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Toward Robust Personalized Alignment for LLMs: Mitigating Persona Drift in Multi-Turn Dialogue

✇cs.AI, q-bio.NC updates on arXiv.org
By: Youyuan Zhang · Siyuan Li · Fangming Liu · Jing Li
arXiv:2609.12373v1 Announce Type: new Abstract: Persona drift remains a central challenge for personalized language models, as user profiles evolve over long interactions rather than remain permanently fixed. Models must therefore revise persistent persona states when preferences genuinely change, while avoiding updates driven by transient, ambiguous, or unresolved observations. We propose CORE, which separates turn-local evidence from persistent persona-state revision and selectively updates grounded user preferences through uncertainty-aware belief revision. We also introduce PERSIST, a held-out post-anchor benchmark for persona-state robustness under sequential interaction stress, covering ambiguity, conflict, and controlled social influence. Across ALOE, PersonaChat, and PERSIST, CORE improves personalized alignment and robustness, with complementary gains in normalized closed-slot state fidelity. Human evaluation and mechanistic controls further support explicit update control beyond stronger generation or persistent memory alone.
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ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xianming Li · Zongxi Li · Tsz-fung Andrew Lee · Jing Li · Haoran Xie · Qing Li
arXiv:2604.19254v2 Announce Type: replace-cross Abstract: Popular low-rank parameter-efficient fine-tuning (PEFT) methods represent adaptation as separate updates to selected backbone weights, without maintaining an explicit task-specific state that is updated and reused across depth. These updates also require the backbone at inference and therefore cannot operate as standalone predictors. We propose ShadowPEFT, which consolidates trainable adaptation into a modular shadow component centered on a compact shadow model and lightweight Transformer layer-specific coupling modules. A persistent shadow state refines the frozen backbone representations and is updated from them in an interactive manner. Because the shadow model is trained as a complete predictor, it can be detached for shadow-only inference without executing the base model and can be initialized from a pretrained model. Experiments on text and image generation and understanding benchmarks show that ShadowPEFT matches or outperforms LoRA and DoRA under comparable trainable-parameter budgets. Additional analyses on shadow pretraining, cross-dataset transfer, parameter scaling, inference latency, and system-level evaluation suggest that centralized layer-space adaptation is a competitive and flexible alternative to conventional low-rank PEFT.
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Right Family, Wrong Skill: Evaluating Risk Exposure in Agent Skill Retrieval

