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Received today — 7 October 2026 ⏭

Stopped-light-enhanced gravitational force sensing

✇Nature Nanotechnology
By: Yicheng Zhu · Qi Geng · Boyi Xue · Yuncong Sun · Ruixiang Zhang · Xianfeng Chen · Xiaoshun Jiang · Farhan Azeem · Miguel Tierz · Wenjie Wan
29 September 2026 at 08:00

Nature Nanotechnology, Published online: 29 September 2026; doi:10.1038/s41565-026-02298-8

A torsion-pendulum gravitational-force sensor with an optical microcavity readout leverages coupled photon–phonon effects to enhance sensitivity to the tiny gravitational pull from a millimetre-sized source mass.
Received — 10 September 2026 ⏭

Immune-endothelial-coagulation crosstalk as a driver of multi-organ dysfunction in severe viral pneumonia

✇Omics In Lung
By: Yuehui Jia · Yide Chen · Jiabao Liao · Fei Qu · Yanming Lu · Chunxiang Niu · Feng Chen
5 September 2026 at 18:00

Front Immunol. 2026 Aug 21;17:1878054. doi: 10.3389/fimmu.2026.1878054. eCollection 2026.

ABSTRACT

Viral burden or pathogen identity alone cannot adequately explain the progression of severe viral pneumonia from a compartmentalized respiratory infection to acute respiratory distress syndrome, multi-organ failure, and death. Maladaptive immunity, endothelial damage, and coagulation dysregulation are all functionally integrated in a host-driven pathological mechanism that mediates disease escalation. Systemic microvascular damage and pulmonary inflammation are linked by immune-endothelial-coagulation interaction. This review investigates the ways in which immunothrombosis and microcirculatory dysfunction are propagated by defective antiviral immunity, alveolar-capillary barrier failure, damage-associated molecular pattern and neutrophil extracellular trap release, endothelial glycocalyx degradation, complement-platelet interactions, coagulation cascade activation, and impaired fibrinolysis. Lung-derived inflammatory signals cause endothelial activation and procoagulant reprogramming in distal organs following systemic dissemination, resulting in organ-specific phenotypes such as acute kidney injury, secondary myocardial injury, ARDS in the lung, neurovascular unit dysfunction, and barrier-disruption-associated inflammatory amplification along the liver-gut axis. This framework may provide a rationale for exploring stage-adapted and phenotype-guided approaches to severe viral pneumonia, including early antiviral therapy, immunomodulation during disease progression, endothelial-coagulation axis targeting, and host-directed strategies. Further longitudinal cohorts, multi-omics analyses, mechanism-based stratification studies, and mechanism-embedded clinical trials will be needed to determine whether immune-endothelial-coagulation coupling can be translated from a mechanistic model into a clinically actionable framework for precision intervention.

PMID:42698821 | PMC:PMC13542883 | DOI:10.3389/fimmu.2026.1878054

Targeting peripheral 5-HT2AR enhances antitumor immunity in colorectal cancer

✇Cell
By: YaTing Wen · LingJie Tang · WenWen Duan · QingFei Ren · YangYue Ni · ShiYang Chen · Jun Chen · Lei Yang · Huan Wang · Yuan Chen · YunZhe Zheng · XingChao Pan · Hui Du · MengWen Huang · An Zeng · Zhaocai Zhou · ZhiGang Zhang · HongBin Ji · GaoXiang Ge · Yi Arial Zeng · Wei Zhang · ShiHui Wang · JianJun Cheng · Sheng Wang · JianFeng Chen
4 August 2026 at 08:00
By selectively targeting peripheral 5-HT2AR without inducing psychedelic effects, a non-brain-penetrant agonist boosts antitumor CD8+ T cell immunity and improves immunotherapy responses in preclinical models of colorectal cancer.
Received — 27 May 2026 ⏭

