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Received — 4 April 2026 ⏭

A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability

✇cs.AI, q-bio.NC updates on arXiv.org
By: Pengyun Wang · Junyu Luo · Yanxin Shen · Ming Zhang · Shaoen Qin · Hanwen Xing · Siyu Heng · Xiao Luo
3 April 2026 at 12:00
arXiv:2406.09031v5 Announce Type: replace-cross Abstract: Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks. Despite the recent surge in graph pooling approaches, there is a lack of standardized experimental settings and fair benchmarks to evaluate their performance. To address this issue, we have constructed a comprehensive benchmark that includes 17 graph pooling methods and 28 different graph datasets. This benchmark systematically assesses the performance of graph pooling methods in three dimensions, i.e., effectiveness, robustness, and generalizability. We first evaluate the performance of these graph pooling approaches across different tasks including graph classification, graph regression and node classification. Then, we investigate their performance under potential noise attacks and out-of-distribution shifts in real-world scenarios. We also involve detailed efficiency analysis, backbone analysis, parameter analysis and visualization to provide more evidence. Extensive experiments validate the strong capability and applicability of graph pooling approaches in various scenarios, which can provide valuable insights and guidance for deep geometric learning research. The source code of our benchmark is available at https://github.com/goose315/Graph_Pooling_Benchmark.
Received — 2 April 2026 ⏭

Dunhuang Daxiefei Decoction ameliorates acute lung injury via the HIF-1alpha/glycolysis/H3K18la axis

✇Omics In Lung
By: Jiayun Wang · Liangbo Jiao · Zhiqiang Yan · Shun Wang · Suwan Wu · Tao Guo · Yuetong Yang · Xiaoyingzi Su · Bo Tan · Aidong Yang
28 March 2026 at 18:00

J Ethnopharmacol. 2026 Mar 26;365:121591. doi: 10.1016/j.jep.2026.121591. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Acute lung injury (ALI) lacks effective therapies. HIF-1α-driven glycolysis can promote histone lactylation and sustain pro-inflammatory (M1) macrophage responses. Daxiefei Decoction (DXFD), a classic traditional Chinese medicine formula, is used for pulmonary inflammatory diseases, but its immunometabolic mechanism remains unclear.

AIM OF THE STUDY: To evaluate the protective efficacy of DXFD against lipopolysaccharide (LPS)-induced ALI and to determine whether it acts through the HIF-1α/glycolysis/histone H3K18 lactylation (H3K18la) axis to regulate macrophage polarization.

MATERIALS & METHODS: DXFD constituents were characterized by UPLC-LTQ-Orbitrap-MS/MS, followed by network pharmacology, molecular docking, and molecular dynamics (MD) simulations. Lung transcriptomics and metabolomics were performed in ALI mice. Efficacy and mechanisms were assessed in LPS-challenged mice and RAW264.7 macrophages using histopathology, ELISA, qRT-PCR, Western blotting, and immunofluorescence. HIF-1α overexpression was used for validation.

RESULTS: DXFD dose-dependently alleviated lung injury and reduced pro-inflammatory cytokines in vivo, and suppressed M1 polarization in vivo and in LPS-stimulated macrophages. Multi-omics indicated activation of HIF-1α-associated inflammatory and glycolytic programs in ALI, which were normalized by DXFD. DXFD decreased glycolytic enzyme expression and reduced histone H3K18 lactylation (H3K18la); these effects were partially reversed by HIF-1α overexpression. Molecular docking and dynamics suggested stable binding of baicalin to HIF-1α.

CONCLUSIONS: DXFD mitigates ALI by dampening HIF-1α-dependent glycolysis and H3K18la, thereby restraining M1-driven inflammatory amplification.

PMID:41903585 | DOI:10.1016/j.jep.2026.121591

Received — 17 March 2026 ⏭

FAPE-IR: Frequency-Aware Planning and Execution Framework for All-in-One Image Restoration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jingren Liu · Shuning Xu · Qirui Yang · Yun Wang · Xiangyu Chen · Zhong Ji
16 March 2026 at 12:00
arXiv:2511.14099v3 Announce Type: replace-cross Abstract: All-in-One Image Restoration (AIO-IR) aims to develop a unified model that can handle multiple degradations under complex conditions. However, existing methods often rely on task-specific designs or latent routing strategies, making it hard to adapt to real-world scenarios with various degradations. We propose FAPE-IR, a Frequency-Aware Planning and Execution framework for image restoration. It uses a frozen Multimodal Large Language Model (MLLM) as a planner to analyze degraded images and generate concise, frequency-aware restoration plans. These plans guide a LoRA-based Mixture-of-Experts (LoRA-MoE) module within a diffusion-based executor, which dynamically selects high- or low-frequency experts, complemented by frequency features of the input image. To further improve restoration quality and reduce artifacts, we introduce adversarial training and a frequency regularization loss. By coupling semantic planning with frequency-based restoration, FAPE-IR offers a unified and interpretable solution for all-in-one image restoration. Extensive experiments show that FAPE-IR achieves state-of-the-art performance across seven restoration tasks and exhibits strong zero-shot generalization under mixed degradations.
Received — 11 March 2026 ⏭