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiandong Ding · Honglei Ji · Ming Liu · Tao Duan
arXiv:2606.10388v3 Announce Type: replace-cross Abstract: Agent skill libraries are becoming routable software assets: a retrieved skill can contribute instructions, scripts, resource bindings, and execution assumptions to an agent. This makes retrieval failures more specific than broad irrelevance. A system can find the right capability family yet expose the wrong same-capability representative. We study this failure as same-capability risk-exposure retrieval. Each benchmark unit pairs a helpful skill with a query-specific risky sibling that shares the capability family but differs on an execution-controlling contract, such as the required resource, precondition, procedure, or artifact. We introduce SameCapRisk-Bench, an auditable benchmark with 1,190 skill-risk units and 1,686 evaluation query cases: 694 marked-sibling units under public library pressure and 496 hard role-flip units where the same two skills swap helpful/risky roles across paired queries. The release records admission evidence, cue/leakage checks, source hashes, family relations, and fixed candidate pools. The benchmark reports helpful ranking together with harmful sibling rate (HSR@K), the top-K exposure of the marked risky sibling. On this benchmark, public SkillRouter, SkillRet, and R3-Skill retrieve helpful skills at high Recall@3 (0.848--0.888) but also expose marked risky siblings frequently (HSR@3 0.346--0.372). A fully public score-and-cluster pipeline lowers HSR@3 to 0.128--0.182, with Recall@3 of 0.713--0.776. Under a benchmark-trained reference scorer, public text-cluster and controlled resolvers reach HSR@3 0.012 and 0.007; the latter attains Recall@3 0.833. Skill retrieval should therefore report both capability matching and same-family risk exposure, with HSR serving as a targeted exposure certificate for fixed skill libraries.
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JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zixiang Chen · Yuheng Lu · Zihao Cheng · Zeming Liu · Jizeng Bai · Ziye Huang · Zhiyin Lin · Zihan Li · Yuhang Guo · Yunhong Wang · Haifeng Wang
arXiv:2609.10451v1 Announce Type: new Abstract: Real-world GUI usage frequently involves workflows that span multiple devices and platforms, requiring the transfer of intermediate results, maintenance of shared state, and coordination across heterogeneous environments. However, existing GUI benchmarks overwhelmingly evaluate agents on single-device, statically defined tasks, thus leaving such cross-device capabilities largely unexamined, resulting in an overly optimistic assessment of agents' readiness for real-world usage. We introduce JarvisGUI, a dynamic benchmark that evaluates GUI agents on cross-device workflows requiring coordinated interaction across heterogeneous platforms, including Android, Windows, and Ubuntu. Specifically, JarvisGUI formulates GUI tasks as input-output transformations under a lightweight type system, which allows us to automatically compose multi-step, cross-device workflows and dynamically evaluate agent performance within a unified framework. By evaluating agents in virtual environments spanning multiple operating systems, JarvisGUI reveals that state-of-the-art open-source GUI agents struggle with the state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management required for real-world workflows, exposing a critical capability gap invisible to existing benchmarks.
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MOSAIC: A Universal Agent-Level Interface for Cross-Paradigm Agent Mixing and Human-AI Collaboration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Abdulhamid M. Mousa · Rakhmonberdi Khajiev · Jalaledin M. Azzabi · Abdulkarim M. Mousa · Peng Yong · Yunusa Haruna · Ming Liu
arXiv:2603.01260v2 Announce Type: replace-cross Abstract: Existing infrastructure cannot deploy agents from different decision-making paradigms within the same environment, making fair cross-paradigm comparison under identical conditions impossible. We present MOSAIC, an open-source platform that enables heterogeneous agents (RL policies, LLMs, VLMs, and human operators) to act within shared reinforcement learning environments in ad-hoc team settings with reproducible results. MOSAIC introduces three contributions. (i) IPC-based worker protocol that wraps native and third-party frameworks as isolated subprocess workers, each executing its own training and inference logic unmodified and communicating through a versioned inter-process protocol. (ii) An operator abstraction that forms an agent-level interface by mapping workers to agent slots: each operator, regardless of whether it is backed by an RL policy, an LLM, or a human, conforms to a minimal universal interface. (iii) A deterministic cross-paradigm evaluation framework with two complementary modes: a manual mode that advances up to $N$ operators in lock-step under shared seeds for fine-grained visual inspection of behavioural differences; and a script mode that drives automated, long-running evaluation via declarative Python scripts for reproducible experiments. Our documentation is released at: https://mosaic-platform.readthedocs.io.
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Multiomic characterization of malignant pulmonary nodules and development of a methylation-based diagnostic Model

✇Pulmonary nodule
By: Wei Zhou · Tongji Xie · Qingjun Wu · Mengmeng Song · Jianzhen Weng · Jieli Zhang · Xuan Gao · Min Zheng · Zheng Wang · Yunzhi Zhou · Yanming Li

J Transl Med. 2026 Jun 8;24(1):776. doi: 10.1186/s12967-026-08382-w.

ABSTRACT

BACKGROUND: The molecular distinction between benign and malignant pulmonary nodules remains a significant diagnostic challenge. While genomic drivers are well studied, multiomic integration of the epigenetic-transcriptional landscape and its translation into noninvasive tools are lacking.

METHODS: We performed a multiomic characterization (genomic, epigenomic, and transcriptomic) of 158 pulmonary nodules. Unsupervised factor analysis integrated these layers to identify core regulatory axes. A 9-gene cell-free DNA (cfDNA) methylation classifier was developed and validated in blood and tissue cohorts.

