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Received — 27 May 2026 ⏭

Auditing Stealth Sycophancy in Mental-Health Dialogue: Structured Clinical-State Diagnostics and Clean Matched Benchmarks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianze Han · Beining Xu · Hanbo Zhang · Yongming Lu
26 May 2026 at 12:00
arXiv:2605.03472v2 Announce Type: replace-cross Abstract: Mental-health dialogue models are increasingly evaluated by AI-based evaluators, yet these evaluators often treat surface empathy, supportiveness, or fluency as evidence of safety. In this paper, we study a hidden failure mode that we call implicit sycophancy: a response may appear empathetic while implicitly reinforcing catastrophizing, avoidance, hopeless prediction, or CBT-style labeling. To examine this problem, we introduce a diagnostic benchmark for implicit-sycophancy detection, built from three representative mental-health dialogue sources covering everyday peer support, counseling-style emotional support, and crisis-oriented interaction, and further construct a leakage-audited clean single-response matched benchmark with 500 contexts and 1,500 matched response windows. We then propose Dynamic Emotional Signature Graphs (DESG), a structured offline audit framework that separates LLM-based state extraction from final scoring and evaluates clinical direction through semantic, affective, and cognitive-distortion state transitions rather than free-form LLM judgment. Unlike metadata, surface-style, lexical, embedding, and rubric-LLM baselines, DESG scores the direction of clinical-state change induced by a response; on the leakage-audited clean matched benchmark, DESG-StateRisk improves over the strongest non-DESG baseline by 0.0488 macro-F1 and achieves the best harmful-risk detection result. These results suggest that evaluating implicit sycophancy requires explicit clinical-state modeling together with leakage checks, shortcut controls, and competitive baselines.

Refined immune-based molecular subtypes of gastric cancer: Integrating mismatch repair status and tumor microenvironment for enhanced immunotherapy prediction

✇Omics in Gastric
By: Ke Shen · Yanqiang Zhang · Zhiqin Wang · Ruixing Xu · Jintao He · Mengya Zhou · Yiming Lu · Danni Zhu · Yizhou Wei · Weiwei Zhu · Qing Yang · Zhiyuan Xu · Can Hu · Siwei Pan · Xiangdong Cheng
18 May 2026 at 18:00

Chin J Cancer Res. 2026 Apr 30;38(2):234-251. doi: 10.21147/j.issn.1000-9604.2026.02.09.

ABSTRACT

OBJECTIVE: Gastric cancer (GC) is heterogeneous, and current mismatch repair (MMR)-based classifications incompletely predict response to immune checkpoint inhibitors (ICIs).

METHODS: RNA sequencing (RNA-seq) and immune infiltration profiles from 189 resected GC were used to derive four refined immune-MMR subtypes (R1-R4) by integrating MMR status, survival, and tumor microenvironment (TME) features. Multi-omics profiling and pathway analysis defined subtype biology. External transcriptomic cohorts and an ICI-treated cohort were classified with Nearest Template Prediction (NTP). Immune response-associated genes were identified from responder vs. non-responder comparisons within the ICI-sensitive subtype and validated by multiplex immunohistochemistry (mIHC).

RESULTS: R1 showed the best prognosis and highest immunotherapy response with objective response rate (ORR) 54.5%, while R4 had the worst prognosis. R2 represented an immune-unresponsive deficient mismatch repair (dMMR) subset, and R3 captured an immune-active proficient mismatch repair (pMMR) subgroup with moderate therapy sensitivity. Multi-omics integration revealed subtype-specific pathways (e.g., ECM remodeling in R1, metabolic reprogramming in R2). Reclassification of pMMR tumors based on transcriptional similarity to R1 identified a New R3 subset with enhanced immune features and higher ICI response. Eight immune response-associated genes (e.g., CXCL10, CXCL11, ELN, GAD1, IL32, MT1E, OR2I1P, SLC3A1) were identified and validated by mIHC for predictive relevance.

CONCLUSIONS: This immune-based molecular framework refines risk stratification beyond conventional MMR categories, identifies ICI-sensitive subsets among both dMMR and pMMR tumors, and proposes candidate biomarkers for patient selection.

PMID:42147371 | PMC:PMC13171420 | DOI:10.21147/j.issn.1000-9604.2026.02.09

Received — 13 April 2026 ⏭

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions

✇Omics In Lung
By: Yin Zhang · Xianzhe Yu · Ping Li · Yaobin Ouyang · Lingling Zhu · Fengming Luo
9 April 2026 at 18:00

BMC Med. 2026 Apr 8. doi: 10.1186/s12916-026-04833-9. Online ahead of print.

