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Received — 10 September 2026 ⏭

No Free Checker: A Survey of Verifiers for Robot Policies

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yang Wan · Xihang Yue · Zhirui Liu · Ziyuan Chu · Shuxun Wang · Yuhan Chen · Xiaonan Jiang · Xukun Zhu · Yubo Dong · Linchao Zhu
10 September 2026 at 12:00
arXiv:2609.09250v1 Announce Type: cross Abstract: A verifier for robot policies reads a candidate behavior and returns a score for how well it did, used both to evaluate vision-language-action policies and to train them. Verifiers range from success detectors and reward models to runtime monitors, safety filters, and temporal-logic specifications. We survey roughly 150 verifiers and compare them along two properties. Availability is how much a verdict costs, how early in a rollout the verdict arrives, and how often a verdict can be asked for. Availability rises as verdicts get cheaper, earlier, and denser. Credibility is how much a high score tells us about the task. Credibility falls as the judgment becomes gameable and self-serving. We group the verifiers by who supplies the judgment: human verifiers, rule-based and formal verifiers, learned and pretrained verifiers, and model-intrinsic verifiers. Across the four families, we find that credibility falls as availability rises. Regardless of who supplies the judgment, there is no free checker. We then examine what validates a verifier itself, and how much a high score tells us. Three measures appear in the literature: agreement with human labels, the performance of the policy it trains, and behavior under reward hacking. We close with nine metrics that make a verifier claim checkable, and coordinates for the verifiers still to be built.

A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jingwen Zhou · Qinghua Lu · Jieshan Chen · Liming Zhu · Xiwei Xu · Zhenchang Xing · Stefan Harrer
10 September 2026 at 12:00
arXiv:2408.02920v2 Announce Type: replace-cross Abstract: The rapid advancement of AI technology has led to widespread applications of agent systems across various domains. However, the need for detailed architecture design poses significant challenges in designing and operating these systems. This paper introduces a taxonomy focused on the architectures of foundation-model-based agents, addressing critical aspects such as functional capabilities and non-functional qualities. We also discuss the operations involved in both design-time and run-time phases, providing a comprehensive view of architectural design and operational characteristics. By unifying and detailing these classifications, our taxonomy aims to improve the design of foundation-model-based agents. Additionally, the paper establishes a decision model that guides critical design and runtime decisions, offering a structured approach to enhance the development of foundation-model-based agents. Our contributions include providing a structured architecture design option and guiding the development process of foundation-model-based agents, thereby addressing current fragmentation in the field.

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
10 September 2026 at 12:00
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.

A programmed cell death learning signature predicts immunotherapy response and identifies AP1S1 as a regulator of immune exclusion in breast cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Guixin Wang · Junming Cao · Parhat Kaysar · Chenglu Lu · Ziyi Chen · Zhaohui Chen · Han Zhang · Yue Yu · Yongjie Xie · Shan Cheng · Pengpeng Zhang · Xin Wang
9 September 2026 at 18:00

Chin J Cancer Res. 2026 Aug 30;38(4):480-500. doi: 10.21147/j.issn.1000-9604.2026.04.08.

ABSTRACT

OBJECTIVE: Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in the breast cancer immune microenvironment remains poorly understood.

METHODS: We integrated multi-omics data from six breast cancer cohorts (N=3,764) to develop a programmed cell death learning signature (PCDsig) using over 100 machine learning combinations. The model was benchmarked against 29 published signatures. Single-cell transcriptomic analysis decoded the immune landscape and cellular crosstalk. The role of adaptor-related protein complex 1 subunit sigma 1 (AP1S1) was validated through a clinical cohort, in vitro functional assays, and in vivo syngeneic mouse models.

