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Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yudong Gao · Linghan Chen · Wenhan Wu · Mia Zhou · Jiyao Wang · Kaiyan Ji · Mingyu Guo · Honglong Chen
arXiv:2608.15475v3 Announce Type: replace-cross Abstract: Quantized Vision-Language-Action (VLA) models expose a weight-fault surface: Rowhammer-style faults can corrupt deployed INT8 bits. We present the first bit-flip attack on a VLA: a few gradient-selected flips reduce closed-loop success to $0\%$, while hundreds of random flips are harmless. Across four model variants spanning three action-head families, damaging bits concentrate in a few action-generating layers, but the empirical budget depends sharply on the head: direct regression and token policies fall in $1$--$5$ flips, whereas the evaluated flow-matching policies require ${\sim}100$--$300$. Our fixed-direction manifold-escape loss cuts \pizero{}'s budget from ${\sim}1000$ to ${\sim}100$ flips, and a matched five-direction sweep shows that the attack is not specific to an all-positive direction. On a direct head, protecting $3.1\%$ of weights preserves $60\%$ success at $K{=}100$, and protecting $5.3\%$ moves the open-loop break threshold from 3 to 100 flips. Finally, task-calibrated emulated $K{=}100$ flips yield $0/20$ real-robot successes, versus $14/20$ clean and $16/20$ global-random. Weight integrity is therefore a security boundary for embodied foundation models. Code is included as ancillary material.
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Perioperative Modulation of the Gut-Liver Axis in Liver Surgery: Clinical Evidence and Future Directions

✇(Multiomics OR Omics) AND (Pancreatic)
By: Yan Sun · Yan Jiao · Wan-Chao Liu

J Vis Exp. 2026 Sep 1;(235). doi: 10.3791/73747.

ABSTRACT

Liver resection and liver transplantation remain cornerstone treatments for many hepatobiliary diseases, yet postoperative infection, impaired liver regeneration, and post-hepatectomy liver failure (PHLF) remain serious complications. Perioperative stressors can disrupt the gut-liver axis by altering the intestinal microbiota, epithelial barrier integrity, microbial metabolites, bile acid signaling, and host immunity. This review examines how these alterations relate to clinical outcomes and evaluates evidence for microbiota-targeted interventions, including probiotics, synbiotics, nutritional optimization, antibiotic stewardship, bile acid modulation, and emerging multiomics strategies. We distinguish liver resection from living-donor and deceased-donor liver transplantation because the patient populations, graft or remnant anatomy, ischemia-reperfusion exposures, immune status, and outcome definitions differ. Clinical evidence most consistently supports selected pro-/synbiotic strategies for reducing postoperative infection in higher-risk settings, whereas microbiome-based prediction of PHLF, fecal microbiota transplantation (FMT), bile acid-directed therapy, and precision multiomics-guided pathways remain investigational. Future work should use transparent literature identification, standardized perioperative protocols, risk-defined populations, external validation, and prospective multicenter trials. A better understanding of gut-liver interactions may help preserve beneficial host-microbial signals while limiting translocation and inflammation during recovery.

