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Received — 10 September 2026 ⏭

RESCUE-BENCH: Towards Relation-Aware Multi-Party Emotional Support Conversation Systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haichuan Hu · Yang Xiao · Mingni Tang · Jiawen Duan · Quanjun Zhang · Congqing He · Hao Zhang · Jiashuo Wang · Johan F. Hoorn · Wenjie Li
10 September 2026 at 12:00
arXiv:2609.09657v1 Announce Type: new Abstract: Existing emotional support conversation systems mainly focus on one-on-one seeker-supporter interactions and individual emotional states, leaving interpersonal relations in multi-party scenarios underexplored. In this work, we introduce relation-aware emotional support conversation, a new task that evaluates whether LLMs can capture and utilize the evolving dynamics of relationships to offer more effective emotional support. We construct RESCUE (Relation-aware Emotional Support Conversation Understanding and Evaluation Benchmark) from real couple and family interview conversations, containing 191 samples, 7,079 annotated turns, and 1,064.8 minutes of video. Based on rich annotations of socio-emotional and support-related dynamics, RESCUE defines six tasks that evaluate two core capabilities required for relation-aware emotional support: Relational Understanding and Relation-Sensitive Support. Experiments with ten LLMs show that current models perform relatively well on tasks relying on local emotional or intervention cues, but struggle with relation-intensive tasks such as relation pattern prediction, viewpoint prediction, and support strategy prediction. These findings reveal the limitations of current LLMs in modeling interpersonal relations and making relation-sensitive support decisions.
Received — 27 May 2026 ⏭

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
26 May 2026 at 18:00

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

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
26 May 2026 at 18:00

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

Received — 11 March 2026 ⏭

UnfoldLDM: Deep Unfolding-based Blind Image Restoration with Latent Diffusion Priors

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chunming He · Rihan Zhang · Zheng Chen · Bowen Yang · Chengyu Fang · Yunlong Lin · Yulun Zhang · Fengyang Xiao · Sina Farsiu
10 March 2026 at 12:00
arXiv:2511.18152v2 Announce Type: replace-cross Abstract: Deep unfolding networks (DUNs) combine the interpretability of model-based methods with the learning ability of deep networks, yet remain limited for blind image restoration (BIR). Existing DUNs suffer from: (1) \textbf{Degradation-specific dependency}, as their optimization frameworks are tied to a known degradation model, making them unsuitable for BIR tasks; and (2) \textbf{Over-smoothing bias}, resulting from the direct feeding of gradient descent outputs, dominated by low-frequency content, into the proximal term, suppressing fine textures. To overcome these issues, we propose UnfoldLDM to integrate DUNs with latent diffusion model (LDM) for BIR. In each stage, UnfoldLDM employs a multi-granularity degradation-aware (MGDA) module as the gradient descent step. MGDA models BIR as an unknown degradation estimation problem and estimates both the holistic degradation matrix and its decomposed forms, enabling robust degradation removal. For the proximal step, we design a degradation-resistant LDM (DR-LDM) to extract compact degradation-invariant priors from the MGDA output. Guided by this prior, an over-smoothing correction transformer (OCFormer) explicitly recovers high-frequency components and enhances texture details. This unique combination ensures the final result is degradation-free and visually rich. Experiments show that our UnfoldLDM achieves a leading place on various BIR tasks and benefits downstream tasks. Moreover, our design is compatible with existing DUN-based methods, serving as a plug-and-play framework. Code will be released.
Received — 5 March 2026 ⏭
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Delegation and Verification Under AI Lingxiao Huang · Wenyang Xiao · Nisheeth K. Vishnoi
    arXiv:2603.02961v1 Announce Type: cross Abstract: As AI systems enter institutional workflows, workers must decide whether to delegate task execution to AI and how much effort to invest in verifying AI outputs, while institutions evaluate workers using outcome-based standards that may misalign with workers' private costs. We model delegation and verification as the solution to a rational worker's optimization problem, and define worker quality by evaluating an institution-centered utility (dist
     
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Delegation and Verification Under AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lingxiao Huang · Wenyang Xiao · Nisheeth K. Vishnoi
4 March 2026 at 13:00
arXiv:2603.02961v1 Announce Type: cross Abstract: As AI systems enter institutional workflows, workers must decide whether to delegate task execution to AI and how much effort to invest in verifying AI outputs, while institutions evaluate workers using outcome-based standards that may misalign with workers' private costs. We model delegation and verification as the solution to a rational worker's optimization problem, and define worker quality by evaluating an institution-centered utility (distinct from the worker's objective) at the resulting optimal action. We formally characterize optimal worker workflows and show that AI induces *phase transitions*, where arbitrarily small differences in verification ability lead to sharply different behaviors. As a result, AI can amplify workers with strong verification reliability while degrading institutional worker quality for others who rationally over-delegate and reduce oversight, even when baseline task success improves and no behavioral biases are present. These results identify a structural mechanism by which AI reshapes institutional worker quality and amplifies quality disparities between workers with different verification reliability.
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