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Normal view

Received — 27 May 2026 ⏭

Integrative multi-omics analysis identifies stromal-immune crosstalk as a determinant of immunotherapy efficacy and establishes a prognostic signature in gastric cancer

✇Omics in Gastric
By: Fan Zhang · Qingqing Zhang · Shuai Shao · Xiaobo Li · Yi Cheng · Xu Cao · Xiaotang Yu · Zhengming Gao
2 May 2026 at 18:00

Comput Biol Chem. 2026 Apr 23;124(Pt 1):109095. doi: 10.1016/j.compbiolchem.2026.109095. Online ahead of print.

ABSTRACT

Immune checkpoint inhibitors like pembrolizumab exhibit variable efficacy in metastatic gastric cancer (GC). This study aimed to identify molecular drivers of pembrolizumab response, explore mechanisms of immune checkpoint inhibitors (ICIs) efficacy, and develop a prognostic signature. Transcriptomic analysis of pembrolizumab-treated GC (TIGER database) identified 165 response-associated differentially expressed genes (DEGs). Functional annotation and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) revealed that responder-upregulated genes (R-DEGs) were enriched in immune activation pathways and mainly localized to CD8 + T/NK cells. In contrast, non-responder-upregulated genes (D-DEGs) were linked to extracellular matrix (ECM) remodeling and mainly expressed in fibroblasts/endothelial cells. CellChat analysis demonstrated that key DEGs mediate immune-stromal crosstalk via MHC-I and collagen/laminin signaling. A prognostic signature (Lasso-StepCox[forward] Riskscore; LSR: APOD, APOH, BATF2, GJA1, MAGED1, SLC5A1, SLCO2A1, VWF, VCAN) was derived and validated in four independent GC cohorts from the GEO and Cancer Genome Atlas (TCGA) database. Multi-omics analyses showed that LSR-high tumors exhibited aggressive clinicopathological features, increased stromal components, reduced cytotoxic immune infiltration, diminished tumor mutational burden (TMB), and poorer prognosis. Immunohistochemistry (IHC) and spatial transcriptomics in GC showed that stromal VWF/VCAN expression correlates with reduced CD8⁺ T cell granzyme B expression, suggesting T cell dysfunction. High VWF expression in GC predicted poor survival, and a combined VWF/VCAN score showed enhanced prognostic stratification. This study highlights stromal-immune crosstalk as a driver of pembrolizumab resistance and provides a signature as a clinical tool for prognosis and personalized therapy in metastatic GC.

PMID:42068630 | DOI:10.1016/j.compbiolchem.2026.109095

Received — 8 April 2026 ⏭

Why Attend to Everything? Focus is the Key

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hengshuai Yao · Xing Chen · Ahmed Murtadha · Jin Li · Shuai Shao · Yasin Abbasi Yadkori · Guan Wang · Mingli Yuan · William Chen · Sen Song
7 April 2026 at 12:00
arXiv:2604.03260v1 Announce Type: cross Abstract: We introduce Focus, a method that learns which token pairs matter rather than approximating all of them. Learnable centroids assign tokens to groups; distant attention is restricted to same-group pairs while local attention operates at full resolution. Because all model weights stay frozen, Focus is purely additive: centroid-only training (as few as 148K parameters) improves domain perplexity with zero degradation on downstream benchmarks--from 124M to 70B parameters, across five attention architectures. No existing efficient attention method achieves this in the retrofit setting. At 124M, Focus surpasses full attention (30.3 vs 31.4 PPL); trained from scratch at 7B scale (2B tokens), Focus again beats full attention (13.82 vs 13.89 PPL). At inference, restricting each token to its top-k highest-scoring groups discretizes the soft routing into a hard sparsity pattern, yielding 2x speedup while beating the pretrained baseline (41.3 vs 42.8 PPL); decomposing this pattern into two standard FlashAttention calls reaches 8.6x wall-clock speedup at 1M tokens with no custom kernels. Unlike LoRA, centroid routing preserves alignment: instruction-tuned models retain TruthfulQA scores after adaptation, while LoRA degrades at every learning rate and rank. Sinkhorn normalization enforces balanced groups as a hard constraint, and the resulting groups discover interpretable linguistic categories without supervision.