PMID:42683887 | DOI:10.3791/73747

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AffordTissue: Dense Affordance Prediction for Tool-Action Specific Tissue Interaction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Aiza Maksutova · Lalithkumar Seenivasan · Hao Ding · Jiru Xu · Chenhao Yu · Chenyan Jing · Yiqing Shen · Mathias Unberath
arXiv:2604.01371v1 Announce Type: cross Abstract: Surgical action automation has progressed rapidly toward achieving surgeon-like dexterous control, driven primarily by advances in learning from demonstration and vision-language-action models. While these have demonstrated success in table-top experiments, translating them to clinical deployment remains challenging: current methods offer limited predictability on where instruments will interact on tissue surfaces and lack explicit conditioning inputs to enforce tool-action-specific safe interaction regions. Addressing this gap, we introduce AffordTissue, a multimodal framework for predicting tool-action specific tissue affordance regions as dense heatmaps during cholecystectomy. Our approach combines a temporal vision encoder capturing tool motion and tissue dynamics across multiple viewpoints, language conditioning enabling generalization across diverse instrument-action pairs, and a DiT-style decoder for dense affordance prediction. We establish the first tissue affordance benchmark by curating and annotating 15,638 video clips across 103 cholecystectomy procedures, covering six unique tool-action pairs involving four instruments (hook, grasper, scissors, clipper) and their associated tasks: dissection, grasping, clipping, and cutting. Experiments demonstrate substantial improvement over vision-language model baselines (20.6 px ASSD vs. 60.2 px for Molmo-VLM), showing that our task-specific architecture outperforms large-scale foundation models for dense surgical affordance prediction. By predicting tool-action specific tissue affordance regions, AffordTissue provides explicit spatial reasoning for safe surgical automation, potentially unlocking explicit policy guidance toward appropriate tissue regions and early safe stop when instruments deviate outside predicted safe zones.
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Targeting sialic acid metabolism: a therapeutic strategy against gastric cancer driven by WZ35

✇Omics in Gastric
By: Boxiang Hong · Ying Wang · Xinran Feng · Lihan Wei · Yichen Huang · Xiawei Zhang · Longyuchen Xu · Pinyan Jin · Liyuan Huang · Rui Li · Xinyu Wu · Zhi Li · Hangyu Ye · Tongke Chen · Xiaoyan Wang · Hao Zhang

Cell Oncol (Dordr). 2026 Mar 23;49(2):60. doi: 10.1007/s13402-026-01194-6.

ABSTRACT

Glycolytic reprogramming is closely associated with the occurrence and progression of gastric cancer. Specifically, the energy derived from glucose metabolism and the cellular proteins by its intermediate products influence gastric cancer development. However, as an important branch of glucose metabolism, sialic acid metabolism and its mediated sialylation modifications remain insufficiently studied in gastric cancer, and their specific relationship with malignant tumor progression requires further exploration. This study employed a multi‑omics approach, integrating metabolomics, single‑cell RNA sequencing, and bulk RNA sequencing analyses, to investigate the metabolic landscape of gastric cancer and its associated alterations. The results indicated that sialic acid is a characteristic metabolite in malignant gastric cancer tissues. It modulates biological functions such as immune response, proliferative activity, and metabolic remodeling within gastric cancer tissues by influencing sialylation modifications. Furthermore, we identified the drug WZ35, which can inhibit the malignant proliferation of gastric cancer by targeting both sialic acid metabolism and sialylated protein modifications. We put forward a conjecture that the metabolism and modification of sialic acid promote the malignant development of gastric cancer, and we discovered that the drug WZ35 has an inhibitory effect on the sialic acid metabolism of gastric cancer.

GRAPHICAL ABSTRACT:

PMID:41870836 | PMC:PMC13009457 | DOI:10.1007/s13402-026-01194-6

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Avoiding Over-smoothing in Social Media Rumor Detection with Pre-trained Propagation Tree Transformer

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chaoqun Cui · Caiyan Jia
arXiv:2603.22854v1 Announce Type: cross Abstract: Deep learning techniques for rumor detection typically utilize Graph Neural Networks (GNNs) to analyze post relations. These methods, however, falter due to over-smoothing issues when processing rumor propagation structures, leading to declining performance. Our investigation into this issue reveals that over-smoothing is intrinsically tied to the structural characteristics of rumor propagation trees, in which the majority of nodes are 1-level nodes. Furthermore, GNNs struggle to capture long-range dependencies within these trees. To circumvent these challenges, we propose a Pre-Trained Propagation Tree Transformer (P2T3) method based on pure Transformer architecture. It extracts all conversation chains from a tree structure following the propagation direction of replies, utilizes token-wise embedding to infuse connection information and introduces necessary inductive bias, and pre-trains on large-scale unlabeled datasets. Experiments indicate that P2T3 surpasses previous state-of-the-art methods in multiple benchmark datasets and performs well under few-shot conditions. P2T3 not only avoids the over-smoothing issue inherent in GNNs but also potentially offers a large model or unified multi-modal scheme for future social media research.
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Global Evolutionary Steering: Refining Activation Steering Control via Cross-Layer Consistency

