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Received — 27 May 2026 ⏭

Bridging the Semantic-Action Gap in Visual Token Pruning for Efficient VLA Inference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziyan Liu · Yeqiu Chen · Hongyi Cai · Tao Lin · Shuo Yang · Zheng Liu · Bo Zhao
26 May 2026 at 12:00
arXiv:2511.16449v5 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown great potential for embodied AI by integrating visual perception, language understanding, and action execution. In real-time deployment, these models must process continuous visual streams, incurring substantial computational overhead. Visual token pruning -- a mainstream technique for accelerating Vision-Language Models (VLMs) by retaining salient tokens while discarding redundant ones -- offers a natural candidate solution to this challenge. However, directly applying VLM-oriented pruning methods to VLA inference can cause severe degradation in manipulation performance. Our analysis attributes this degradation to a key mismatch: VLA inference exhibits distinct attention patterns between the vision-language prefill stage and the action-decode stage, so pruning based only on context-prefill semantic salience is biased toward semantic cues and may remove action-critical visual tokens. Motivated by this observation, we propose VLA-Pruner, an effective plug-and-play token pruning method grounded in the visual requirements of VLA inference, further exploiting the temporal continuity of robot manipulation. Specifically, VLA-Pruner estimates visual-token importance from both semantic prefilling and temporally smoothed action relevance, and then applies a Combine-then-Filter strategy to retain compact, non-redundant tokens under the compute budget. Experiments show that VLA-Pruner outperforms state-of-the-art approaches across multiple VLA architectures, achieving up to 1.99x speedup with comparable manipulation quality.
Received — 8 April 2026 ⏭

Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zijun Wang · Haoqin Tu · Letian Zhang · Hardy Chen · Juncheng Wu · Xiangyan Liu · Zhenlong Yuan · Tianyu Pang · Michael Qizhe Shieh · Fengze Liu · Zeyu Zheng · Huaxiu Yao · Yuyin Zhou · Cihang Xie
7 April 2026 at 12:00
arXiv:2604.04759v1 Announce Type: cross Abstract: OpenClaw, the most widely deployed personal AI agent in early 2026, operates with full local system access and integrates with sensitive services such as Gmail, Stripe, and the filesystem. While these broad privileges enable high levels of automation and powerful personalization, they also expose a substantial attack surface that existing sandboxed evaluations fail to capture. To address this gap, we present the first real-world safety evaluation of OpenClaw and introduce the CIK taxonomy, which unifies an agent's persistent state into three dimensions, i.e., Capability, Identity, and Knowledge, for safety analysis. Our evaluations cover 12 attack scenarios on a live OpenClaw instance across four backbone models (Claude Sonnet 4.5, Opus 4.6, Gemini 3.1 Pro, and GPT-5.4). The results show that poisoning any single CIK dimension increases the average attack success rate from 24.6% to 64-74%, with even the most robust model exhibiting more than a threefold increase over its baseline vulnerability. We further assess three CIK-aligned defense strategies alongside a file-protection mechanism; however, the strongest defense still yields a 63.8% success rate under Capability-targeted attacks, while file protection blocks 97% of malicious injections but also prevents legitimate updates. Taken together, these findings show that the vulnerabilities are inherent to the agent architecture, necessitating more systematic safeguards to secure personal AI agents. Our project page is https://ucsc-vlaa.github.io/CIK-Bench.
Received — 4 April 2026 ⏭

Pyruvate is a natural suppressor of interferon signaling by inducing STAT1 protein pyruvylation

✇Cell
By: Yibo Zuo · Qin Wang · Wanying Tian · Xinhe Wang · Zhijin Zheng · Wei He · Renxia Zhang · Qian Zhao · Ying Miao · Yukang Yuan · Tingting Zhang · Qun Cui · Yuerong Zhang · Chunyan Liu · Haiyan Zhou · Hui Zheng
27 February 2026 at 08:00
Yibo et al. identify protein pyruvylation as a post-translational modification that can modulate immune signaling and host antiviral response.
Received — 14 March 2026 ⏭

NFATC2::NUTM2 Fusion Defines a Novel Primary Pulmonary Epithelial Tumor With a Distinctive Immunophenotype

✇Pulmonary nodule
By: Lanlan Feng · Qiao Liu · Xiaoyan Liu · Shumei Wang · Junting Li · Yu Han · Xiaorong Mu · Yuanyuan Wang · Junfeng Jiang · Chun Wu · Xiujuan Han · Shaojun Zhu · Fuqin Zhang · Jing Tian · Zhuo Wang · Wei Zhang · Li Gong
13 March 2026 at 18:00

Am J Surg Pathol. 2026 Jun 1;50(6):695-704. doi: 10.1097/PAS.0000000000002533. Epub 2026 Mar 13.