Automated Detection and Classification of Delusion-related Content in Naturalistic Audio Diaries Using Multi-Agent Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Feng Chen · Justin Tauscher · Changye Li · Meliha Yetisgen · Alex Cohen · Adam Kuczynski · Angelina Pei-Tzu Tsai · Benjamin Buck · Dror Ben-Zeev · Trevor Cohen
26 May 2026 at 12:00
arXiv:2605.24755v1 Announce Type: new Abstract: Speech monologues recorded in naturalistic settings provide opportunities to characterize mental illness phenomenology and detect symptom exacerbation. Large language models (LLMs) offer new possibilities for automating this process, as they require annotated data primarily for evaluation rather than training. In this paper, we present a novel automated, multi-agent LLM pipeline for the fine-grained, multi-label extraction of language suggestive of delusional beliefs, associated affective responses, and behavioral responses from transcripts of naturalistic audio diaries collected from people with moderate persecutory ideation. Evaluating an ensemble of three foundation models, we demonstrate that detailed diagnostic prompt instructions successfully reduce false positives for delusional theme classification, but also constrain the interpretation of affective or behavioral responses. Furthermore, comparing multi-agent adjudication frameworks shows that complex conversational debate between agents diminishes accuracy on clinically ambiguous text by inducing premature consensus. Instead, majority voting establishes robust performance (Micro F1 of 0.872 and 0.779 for delusion detection and classification respectively). This work provides a validated and scalable pipeline for the automated detection and characterization of content suggesting delusional beliefs in naturalistic speech.

AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaqi Liu · Shi Qiu · Mairui Li · Bingzhou Li · Haonian Ji · Siwei Han · Xinyu Ye · Peng Xia · Zihan Dong · Meng Chen · Congyu Zhang · Letian Zhang · Guiming Chen · Haoqin Tu · Xinyu Yang · Lu Feng · Xujiang Zhao · Haifeng Chen · Jiawei Zhou · Xiao Wang · Weitong Zhang · Hongtu Zhu · Yun Li · Jieru Mei · Hongliang Fei · Jiaheng Zhang · Linjie Li · Linjun Zhang · Yuyin Zhou · Sheng Wang · Caiming Xiong · James Zou · Zeyu Zheng · Cihang Xie · Mingyu Ding · Huaxiu Yao
26 May 2026 at 12:00
arXiv:2605.20025v2 Announce Type: replace Abstract: Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail and inform the next attempt, and lessons accumulate across cycles. Existing autonomous research systems often model this process as a linear pipeline: they rely on single-agent reasoning, stop when execution fails, and do not carry experience across runs. We present AutoResearchClaw, a multi-agent autonomous research pipeline built on five mechanisms: structured multi-agent debate for hypothesis generation and result analysis, a self-healing executor with a \textsc{Pivot}/\textsc{Refine} decision loop that transforms failures into information, verifiable result reporting that prevents fabricated numbers and hallucinated citations, human-in-the-loop collaboration with seven intervention modes spanning full autonomy to step-by-step oversight, and cross-run evolution that converts past mistakes into future safeguards. On ARC-Bench, a 25-topic experiment-stage benchmark, AutoResearchClaw outperforms AI Scientist v2 by 54.7%. A human-in-the-loop ablation across seven intervention modes reveals that precise, targeted collaboration at high-leverage decision points consistently outperforms both full autonomy and exhaustive step-by-step oversight. We position AutoResearchClaw as a research amplifier that augments rather than replaces human scientific judgment. Code is available at https://github.com/aiming-lab/AutoResearchClaw.
Received — 6 April 2026 ⏭

<i>TWIST1</i> mediated transcriptional activation of <i>SPON2</i> drives colorectal cancer peritoneal metastasis through stromal cell signaling network

✇Oncogene - Issue - nature.com science feeds
By: Zhuan Zhou · Alessandro La Ferlita · Manoj H. Palavalli · Xiongfeng Chen · Lauren Tyler · Aslam Ejaz · Joal Beane · Patricio M. Polanco · Huocong Huang · Alex C. Kim
3 April 2026 at 08:00

Oncogene, Published online: 03 April 2026; doi:10.1038/s41388-026-03743-7

TWIST1 mediated transcriptional activation of SPON2 drives colorectal cancer peritoneal metastasis through stromal cell signaling network
Received — 2 April 2026 ⏭