RESULTS: PCDsig significantly stratified patient prognosis across all cohorts, consistently outperforming 29 existing models. High PCDsig scores correlated with immune-excluded phenotypes, reduced CD8+ T cell infiltration, and lower immunophenoscores. Single-cell analysis revealed that high-PCDsig tumors utilize vascular endothelial growth factor A (VEGFA) signaling to foster an immunosuppressive microenvironment. AP1S1 was identified as the core driver of immune exclusion. And our clinical cohort supported the immune exclusion effect of AP1S1. AP1S1 knockdown impaired tumor progression in vitro and fundamentally remodeled the tumor immune ecosystem in vivo. Combining AP1S1 inhibition with anti-programmed cell death ligand 1 (anti-PD-L1) therapy exerted profound synergistic effects, driven by massive infiltration and functional activation of cytotoxic Granzyme B (GZMB)+CD8+ T cells.

CONCLUSIONS: Our study establishes the PCDsig we developed is a potential prognostic and predictive biomarker for breast cancer. We provide the first evidence of AP1S1 as a core immunomodulatory oncogene that mediates immune exclusion. Targeting AP1S1 represents a highly promising strategy to sensitize cold breast tumors to ICB, offering a new perspective for precision immunotherapy.

PMID:42712842 | PMC:PMC13551362 | DOI:10.21147/j.issn.1000-9604.2026.04.08

A programmed cell death learning signature predicts immunotherapy response and identifies AP1S1 as a regulator of immune exclusion in breast cancer

✇(Multiomics OR Omics) AND (Pancreatic)
By: Guixin Wang · Junming Cao · Parhat Kaysar · Chenglu Lu · Ziyi Chen · Zhaohui Chen · Han Zhang · Yue Yu · Yongjie Xie · Shan Cheng · Pengpeng Zhang · Xin Wang
9 September 2026 at 18:00

Chin J Cancer Res. 2026 Aug 30;38(4):480-500. doi: 10.21147/j.issn.1000-9604.2026.04.08.

ABSTRACT

OBJECTIVE: Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in the breast cancer immune microenvironment remains poorly understood.

METHODS: We integrated multi-omics data from six breast cancer cohorts (N=3,764) to develop a programmed cell death learning signature (PCDsig) using over 100 machine learning combinations. The model was benchmarked against 29 published signatures. Single-cell transcriptomic analysis decoded the immune landscape and cellular crosstalk. The role of adaptor-related protein complex 1 subunit sigma 1 (AP1S1) was validated through a clinical cohort, in vitro functional assays, and in vivo syngeneic mouse models.

RESULTS: PCDsig significantly stratified patient prognosis across all cohorts, consistently outperforming 29 existing models. High PCDsig scores correlated with immune-excluded phenotypes, reduced CD8+ T cell infiltration, and lower immunophenoscores. Single-cell analysis revealed that high-PCDsig tumors utilize vascular endothelial growth factor A (VEGFA) signaling to foster an immunosuppressive microenvironment. AP1S1 was identified as the core driver of immune exclusion. And our clinical cohort supported the immune exclusion effect of AP1S1. AP1S1 knockdown impaired tumor progression in vitro and fundamentally remodeled the tumor immune ecosystem in vivo. Combining AP1S1 inhibition with anti-programmed cell death ligand 1 (anti-PD-L1) therapy exerted profound synergistic effects, driven by massive infiltration and functional activation of cytotoxic Granzyme B (GZMB)+CD8+ T cells.

CONCLUSIONS: Our study establishes the PCDsig we developed is a potential prognostic and predictive biomarker for breast cancer. We provide the first evidence of AP1S1 as a core immunomodulatory oncogene that mediates immune exclusion. Targeting AP1S1 represents a highly promising strategy to sensitize cold breast tumors to ICB, offering a new perspective for precision immunotherapy.