RESULTS: Genomic profiling revealed EGFR mutations (exclusive to malignant nodules) and MYC amplification as fundamental initiators of malignancy. Multiomic factor analysis (Factor 1) revealed profound genetic‒epigenetic synergy, in which these alterations dictate a permissive methylome, leading to aberrant epigenetic programming of chromatin accessibility, as well as epigenetic-transcriptional effects: hypomethylation at the promoters of cell cycle genes that augments their expression, and hypermethylation at immune related pathways gene loci that silences their transcription. This effect orchestrates formation of proproliferative (E2F target/G2M checkpoint) and "immune-cold" malignant phenotype, characterized by elevated Treg/CD8+ ratios and fibroblast recruitment. Notably, we observed a gradual accumulation of methylation aberrations along the premalignant-to-invasive continuum (adenocarcinoma in situ [AIS]→minimally invasive adenocarcinoma [MIA]→adenocarcinoma [ADC]), identifying progressive epigenetic dysregulation as a hallmark of tumor aggressiveness. Global methylome remodeling drives ADC progression through hypermethylation-mediated silencing of tumor suppressors (RASA3 and PPARG) and hypomethylation-activated oncogenic axes, specifically the GDF15 axis, which independently predict poor survival in patients with lung ADC in the TCGA cohort. We translated these tissue-derived insights into a 9-gene cfDNA methylation classifier, which achieved exceptional diagnostic accuracy across independent cohorts (training AUC = 1.00; test AUC = 0.93; tissue AUC = 0.96). Rooted in the biological "ground truth" of tissue dysregulation, this classifier functions specifically as a functional readout of the core cell cycle and proliferative pathways, offering a robust, noninvasive tool for the biology-informed risk assessment of pulmonary nodules.

CONCLUSIONS: This study delineates an epigenetic-transcriptional regulatory network that drives nodule malignancy. Our findings provide a robust theoretical foundation and a high-performance liquid biopsy tool for the precise, noninvasive diagnosis of pulmonary nodules.

PMID:42260586 | PMC:PMC13274191 | DOI:10.1186/s12967-026-08382-w

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A Non-Canonical Role of SMAD4 in Regulating 3D Genome Architecture to Inhibit Lung Squamous Cell Carcinoma Development

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Qian Tang · Chen Lian · Xinyan Han · Xuhan Zhang · Zihan Wang · Boyu Wang · Taoyu Zhu · Xinrui Lin · Xiaolei Wang · Yaping Xu · Manyu Xiao · Zijin Wang · Junming Li · Silin Chen · Yunze Wang · Yufei Liu · Songsong Li · Zuolin Shen · Xi Lu · Xueqi Han · Yilu Zhou · Mingyang Xiao · Jiayi Ran · Xiaoran Cao · Xinyi Xu · Hugo Sámano-Sánchez · Alfredo Rodríguez · Lian Wang · Shuifang Chen · Zhanyu Xu · Shirong Zhang · Nuo Yang · Yong Tang · Jian Liu

Adv Sci (Weinh). 2026 May 26:e75839. doi: 10.1002/advs.75839. Online ahead of print.

ABSTRACT

Lung squamous cell carcinoma (LUSC) lacks clearly defined key drivers and effective targeted therapies, reflecting an incomplete understanding of its molecular pathogenesis. Here, we identify SMAD4 as a critical regulator of three-dimensional (3D) genome organization in LUSC and uncover a mechanistic link between tumor suppressor loss and oncogenic transcriptional activation. By integrating clinical datasets, genetically engineered mouse models, human and murine LUSC cell lines, and multi-omics analyses, we demonstrate that SMAD4 deficiency promotes LUSC progression by unleashing EP300-mediated enhancer-promoter looping at the SOX2 locus. Mechanistically, SMAD4 does not directly bind SOX2 regulatory elements but instead constrains chromatin looping by sequestering EP300 away from loop anchor regions. Loss of SMAD4 leads to enhanced H3K27ac deposition, aberrant SOX2 activation, and increased LUSC tumor cell proliferation. Together, these findings reveal a non-canonical role for a transcription factor (e.g., SMAD4) in regulating dysregulated 3D genome architecture to inhibit tumor development.

PMID:42189071 | DOI:10.1002/advs.75839

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A Non-Canonical Role of SMAD4 in Regulating 3D Genome Architecture to Inhibit Lung Squamous Cell Carcinoma Development

✇Omics In Lung
By: Qian Tang · Chen Lian · Xinyan Han · Xuhan Zhang · Zihan Wang · Boyu Wang · Taoyu Zhu · Xinrui Lin · Xiaolei Wang · Yaping Xu · Manyu Xiao · Zijin Wang · Junming Li · Silin Chen · Yunze Wang · Yufei Liu · Songsong Li · Zuolin Shen · Xi Lu · Xueqi Han · Yilu Zhou · Mingyang Xiao · Jiayi Ran · Xiaoran Cao · Xinyi Xu · Hugo Sámano-Sánchez · Alfredo Rodríguez · Lian Wang · Shuifang Chen · Zhanyu Xu · Shirong Zhang · Nuo Yang · Yong Tang · Jian Liu

Adv Sci (Weinh). 2026 May 26:e75839. doi: 10.1002/advs.75839. Online ahead of print.