ABSTRACT

With the application of molecular techniques in pathologic diagnosis, several novel primary pulmonary epithelial tumors have been continuously discovered and classified under the WHO classification of thoracic tumors. Recently, a pulmonary tumor with NFATC2 :: NUTM2B fusion was first documented, but the spectrum of NFATC2::NUTM2 fusion variants and their associated pathologic features remains incompletely characterized. Coincidentally, we also found and described 6 primary pulmonary tumors harboring recurrent NFATC2::NUTM2A/E fusions through integrated genomic analysis. These patients, including 4 females and 2 males, with a median age of 53 years, presented with incidentally detected peripheral lung nodules composed of monotonous epithelioid cells arranged in cords, nests, and trabeculae within a prominent desmoplastic stroma. All tumors exhibited a consistent immunophenotype: CK5/6+/GATA3+/calponin+/EMA+/DOG1 (perinuclear dot-like staining)/p63-. High-throughput chromosome conformation capture (Hi-C) analysis showed the structural variation of NFATC2::NUTM2E in all 6 cases, whereas RNA sequencing detected the fusion transcripts in 5 cases ( NFATC2::NUTM2A , n=2; NFATC2::NUTM2E , n=3). Ultrastructural examination of 1 case suggested epithelial differentiation. All patients remained disease-free after complete resection (median follow-up: 24 mo; range: 9 to 41 mo). These findings define a novel primary pulmonary tumor entity driven by NFATC2::NUTM2 fusions, and characterized by a distinctive immunophenotype, expanding the spectrum of NUTM2 -associated neoplasms. Our study underscores the utility of multiomics approaches for characterizing rare neoplasms and provides a diagnostic framework for this entity.

PMID:41821426 | DOI:10.1097/PAS.0000000000002533

Targeting the OXNAD1-PTGS2 axis with resveratrol overcomes ferroptosis Inhibition and reverses 5-FU resistance in gastric cancer

✇Omics in Gastric
By: Jinzhou Wang · Bishuang Xu · Xiaohan Lin · Biyu Chen · Xiaoyan Liu · Wei Wu
13 March 2026 at 18:00

Gastric Cancer. 2026 Mar 13. doi: 10.1007/s10120-026-01718-x. Online ahead of print.

ABSTRACT

BACKGROUND: 5-Fluorouracil (5-FU) remains a cornerstone of first-line chemotherapy for gastric cancer, yet the emergence of resistance severely compromises its clinical efficacy. Although ferroptosis suppression has been recognized as a pivotal mechanism of chemoresistance, the mitochondrial regulatory processes involved remain poorly understood.

METHODS: We integrated clinical specimen analysis, in vitro and in vivo functional assays, multi-omics profiling, and molecular docking to delineate the role of the mitochondrial oxidoreductase OXNAD1 in mediating 5-FU resistance in gastric cancer, and to assess the therapeutic potential of the natural polyphenol resveratrol as a chemosensitizing agent.

RESULTS: OXNAD1 was found to be significantly overexpressed in gastric cancer tissues and cell lines, correlating with unfavorable prognosis and enhanced 5-FU resistance. Mechanistically, OXNAD1 directly bound to and suppressed the ferroptosis driver PTGS2, thereby attenuating lipid peroxidation and mitochondrial damage, ultimately restraining ferroptosis and promoting drug resistance. Notably, resveratrol disrupted the OXNAD1-PTGS2 interaction by directly binding OXNAD1, reinstating ferroptotic activity, markedly enhancing the cytotoxic effect of 5-FU in resistant cells, and potentiating the antitumor efficacy of 5-FU in xenograft models.