Targeting FTO shows therapeutic potential in esophageal squamous cell carcinoma by modulating microRNA biogenesis

✇Oncogene - Issue - nature.com science feeds
By: Wei Zhou · Chunyan Wang · Changhao Li · Pengxiang Chen · Yuchen Liu · Hongyuan Mao · Pengfei Zhu · Yuxiao Gong · Yufeng Cheng · Lin Zhang
1 April 2026 at 08:00

Oncogene, Published online: 01 April 2026; doi:10.1038/s41388-026-03754-4

Targeting FTO shows therapeutic potential in esophageal squamous cell carcinoma by modulating microRNA biogenesis
Received — 26 March 2026 ⏭

KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Songming Zhang · Xue Zhang · Tong Zhang · Bojie Hu · Yufeng Chen · Jinan Xu
25 March 2026 at 12:00
arXiv:2603.01875v2 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is an essential technique to compress large language models (LLMs) into smaller ones. However, despite the distinct roles of the student model and the teacher model in KD, most existing frameworks still use a homogeneous training backend (e.g., FSDP and DeepSpeed) for both models, leading to suboptimal training efficiency. In this paper, we present a novel framework for LLM distillation, termed \textbf{KDFlow}, which features a decoupled architecture and employs SGLang for teacher inference. By bridging the training efficiency of FSDP2 and the inference efficiency of SGLang, KDFlow achieves full utilization of both advantages in a unified system. Moreover, instead of transferring full logits across different processes, our framework only transmits the teacher's hidden states using zero-copy data transfer and recomputes the logits on the student side, effectively balancing the communication cost and KD performance. Furthermore, our framework supports both off-policy and on-policy distillation and incorporates KD algorithms for cross-tokenizer KD through highly extensible and user-friendly APIs. Experiments show that KDFlow can achieve \textbf{1.44$\times$ to 6.36$\times$} speedup compared to current KD frameworks, enabling researchers to rapidly prototype and scale LLM distillation with minimal engineering overhead. Code is available at: https://github.com/songmzhang/KDFlow

Sustained nitric oxide production by engineered <i>E. coli</i> remodels the tumor microenvironment and potentiates immunotherapy

✇Nature Biotechnology - Issue - nature.com science feeds
By: Shuyu Xu · Tianjiao Zhang · Yang Song · Mengxin Wang · Ruiqi Wu · Mofan Li · Haonan Wang · Xinxin Xie · Qingfeng Chen · Xiaotu Ma · Xiaolong Liang
18 March 2026 at 08:00

Nature Biotechnology, Published online: 18 March 2026; doi:10.1038/s41587-026-03054-y

Solid tumors are sensitized to anti‑PD‑L1 immunotherapy by engineered E. coli to produce nitric oxide.
Received — 17 March 2026 ⏭

CRAFT-GUI: Curriculum-Reinforced Agent For GUI Tasks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Songqin Nong · Xiaoxuan Tang · Jingxuan Xu · Sheng Zhou · Jianfeng Chen · Tao Jiang · Wenhao Xu
16 March 2026 at 12:00
arXiv:2508.11360v2 Announce Type: replace Abstract: As autonomous agents become adept at understanding and interacting with graphical user interface (GUI) environments, a new era of automated task execution is emerging. Recent studies have demonstrated that Reinforcement Learning (RL) can effectively enhance agents' performance in dynamic interactive GUI environments. However, these methods face two key limitations: (1) they overlook the significant variation in difficulty across different GUI tasks by treating the entire training data as a uniform set, which hampers the agent's ability to adapt its learning process; and (2) most approaches collapse task-specific nuances into a single, coarse reward, leaving the agent with a uniform signal that yields inefficient policy updates. To address these limitations, we propose CRAFT-GUI, a curriculum learning framework based on Group Relative Policy Optimization (GRPO) that explicitly accounts for the varying difficulty across trajectories. To enable more fine-grained policy optimization, we design a reward function that combines simple rule-based signals with model-judged evaluation, providing richer and more nuanced feedback during training. Experimental results demonstrate that our method achieves significant improvements over previous state-of-the-art approaches, outperforming them by 5.6% on public benchmarks Android Control and 10.3% on our internal online benchmarks, respectively. These findings empirically validate the effectiveness of integrating reinforcement learning with curriculum learning in GUI interaction tasks.