ABSTRACT

Lung squamous cell carcinoma (LUSC) lacks clearly defined key drivers and effective targeted therapies, reflecting an incomplete understanding of its molecular pathogenesis. Here, we identify SMAD4 as a critical regulator of three-dimensional (3D) genome organization in LUSC and uncover a mechanistic link between tumor suppressor loss and oncogenic transcriptional activation. By integrating clinical datasets, genetically engineered mouse models, human and murine LUSC cell lines, and multi-omics analyses, we demonstrate that SMAD4 deficiency promotes LUSC progression by unleashing EP300-mediated enhancer-promoter looping at the SOX2 locus. Mechanistically, SMAD4 does not directly bind SOX2 regulatory elements but instead constrains chromatin looping by sequestering EP300 away from loop anchor regions. Loss of SMAD4 leads to enhanced H3K27ac deposition, aberrant SOX2 activation, and increased LUSC tumor cell proliferation. Together, these findings reveal a non-canonical role for a transcription factor (e.g., SMAD4) in regulating dysregulated 3D genome architecture to inhibit tumor development.

PMID:42189071 | DOI:10.1002/advs.75839

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Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Heqiang Wang · Weihong Yang · Zheyuan Yang · Jia Zhou · Xiaoxiong Zhong · Fangming Liu · Weizhe Zhang
arXiv:2605.23984v1 Announce Type: cross Abstract: Industrial anomaly detection has attracted significant attention as a fundamental challenge in industrial systems. The rapid advancement of heterogeneous industrial sensors has driven industrial anomaly detection from unimodal to multimodal paradigms. However, existing methods are primarily designed for centralized and offline settings, overlooking the distributed and continuously generated data characteristic of real-world industrial environments. With the advancement of edge intelligence, modern edge devices are increasingly capable of not only data acquisition but also distributed model training, enabling collaborative intelligence across the system. Industrial anomaly detection represents a critical application in this context. Motivated by these challenges, we propose a novel framework termed Multimodal Online Distributed Industrial Anomaly Detection (MODIAD). We first present a comprehensive workflow for MODIAD and then formulate a Multi-class Intelligent Scheduling (MIS) problem to coordinate cross class model updates by balancing data sufficiency and class update frequency. To efficiently solve this problem, we design a Sequential Marginal Gain Greedy (SMG) algorithm that enables effective multi-class training under resource constraints. Furthermore, to improve the computational and communication efficiency during training, we propose an Resource Efficient Class-Wise Low Rank Adaptation (REC-LoRA) strategy, which significantly reduces system overhead while preserving detection performance. Extensive experiments on two representative multimodal industrial anomaly detection datasets, MVTec 3D-AD and Eyecandies demonstrate that the proposed approach achieves superior performance and efficiency under the MODIAD scenario.
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A World Model of Radiologist Reading for Medical Image Representation Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiwei Li · Zihao Wu · Huaqin Zhao · Yifan Zhou · Chao Cao · Dajiang Zhu · Tianming Liu · Lin Zhao
arXiv:2605.23992v1 Announce Type: cross Abstract: Radiologist eye-tracking data provide a rich record of how experts search, compare, and accumulate evidence during image reading; yet, existing methods exploit this signal only partially, either as a static spatial prior or as an auxiliary prediction target decoupled from diagnosis. We propose GazeWorld, a medical imaging world model that treats the image as the world and the radiologist's fixation sequence as a trajectory through it. GazeWorld autoregressively predicts the latent representation of the next fixated patch from all previously visited ones, while a spatial-completion branch covers unvisited regions. At inference, GazeWorld generates a sequence of patch representations from the image alone without requiring real gaze data. Frozen GazeWorld features achieve state-of-the-art diagnostic accuracy across all nine supervised settings on CheXpert, RSNA Pneumonia, and SIIM-ACR Pneumothorax, as well as the highest zero-shot accuracy on all three benchmarks. On the GazeSearch benchmark, a generic decoder trained on the same frozen features outperforms the purpose-built LogitGaze-Med by over 16\% in ScanMatch and 22\% in SED, despite not being explicitly trained to predict gaze. GazeWorld demonstrates that modeling how experts read, not just what they conclude, offers a promising pretraining paradigm for medical imaging AI.
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Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory

✇cs.AI, q-bio.NC updates on arXiv.org
By: Quanjiang Li · Zhiming Liu · Wei Luo · Tingjin Luo · Chenping Hou
arXiv:2605.24602v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) frequently suffer from object hallucinations, yet the visual perceptual mechanism underlying this failure remains poorly understood. In this work, we reveal that hallucinations are strongly associated with a human-like attention distraction phenomenon, where humans under divided focus experience degraded visual clarity and produce inaccurate descriptions, while in models the same mechanism manifests as spatial inconsistency in multi-head attention and temporal fading of attention to image tokens during decoding. We further provide theoretical insights that attention dispersion increases model complexity and degrades classification generalization. Motivated by these findings, we propose an Attention-Focused Approach for Improved Image Perception (AFIP), which corrects attention distraction via cross-head attention enrichment and reinforces visual grounding through dynamic historical attention enhancement. Extensive experiments on multiple benchmarks and models validate the effectiveness of AFIP without additional training.
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Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jinjin Chi · Lei Feng · Lulu Zhang · Yongcheng Jing · Yiming Wang · Ximing Li · Jialie Shen · Leszek Rutkowski · Dacheng Tao
arXiv:2605.24911v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) have recently achieved strong zero-shot forecasting performance through large-scale pretraining and retrieval-augmented prediction. However, our empirical analysis reveals a non-trivial limitation of retrieval-based forecasting: retrieval tends to induce more oscillatory predictions, improving performance on highly fluctuating series while degrading accuracy on smoother, trend-dominated ones. This suggests that retrieved information may be fused into prediction without explicitly distinguishing stable temporal structure from instance-specific variations, which can reduce robustness under distribution shifts. We propose a Retrieval-guided Invariant-Dynamic DEcomposition framework for time series forecasting. Rather than using retrieval as auxiliary predictive context, we leverage retrieved sequences as implicit samples from related environments to guide representation decomposition. Specifically, we first construct a retrieval-aware representation via attention-based aggregation, and then introduce a retrieval-guided routing mechanism to decompose it into an invariant component capturing stable shared structure and a dynamic component modeling context-dependent variations. These two components are forecast separately and fused for final prediction, enabling the model to preserve transferable patterns while remaining adaptive to evolving dynamics. We further design training objectives that encourage invariant learning and disentanglement, and provide theoretical insight showing that retrieval aggregation reduces variance and approximates invariant representation learning without explicit environment supervision. Extensive experiments demonstrate that our method consistently improves robustness under distribution shifts and outperforms existing TSFMs and retrieval-based baselines in zero-shot forecasting settings.
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MinerU-Popo: Universal Post-Processing Model for Structured Document Parsing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bangrui Xu · Ziyang Miao · Xuanhe Zhou · Yiming Lin · Zirui Tang · Xiaomeng Zhao · Fan Wu · Cheng Tan · Fan Wu · Bin Wang · Conghui He
arXiv:2605.24973v1 Announce Type: cross Abstract: VLM-based OCR models have become the de facto choice for document parsing, as they can accurately extract page-level elements (e.g., paragraphs within individual pages) together with their bounding boxes and textual content. However, downstream applications such as RAG require coherent document-level information, whereas these models often break cross-page continuity and fail to recover disrupted structures, such as paragraphs and tables truncated by page boundaries. Such relationships are not confined to a single page; instead, they require joint analysis of titles, paragraphs, tables, and images spanning multiple pages. A natural solution is therefore to reuse existing OCR outputs and reconstruct document-level logical structures through post-processing. To this end, we propose MinerU-Popo, a lightweight and universal framework for POst-Processing OCR outputs, which converts page-level results from diverse parsers into coherent document-level structures. MinerU-Popo decomposes the problem into four focused subtasks: text truncation recovery, table truncation recovery, title hierarchy reconstruction, and image-text association. To address these effectively, we build a task-oriented data engine with task-specific input filtering, and use the generated data (30K) to fine-tune a lightweight post-processing model (Qwen3-VL-4B). To support long documents, we introduce dynamic chunking with overlap-based synchronization, which aligns chunk-level outputs from the fine-tuned model and preserves global consistency. Finally, we assemble the aligned outputs into a tree-structured document representation, further enriched with node chunking and summaries for downstream retrieval and analysis. Empirical results show MinerU-Popo improves title-hierarchy TEDS by at least 20% across all five tested OCR models, improves RAG accuracy and reduces per-query latency.
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CollectionLoRA: Collecting 50 Effects in 1 LoRA via Multi-Teacher On-Policy Distillation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fangtai Wu · Hailong Guo · Shijie Huang · Jiayi Song · Yubo Huang · Mushui Liu · Zhao Wang · Yunlong Yu · Jiaming Liu · Ruihua Huang
arXiv:2605.25378v1 Announce Type: cross Abstract: Customized image editing aims to equip pre-trained diffusion models with specific visual effects using limited paired data, typically via Low-Rank Adaptation (LoRA). As the number of desired effects grows, storing and dynamically loading numerous these effect LoRAs significantly increases deployment overhead. Furthermore, current pipelines typically cascade these effect LoRAs with acceleration modules for fast generation, which triggers severe parameter interference and results in concept bleeding and style degradation. We propose CollectionLoRA, a multi-teacher on-policy distillation framework capable of distilling the concepts of up to 50 different effect LoRAs along with few-step generation capabilities into a single LoRA. This fundamentally resolves the feature interference issue and significantly reduces deployment costs. Specifically, the method introduces (i) a Probabilistic Dual-Stream Routing mechanism that enables the model to randomly switch between data sources during training, effectively enhancing its generalization in unseen scenarios; (ii) an Asymmetric Orthogonal Prompting strategy to achieve concept isolation within the prompt space; (iii) a Coarse-to-Fine Distillation Objective to mitigate the distribution gap between the teacher and student models. Extensive evaluations show that CollectionLoRA distills all customized effects and few-step generation into a single LoRA, reducing deployment overhead while achieving concept fidelity comparable to or better than independently trained teacher models.
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Channel-wise Vector Quantization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wei Song · Tianhang Wang · Yitong Chen · Tong Zhang · Zuxuan Wu · Ming Li · Jiaqi Wang · Kaicheng Yu
arXiv:2605.26089v1 Announce Type: cross Abstract: We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens. Unlike conventional vector quantization, which assigns a discrete token to each patch feature vector, CVQ quantizes each channel of the feature map. This formulation represents an image as discrete levels of visual details, rather than as a grid of spatial patches. Based on CVQ, we introduce a new visual autoregressive framework with "next-channel prediction". Instead of rendering images patch by patch in raster order, our Channel-wise Autoregressive (CAR) model predicts image channels sequentially, producing progressively enriched visual details. Specifically, it first sketches global structure and then refines fine-grained attributes, akin to a human artist's workflow. Empirically, we show that: (1) CVQ achieves 100% codebook utilization with a 16K+ codebook size without any bells and whistles, and substantially improves reconstruction quality over conventional VQ; and (2) CAR attains a DPG score of 86.7 and a GenEval score of 0.79, demonstrating strong effectiveness for text-to-image generation.
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From Prompt Optimization to Multi-Dimensional Credibility Evaluation: Enhancing Trustworthiness of Chinese LLM-Generated Liver MRI Reports -- with Preliminary Extension to Lung Cancer