ABSTRACT

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chronic obstructive pulmonary disease, lung cancer, pulmonary fibrosis, asthma, and respiratory tract infections. This comprehensive review examines the hallmarks of immunosenescence, and illustrates the association between immunosenescence and the pathogenesis of lung diseases. In addition, we discuss current and emerging therapeutic strategies that have been evaluated in human clinical trials for targeting immunosenescence in lung diseases. Specifically, this review provides in-depth insights into the therapeutic strategies, including senolytics and senomorphics, immunotherapy, stem cell therapy, thymic rejuvenation, probiotics, and lifestyle. We also highlight the potential of personalized approaches integrating multi-omics data and artificial intelligence to guide biomarker-driven interventions, enabling truly personalized therapeutic strategies. Finally, this review underscores the imperative for rigorously designed clinical trials to develop and validate interventions that specifically target immunosenescence, with the ultimate goal of improving clinical outcomes for the aged population with lung diseases.

PMID:41952158 | DOI:10.1186/s12916-026-04833-9

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yin Zhang · Xianzhe Yu · Ping Li · Yaobin Ouyang · Lingling Zhu · Fengming Luo
9 April 2026 at 18:00

BMC Med. 2026 Apr 8. doi: 10.1186/s12916-026-04833-9. Online ahead of print.

ABSTRACT

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chronic obstructive pulmonary disease, lung cancer, pulmonary fibrosis, asthma, and respiratory tract infections. This comprehensive review examines the hallmarks of immunosenescence, and illustrates the association between immunosenescence and the pathogenesis of lung diseases. In addition, we discuss current and emerging therapeutic strategies that have been evaluated in human clinical trials for targeting immunosenescence in lung diseases. Specifically, this review provides in-depth insights into the therapeutic strategies, including senolytics and senomorphics, immunotherapy, stem cell therapy, thymic rejuvenation, probiotics, and lifestyle. We also highlight the potential of personalized approaches integrating multi-omics data and artificial intelligence to guide biomarker-driven interventions, enabling truly personalized therapeutic strategies. Finally, this review underscores the imperative for rigorously designed clinical trials to develop and validate interventions that specifically target immunosenescence, with the ultimate goal of improving clinical outcomes for the aged population with lung diseases.

PMID:41952158 | DOI:10.1186/s12916-026-04833-9

Received — 8 April 2026 ⏭

Agile Deliberation: Concept Deliberation for Subjective Visual Classification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Leijie Wang · Otilia Stretcu · Wei Qiao · Thomas Denby · Krishnamurthy Viswanathan · Enming Luo · Chun-Ta Lu · Tushar Dogra · Ranjay Krishna · Ariel Fuxman
7 April 2026 at 12:00
arXiv:2512.10821v2 Announce Type: replace Abstract: From content moderation to content curation, applications requiring vision classifiers for visual concepts are rapidly expanding. Existing human-in-the-loop approaches typically assume users begin with a clear, stable concept understanding to be able to provide high-quality supervision. In reality, users often start with a vague idea and must iteratively refine it through "concept deliberation", a practice we uncovered through structured interviews with content moderation experts. We operationalize the common strategies in deliberation used by real content moderators into a human-in-the-loop framework called "Agile Deliberation" that explicitly supports evolving and subjective concepts. The system supports users in defining the concept for themselves by exposing them to borderline cases. The system does this with two deliberation stages: (1) concept scoping, which decomposes the initial concept into a structured hierarchy of sub-concepts, and (2) concept iteration, which surfaces semantically borderline examples for user reflection and feedback to iteratively align an image classifier with the user's evolving intent. Since concept deliberation is inherently subjective and interactive, we painstakingly evaluate the framework through 18 user sessions, each 1.5h long, rather than standard benchmarking datasets. We find that Agile Deliberation achieves 7.5% higher F1 scores than automated decomposition baselines and more than 3% higher than manual deliberation, while participants reported clearer conceptual understanding and lower cognitive effort.
Received — 2 April 2026 ⏭