PMID:42712842 | PMC:PMC13551362 | DOI:10.21147/j.issn.1000-9604.2026.04.08

An operational perturbation proteomics-based virtual cell model

✇Nature - Issue - nature.com science feeds
By: Rui Sun · Liujia Qian · Yongge Li · Tong Liu · Honghan Cheng · Xuedong Zhang · Xueya Zhou · Yuecheng Zhan · Guangmei Zhang · Zhengchao Luo · Kunpeng Ma · Chunlong Wu · Dongchen Ji · Zhangzhi Xue · Hongxue Meng · Yuhang Xiang · Dingwei Lei · Qianhe Zhou · Wenbin Hu · Yuhan Deng · Lingling Tan · Qi Xiao · Zhiwei Liu · Lei Zeng · Liqin Qian · Xuan Zheng · Qiong Hu · Nianzi Luo · Weinan E · Peijie Zhou · Han Wen · Yi Zhu · Tiannan Guo
9 September 2026 at 08:00

Nature, Published online: 09 September 2026; doi:10.1038/s41586-026-11001-9

Temporal protein-abundance measurements from systematically perturbed breast cancer cell lines were generated to develop ProteinTalks, a virtual cell model that functions as an operational tool for diverse drug discovery tasks.
Received — 27 May 2026 ⏭

MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Han Chen · Zining Zhang · Wenqi Pei · Bingsheng He · Ming Wu · Jason Zeng · Michael Heinrich · Wei Wu · Hongbao Zhang
26 May 2026 at 12:00
arXiv:2605.23986v1 Announce Type: cross Abstract: Memory is a fundamental component for enabling long-context LLM agents, supporting persistent state across interactions through a continuous serve-and-update lifecycle. Despite substantial prior work, existing systems suffer from significant maintenance overhead due to two key limitations: coarse-grained state management and inherently sequential update pipelines. In particular, updates are often tightly coupled with LLM inference and require full-state rewrites, leading to poor scalability and growing latency as memory accumulates. To address these challenges, we present MemForest, a memory framework that reformulates agent memory as a write-efficient temporal data management problem. MemForest breaks the sequential bottleneck via parallel chunk extraction, decoupling memory construction into concurrent, independent operations. To further eliminate coarse-grained maintenance, we introduce MemTree, a hierarchical temporal index that organizes memory as time-ordered trees rather than flat global summaries. This design replaces full-state rewrites with localized per-node updates, reducing maintenance cost to the affected tree paths while naturally preserving temporally evolving states. We evaluate MemForest on two long-context memory benchmarks, LongMemEval-S and LoCoMo. On LongMemEval-S, MemForest achieves the best overall performance among stateful baselines, reaching 79.8% pass@1 accuracy while sustaining a memory construction throughput approximately 6x higher than state-of-the-art approaches including EverMemOS.

HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Han Chen · Hanchen Wang · Hongmei Chen · Ying Zhang · Lu Qin · Wenjie Zhang
26 May 2026 at 12:00
arXiv:2509.02113v2 Announce Type: replace-cross Abstract: The advancement of graph-based malware analysis is critically limited by the absence of large-scale datasets that capture the inherent hierarchical structure of software. Existing methods often oversimplify programs into single level graphs, failing to model the crucial semantic relationship between high-level functional interactions and low-level instruction logic. To bridge this gap, we introduce \dataset, the largest public hierarchical graph dataset for malware analysis, comprising over \textbf{200M} Control Flow Graphs (CFGs) nested within \textbf{595K} Function Call Graphs (FCGs). This two-level representation preserves structural semantics essential for building robust detectors resilient to code obfuscation and malware evolution. We demonstrate HiGraph's utility through a large-scale analysis that reveals distinct structural properties of benign and malicious software, establishing it as a foundational benchmark for the community. The dataset and tools are publicly available at https://higraph.org.

