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Received — 26 March 2026 ⏭

Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jihyun Janice Ahn · Ryo Kamoi · Berk Atil · Renze Lou · WonWoo Kang · Heehyun Park · Sarkar Snigdha Sarathi Das · Zhuoyang Zou · Xiaoxin Lu · Yusen Zhang · Asfahan Shah · Ridwanul Hasan Tanvir · Lingxiao Zhao · Hongxi Huang · Vignesh Venkatesh · Dianjun Lin · Hamid Shah · Wentao Wang · Zhanpeng Song · Joshua Reed Bassin · Dax Patel · Ishan Appareddy Agrahar · Sahil Pardasani · Xin Dong · Fatemeh Rahbari · Benjamin David Rishel · Soochan Andrew Lee · Yuv Boghani · Ali B. AlNaseeb · Pranav Suby · Seokhyeon Bae · Shreya Buddharaju · Damien Kula · Soumyadeep Das · Hanyang Frank Liu · Faye Mo · Wenpeng Yin
25 March 2026 at 12:00
arXiv:2603.22619v1 Announce Type: new Abstract: LLMs often generate seemingly valid answers to flawed or ill-posed inputs. This is not due to missing knowledge: under discriminative prompting, the same models can mostly identify such issues, yet fail to reflect this in standard generative responses. This reveals a fundamental know-act gap between discriminative recognition and generative behavior. Prior work largely characterizes this issue in narrow settings, such as math word problems or question answering, with limited focus on how to integrate these two modes. In this work, we present a comprehensive analysis using FaultyScience, a newly constructed large-scale, cross-disciplinary benchmark of faulty scientific questions. We show that the gap is pervasive and stems from token-level autoregression, which entangles task selection (validate vs. answer) with content generation, preventing discriminative knowledge from being utilized. To address this, we propose DeIllusionLLM, a task-level autoregressive framework that explicitly models this decision. Through self-distillation, the model unifies discriminative judgment and generative reasoning within a single backbone. Empirically, DeIllusionLLM substantially reduces answer-despite-error failures under natural prompting while maintaining general reasoning performance, demonstrating that self-distillation is an effective and scalable solution for bridging the discriminative-generative know-act gap

From Noisy Labels to Intrinsic Structure: A Geometric-Structural Dual-Guided Framework for Noise-Robust Medical Image Segmentation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tao Wang · Zhenxuan Zhang · Yuanbo Zhou · Xinlin Zhang · Yuanbin Chen · Tao Tan · Guang Yang · Tong Tong
25 March 2026 at 12:00
arXiv:2509.02419v2 Announce Type: replace-cross Abstract: The effectiveness of convolutional neural networks in medical image segmentation relies on large-scale, high-quality annotations, which are costly and time-consuming to obtain. Even expert-labeled datasets inevitably contain noise arising from subjectivity and coarse delineations, which disrupt feature learning and adversely impact model performance. To address these challenges, this study propose a Geometric-Structural Dual-Guided Network (GSD-Net), which integrates geometric and structural cues to improve robustness against noisy annotations. It incorporates a Geometric Distance-Aware module that dynamically adjusts pixel-level weights using geometric features, thereby strengthening supervision in reliable regions while suppressing noise. A Structure-Guided Label Refinement module further refines labels with structural priors, and a Knowledge Transfer module enriches supervision and improves sensitivity to local details. To comprehensively assess its effectiveness, we evaluated GSD-Net on six publicly available datasets: four containing three types of simulated label noise, and two with multi-expert annotations that reflect real-world subjectivity and labeling inconsistencies. Experimental results demonstrate that GSD-Net achieves state-of-the-art performance under noisy annotations, achieving improvements of 1.58% on Kvasir, 22.76% on Shenzhen, 8.87% on BU-SUC, and 1.77% on BraTS2020 under SR simulated noise. The codes of this study are available at https://github.com/ortonwang/GSD-Net.

A Review of the Role of Zeqi Decoction in the Treatment of Non-Small Cell Lung Cancer

✇Pulmonary nodule
By: Lan Zhang · Yanhui Jia · Lei Song · Yiman Li · Yawei Liu · Yan Qin · Tao Wang · Na Li
18 March 2026 at 18:00

J Multidiscip Healthc. 2026 Mar 11;19:584071. doi: 10.2147/JMDH.S584071. eCollection 2026.