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinyan Jiang · Wenjing Yu · Di Wang · Lijie Hu
arXiv:2603.12298v1 Announce Type: cross Abstract: Activation engineering enables precise control over Large Language Models (LLMs) without the computational cost of fine-tuning. However, existing methods deriving vectors from static activation differences are susceptible to high-dimensional noise and layer-wise semantic drift, often capturing spurious correlations rather than the target intent. To address this, we propose Global Evolutionary Refined Steering (GER-steer), a training-free framework that grounded in the geometric stability of the network's representation evolution. GER-steer exploits this global signal to rectify raw steering vectors, effectively decoupling robust semantic intent from orthogonal artifacts. Extensive evaluations confirm that GER-steer consistently outperforms baselines, delivering superior efficacy and generalization without layer-specific tuning, establishing a universal solution for reliable model alignment.
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SAW: Toward a Surgical Action World Model via Controllable and Scalable Video Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sampath Rapuri · Lalithkumar Seenivasan · Dominik Schneider · Roger Soberanis-Mukul · Yufan He · Hao Ding · Jiru Xu · Chenhao Yu · Chenyan Jing · Pengfei Guo · Daguang Xu · Mathias Unberath
arXiv:2603.13024v1 Announce Type: cross Abstract: A surgical world model capable of generating realistic surgical action videos with precise control over tool-tissue interactions can address fundamental challenges in surgical AI and simulation -- from data scarcity and rare event synthesis to bridging the sim-to-real gap for surgical automation. However, current video generation methods, the very core of such surgical world models, require expensive annotations or complex structured intermediates as conditioning signals at inference, limiting their scalability. Other approaches exhibit limited temporal consistency across complex laparoscopic scenes and do not possess sufficient realism. We propose Surgical Action World (SAW) -- a step toward surgical action world modeling through video diffusion conditioned on four lightweight signals: language prompts encoding tool-action context, a reference surgical scene, tissue affordance mask, and 2D tool-tip trajectories. We design a conditional video diffusion approach that reformulates video-to-video diffusion into trajectory-conditioned surgical action synthesis. The backbone diffusion model is fine-tuned on a custom-curated dataset of 12,044 laparoscopic clips with lightweight spatiotemporal conditioning signals, leveraging a depth consistency loss to enforce geometric plausibility without requiring depth at inference. SAW achieves state-of-the-art temporal consistency (CD-FVD: 199.19 vs. 546.82) and strong visual quality on held-out test data. Furthermore, we demonstrate its downstream utility for (a) surgical AI, where augmenting rare actions with SAW-generated videos improves action recognition (clipping F1-score: 20.93% to 43.14%; cutting: 0.00% to 8.33%) on real test data, and (b) surgical simulation, where rendering tool-tissue interaction videos from simulator-derived trajectory points toward a visually faithful simulation engine.
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METTL16 enhances proteasome inhibitor resistance in multiple myeloma by inhibiting eIF2α-PERK interaction and promoting PSMB5 translation

✇Oncogene - Issue - nature.com science feeds
By: Guanli Wang · Xuejie Gao · Hui Zhang · Ke Hu · Qilin Feng · Yujie Liu · Chaolu Hu · Shushan Guo · Dandan Yu · Shuaikang Chang · Xiaosong Wu · Xinyan Jia · Dong An · Yu Peng · Yi Tao · Haiyan Cai · Gege Chen · Li Zhang · Jumei Shi