$M^3-Verse$: A "Spot the Difference" Challenge for Large Multimodal Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Kewei Wei · Bocheng Hu · Jie Cao · Xiaohan Chen · Zhengxi Lu · Wubing Xia · Weili Xu · Jiaao Wu · Junchen He · Mingyu Jia · Ciyun Zhao · Ye Sun · Yizhi Li · Zhonghan Zhao · Jian Zhang · Gaoang Wang
26 May 2026 at 12:00
arXiv:2512.18735v2 Announce Type: replace-cross Abstract: Modern Large Multimodal Models (LMMs) have demonstrated extraordinary ability in static image and single-state spatial-temporal understanding. However, their capacity to comprehend the dynamic changes of objects within a shared spatial context between two distinct video observations, remains largely unexplored. This ability to reason about transformations within a consistent environment is particularly crucial for advancements in the field of spatial intelligence. In this paper, we introduce $M^3-Verse$, a Multi-Modal, Multi-State, Multi-Dimensional benchmark, to formally evaluate this capability. It is built upon paired videos that provide multi-perspective observations of an indoor scene before and after a state change. The benchmark contains a total of 270 scenes and 2,932 questions, which are categorized into over 50 subtasks that probe 4 core capabilities. We evaluate 16 state-of-the-art LMMs and observe their limitations in tracking state transitions. To address these challenges, we further propose a simple yet effective baseline that achieves significant performance improvements in multi-state perception. $M^3-Verse$ thus provides a challenging new testbed to catalyze the development of next-generation models with a more holistic understanding of our dynamic visual world. You can get the construction pipeline from https://github.com/Wal-K-aWay/M3-Verse_pipeline and full benchmark data from https://www.modelscope.cn/datasets/WalKaWay/M3-Verse.

Can China’s Great Green Wall shape efforts to keep the world’s deserts at bay?

✇Nature - Issue - nature.com science feeds
By: Lilin Zheng · Annah Lake Zhu · Qiuxia Dong · Michael E. Meadows · David S. G. Thomas · Xin Gao · Jiaojun Zhu · Xiaobo Wang · Zilong Xia · Alex De Sherbinin · Ruishan Chen
15 April 2026 at 08:00

Nature, Published online: 15 April 2026; doi:10.1038/d41586-026-01102-w

Grand anti-desertification schemes often fail when trees die and funding dries up — yet one project has broken the mould.
Received — 8 April 2026 ⏭

MAVEN: A Mesh-Aware Volumetric Encoding Network for Simulating 3D Flexible Deformation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhe Feng · Shilong Tao · Haonan Sun · Shaohan Chen · Zhanxing Zhu · Yunhuai Liu
7 April 2026 at 12:00
arXiv:2604.04474v1 Announce Type: cross Abstract: Deep learning-based approaches, particularly graph neural networks (GNNs), have gained prominence in simulating flexible deformations and contacts of solids, due to their ability to handle unstructured physical fields and nonlinear regression on graph structures. However, existing GNNs commonly represent meshes with graphs built solely from vertices and edges. These approaches tend to overlook higher-dimensional spatial features, e.g., 2D facets and 3D cells, from the original geometry. As a result, it is challenging to accurately capture boundary representations and volumetric characteristics, though this information is critically important for modeling contact interactions and internal physical quantity propagation, particularly under sparse mesh discretization. In this paper, we introduce MAVEN, a mesh-aware volumetric encoding network for simulating 3D flexible deformation, which explicitly models geometric mesh elements of higher dimension to achieve a more accurate and natural physical simulation. MAVEN establishes learnable mappings among 3D cells, 2D facets, and vertices, enabling flexible mutual transformations. Explicit geometric features are incorporated into the model to alleviate the burden of implicitly learning geometric patterns. Experimental results show that MAVEN consistently achieves state-of-the-art performance across established datasets and a novel metal stretch-bending task featuring large deformations and prolonged contacts.