Stronger Normalization-Free Transformers

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mingzhi Chen · Taiming Lu · Jiachen Zhu · Mingjie Sun · Zhuang Liu
1 April 2026 at 12:00
arXiv:2512.10938v2 Announce Type: replace-cross Abstract: Although normalization layers have long been viewed as indispensable components of deep learning architectures, the recent introduction of Dynamic Tanh (DyT) has demonstrated that alternatives are possible. The point-wise function DyT constrains extreme values for stable convergence and reaches normalization-level performance; this work seeks further for function designs that can surpass it. We first study how the intrinsic properties of point-wise functions influence training and performance. Building on these findings, we conduct a large-scale search for a more effective function design. Through this exploration, we introduce $\mathrm{Derf}(x) = \mathrm{erf}(\alpha x + s)$, where $\mathrm{erf}(x)$ is the rescaled Gaussian cumulative distribution function, and identify it as the most performant design. Derf outperforms LayerNorm, RMSNorm, and DyT across a wide range of domains, including visual recognition and generation, speech representation, and DNA sequence modeling. Our analysis also suggests that the performance gains of Derf largely stem from its improved generalization rather than stronger fitting capacity. Its simplicity and stronger performance make Derf a practical choice for normalization-free Transformer architectures.
Received — 11 March 2026 ⏭

Rethinking Driving World Model as Synthetic Data Generator for Perception Tasks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Kai Zeng · Zhanqian Wu · Kaixin Xiong · Xiaobao Wei · Xiangyu Guo · Zhenxin Zhu · Kalok Ho · Lijun Zhou · Bohan Zeng · Ming Lu · Haiyang Sun · Bing Wang · Guang Chen · Hangjun Ye · Wentao Zhang
10 March 2026 at 12:00
arXiv:2510.19195v4 Announce Type: replace-cross Abstract: Recent advancements in driving world models enable controllable generation of high-quality RGB videos or multimodal videos. Existing methods primarily focus on metrics related to generation quality and controllability. However, they often overlook the evaluation of downstream perception tasks, which are $\mathbf{really\ crucial}$ for the performance of autonomous driving. Existing methods usually leverage a training strategy that first pretrains on synthetic data and finetunes on real data, resulting in twice the epochs compared to the baseline (real data only). When we double the epochs in the baseline, the benefit of synthetic data becomes negligible. To thoroughly demonstrate the benefit of synthetic data, we introduce Dream4Drive, a novel synthetic data generation framework designed for enhancing the downstream perception tasks. Dream4Drive first decomposes the input video into several 3D-aware guidance maps and subsequently renders the 3D assets onto these guidance maps. Finally, the driving world model is fine-tuned to produce the edited, multi-view photorealistic videos, which can be used to train the downstream perception models. Dream4Drive enables unprecedented flexibility in generating multi-view corner cases at scale, significantly boosting corner case perception in autonomous driving. To facilitate future research, we also contribute a large-scale 3D asset dataset named DriveObj3D, covering the typical categories in driving scenarios and enabling diverse 3D-aware video editing. We conduct comprehensive experiments to show that Dream4Drive can effectively boost the performance of downstream perception models under various training epochs. Page: https://wm-research.github.io/Dream4Drive/ GitHub Link: https://github.com/wm-research/Dream4Drive
Received — 25 February 2026 ⏭

From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianxi Wan · Jiaming Luo · Siyuan Chen · Kunyao Lan · Jianhua Chen · Haiyang Geng · Mengyue Wu
24 February 2026 at 13:00
arXiv:2510.25232v2 Announce Type: replace Abstract: Psychiatric comorbidity is clinically significant yet challenging due to the complexity of multiple co-occurring disorders. To address this, we develop a novel approach integrating synthetic patient electronic medical record (EMR) construction and multi-agent diagnostic dialogue generation. We create 502 synthetic EMRs for common comorbid conditions using a pipeline that ensures clinical relevance and diversity. Our multi-agent framework transfers the clinical interview protocol into a hierarchical state machine and context tree, supporting over 130 diagnostic states while maintaining clinical standards. Through this rigorous process, we construct PsyCoTalk, the first large-scale dialogue dataset supporting comorbidity, containing 3,000 multi-turn diagnostic dialogues validated by psychiatrists. This dataset enhances diagnostic accuracy and treatment planning, offering a valuable resource for psychiatric comorbidity research. Compared to real-world clinical transcripts, PsyCoTalk exhibits high structural and linguistic fidelity in terms of dialogue length, token distribution, and diagnostic reasoning strategies. Licensed psychiatrists confirm the realism and diagnostic validity of the dialogues. This dataset enables the development and evaluation of models capable of multi-disorder psychiatric screening in a single conversational pass.
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