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Received — 27 May 2026 ⏭

Neural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables

✇cs.AI, q-bio.NC updates on arXiv.org
By: Weizhi Fei · Hang Yin · Zihao Wang · Shukai Zhao · Wei Zhang · Yangqiu Song
26 May 2026 at 12:00
arXiv:2605.25985v1 Announce Type: new Abstract: Complex Query Answering (CQA) is a fundamental knowledge representation and reasoning task over incomplete knowledge graphs (KGs). Answering existential first-order queries with $k$ free variables (i.e., $\text{EFO}_k$ queries) is a crucial yet challenging problem, as it requires ranking answer tuples in $\mathcal{E}^k$, where $\mathcal{E}$ denotes the entity set of a KG. This quickly becomes intractable as $k$ grows. Consequently, existing benchmarks and methods rely on marginal rankings over individual variables; however, marginal rankings are a poor proxy for the true joint ranking of tuples. Building on neural symbolic search for $\text{EFO}_1$ queries, we propose Neural Scalable Symbolic Search (NS3), a budgeted framework that approximates joint ranking without enumerating $\mathcal{E}^k$. NS3 (i) answers marginalized sub-queries to obtain necessary candidate sets, (ii) merges multiple free variables into hypernodes whose domains are pruned and controlled by a dynamic budget $B$, and (iii) progressively reduces an $\text{EFO}_k$ query to an $\text{EFO}_{k-1}$ query over a budgeted reduced domain. Across three standard KG datasets, NS3 substantially improves joint ranking performance while retaining strong marginal accuracy. We further release a joint-ranking benchmark that extends existing $\text{EFO}_1$ datasets to $k=3$, enabling systematic evaluation of multi-variable queries. Our code is provided in https://github.com/HKUST-KnowComp/NS3_KDD2026.

Towards Evaluation Engineering: An Empirical Study of ML Evaluation Harnesses in the Wild

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhimin Zhao · Zehao Wang · Abdul Ali Bangash · Bram Adams · Ahmed E. Hassan
26 May 2026 at 12:00
arXiv:2605.24213v1 Announce Type: cross Abstract: Evaluation harnesses are software systems that orchestrate model evaluation by managing model invocation, data loading, metric computation, and result reporting. Despite their critical role in machine learning infrastructure, their operational challenges and engineering concerns have received limited attention so far. We present an empirical study of 57 evaluation harnesses, deriving a five-stage harness model and classifying 16,560 issues by workflow stage and root cause. Most harness operational challenges concentrate in the Specification stage (41.4% of issues), where harnesses integrate external models, datasets, and scoring judges. The three most frequent root causes of operational challenges are unimplemented features (24.3%), documentation gaps (20.3%), and missing input validation (17.2%), which together account for 61.7% of classified issues, spanning both defects in existing functionality and capability gaps that block intended workflows. Root causes also vary by workflow stage: environment incompatibility and external dependency breakage account for 36.2% of provisioning issues, whereas algorithmic error (25.9%) and validation gap (22.5%) dominate assessment issues. Together, these contributions establish an empirical foundation for treating evaluation engineering as a distinct software engineering concern.

CollectionLoRA: Collecting 50 Effects in 1 LoRA via Multi-Teacher On-Policy Distillation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fangtai Wu · Hailong Guo · Shijie Huang · Jiayi Song · Yubo Huang · Mushui Liu · Zhao Wang · Yunlong Yu · Jiaming Liu · Ruihua Huang
26 May 2026 at 12:00
arXiv:2605.25378v1 Announce Type: cross Abstract: Customized image editing aims to equip pre-trained diffusion models with specific visual effects using limited paired data, typically via Low-Rank Adaptation (LoRA). As the number of desired effects grows, storing and dynamically loading numerous these effect LoRAs significantly increases deployment overhead. Furthermore, current pipelines typically cascade these effect LoRAs with acceleration modules for fast generation, which triggers severe parameter interference and results in concept bleeding and style degradation. We propose CollectionLoRA, a multi-teacher on-policy distillation framework capable of distilling the concepts of up to 50 different effect LoRAs along with few-step generation capabilities into a single LoRA. This fundamentally resolves the feature interference issue and significantly reduces deployment costs. Specifically, the method introduces (i) a Probabilistic Dual-Stream Routing mechanism that enables the model to randomly switch between data sources during training, effectively enhancing its generalization in unseen scenarios; (ii) an Asymmetric Orthogonal Prompting strategy to achieve concept isolation within the prompt space; (iii) a Coarse-to-Fine Distillation Objective to mitigate the distribution gap between the teacher and student models. Extensive evaluations show that CollectionLoRA distills all customized effects and few-step generation into a single LoRA, reducing deployment overhead while achieving concept fidelity comparable to or better than independently trained teacher models.

Efficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering

✇cs.AI, q-bio.NC updates on arXiv.org
By: Weizhi Fei · Zihao Wang · hang Yin · Shukai Zhao · Wei Zhang · Yangqiu Song
26 May 2026 at 12:00
arXiv:2505.08155v4 Announce Type: replace Abstract: Complex Query Answering (CQA) is a crucial reasoning task over Knowledge Graphs (KGs), which aims to answer first-order logical queries from incomplete KGs. While existing neural-symbolic methods achieve strong performance, they face significant complexity bottlenecks: quadratic data complexity scaling with the number of entities, and NP-hard query complexity for cyclic queries. Consequently, these approaches struggle to scale effectively to large knowledge graphs and complex queries. To address these limitations, we propose an efficient and scalable symbolic search method comprising two key components: (1) constraint strategies that drastically reduce the variable search domain, lowering data complexity; and (2) a local search algorithm that approximately solves NP-hard cyclic queries. Experiments on various CQA benchmarks demonstrate that, for tree-form queries, our method achieves 97% relative MRR with a 10$\times$ speedup using only 10% of the search space. Furthermore, it demonstrates robust performance on complex cyclic queries and large-scale KGs, effectively alleviating efficiency and scalability challenges. Our code is provided in https://github.com/HKUST-KnowComp/NLISA_KDD2026.

Kolmogorov-Arnold Fourier Networks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jusheng Zhang · Yijia Fan · Kaitong Cai · Keze Wang · Wenhao Wang
26 May 2026 at 12:00
arXiv:2502.06018v3 Announce Type: replace-cross Abstract: Although Kolmogorov-Arnold-based interpretable networks (KANs) possess strong theoretical expressiveness, they suffer from severe parameter explosion and limited ability to capture high-frequency features in high-dimensional tasks. To address these issues, we propose the Kolmogorov-Arnold Fourier Network (KAF), which fundamentally redefines the KAN paradigm through spectral reparameterization. Our key contributions include: (1) proposing a fundamental basis transformation from the local, grid-based B-spline representation to a global, adaptive spectral representation. This shift changes the network's inductive bias, reducing parameter complexity from $O(G)$ to $O(1)$ while preserving expressiveness; (2) introducing trainable Random Fourier Features (RFF) initialized via a spectral alignment strategy, which allows the model to break the smoothness limitation of fixed kernels and accurately capture high-frequency components; and (3) implementing an adaptive hybrid GELU-Fourier activation mechanism that progressively enhances frequency representation during training. Comprehensive experiments demonstrate the superiority of KAF across computer vision (CV), natural language processing (NLP), audio, and partial differential equation (PDE) solving tasks, achieving state-of-the-art performance with improved efficiency. The code is available at https://github.com/kolmogorovArnoldFourierNetwork/KAF.

PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data across Nodes

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiming Zhou · Jiahao Wang · Mingyue Cheng · Hao Wang · Defu Lian · Enhong Chen
26 May 2026 at 12:00
arXiv:2601.05613v2 Announce Type: replace-cross Abstract: While collaborative forecasting on distributed time series is highly desirable, directly pooling localized datasets is often impractical due to data sharing constraints. Federated learning offers a promising alternative, yet conventional federated learning algorithms require homogeneous model architectures, which are incompatible with the structural discrepancies, such as unaligned temporal resolutions and mismatched variable channels, commonly observed across decentralized nodes. To bridge this gap, we introduce PiXTime, a novel Transformer-based framework designed to natively accommodate and leverage structurally heterogeneous temporal data. At its core, PiXTime adopts a parameter-decoupling architecture, strategically partitioning the model into localized personalized modules and a globally aggregated shared backbone. Specifically, node-specific local modules act as dimensional adapters, projecting raw sequences of diverse lengths into a unified representation space. Concurrently, a globally synchronized VE Table injects consistent categorical identities into the feature space, allowing the shared backbone to collaboratively learn and generalize representations across inconsistent variable distributions. Comprehensive evaluations on multiple benchmarks demonstrate that PiXTime achieves state-of-the-art performance in heterogeneous federated environments, while maintaining robust superiority in standard homogeneous and centralized forecasting settings.

BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Weiqin Yang · Bohao Wang · Zhenxiang Xu · Jiawei Chen · Shengjia Zhang · Jingbang Chen · Canghong Jin · Can Wang
26 May 2026 at 12:00
arXiv:2601.22925v3 Announce Type: replace-cross Abstract: Recent years have seen a rapid surge in research leveraging Large Language Models (LLMs) for recommendation. These methods typically employ supervised fine-tuning (SFT) to adapt LLMs to recommendation scenarios, and utilize beam search during inference to efficiently retrieve $B$ top-ranked recommended items. However, we identify a critical training-inference inconsistency: while SFT optimizes the overall probability of positive items, it does not guarantee that such items will be retrieved by beam search even if they possess high overall probabilities. Due to the greedy pruning mechanism, beam search can prematurely discard a positive item once its prefix probability is insufficient. To address this inconsistency, we propose BEAR (Beam-SEarch-Aware Regularization), a novel fine-tuning objective that explicitly accounts for beam search behavior during training. Rather than directly simulating beam search for each instance during training, which is computationally prohibitive, BEAR enforces a relaxed necessary condition: each token in a positive item must rank within the top-$B$ candidate tokens at each decoding step. This objective effectively mitigates the risk of incorrect pruning while incurring negligible computational overhead compared to standard SFT. Extensive experiments across four real-world datasets demonstrate that BEAR significantly outperforms strong baselines. Code is available at https://github.com/Tiny-Snow/BEAR-SIGIR-2026 .

Prism: Spectral-Aware Block-Sparse Attention

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinghao Wang · Pengyu Wang · Xiaoran Liu · Fangxu Liu · Jason Chu · Kai Song · Xipeng Qiu
26 May 2026 at 12:00
arXiv:2602.08426v2 Announce Type: replace-cross Abstract: Block-sparse attention is promising for accelerating long-context LLM pre-filling, yet identifying relevant blocks efficiently remains a bottleneck. Existing methods typically employ coarse-grained attention as a proxy for block importance estimation, but often resort to expensive token-level searching or scoring, resulting in significant selection overhead. In this work, we trace the inaccuracy of standard coarse-grained attention via mean pooling to a theoretical root cause: the interaction between mean pooling and Rotary Positional Embeddings (RoPE). We prove that mean pooling acts as a low-pass filter that induces destructive interference in high-frequency dimensions, effectively creating a "blind spot" for local positional information (e.g., slash patterns). To address this, we introduce Prism, a training-free spectral-aware approach that decomposes block selection into high-frequency and low-frequency branches. By applying energy-based temperature calibration, Prism restores the attenuated positional signals directly from pooled representations, enabling block importance estimation using purely block-level operations, thereby improving efficiency. Extensive evaluations confirm that Prism maintains accuracy parity with full attention while delivering up to $\mathbf{5.1\times}$ speedup.

PRXL2B facilitates the progression of hepatocellular carcinoma and the therapeutic efficacy of oncolytic adenovirus H101 through the PI3K/AKT/PD-L1 axis

✇Omics in Hepatocellular
By: Ziqi Zhang · Hongli Chen · Yangming Liu · Hao Wang · You Yan · Yanxi Yu · Zuotian Huang · Yanyao Liu · Wei Li · Zhongjun Wu · Meiqing Kang · Xiaoyong Luo · Ping Che · Rui Liao
20 May 2026 at 18:00

Biosci Trends. 2026 May 21. doi: 10.5582/bst.2026.01000. Online ahead of print.

ABSTRACT

Oncolytic adenovirus H101 has shown antitumor activity in hepatocellular carcinoma (HCC), but the molecular determinants of treatment response remain unclear. In this study, a Hepa1-6 subcutaneous tumor model was established in C57BL/6 mice and treated with intratumoral H101, followed by integrated transcriptomic and proteomic analyses to identify candidate genes associated with H101 response. PRXL2B was selected for further investigation using public multi-omics datasets, tissue microarray-based immunohistochemistry, in vitro functional assays, mechanistic analyses, and in vivo validation experiments. Integrated multi-omics analyses identified PRXL2B as a candidate gene downregulated after H101 treatment. Public datasets and tissue-based validation further showed that PRXL2B was upregulated in HCC tissues. In MHCC97H and HCCLM3 cells, PRXL2B knockdown inhibited proliferation, migration, and invasion, promoted apoptosis and cell-cycle arrest, and enhanced the antitumor effect of H101. Mechanistically, PRXL2B silencing reduced AKT phosphorylation and PD-L1 expression. In vivo, PRXL2B knockdown suppressed tumor growth, and the combination of PRXL2B knockdown and H101 produced the strongest antitumor effect. These findings indicate that PRXL2B promotes malignant phenotypes in HCC and may modulate H101 efficacy through the PI3K/AKT/PD-L1 axis. Targeting PRXL2B may therefore represent a potential strategy to enhance the therapeutic efficacy of oncolytic virus therapy in HCC.