Cardinality Estimation for High Dimensional Similarity Queries with Adaptive Bucket Probing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhonghan Chen · Qintian Guo · Ruiyuan Zhang · Xiaofang Zhou
7 April 2026 at 12:00
arXiv:2604.04603v1 Announce Type: cross Abstract: In this work, we address the problem of cardinality estimation for similarity search in high-dimensional spaces. Our goal is to design a framework that is lightweight, easy to construct, and capable of providing accurate estimates with satisfying online efficiency. We leverage locality-sensitive hashing (LSH) to partition the vector space while preserving distance proximity. Building on this, we adopt the principles of classical multi-probe LSH to adaptively explore neighboring buckets, accounting for distance thresholds of varying magnitudes. To improve online efficiency, we employ progressive sampling to reduce the number of distance computations and utilize asymmetric distance computation in product quantization to accelerate distance calculations in high-dimensional spaces. In addition to handling static datasets, our framework includes updating algorithm designed to efficiently support large-scale dynamic scenarios of data updates.Experiments demonstrate that our methods can accurately estimate the cardinality of similarity queries, yielding satisfying efficiency.

FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tommy Sha (Kindred) · Zhan Cheng (Kindred) · Haotian Zhai (Kindred) · Xuwei Ding (Kindred) · Junnan Li (Kindred) · Haixiang Tang (Kindred) · Zaoting Sun (Kindred) · Yanchuan Tang (Kindred) · Yongzhe (Kindred) · Yi · Yuan Gao · Anhao Li
7 April 2026 at 12:00
arXiv:2511.08887v4 Announce Type: replace-cross Abstract: Stroke is an acute cerebrovascular disease, and timely diagnosis significantly improves patient survival. However, existing automated diagnosis methods suffer from fairness issues across demographic groups, potentially exacerbating healthcare disparities. In this work we propose FAST-CAD, a theoretically grounded framework that combines domain-adversarial training (DAT) with group distributionally robust optimization (Group-DRO) for fair and accurate non-contact stroke diagnosis. Our approach is built on domain adaptation and minimax fairness theory and provides convergence guarantees and fairness bounds. We curate a multimodal dataset covering 12 demographic subgroups defined by age, gender, and posture. FAST-CAD employs self-supervised encoders with adversarial domain discrimination to learn demographic-invariant representations, while Group-DRO optimizes worst-group risk to ensure robust performance across all subgroups. Extensive experiments show that our method achieves superior diagnostic performance while maintaining fairness across demographic groups, and our theoretical analysis supports the effectiveness of the unified DAT + Group-DRO framework. This work provides both practical advances and theoretical insights for fair medical AI systems.
Received — 6 April 2026 ⏭

HoloTrauma 3X Triadic AI Co reasoning for robot assisted emergency maxillofacial reconstruction

✇npj Digital Medicine
By: Qiao Zhou · Yao Liu · Chenfei Wang · Liang Wang · Zhenghan Chen · Yiyi Chen · Chi Yang · Dong Liu · Yuxin Zhang · Jisi Zheng · Xingmei Feng
4 April 2026 at 08:00

npj Digital Medicine, Published online: 04 April 2026; doi:10.1038/s41746-026-02573-x

HoloTrauma 3X Triadic AI Co reasoning for robot assisted emergency maxillofacial reconstruction
Received — 4 April 2026 ⏭

Not All Tokens See Equally: Perception-Grounded Policy Optimization for Large Vision-Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zekai Ye · Qiming Li · Xiaocheng Feng · Ruihan Chen · Ziming Li · Haoyu Ren · Kun Chen · Dandan Tu · Bing Qin
3 April 2026 at 12:00
arXiv:2604.01840v1 Announce Type: new Abstract: While Reinforcement Learning from Verifiable Rewards (RLVR) has advanced reasoning in Large Vision-Language Models (LVLMs), prevailing frameworks suffer from a foundational methodological flaw: by distributing identical advantages across all generated tokens, these methods inherently dilute the learning signals essential for optimizing the critical, visually-grounded steps of multimodal reasoning. To bridge this gap, we formulate \textit{Token Visual Dependency}, quantifying the causal information gain of visual inputs via the Kullback-Leibler (KL) divergence between visual-conditioned and text-only predictive distributions. Revealing that this dependency is highly sparse and semantically pivotal, we introduce Perception-Grounded Policy Optimization (PGPO), which is a novel fine-grained credit assignment framework that dynamically reshapes advantages at the token level. Through a threshold-gated, mass-conserving mechanism, PGPO actively amplifies learning signals for visually-dependent tokens while suppressing gradient noise from linguistic priors. Extensive experiments based on the Qwen2.5-VL series across seven challenging multimodal reasoning benchmarks demonstrate that PGPO boosts models by 18.7% on average. Both theoretical and empirical analyses confirm that PGPO effectively reduces gradient variance, prevents training collapse, and acts as a potent regularizer for robust, perception-grounded multimodal reasoning. Code will be published on https://github.com/Yzk1114/PGPO.
Received — 2 April 2026 ⏭