Agentization of Digital Assets for the Agentic Web: Concepts, Techniques, and Benchmark

✇cs.AI, q-bio.NC updates on arXiv.org
By: Linyao Chen · Bo Huang · Qinlao Zhao · Shuai Shao · Zhi Han · Zicai Cui · Ziheng Zhang · Guangtao Zeng · Wenzheng Tang · Yikun Wang · Yuanjian Zhou · Zimian Peng · Yong Yu · Weiwen Liu · Hiroki Kobayashi · Weinan Zhang
7 April 2026 at 12:00
arXiv:2604.04226v1 Announce Type: cross Abstract: Agentic Web, as a new paradigm that redefines the internet through autonomous, goal-driven interactions, plays an important role in group intelligence. As the foundational semantic primitives of the Agentic Web, digital assets encapsulate interactive web elements into agents, which expand the capacities and coverage of agents in agentic web. The lack of automated methodologies for agent generation limits the wider usage of digital assets and the advancement of the Agentic Web. In this paper, we first formalize these challenges by strictly defining the A2A-Agentization process, decomposing it into critical stages and identifying key technical hurdles on top of the A2A protocol. Based on this framework, we develop an Agentization Agent to agentize digital assets for the Agentic Web. To rigorously evaluate this capability, we propose A2A-Agentization Bench, the first benchmark explicitly designed to evaluate agentization quality in terms of fidelity and interoperability. Our experiments demonstrate that our approach effectively activates the functional capabilities of digital assets and enables interoperable A2A multi-agent collaboration. We believe this work will further facilitate scalable and standardized integration of digital assets into the Agentic Web ecosystem.
Received — 4 April 2026 ⏭

ATBench: A Diverse and Realistic Trajectory Benchmark for Long-Horizon Agent Safety

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yu Li · Haoyu Luo · Yuejin Xie · Yuqian Fu · Zhonghao Yang · Shuai Shao · Qihan Ren · Wanying Qu · Yanwei Fu · Yujiu Yang · Jing Shao · Xia Hu · Dongrui Liu
3 April 2026 at 12:00
arXiv:2604.02022v1 Announce Type: new Abstract: Evaluating the safety of LLM-based agents is increasingly important because risks in realistic deployments often emerge over multi-step interactions rather than isolated prompts or final responses. Existing trajectory-level benchmarks remain limited by insufficient interaction diversity, coarse observability of safety failures, and weak long-horizon realism. We introduce ATBench, a trajectory-level benchmark for structured, diverse, and realistic evaluation of agent safety. ATBench organizes agentic risk along three dimensions: risk source, failure mode, and real-world harm. Based on this taxonomy, we construct trajectories with heterogeneous tool pools and a long-context delayed-trigger protocol that captures realistic risk emergence across multiple stages. The benchmark contains 1,000 trajectories (503 safe and 497 unsafe), averaging 9.01 turns and 3.95k tokens, with 1,954 invoked tools drawn from pools spanning 2,084 available tools. Data quality is supported by rule-based and LLM-based filtering plus full human audit. Experiments on frontier LLMs, open-source models, and specialized guard systems show that ATBench is challenging even for strong evaluators, while enabling taxonomy-stratified analysis, cross-benchmark comparison, and diagnosis of long-horizon failure patterns.
Received — 26 March 2026 ⏭

Towards Self-Evolving Benchmarks: Synthesizing Agent Trajectories via Test-Time Exploration under Validate-by-Reproduce Paradigm

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dadi Guo · Tianyi Zhou · Dongrui Liu · Chen Qian · Qihan Ren · Shuai Shao · Zhiyuan Fan · Yi R. Fung · Kun Wang · Linfeng Zhang · Jing Shao
25 March 2026 at 12:00
arXiv:2510.00415v3 Announce Type: replace Abstract: Recent advances in large language models (LLMs) and agent system designs have empowered agents with unprecedented levels of capability. However, existing agent benchmarks are showing a trend of rapid ceiling-hitting by newly developed agents, making it difficult to meet the demands for evaluating agent abilities. To address this problem, we propose the Trajectory-based Validated-by-Reproducing Agent-benchmark Complexity Evolution (TRACE) framework. This framework takes an original task from an existing benchmark and encourages agents to freely explore and evolve it into a new task with higher difficulty while recording validatable agent trajectories. The framework proceeds in three stages: (1) evolutionary proposal mining, which provides task evolution proposals through preliminary exploration and divergent thinking; (2) problem formation and free exploration, where proposals are conceptualized into feasible problem candidates and the agents then explore them freely while recording their execution trajectories; and (3) multi-level validation, which ensures that the evolved tasks are accompanied by validatable and reproducible trajectories. Experiments on the GAIA benchmark demonstrate that the TRACE framework consistently enhances task complexity while improving the reliability of correctness through validatable execution trajectories. In addition, our framework can successfully adapt to and improve reasoning datasets represented by AIME-2024. This work marks a paradigm shift from static, manually curated benchmarks to dynamic, self-evolving evaluation systems, providing a sustainable and challenging runway for agent development