ABSTRACT

Non-small cell lung cancer (NSCLC) is one of the malignant tumors with the highest incidence and mortality rates. Zeqi Decoction has the functions of "promoting diuresis and reducing swelling, resolving phlegm and dispersing nodules", embodying the unique approach of traditional Chinese medicine in treating lung cancer by "strengthening the body's resistance and eliminating pathogenic factors". Modern research shows that Zeqi Decoction exerts anti-NSCLC effects through multiple pathways and targets. In terms of the material basis of its efficacy, its active ingredients (such as diterpene esters and flavonoids contained in Zeqi) have the ability to directly inhibit the proliferation, invasion and migration of tumor cells and induce apoptosis. In terms of the mechanism of action, basic experiments have revealed that Zeqi Decoction can down-regulate the S100A9/STAT3 signaling pathway, inhibit the immunosuppressive activity of myelium-derived suppressor cells (MDSCs), reshape the tumor microenvironment, thereby enhancing the cytotoxic function of CD8⁺T cells, and can also regulate the EGFR/PI3K/Akt pathway to affect PD-L1 expression. Intervene in tumor immune escape; In terms of clinical transformation, the combination of Zexi Decoction with chemotherapy and targeted therapy can improve patients' symptoms such as cough and pleural effusion, prolong progression-free survival, and alleviate the toxic and side effects of Western medical treatment. In addition, Zexi Decoction also shows potential value in reversing drug resistance such as gemcitabine. At present, there are still problems such as the lack of standardized protocols and unclear molecular mechanisms in the research. In the future, it is necessary to combine new technologies such as network pharmacology and multi-omics analysis to deepen the research on the pharmacological material basis, dose-effect relationship and evidence-based medicine of Zeqi Decoction, so as to promote the clinical application and transformation of the combination of traditional Chinese and Western medicine in the treatment of NSCLC.

PMID:41847115 | PMC:PMC12991379 | DOI:10.2147/JMDH.S584071

Received — 14 March 2026 ⏭

Inhibition of ZBTB7B-mediated ADPGK transcription by NEDD4 impedes glycolysis and progression of lung adenocarcinoma

✇Oncogenesis - nature.com science feeds
By: Fang-Peng Liu · Ting Huang · Hai-Yan Yang · Jian-Hua Zha · Chuan Xu · Cheng-Peng Qiu · Xing Wang · Zhuo Lu · Tao Wang
11 March 2026 at 08:00

Oncogenesis, Published online: 11 March 2026; doi:10.1038/s41389-026-00605-5

Inhibition of ZBTB7B-mediated ADPGK transcription by NEDD4 impedes glycolysis and progression of lung adenocarcinoma
Received — 5 March 2026 ⏭

Agentic Peer-to-Peer Networks: From Content Distribution to Capability and Action Sharing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Taotao Wang · Lizhao You · Jingwen Tong · Chonghe Zhao · Shengli Zhang
5 March 2026 at 13:00
arXiv:2603.03753v1 Announce Type: cross Abstract: The ongoing shift of AI models from centralized cloud APIs to local AI agents on edge devices is enabling \textit{Client-Side Autonomous Agents (CSAAs)} -- persistent personal agents that can plan, access local context, and invoke tools on behalf of users. As these agents begin to collaborate by delegating subtasks directly between clients, they naturally form \emph{Agentic Peer-to-Peer (P2P) Networks}. Unlike classic file-sharing overlays where the exchanged object is static, hash-indexed content (e.g., files in BitTorrent), agentic overlays exchange \emph{capabilities and actions} that are heterogeneous, state-dependent, and potentially unsafe if delegated to untrusted peers. This article outlines the networking foundations needed to make such collaboration practical. We propose a plane-based reference architecture that decouples connectivity/identity, semantic discovery, and execution. Besides, we introduce signed, soft-state capability descriptors to support intent- and constraint-aware discovery. To cope with adversarial settings, we further present a \textit{tiered verification} spectrum: Tier~1 relies on reputation signals, Tier~2 applies lightweight canary challenge-response with fallback selection, and Tier~3 requires evidence packages such as signed tool receipts/traces (and, when applicable, attestation). Using a discrete-event simulator that models registry-based discovery, Sybil-style index poisoning, and capability drift, we show that tiered verification substantially improves end-to-end workflow success while keeping discovery latency near-constant and control-plane overhead modest.
Received — 25 February 2026 ⏭