Robust transcriptomic hallmarks targeting intratumor heterogeneity in intrahepatic cholangiocarcinoma

✇(Multiomics OR Omics) AND (Pancreatic)
By: Youpei Lin · Lihua Peng · Haichao Zhao · Penghui Lin · Lv Chen · Zunyun Gong · Jian Lin · Dongbing Liu · Xupeng Yang · Xiangze Song · Mao Zhang · Shuyi Ji · Junhua Rao · Huijuan Luo · Yifei Cheng · Xiaofang Chen · Guohe Song · Hao Hou · Dongning Rao · Tiancheng Zhang · Weiren Liu · Shu Zhang · Gaoyang Xu · Juan Zhang · Liyun Xiao · Yan Liang · Yang Liu · Yuhan Chen · Zhirou Cui · Xiaoying Wang · Jian Zhou · Qiang Gao · Kui Wu · Jia Fan
31 March 2026 at 18:00

Cell Rep Med. 2026 Mar 30:102708. doi: 10.1016/j.xcrm.2026.102708. Online ahead of print.

ABSTRACT

Intratumor heterogeneity (ITH) undermines transcriptome-based stratification in intrahepatic cholangiocarcinoma (iCCA). Here, we integrate multi-omics data from multi-region, single-region, and single-cell RNA sequencing cohorts to systematically characterize gene expression ITH. We uncover that immune and stromal heterogeneity are primary drivers of ITH, leading to misclassification of a median 27.8% of tumors by existing subtyping systems. To overcome this, we identify a low-intratumor-heterogeneity/high-intertumor-variability (LIHV) gene set and develop an ITH-insensitive classification system defining five subgroups: inflammatory (SI), metabolic (SII), atypical (SIII-1), immune-silent (SIII-2), and neurodegenerative (SIII-3). These subgroups exhibit distinct clinical outcomes, molecular features, immune landscapes, and therapeutic vulnerabilities. GPRC5A and VTCN1 serve as robust immunohistochemical biomarkers for SI and SIII tumors, while serum CEA and CA19-9 identify inflammatory iCCA. Therapeutically, HSP90 inhibition synergizes with anti-PD1 in inflammatory iCCA, whereas combined anti-PD1 and anti-TIM3 suppresses neurodegenerative iCCA. Collectively, our study provides a robust molecular framework and actionable therapeutic strategies for iCCA.

PMID:41916296 | DOI:10.1016/j.xcrm.2026.102708

Received — 26 March 2026 ⏭

1S-DAug: One-Shot Data Augmentation for Robust Few-Shot Generalization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yunwei Bai · Ying Kiat Tan · Yao Shu · Tsuhan Chen
25 March 2026 at 12:00
arXiv:2602.00114v3 Announce Type: replace-cross Abstract: Few-shot learning (FSL) challenges model generalization to novel classes based on just a few shots of labeled examples, a testbed where traditional test-time augmentations fail to be effective. We introduce 1S-DAug, a one-shot generative augmentation operator that synthesizes diverse yet faithful variants from just one example image at test time. 1S-DAug couples traditional geometric perturbations with controlled noise injection and a denoising diffusion process conditioned on the original image. The generated images are then encoded and aggregated, alongside the original image, into a combined representation for more robust FSL predictions. Integrated as a training-free model-agnostic plugin, 1S-DAug consistently improves FSL across standard benchmarks of 4 different datasets without any model parameter update, including achieving up to 20% relative accuracy improvement on the miniImagenet 5-way-1-shot benchmark. Code will be released.
Received — 17 March 2026 ⏭