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qiuli Wang · Xinhuang Sun · Yonglin Chen · Jie Cheng · Yongxu Liu · Xingpeng Zhang · Xiaoming Li · Wei Chen
arXiv:2510.23008v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated promising performance in generating diagnostic conclusions from imaging findings, thereby supporting radiology reporting, trainee education, and quality control. However, systematic guidance on how to optimize prompt design across different clinical contexts remains underexplored. Moreover, a comprehensive and standardized framework for assessing the trustworthiness of LLM-generated radiology reports is yet to be established. This study aims to enhance the trustworthiness of LLM-generated liver MRI reports by introducing a Multi-Dimensional Credibility Assessment (MDCA) framework and providing guidance on institution-specific prompt optimization. The proposed framework is applied to evaluate and compare the performance of several advanced LLMs, including Kimi-K2-Instruct-0905, Qwen3-235B-A22B-Instruct-2507, DeepSeek-V3, and ByteDance-Seed-OSS-36B-Instruct, using the SiliconFlow platform.
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TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Quang Duc Nguyen · Siyuan Liang · Yiming Li · Fushuo Huo · Dacheng Tao
arXiv:2605.22365v2 Announce Type: replace-cross Abstract: Time Series Forecasting (TSF) is highly vulnerable to backdoor attacks, yet effective defenses remain underexplored due to challenges arising from data entanglement and shifts in task formulation. To fill this gap, we conduct a systematic evaluation of thirteen representative backdoor defenses across the TSF life cycle and analyze their failure modes. Our results reveal two fundamental issues: (1) data entanglement induces channel-level signal dilution, rendering sample-filtering and trigger-synthesis defenses ineffective at localizing backdoors; and (2) task-formulation shift leads to training-loss degeneration, causing poisoned and clean windows to become indistinguishable at training stages. Based on these findings, we propose a training-time backdoor defense for TSF, termed TimeGuard. Our method adopts channel-wise pool training as the core paradigm and initializes a high-confidence pool using time-aware criteria to mitigate signal dilution. Moreover, we introduce distance-regularized loss selection to progressively expand the reliable pool during training and ease loss degeneration. Extensive experiments across multiple datasets, forecasting architectures, and TSF backdoor attacks demonstrate that TimeGuard substantially improves robustness, boosting $\mathrm{MAE}_\mathrm{P}$ by $1.96\times$ over the leading baseline, while preserving clean performance within 5% $\mathrm{MAE}_\mathrm{C}$.
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A framework for building a synthetic cell from the SynCell Asia Initiative