Tuning the sensitivity of mechanosensory receptors through histidine scanning

✇Cell
By: Yuanhao Wang · Yuhan Wang · Wenjie Yuan · Mingyu Fan · Xiaojing Wang · Anhui Wang · Yanling Bao · Yajing Zhang · Jia Chi Tan · Jianglai Wang · Junshuang Liu · Tianqi Huang · Zixuan Han · Biling Pei · Lijuan Chen · Zhengxu Ren · Xueqing Wang · Lanxin Hu · Siqi Wu · Mengke Pang · Sifan Wang · Zihuan Yang · Jiaze Li · Delian Huang · Shuai Shao · Huairui Yuan · Ling Wu · Yinnian Feng · Penghui Zhou · Guohui Li · Bo Sun · Chenqi Xu · Nicholas R.J. Gascoigne · Xiang Zhao
18 February 2026 at 08:00
Histidine scanning represents a broadly applicable technique for the identification of critical interaction sites within TCRs and other mechanosensory receptors to enhance receptor signaling strength and augment therapeutic efficacy via the catch bond mechanism.
Received — 11 March 2026 ⏭

Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shuai Shao · Qihan Ren · Chen Qian · Boyi Wei · Dadi Guo · Jingyi Yang · Xinhao Song · Linfeng Zhang · Weinan Zhang · Dongrui Liu · Jing Shao
10 March 2026 at 12:00
arXiv:2509.26354v2 Announce Type: replace Abstract: Advances in Large Language Models (LLMs) have enabled a new class of self-evolving agents that autonomously improve through interaction with the environment, demonstrating strong capabilities. However, self-evolution also introduces novel risks overlooked by current safety research. In this work, we study the case where an agent's self-evolution deviates in unintended ways, leading to undesirable or even harmful outcomes. We refer to this as Misevolution. To provide a systematic investigation, we evaluate misevolution along four key evolutionary pathways: model, memory, tool, and workflow. Our empirical findings reveal that misevolution is a widespread risk, affecting agents built even on top-tier LLMs (e.g., Gemini-2.5-Pro). Different emergent risks are observed in the self-evolutionary process, such as the degradation of safety alignment after memory accumulation, or the unintended introduction of vulnerabilities in tool creation and reuse. To our knowledge, this is the first study to systematically conceptualize misevolution and provide empirical evidence of its occurrence, highlighting an urgent need for new safety paradigms for self-evolving agents. Finally, we discuss potential mitigation strategies to inspire further research on building safer and more trustworthy self-evolving agents. Our code and data are available at https://github.com/ShaoShuai0605/Misevolution . Warning: this paper includes examples that may be offensive or harmful in nature.
Received — 25 February 2026 ⏭

Luna-2: Scalable Single-Token Evaluation with Small Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Vatsal Goel · Rishon Dsouza · Nikhil Ega · Amey Ramesh Rambatla · Rob Friel · Shuai Shao · Yash Sheth
24 February 2026 at 13:00
arXiv:2602.18583v1 Announce Type: cross Abstract: Real-time guardrails require evaluation that is accurate, cheap, and fast - yet today's default, LLM-as-a-judge (LLMAJ), is slow, expensive, and operationally non-deterministic due to multi-token generation. We present Luna-2, a novel architecture that leverages decoder-only small language models (SLMs) into a deterministic evaluation model to reliably compute complex task-specific LLMAJ metrics (e.g. toxicity, hallucination, tool selection quality, etc.) at an accuracy at par or higher than LLMAJ using frontier LLMs while drastically reducing the cost and latency of computation. Each metric is implemented as a lightweight LoRA/PEFT head on top of a shared SLM backbone, enabling hundreds of specialized metrics to run concurrently on a single GPU, deployable locally next to AI systems in a privacy-preserving and latency optimizing manner. Across content safety and hallucination benchmarks, Luna-2 matches the accuracy of state-of-the-art LLM-based evaluators while reducing inference cost by over 80x and latency by over 20x. In this paper, we outline the model architecture, training methodology and report real-world empirical results on accuracy, latency, and throughput results. In production, Luna-2 is protecting 100M+ AI sessions and processing over 100B tokens per month for our customers with eval cost savings of over $30M annually.
Received — 17 February 2026 ⏭

OmniVideo-R1: Reinforcing Audio-visual Reasoning with Query Intention and Modality Attention

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhangquan Chen · Jiale Tao · Ruihuang Li · Yihao Hu · Ruitao Chen · Zhantao Yang · Xinlei Yu · Haodong Jing · Manyuan Zhang · Shuai Shao · Biao Wang · Qinglin Lu · Ruqi Huang
17 February 2026 at 13:00
arXiv:2602.05847v2 Announce Type: replace Abstract: While humans perceive the world through diverse modalities that operate synergistically to support a holistic understanding of their surroundings, existing omnivideo models still face substantial challenges on audio-visual understanding tasks. In this paper, we propose OmniVideo-R1, a novel reinforced framework that improves mixed-modality reasoning. OmniVideo-R1 empowers models to "think with omnimodal cues" by two key strategies: (1) query-intensive grounding based on self-supervised learning paradigms; and (2) modality-attentive fusion built upon contrastive learning paradigms. Extensive experiments on multiple benchmarks demonstrate that OmniVideo-R1 consistently outperforms strong baselines, highlighting its effectiveness and robust generalization capabilities.
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