CONCLUSION: The OXNAD1-PTGS2 axis constitutes a critical metabolic-cell death cross-regulatory pathway underlying 5-FU resistance in gastric cancer. Targeting this axis with resveratrol provides a promising combinatorial strategy to overcome chemoresistance.

PMID:41824193 | DOI:10.1007/s10120-026-01718-x

NFATC2::NUTM2 Fusion Defines a Novel Primary Pulmonary Epithelial Tumor With a Distinctive Immunophenotype

✇Omics In Lung
By: Lanlan Feng · Qiao Liu · Xiaoyan Liu · Shumei Wang · Junting Li · Yu Han · Xiaorong Mu · Yuanyuan Wang · Junfeng Jiang · Chun Wu · Xiujuan Han · Shaojun Zhu · Fuqin Zhang · Jing Tian · Zhuo Wang · Wei Zhang · Li Gong
13 March 2026 at 18:00

Am J Surg Pathol. 2026 Mar 13. doi: 10.1097/PAS.0000000000002533. Online ahead of print.

ABSTRACT

With the application of molecular techniques in pathologic diagnosis, several novel primary pulmonary epithelial tumors have been continuously discovered and classified under the WHO classification of thoracic tumors. Recently, a pulmonary tumor with NFATC2::NUTM2B fusion was first documented, but the spectrum of NFATC2::NUTM2 fusion variants and their associated pathologic features remains incompletely characterized. Coincidentally, we also found and described 6 primary pulmonary tumors harboring recurrent NFATC2::NUTM2A/E fusions through integrated genomic analysis. These patients, including 4 females and 2 males, with a median age of 53 years, presented with incidentally detected peripheral lung nodules composed of monotonous epithelioid cells arranged in cords, nests, and trabeculae within a prominent desmoplastic stroma. All tumors exhibited a consistent immunophenotype: CK5/6+/GATA3+/calponin+/EMA+/DOG1 (perinuclear dot-like staining)/p63-. High-throughput chromosome conformation capture (Hi-C) analysis showed the structural variation of NFATC2::NUTM2E in all 6 cases, whereas RNA sequencing detected the fusion transcripts in 5 cases (NFATC2::NUTM2A, n=2; NFATC2::NUTM2E, n=3). Ultrastructural examination of 1 case suggested epithelial differentiation. All patients remained disease-free after complete resection (median follow-up: 24 mo; range: 9 to 41 mo). These findings define a novel primary pulmonary tumor entity driven by NFATC2::NUTM2 fusions, and characterized by a distinctive immunophenotype, expanding the spectrum of NUTM2-associated neoplasms. Our study underscores the utility of multiomics approaches for characterizing rare neoplasms and provides a diagnostic framework for this entity.

PMID:41821426 | DOI:10.1097/PAS.0000000000002533

Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wei Yang · Defu Cao · Jiacheng Pang · Muyan Weng · Yan Liu
10 March 2026 at 12:00
arXiv:2603.07972v1 Announce Type: new Abstract: While scaling individual Large Language Models (LLMs) has delivered remarkable progress, the next frontier lies in scaling collaboration through multi-agent systems (MAS). However, purely autonomous MAS remain ''closed-world'' systems, constrained by the static knowledge horizon of pre-trained models. This limitation makes them brittle on tasks requiring knowledge beyond training data, often leading to collective failure under novel challenges. To address this, we propose the Human-In-the-Loop Multi-Agent Collaboration (HILA) framework, a principled paradigm for human--agent collaboration. HILA trains agents to learn a metacognitive policy that governs when to solve problems autonomously and when to defer to a human expert. To operationalize this policy, we introduce Dual-Loop Policy Optimization, which disentangles immediate decision-making from long-term capability growth. The inner loop applies Group Relative Policy Optimization (GRPO) with a cost-aware reward to optimize deferral decisions, while the outer loop implements continual learning, transforming expert feedback into high-quality supervised signals that strengthen the agent's reasoning ability. Experiments on challenging mathematical and problem-solving benchmarks show that HILA, equipped with Dual-Loop Policy Optimization, consistently outperforms advanced MAS, establishing a principled foundation for collaborative and continually improving agentic systems.
Received — 5 March 2026 ⏭

