. To solve these issues, we propose VideoTemp-o3, a unified agentic thinking-with-videos framework that jointly models video grounding and question answering. VideoTemp-o3 exhibits strong localization capability, supports on-demand clipping, and can refine inaccurate localizations. Specifically, in the supervised fine-tuning stage, we design a unified masking mechanism that encourages exploration while preventing noise. For reinforcement learning, we introduce dedicated rewards to mitigate reward hacking. Besides, from the data perspective, we develop an effective pipeline to construct high-quality long video grounded QA data, along with a corresponding benchmark for systematic evaluation across various video durations. Experimental results demonstrate that our method achieves remarkable performance on both long video understanding and grounding.
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