Foundations of Top-$k$ Decoding For Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Georgy Noarov · Soham Mallick · Tao Wang · Sunay Joshi · Yan Sun · Yangxinyu Xie · Mengxin Yu · Edgar Dobriban
24 February 2026 at 13:00
arXiv:2505.19371v2 Announce Type: replace Abstract: Top-$k$ decoding is a widely used method for sampling from LLMs: at each token, only the largest $k$ next-token-probabilities are kept, and the next token is sampled after re-normalizing them to sum to unity. Top-$k$ and other sampling methods are motivated by the intuition that true next-token distributions are sparse, and the noisy LLM probabilities need to be truncated. However, to our knowledge, a precise theoretical motivation for the use of top-$k$ decoding is missing. In this work, we develop a theoretical framework that both explains and generalizes top-$k$ decoding. We view decoding at a fixed token as the recovery of a sparse probability distribution. We consider \emph{Bregman decoders} obtained by minimizing a separable Bregman divergence (for both the \emph{primal} and \emph{dual} cases) with a sparsity-inducing $\ell_0$ regularization. Despite the combinatorial nature of the objective, we show how to optimize it efficiently for a large class of divergences. We show that the optimal decoding strategies are greedy, and further that the loss function is discretely convex in $k$, so that binary search provably and efficiently finds the optimal $k$. We show that top-$k$ decoding arises as a special case for the KL divergence, and identify new decoding strategies that have distinct behaviors (e.g., non-linearly up-weighting larger probabilities after re-normalization).
Received — 17 February 2026 ⏭

OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Caorui Li · Yu Chen · Yiyan Ji · Jin Xu · Zhenyu Cui · Shihao Li · Yuanxing Zhang · Wentao Wang · Zhenghao Song · Dingling Zhang · Ying He · Haoxiang Liu · Yuxuan Wang · Qiufeng Wang · Jiafu Tang · Zhenhe Wu · Jiehui Luo · Zhiyu Pan · Weihao Xie · Chenchen Zhang · Zhaohui Wang · Jiayi Tian · Yanghai Wang · Zhe Cao · Minxin Dai · Ke Wang · Runzhe Wen · Yinghao Ma · Yaning Pan · Sungkyun Chang · Termeh Taheri · Haiwen Xia · Christos Plachouras · Emmanouil Benetos · Yizhi Li · Ge Zhang · Jian Yang · Tianhao Peng · Zili Wang · Minghao Liu · Junran Peng · Zhaoxiang Zhang · Jiaheng Liu
17 February 2026 at 13:00
arXiv:2510.10689v2 Announce Type: replace Abstract: Recent advances in multimodal large language models (MLLMs) have demonstrated substantial potential in video understanding. However, existing benchmarks fail to comprehensively evaluate synergistic reasoning capabilities across audio and visual modalities, often neglecting either one of the modalities or integrating them in a logically inconsistent manner. To bridge this gap, we introduce OmniVideoBench, a large-scale and rigorously designed benchmark dedicated to assessing synergistic audio-visual understanding, with a strong emphasis on modality complementarity and logical consistency. Specifically, OmniVideoBench comprises 1000 high-quality question-answer(QA) pairs, each annotated with step-by-step reasoning traces, derived from 628 diverse videos ranging from several seconds to 30 minutes, and manually verified to guarantee complete correctness and uniqueness. Moreover, OmniVideoBench encompasses 13 carefully designed question types, covering temporal reasoning, spatial localization, counting, causal inference, summarization, and beyond, thereby capturing the essential challenges of video understanding. Evaluation of multiple MLLMs on OmniVideoBench reveals a pronounced gap between model performance and human reasoning, with open-source models lagging significantly behind their closed-source counterparts, underscoring the inherent difficulty of genuine audio-visual reasoning. We will release OmniVideoBench to foster the development of MLLMs with stronger and more generalizable reasoning capabilities.
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