Depth Charge: Jailbreak Large Language Models from Deep Safety Attention Heads

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jinman Wu · Yi Xie · Shiqian Zhao · Xiaofeng Chen
16 March 2026 at 12:00
arXiv:2603.05772v2 Announce Type: replace-cross Abstract: Currently, open-sourced large language models (OSLLMs) have demonstrated remarkable generative performance. However, as their structure and weights are made public, they are exposed to jailbreak attacks even after alignment. Existing attacks operate primarily at shallow levels, such as the prompt or embedding level, and often fail to expose vulnerabilities rooted in deeper model components, which creates a false sense of security for successful defense. In this paper, we propose \textbf{\underline{S}}afety \textbf{\underline{A}}ttention \textbf{\underline{H}}ead \textbf{\underline{A}}ttack (\textbf{SAHA}), an attention-head-level jailbreak framework that explores the vulnerability in deeper but insufficiently aligned attention heads. SAHA contains two novel designs. Firstly, we reveal that deeper attention layers introduce more vulnerability against jailbreak attacks. Based on this finding, \textbf{SAHA} introduces \textit{Ablation-Impact Ranking} head selection strategy to effectively locate the most vital layer for unsafe output. Secondly, we introduce a boundary-aware perturbation method, \textit{i.e. Layer-Wise Perturbation}, to probe the generation of unsafe content with minimal perturbation to the attention. This constrained perturbation guarantees higher semantic relevance with the target intent while ensuring evasion. Extensive experiments show the superiority of our method: SAHA improves ASR by 14\% over SOTA baselines, revealing the vulnerability of the attack surface on the attention head. Our code is available at https://anonymous.4open.science/r/SAHA.

Knowing without Acting: The Disentangled Geometry of Safety Mechanisms in Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jinman Wu · Yi Xie · Shen Lin · Shiqian Zhao · Xiaofeng Chen
16 March 2026 at 12:00
arXiv:2603.05773v2 Announce Type: replace-cross Abstract: Safety alignment is often conceptualized as a monolithic process wherein harmfulness detection automatically triggers refusal. However, the persistence of jailbreak attacks suggests a fundamental mechanistic decoupling. We propose the \textbf{\underline{D}}isentangled \textbf{\underline{S}}afety \textbf{\underline{H}}ypothesis \textbf{(DSH)}, positing that safety computation operates on two distinct subspaces: a \textit{Recognition Axis} ($\mathbf{v}_H$, ``Knowing'') and an \textit{Execution Axis} ($\mathbf{v}_R$, ``Acting''). Our geometric analysis reveals a universal ``Reflex-to-Dissociation'' evolution, where these signals transition from antagonistic entanglement in early layers to structural independence in deep layers. To validate this, we introduce \textit{Double-Difference Extraction} and \textit{Adaptive Causal Steering}. Using our curated \textsc{AmbiguityBench}, we demonstrate a causal double dissociation, effectively creating a state of ``Knowing without Acting.'' Crucially, we leverage this disentanglement to propose the \textbf{Refusal Erasure Attack (REA)}, which achieves State-of-the-Art attack success rates by surgically lobotomizing the refusal mechanism. Furthermore, we uncover a critical architectural divergence, contrasting the \textit{Explicit Semantic Control} of Llama3.1 with the \textit{Latent Distributed Control} of Qwen2.5. The code and dataset are available at https://anonymous.4open.science/r/DSH.
Received — 25 February 2026 ⏭

Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dongming Jiang · Yi Li · Songtao Wei · Jinxin Yang · Ayushi Kishore · Alysa Zhao · Dingyi Kang · Xu Hu · Feng Chen · Qiannan Li · Bingzhe Li
24 February 2026 at 13:00
arXiv:2602.19320v1 Announce Type: cross Abstract: Agentic memory systems enable large language model (LLM) agents to maintain state across long interactions, supporting long-horizon reasoning and personalization beyond fixed context windows. Despite rapid architectural development, the empirical foundations of these systems remain fragile: existing benchmarks are often underscaled, evaluation metrics are misaligned with semantic utility, performance varies significantly across backbone models, and system-level costs are frequently overlooked. This survey presents a structured analysis of agentic memory from both architectural and system perspectives. We first introduce a concise taxonomy of MAG systems based on four memory structures. Then, we analyze key pain points limiting current systems, including benchmark saturation effects, metric validity and judge sensitivity, backbone-dependent accuracy, and the latency and throughput overhead introduced by memory maintenance. By connecting the memory structure to empirical limitations, this survey clarifies why current agentic memory systems often underperform their theoretical promise and outlines directions for more reliable evaluation and scalable system design.
Received — 17 February 2026 ⏭

DAIAN: Deep Adaptive Intent-Aware Network for CTR Prediction in Trigger-Induced Recommendation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhihao Lv · Longtao Zhang · Ailong He · Shuzhi Cao · Shuguang Han · Jufeng Chen
17 February 2026 at 13:00
arXiv:2602.13971v1 Announce Type: cross Abstract: Recommendation systems are essential for personalizing e-commerce shopping experiences. Among these, Trigger-Induced Recommendation (TIR) has emerged as a key scenario, which utilizes a trigger item (explicitly represents a user's instantaneous interest), enabling precise, real-time recommendations. Although several trigger-based techniques have been proposed, most of them struggle to address the intent myopia issue, that is, a recommendation system overemphasizes the role of trigger items and narrowly focuses on suggesting commodities that are highly relevant to trigger items. Meanwhile, existing methods rely on collaborative behavior patterns between trigger and recommended items to identify the user's preferences, yet the sparsity of ID-based interaction restricts their effectiveness. To this end, we propose the Deep Adaptive Intent-Aware Network (DAIAN) that dynamically adapts to users' intent preferences. In general, we first extract the users' personalized intent representations by analyzing the correlation between a user's click and the trigger item, and accordingly retrieve the user's related historical behaviors to mine the user's diverse intent. Besides, sparse collaborative behaviors constrain the performance in capturing items associated with user intent. Hence, we reinforce similarity by leveraging a hybrid enhancer with ID and semantic information, followed by adaptive selection based on varying intents. Experimental results on public datasets and our industrial e-commerce datasets demonstrate the effectiveness of DAIAN.

Uncertainty-Aware Measurement of Scenario Suite Representativeness for Autonomous Systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Robab Aghazadeh Chakherlou · Siddartha Khastgir · Xingyu Zhao · Jerein Jeyachandran · Shufeng Chen
17 February 2026 at 13:00
arXiv:2511.14853v2 Announce Type: replace Abstract: Assuring the trustworthiness and safety of AI systems, e.g., autonomous vehicles (AV), depends critically on the data-related safety properties, e.g., representativeness, completeness, etc., of the datasets used for their training and testing. Among these properties, this paper focuses on representativeness-the extent to which the scenario-based data used for training and testing, reflect the operational conditions that the system is designed to operate safely in, i.e., Operational Design Domain (ODD) or expected to encounter, i.e., Target Operational Domain (TOD). We propose a probabilistic method that quantifies representativeness by comparing the statistical distribution of features encoded by the scenario suites with the corresponding distribution of features representing the TOD, acknowledging that the true TOD distribution is unknown, as it can only be inferred from limited data. We apply an imprecise Bayesian method to handle limited data and uncertain priors. The imprecise Bayesian formulation produces interval-valued, uncertainty-aware estimates of representativeness, rather than a single value. We present a numerical example comparing the distributions of the scenario suite and the inferred TOD across operational categories-weather, road type, time of day, etc., under dependencies and prior uncertainty. We estimate representativeness locally (between categories) and globally as an interval.
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