PROSPECT: Unified Streaming Vision-Language Navigation via Semantic--Spatial Fusion and Latent Predictive Representation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zehua Fan · Wenqi Lyu · Wenxuan Song · Linge Zhao · Yifei Yang · Xi Wang · Junjie He · Lida Huang · Haiyan Liu · Bingchuan Sun · Guangjun Bao · Xuanyao Mao · Liang Xu · Yan Wang · Feng Gao
5 March 2026 at 13:00
arXiv:2603.03739v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have advanced zero-shot end-to-end Vision-Language Navigation (VLN), yet robust navigation requires not only semantic understanding but also predictive modeling of environment dynamics and spatial structure. We propose PROSPECT, a unified streaming navigation agent that couples a streaming Vision-Language-Action (VLA) policy with latent predictive representation learning. PROSPECT uses CUT3R as a streaming 3D foundation spatial encoder to produce long-context, absolute-scale spatial features, and fuses them with SigLIP semantic features via cross-attention. During training, we introduce learnable stream query tokens that query the streaming context and predict next-step 2D and 3D latent features (rather than pixels or explicit modalities), supervised in the latent spaces of frozen SigLIP and CUT3R teachers. The predictive branch shapes internal representations without inference overhead. Experiments on VLN-CE benchmarks and real-robot deployment demonstrate state-of-the-art performance and improved long-horizon robustness under diverse lighting. We will release code for the community soon.

EnECG: Efficient Ensemble Learning for Electrocardiogram Multi-task Foundation Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuhao Xu · Xiaoda Wang · Jiaying Lu · Sirui Ding · Defu Cao · Huaxiu Yao · Yan Liu · Xiao Hu · Carl Yang
5 March 2026 at 13:00
arXiv:2511.22935v2 Announce Type: replace-cross Abstract: Electrocardiogram (ECG) analysis plays a vital role in the early detection, monitoring, and management of various cardiovascular conditions. While existing models have achieved notable success in ECG interpretation, they fail to leverage the interrelated nature of various cardiac abnormalities. Conversely, developing a specific model capable of extracting all relevant features for multiple ECG tasks remains a significant challenge. Large-scale foundation models, though powerful, are not typically pretrained on ECG data, making full re-training or fine-tuning computationally expensive. To address these challenges, we propose EnECG(Mixture of Experts-based Ensemble Learning for ECG Multi-tasks), an ensemble-based framework that integrates multiple specialized foundation models, each excelling in different aspects of ECG interpretation. Instead of relying on a single model or single task, EnECG leverages the strengths of multiple specialized models to tackle a variety of ECG-based tasks. To mitigate the high computational cost of full re-training or fine-tuning, we introduce a lightweight adaptation strategy: attaching dedicated output layers to each foundation model and applying Low-Rank Adaptation (LoRA) only to these newly added parameters. We then adopt a Mixture of Experts (MoE) mechanism to learn ensemble weights, effectively combining the complementary expertise of individual models. Our experimental results demonstrate that by minimizing the scope of fine-tuning, EnECG can help reduce computational and memory costs while maintaining the strong representational power of foundation models. This framework not only enhances feature extraction and predictive performance but also ensures practical efficiency for real-world clinical applications. The code is available at https://github.com/yuhaoxu99/EnECG.git.
Received — 17 February 2026 ⏭

ShallowJail: Steering Jailbreaks against Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shang Liu · Hanyu Pei · Zeyan Liu
17 February 2026 at 13:00
arXiv:2602.07107v2 Announce Type: replace-cross Abstract: Large Language Models(LLMs) have been successful in numerous fields. Alignment has usually been applied to prevent them from harmful purposes. However, aligned LLMs remain vulnerable to jailbreak attacks that deliberately mislead them into producing harmful outputs. Existing jailbreaks are either black-box, using carefully crafted, unstealthy prompts, or white-box, requiring resource-intensive computation. In light of these challenges, we introduce ShallowJail, a novel attack that exploits shallow alignment in LLMs. ShallowJail can misguide LLMs' responses by manipulating the initial tokens during inference. Through extensive experiments, we demonstrate the effectiveness of ShallowJail, which substantially degrades the safety of state-of-the-art LLM responses. Our code is available at https://github.com/liuup/ShallowJail.
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