✇Nature Biotechnology - Issue - nature.com science feeds
By: Zhuojun Dai · Wataru Aoki · Pimchai Chaiyen · Matthew Wook Chang · Xixian Chen · Zibo Chen · Byung-Kwan Cho · Nan-Nan Deng · Shishen Du · Jee Loon Foo · Julius Fredens · Meifang Fu · Xiaojun Han · Tomohisa Hasunuma · Norikazu Ichihashi · Nurulfiza Mat Isa · Daisuke Kiga · Dong-Myung Kim · Jongmin Kim · Akihiko Kondo · Yuishin Kosaka · Kobkul Laoteng · Joongoo Lee · Jian Li · Junbai Li · Ming Li · Sierin Lim · Jianbo Liu · Xingguo Liu · Yuan Lu · Nan Luo · Tomoaki Matsuura · Wataru Mizunashi · Yan Qiao · Ahmad Bazli Ramzi · Sang Woo Seo · Yoshihiro Shimizu · Kwanwoo Shin · Liangfei Tian · Wanwipa Vongsangnak · Baojun Wang · Cheng-Cai Zhang · Xian-En Zhang · Guoping Zhao · Bo Zheng · Yaoqi Zhou · Chenli Liu

Nature Biotechnology, Published online: 26 May 2026; doi:10.1038/s41587-026-03153-w

Building a living cell from scratch requires overcoming a bottleneck that has remained unresolved despite decades of progress: orchestrating the spatiotemporal integration of core functional modules. To tackle this barrier, the SynCell Asia Initiative outlines a strategy for developing core functional modules followed by their systems-level integration through the establishment of a centralized, artificial intelligence (AI)-driven biofoundry.
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Multi-omics biomarkers for predicting resistance, hyperprogression, and immune-related toxicity during PD-1/PD-L1 therapy in lung cancer: a literature review

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Na Hu · Ziyue Wang · Ming Liu · Dong Guo · Chenxue Jiang · Yun Chen · Zhenshan Zhang · Menglin Bai · Bo Cheng · Jessica C Hsu · Leilei Wu · Xiujie Sui

Front Immunol. 2026 May 8;17:1780459. doi: 10.3389/fimmu.2026.1780459. eCollection 2026.

