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Received — 10 September 2026 ⏭

ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiafan Li · Mengxue Yang · Jiaqi Zhu · Liang Chang · Ying Li · Hongan Wang
10 September 2026 at 12:00
arXiv:2608.05833v3 Announce Type: replace Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images. Traditional representation learning approaches follow the embedding-based paradigm and may struggle when relation-specific evidence is limited. Meanwhile, LLM-based reasoning methods typically linearize graph structures into textual prompts, which obscures structural topology and neglects vital visual information. While vision-language models (VLMs) excel at multimodal reasoning, they cannot natively interpret structured graph topology, particularly when it comes to knowledge graphs where nodes and edges carry complex semantics. To bridge this gap, we propose ViSR-KGC, a visual subgraph reasoning approach for KGC. It integrates three complementary capabilities to capture semantic correlations: identifying global topology dependencies via representation learning, analyzing local multimodal evidence using VLMs, and providing necessary commonsense knowledge inherent in pre-trained models. Based on learned multimodal embeddings, our framework first extracts a compact and query-aware subgraph from the MMKG. Then, this subgraph is transformed into a visually interpretable image using a layout strategy selected through empirical comparison. Finally, the visualized subgraph, entity images, textual descriptions, and candidate answers are combined into a unified prompt, enabling the VLM to infer the missing entity.

Artificial intelligence-assisted early screening of lung cancer and accurate diagnosis of pulmonary nodules: research progress and clinical prospects from radiomics to multi-omics integration: a narrative review

✇Pulmonary nodule
By: Junwei Wang · Guixue Yang · Zhuoxin Dai · Miao Yang · Peng Dai · Mengyang Chang · Quan-Xing Liu · Ji-Gang Dai
17 June 2026 at 18:00

J Thorac Dis. 2026 May 31;18(5):537. doi: 10.21037/jtd-2026-1-0315. Epub 2026 Apr 30.

ABSTRACT

BACKGROUND AND OBJECTIVE: Lung cancer remains one of the leading causes of cancer-related death worldwide. Although low-dose computed tomography (LDCT) has improved early detection, false-positive results, overdiagnosis, and interobserver variability continue to limit screening efficiency and downstream management of pulmonary nodules. This narrative review summarizes recent progress in artificial intelligence (AI)-assisted screening, radiomics-based nodule characterization, and multi-omics integration for the precision diagnosis of lung cancer.

METHODS: A narrative review with thematic analysis was conducted using representative literature on AI-assisted lung cancer screening, quantitative imaging analysis of pulmonary nodules, radiogenomic and multi-omics integration, and clinical translation challenges. Studies were synthesized to highlight technical advances, diagnostic performance, strengths, limitations, and barriers to implementation.

KEY CONTENT AND FINDINGS: AI improves nodule detection, second-reader support, workflow efficiency, and malignancy-risk estimation in LDCT screening. Radiomics converts CT images into quantitative features that can improve discrimination between benign and malignant nodules, especially when combined with clinical variables or deep-learning models. Beyond imaging alone, radiogenomic and other multi-omics approaches link imaging phenotypes with molecular alterations, treatment response, and prognosis, thereby supporting more individualized management. However, current evidence remains limited by dataset heterogeneity, retrospective design, limited interpretability, and insufficient multicenter prospective validation.

CONCLUSIONS: AI-based imaging and multi-omics integration offer a promising pathway toward earlier detection and more precise diagnosis of lung cancer. Broader clinical adoption will depend on standardized data acquisition, robust external validation, interpretable models, and careful governance of privacy, ethics, and workflow integration.