ELHPlan: Efficient Long-Horizon Task Planning for Multi-Agent Collaboration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shaobin Ling · Yun Wang · Chenyou Fan · Tin Lun Lam · Junjie Hu
10 March 2026 at 12:00
arXiv:2509.24230v2 Announce Type: replace Abstract: Large Language Models (LLMs) enable intelligent multi-robot collaboration but face fundamental trade-offs: open-loop methods that compile tasks into formal representations for external executors produce sound plans but lack adaptability in partially observable environments, while iterative methods incur prohibitive computational costs that scale poorly with team size and task complexity. In this paper, we propose Efficient Long-Horizon Planning (ELHPlan), a novel framework that introduces Action Chains, sequences of actions explicitly bound to sub-goal intentions, as the fundamental planning primitive. ELHPlan operates via a cyclical process: 1) constructing intention-bound action sequences, 2) proactively validating for conflicts and feasibility, 3) refining issues through targeted mechanisms, and 4) executing validated actions. This design balances adaptability and efficiency by providing intention-bound action sequences with longer lookahead while avoiding expensive full re-planning. We further advocate comprehensive efficiency metrics, including token consumption and planning time, to more holistically evaluate multi-agent collaboration. Our experiments on benchmarks TDW-MAT and C-WAH demonstrate that ELHPlan achieves comparable task success rates while consuming only 30-40% of the tokens required by state-of-the-art methods. Our research establishes a new efficiency-effectiveness frontier for LLM-based multi-agent planning systems.

Video-EM: Event-Centric Episodic Memory for Long-Form Video Understanding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yun Wang · Long Zhang · Jingren Liu · Jiaqi Yan · Zhanjie Zhang · Jiahao Zheng · Ao Ma · Run Ling · Xun Yang · Dapeng Wu · Xiangyu Chen · Xuelong Li
10 March 2026 at 12:00
arXiv:2508.09486v2 Announce Type: replace-cross Abstract: Video Large Language Models (Video-LLMs) have shown strong video understanding, yet their application to long-form videos remains constrained by limited context windows. A common workaround is to compress long videos into a handful of representative frames via retrieval or summarization. However, most existing pipelines score frames in isolation, implicitly assuming that frame-level saliency is sufficient for downstream reasoning. This often yields redundant selections, fragmented temporal evidence, and weakened narrative grounding for long-form video question answering. We present \textbf{Video-EM}, a training-free, event-centric episodic memory framework that reframes long-form VideoQA as \emph{episodic event construction} followed by \emph{memory refinement}. Instead of treating retrieved keyframes as independent visuals, Video-EM employs an LLM as an active memory agent to orchestrate off-the-shelf tools: it first localizes query-relevant moments via multi-grained semantic matching, then groups and segments them into temporally coherent events, and finally encodes each event as a grounded episodic memory with explicit temporal indices and spatio-temporal cues (capturing \emph{when}, \emph{where}, \emph{what}, and involved entities). To further suppress verbosity and noise from imperfect upstream signals, Video-EM integrates a reasoning-driven self-reflection loop that iteratively verifies evidence sufficiency and cross-event consistency, removes redundancy, and adaptively adjusts event granularity. The outcome is a compact yet reliable \emph{event timeline} -- a minimal but sufficient episodic memory set that can be directly consumed by existing Video-LLMs without additional training or architectural changes.

LMMRec: LLM-driven Motivation-aware Multimodal Recommendation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yicheng Di · Zhanjie Zhang · Yun Wang · Jinren Liu · Jiaqi Yan · Jiyu Wei · Xiangyu Chen · Yuan Liu
10 March 2026 at 12:00
arXiv:2602.05474v3 Announce Type: replace-cross Abstract: Motivation-based recommendation systems uncover user behavior drivers. Motivation modeling, crucial for decision-making and content preference, explains recommendation generation. Existing methods often treat motivation as latent variables from interaction data, neglecting heterogeneous information like review text. In multimodal motivation fusion, two challenges arise: 1) achieving stable cross-modal alignment amid noise, and 2) identifying features reflecting the same underlying motivation across modalities. To address these, we propose LLM-driven Motivation-aware Multimodal Recommendation (LMMRec), a model-agnostic framework leveraging large language models for deep semantic priors and motivation understanding. LMMRec uses chain-of-thought prompting to extract fine-grained user and item motivations from text. A dual-encoder architecture models textual and interaction-based motivations for cross-modal alignment, while Motivation Coordination Strategy and Interaction-Text Correspondence Method mitigate noise and semantic drift through contrastive learning and momentum updates. Experiments on three datasets show LMMRec achieves up to a 4.98\% performance improvement.
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