ABSTRACT

Immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) and its ligand programmed death-ligand 1 (PD-L1) have transformed the management of advanced lung cancer, yet most patients experience primary resistance, hyperprogressive disease (HPD), or clinically significant immune-related adverse events (irAEs). Multi-omics technologies now enable integrated interrogation of tumor, microenvironmental, host, and clinical determinants of these divergent outcomes. In this review, we first discuss the biological and clinical foundations of PD-1/PD-L1 blockade in non-small cell and small cell lung cancer, and summarize the spectrum of resistance, HPD, and irAEs observed in trials and real-world practice. We then describe multi-omics study frameworks that connect genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, and microbiome profiling with these outcome phenotypes. Building on this foundation, we synthesize evidence for composite biomarkers of primary and acquired resistance, delineate emerging multi-omics signatures of HPD, and examine host- and tumor-derived multi-omics correlates of organ-specific and systemic irAEs. We further propose an efficacy-risk quadrant framework to guide clinical decision-making when favorable efficacy predictors coexist with elevated risk of severe adverse outcomes, and outline a three-step approach for high-efficacy/high-risk patients: joint probability reporting, multi-omics guided mitigation, and dynamic reassessment. Finally, we evaluate translational strategies that integrate multi-omics scores into baseline risk stratification, dynamic monitoring with attention to technical challenges such as distinguishing true progression from ctDNA pseudoprogression, and biomarker-driven trial design, while assessing the evidence level and translational readiness of candidate assays from retrospective discovery to clinical implementation. A clinical case illustrates how multi-omics can link baseline risk stratification, regimen selection, and longitudinal monitoring into a coherent action plan, while acknowledging that artificial intelligence-driven models remain investigational and real-world application still relies on clinician judgment. Collectively, this review defines how integrated multi-omics biomarkers can be leveraged to predict resistance, HPD, and immune-related toxicity, and to refine patient selection and management during PD-1/PD-L1 therapy in lung cancer.

PMID:42183274 | PMC:PMC13194140 | DOI:10.3389/fimmu.2026.1780459

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Multi-omics biomarkers for predicting resistance, hyperprogression, and immune-related toxicity during PD-1/PD-L1 therapy in lung cancer: a literature review

✇Omics In Lung
By: Na Hu · Ziyue Wang · Ming Liu · Dong Guo · Chenxue Jiang · Yun Chen · Zhenshan Zhang · Menglin Bai · Bo Cheng · Jessica C Hsu · Leilei Wu · Xiujie Sui

Front Immunol. 2026 May 8;17:1780459. doi: 10.3389/fimmu.2026.1780459. eCollection 2026.

ABSTRACT

Immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) and its ligand programmed death-ligand 1 (PD-L1) have transformed the management of advanced lung cancer, yet most patients experience primary resistance, hyperprogressive disease (HPD), or clinically significant immune-related adverse events (irAEs). Multi-omics technologies now enable integrated interrogation of tumor, microenvironmental, host, and clinical determinants of these divergent outcomes. In this review, we first discuss the biological and clinical foundations of PD-1/PD-L1 blockade in non-small cell and small cell lung cancer, and summarize the spectrum of resistance, HPD, and irAEs observed in trials and real-world practice. We then describe multi-omics study frameworks that connect genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, and microbiome profiling with these outcome phenotypes. Building on this foundation, we synthesize evidence for composite biomarkers of primary and acquired resistance, delineate emerging multi-omics signatures of HPD, and examine host- and tumor-derived multi-omics correlates of organ-specific and systemic irAEs. We further propose an efficacy-risk quadrant framework to guide clinical decision-making when favorable efficacy predictors coexist with elevated risk of severe adverse outcomes, and outline a three-step approach for high-efficacy/high-risk patients: joint probability reporting, multi-omics guided mitigation, and dynamic reassessment. Finally, we evaluate translational strategies that integrate multi-omics scores into baseline risk stratification, dynamic monitoring with attention to technical challenges such as distinguishing true progression from ctDNA pseudoprogression, and biomarker-driven trial design, while assessing the evidence level and translational readiness of candidate assays from retrospective discovery to clinical implementation. A clinical case illustrates how multi-omics can link baseline risk stratification, regimen selection, and longitudinal monitoring into a coherent action plan, while acknowledging that artificial intelligence-driven models remain investigational and real-world application still relies on clinician judgment. Collectively, this review defines how integrated multi-omics biomarkers can be leveraged to predict resistance, HPD, and immune-related toxicity, and to refine patient selection and management during PD-1/PD-L1 therapy in lung cancer.

PMID:42183274 | PMC:PMC13194140 | DOI:10.3389/fimmu.2026.1780459

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