PMID:42306713 | PMC:PMC13266817 | DOI:10.21037/jtd-2026-1-0315

Received — 27 May 2026 ⏭

SkillOpt: Executive Strategy for Self-Evolving Agent Skills

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yifan Yang · Ziyang Gong · Weiquan Huang · Qihao Yang · Ziwei Zhou · Zisu Huang · Yan Li · Xuemei Gao · Qi Dai · Bei Liu · Kai Qiu · Yuqing Yang · Dongdong Chen · Xue Yang · Chong Luo
26 May 2026 at 12:00
arXiv:2605.23904v2 Announce Type: replace Abstract: Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting point under feedback. We argue the skill should instead be trained as the external state of a frozen agent, with the same discipline that makes weight-space optimization reproducible. SkillOpt is, to our knowledge, the first systematic controllable text-space optimizer for agent skills: a separate optimizer model turns scored rollouts into bounded add/delete/replace edits on a single skill document, and an edit is accepted only when it strictly improves a held-out validation score. A textual learning-rate budget, rejected-edit buffer, and epoch-wise slow/meta update make skill training stable while adding zero inference-time model calls at deployment. Across six benchmarks, seven target models, and three execution harnesses (direct chat, Codex, Claude Code), SkillOpt is best or tied on all 52 evaluated (model, benchmark, harness) cells and beats every per-cell competitor among human, one-shot LLM, Trace2Skill, TextGrad, GEPA, and EvoSkill skills. On GPT-5.5 it lifts the average no-skill accuracy by +23.5 points in direct chat, by +24.8 inside the Codex agentic loop, and by +19.1 inside Claude Code. Transfer experiments further show that optimized skill artifacts retain value when moved across model scales, between Codex and Claude Code execution environments, and to a nearby math benchmark without further optimization. Code: https://aka.ms/skillopt
Received — 4 April 2026 ⏭

Robust Graph Representation Learning via Adaptive Spectral Contrast

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhuolong Li · Boxue Yang · Haopeng Chen
3 April 2026 at 12:00
arXiv:2604.01878v1 Announce Type: cross Abstract: Spectral graph contrastive learning has emerged as a unified paradigm for handling both homophilic and heterophilic graphs by leveraging high-frequency components. However, we identify a fundamental spectral dilemma: while high-frequency signals are indispensable for encoding heterophily, our theoretical analysis proves they exhibit significantly higher variance under spectrally concentrated perturbations. We derive a regret lower bound showing that existing global (node-agnostic) spectral fusion is provably sub-optimal: on mixed graphs with separated node-wise frequency preferences, any global fusion strategy incurs non-vanishing regret relative to a node-wise oracle. To escape this bound, we propose ASPECT, a framework that resolves this dilemma through a reliability-aware spectral gating mechanism. Formulated as a minimax game, ASPECT employs a node-wise gate that dynamically re-weights frequency channels based on their stability against a purpose-built adversary, which explicitly targets spectral energy distributions via a Rayleigh quotient penalty. This design forces the encoder to learn representations that are both structurally discriminative and spectrally robust. Empirical results show that ASPECT achieves new state-of-the-art performance on 8 out of 9 benchmarks, effectively decoupling meaningful structural heterophily from incidental noise.
Received — 17 February 2026 ⏭

GeoEyes: On-Demand Visual Focusing for Evidence-Grounded Understanding of Ultra-High-Resolution Remote Sensing Imagery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fengxiang Wang · Mingshuo Chen · Yueying Li · Yajie Yang · Yifan Zhang · Long Lan · Xue Yang · Hongda Sun · Yulin Wang · Di Wang · Jun Song · Jing Zhang · Bo Du
17 February 2026 at 13:00
arXiv:2602.14201v1 Announce Type: cross Abstract: The "thinking-with-images" paradigm enables multimodal large language models (MLLMs) to actively explore visual scenes via zoom-in tools. This is essential for ultra-high-resolution (UHR) remote sensing VQA, where task-relevant cues are sparse and tiny. However, we observe a consistent failure mode in existing zoom-enabled MLLMs: Tool Usage Homogenization, where tool calls collapse into task-agnostic patterns, limiting effective evidence acquisition. To address this, we propose GeoEyes, a staged training framework consisting of (1) a cold-start SFT dataset, UHR Chain-of-Zoom (UHR-CoZ), which covers diverse zooming regimes, and (2) an agentic reinforcement learning method, AdaZoom-GRPO, that explicitly rewards evidence gain and answer improvement during zoom interactions. The resulting model learns on-demand zooming with proper stopping behavior and achieves substantial improvements on UHR remote sensing benchmarks, with 54.23% accuracy on XLRS-Bench.
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