Oncogene, Published online: 13 March 2026; doi:10.1038/s41388-026-03706-y

METTL16 enhances proteasome inhibitor resistance in multiple myeloma by inhibiting eIF2α-PERK interaction and promoting PSMB5 translation
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CMMR-VLN: Vision-and-Language Navigation via Continual Multimodal Memory Retrieval

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haozhou Li · Xiangyu Dong · Huiyan Jiang · Yaoming Zhou · Xiaoguang Ma
arXiv:2603.07997v1 Announce Type: new Abstract: Although large language models (LLMs) are introduced into vision-and-language navigation (VLN) to improve instruction comprehension and generalization, existing LLM- based VLN lacks the ability to selectively recall and use relevant priori experiences to help navigation tasks, limiting their performance in long-horizon and unfamiliar scenarios. In this work, we propose CMMR-VLN (Continual Multimodal Memory Retrieval based VLN), a VLN framework that endows LLM agents with structured memory and reflection capabilities. Specifically, the CMMR-VLN constructs a multimodal experi- ence memory indexed by panoramic visual images and salient landmarks to retrieve relevant experiences during navigation, introduces a retrieved-augmented generation pipeline to mimick how experienced human navigators leverage priori knowledge, and incorporates a reflection-based memory update strategy that selectively stores complete successful paths and the key initial mistake in failure cases. Comprehensive tests illustrate average success rate improvements of 52.9%, 20.9% and 20.9%, and 200%, 50% and 50% over the NavGPT, the MapGPT, and the DiscussNav in simulation and real tests, respectively eluci- dating the great potential of the CMMR-VLN as a backbone VLN framework.
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OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Caorui Li · Yu Chen · Yiyan Ji · Jin Xu · Zhenyu Cui · Shihao Li · Yuanxing Zhang · Wentao Wang · Zhenghao Song · Dingling Zhang · Ying He · Haoxiang Liu · Yuxuan Wang · Qiufeng Wang · Jiafu Tang · Zhenhe Wu · Jiehui Luo · Zhiyu Pan · Weihao Xie · Chenchen Zhang · Zhaohui Wang · Jiayi Tian · Yanghai Wang · Zhe Cao · Minxin Dai · Ke Wang · Runzhe Wen · Yinghao Ma · Yaning Pan · Sungkyun Chang · Termeh Taheri · Haiwen Xia · Christos Plachouras · Emmanouil Benetos · Yizhi Li · Ge Zhang · Jian Yang · Tianhao Peng · Zili Wang · Minghao Liu · Junran Peng · Zhaoxiang Zhang · Jiaheng Liu
arXiv:2510.10689v2 Announce Type: replace Abstract: Recent advances in multimodal large language models (MLLMs) have demonstrated substantial potential in video understanding. However, existing benchmarks fail to comprehensively evaluate synergistic reasoning capabilities across audio and visual modalities, often neglecting either one of the modalities or integrating them in a logically inconsistent manner. To bridge this gap, we introduce OmniVideoBench, a large-scale and rigorously designed benchmark dedicated to assessing synergistic audio-visual understanding, with a strong emphasis on modality complementarity and logical consistency. Specifically, OmniVideoBench comprises 1000 high-quality question-answer(QA) pairs, each annotated with step-by-step reasoning traces, derived from 628 diverse videos ranging from several seconds to 30 minutes, and manually verified to guarantee complete correctness and uniqueness. Moreover, OmniVideoBench encompasses 13 carefully designed question types, covering temporal reasoning, spatial localization, counting, causal inference, summarization, and beyond, thereby capturing the essential challenges of video understanding. Evaluation of multiple MLLMs on OmniVideoBench reveals a pronounced gap between model performance and human reasoning, with open-source models lagging significantly behind their closed-source counterparts, underscoring the inherent difficulty of genuine audio-visual reasoning. We will release OmniVideoBench to foster the development of MLLMs with stronger and more generalizable reasoning capabilities.
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