MovieTeller: Tool-augmented Movie Synopsis with ID Consistent Progressive Abstraction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yizhi Li · Xiaohan Chen · Miao Jiang · Wentao Tang · Gaoang Wang
16 March 2026 at 12:00
arXiv:2602.23228v2 Announce Type: replace-cross Abstract: With the explosive growth of digital entertainment, automated video summarization has become indispensable for applications such as content indexing, personalized recommendation, and efficient media archiving. Automatic synopsis generation for long-form videos, such as movies and TV series, presents a significant challenge for existing Vision-Language Models (VLMs). While proficient at single-image captioning, these general-purpose models often exhibit critical failures in long-duration contexts, primarily a lack of ID-consistent character identification and a fractured narrative coherence. To overcome these limitations, we propose MovieTeller, a novel framework for generating movie synopses via tool-augmented progressive abstraction. Our core contribution is a training-free, tool-augmented, fact-grounded generation process. Instead of requiring costly model fine-tuning, our framework directly leverages off-the-shelf models in a plug-and-play manner. We first invoke a specialized face recognition model as an external "tool" to establish Factual Groundings--precise character identities and their corresponding bounding boxes. These groundings are then injected into the prompt to steer the VLM's reasoning, ensuring the generated scene descriptions are anchored to verifiable facts. Furthermore, our progressive abstraction pipeline decomposes the summarization of a full-length movie into a multi-stage process, effectively mitigating the context length limitations of current VLMs. Experiments demonstrate that our approach yields significant improvements in factual accuracy, character consistency, and overall narrative coherence compared to end-to-end baselines.
Received — 25 February 2026 ⏭

Think with Grounding: Curriculum Reinforced Reasoning with Video Grounding for Long Video Understanding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Houlun Chen · Xin Wang · Guangyao Li · Yuwei Zhou · Yihan Chen · Jia Jia · Wenwu Zhu
24 February 2026 at 13:00
arXiv:2602.18702v1 Announce Type: cross Abstract: Long video understanding is challenging due to rich and complicated multimodal clues in long temporal range.Current methods adopt reasoning to improve the model's ability to analyze complex video clues in long videos via text-form reasoning.However,the existing literature suffers from the fact that the text-only reasoning under fixed video context may exacerbate hallucinations since detailed crucial clues are often ignored under limited video context length due to the temporal redundancy of long videos.To address this gap,we propose Video-TwG,a curriculum reinforced framework that employs a novel Think-with-Grounding paradigm,enabling video LLMs to actively decide when to perform on-demand grounding during interleaved text-video reasoning, selectively zooming into question-relevant clips only when necessary.Video-TwG can be trained end-to-end in a straightforward manner, without relying on complex auxiliary modules or heavily annotated reasoning tracesIn detail,we design a Two-stage Reinforced Curriculum Strategy, where the model first learns think-with-grounding behavior on a small short-video GQA dataset with grounding labels,and then scales to diverse general QA data with videos of diverse domains to encourage generalization. Further, to handle complex think-with-grounding reasoning for various kinds of data,we propose TwG-GRPO algorithm which features the fine-grained grounding reward, self-confirmed pseudo reward and accuracy-gated mechanism.Finally,we propose to construct a new TwG-51K dataset that facilitates training. Experiments on Video-MME, LongVideoBench, and MLVU show that Video-TwG consistently outperforms strong LVU baselines.Further ablation validates the necessity of our Two-stage Reinforced Curriculum Strategy and shows our TwG-GRPO better leverages diverse unlabeled data to improve grounding quality and reduce redundant groundings without sacrificing QA performance.
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