PMID:42161529 | DOI:10.5582/bst.2026.01000

Proteomic and lipidomic analyses reveal molecular subtypes and potential targets in early-stage lung adenocarcinoma among non-smokers

✇Pulmonary nodule
By: Yaohui Sun · Jian Liu · Xin Xu · Qiqi Liu · Hua Cheng · Wenwen Huo · Ronghao Wang · Qingdong Cao
29 April 2026 at 18:00

Cell Rep. 2026 May 26;45(5):117215. doi: 10.1016/j.celrep.2026.117215. Epub 2026 Apr 28.

ABSTRACT

Early-stage lung adenocarcinoma (LUAD) in never smokers exhibits distinct biological features, yet the metabolic programs driving early invasion remain unclear. We integrate proteomic and lipidomic profiling of primary LUAD tumors from never smokers, matched normal adjacent tissues (NATs), and benign pulmonary nodules (BPNs). Integrated multi-omics analysis reveals coordinated dysregulation of lipid metabolism and immune signaling in early LUAD. Proteome-based network fusion stratifies invasive LUAD into immune-metabolic synergistic (IMS) and metabolic-stress-driven (MSD) subtypes. IMS tumors retain apolipoprotein-associated lipid modules and favorable immune features, whereas MSD tumors exhibit stress-response programs. Mechanistically, APOA1 and APOC1 emerge as key nodes linking lipid homeostasis to invasion, and their depletion promotes LUAD cell migration and invasion. We establish a two-protein, four-lipid diagnostic panel demonstrating robust performance across tissue and plasma cohorts. These findings provide a molecular basis for early detection and risk stratification in never smokers.

PMID:42054209 | DOI:10.1016/j.celrep.2026.117215

SREBP2 regulates CCDC25 expression and promotes tumor metastasis in Triple-Negative Breast Cancer

✇Oncogenesis - nature.com science feeds
By: Xingxian Lv · Hao Wang · Beibei Liang · Liyun Yang · Zhuozhuo Han · Wei Xie · Xiuhong Lu · Hao Yang · Gang Huang · Jian Zhao
13 April 2026 at 08:00

Oncogenesis, Published online: 13 April 2026; doi:10.1038/s41389-026-00614-4

SREBP2 regulates CCDC25 expression and promotes tumor metastasis in Triple-Negative Breast Cancer
Received — 8 April 2026 ⏭

Safe Decentralized Operation of EV Virtual Power Plant with Limited Network Visibility via Multi-Agent Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chenghao Huang · Jiarong Fan · Weiqing Wang · Hao Wang
7 April 2026 at 12:00
arXiv:2604.03278v1 Announce Type: cross Abstract: As power systems advance toward net-zero targets, behind-the-meter renewables are driving rapid growth in distributed energy resources (DERs). Virtual power plants (VPPs) increasingly coordinate these resources to support power distribution network (PDN) operation, with EV charging stations (EVCSs) emerging as a key asset due to their strong impact on local voltages. However, in practice, VPPs must make operational decisions with only partial visibility of PDN states, relying on limited, aggregated information shared by the distribution system operator. This work proposes a safety-enhanced VPP framework for coordinating multiple EVCSs under such realistic information constraints to ensure voltage security while maintaining economic operation. We develop Transformer-assisted Lagrangian Multi-Agent Proximal Policy Optimization (TL-MAPPO), in which EVCS agents learn decentralized charging policies via centralized training with Lagrangian regularization to enforce voltage and demand-satisfaction constraints. A transformer-based embedding layer deployed on each EVCS agent captures temporal correlations among prices, loads, and charging demand to improve decision quality. Experiments on a realistic 33-bus PDN show that the proposed framework reduces voltage violations by approximately 45% and operational costs by approximately 10% compared to representative multi-agent DRL baselines, highlighting its potential for practical VPP deployment.

SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hao Wang, Niels M\"undler, Mark Vero, Jingxuan He, Dawn Song, Martin Vechev
7 April 2026 at 12:00
arXiv:2604.03587v1 Announce Type: cross Abstract: Reasoning language models (RLMs) are increasingly used in programming. Yet, even state-of-the-art RLMs frequently introduce critical security vulnerabilities in generated code. Prior training-based approaches for secure code generation face a critical limitation that prevents their direct application to RLMs: they rely on costly, manually curated security datasets covering only a limited set of vulnerabilities. At the inference level, generic security reminders consistently degrade functional correctness while triggering only shallow ad-hoc vulnerability analysis. To address these problems, we present SecPI, a fine-tuning pipeline that teaches RLMs to internalize structured security reasoning, producing secure code by default without any security instructions at inference time. SecPI filters existing general-purpose coding datasets for security-relevant tasks using an LLM-based classifier, generates high-quality security reasoning traces with a teacher model guided by a structured prompt that systematically enumerates relevant CWEs and mitigations, and fine-tunes the target model on pairs of inputs with no security prompt and teacher reasoning traces -- as a result, the model learns to reason about security autonomously rather than in response to explicit instructions. An extensive evaluation on security benchmarks with state-of-the-art open-weight reasoning models validates the effectiveness of our approach. For instance, SecPI improves the percentage of functionally correct and secure generations for QwQ 32B from 48.2% to 62.2% (+14.0 points) on CWEval and from 18.2% to 22.0% on BaxBench. Further investigation also reveals strong cross-CWE and cross-language generalization beyond training vulnerabilities. Even when trained only on injection-related CWEs, QwQ 32B generates correct and secure code 9.9% more frequently on held-out memory-safety CWEs.
Received — 4 April 2026 ⏭

Restoring circadian rhythms in the hypothalamic paraventricular nucleus reverses aging biomarkers and extends lifespan in male mice

✇Cell
By: Haijiao Zhao · Meimei Liao · Ran Huo · Ting He · Hongni Tian · Zeqi Li · Chen Chen · Ziqing Yu · Juan Chai · Xiaocui Song · Ruichao Shao · Shuhua Ying · Wen Gao · Ling Liu · Di Sang · Qi Li · Haohong Li · Fengchao Wang · Dapeng Ju · Eric Erquan Zhang
4 March 2026 at 08:00
Enhancing circadian amplitude in mouse hypothalamic paraventricular nucleus neurons by 3′-deoxyadenosine treatment alleviates age-related pathologies and extends lifespan.

Genetically encoded fluorescent reporters to visualize α-synuclein pathology in live brain

✇Cell
By: Li Zhang · Minhui Yu · Guoqing Chen · Siyuan Ge · Mengdi Wang · Xianying Zhang · Miao Zhao · Huating Gu · Meizhu Huang · Aixue Liu · Gengxin Ran · Zeyuan Liu · Tiepeng Liao · Qi Chen · Chenjian Miao · Yao Lu · Yibing Wang · Fengchao Wang · Zhihui Liu · Hongying Zhu · Qian Wu · Zhuan Zhou · Wei Xiong · Xiaoqun Wang · Peng Cao
4 March 2026 at 08:00
The development of genetically encoded fluorescent reporters, along with their corresponding knock-in mouse lines for labeling α-Syn inclusions, enables diverse applications in studying the propagation and pathological effects of α-Syn inclusions in the live brain.
Received — 2 April 2026 ⏭

Interview-Informed Generative Agents for Product Discovery: A Validation Study

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zichao Wang · Alexa Siu
1 April 2026 at 12:00
arXiv:2603.29890v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong performance on standardized social science instruments, but their value for product discovery remains unclear. We investigate whether interview-informed generative agents can simulate user responses in concept testing scenarios. Using in-depth workflow interviews with knowledge workers, we created personalized agents and compared their evaluations of novel AI concepts against the same participants' responses. Our results show that agents are distribution-calibrated but identity-imprecise: they fail to replicate the specific individual they are grounded in, yet approximate population-level response distributions. These findings highlight both the potential and the limits of LLM simulation in design research. While unsuitable as a substitute for individual-level insights, simulation may provide value for early-stage concept screening and iteration, where distributional accuracy suffices. We discuss implications for integrating simulation responsibly into product development workflows.

Generative Data Transformation: From Mixed to Unified Data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaqing Zhang · Mingjia Yin · Hao Wang · Yuxin Tian · Yuyang Ye · Yawen Li · Wei Guo · Yong Liu · Enhong Chen
1 April 2026 at 12:00
arXiv:2602.22743v2 Announce Type: replace Abstract: Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start, recent researchs have leveraged data from multiple auxiliary domains to enrich information within the target domain. However, inherent domain gaps can degrade the quality of mixed-domain data, leading to negative transfer and diminished model performance. Existing prevailing \emph{model-centric} paradigm -- which relies on complex, customized architectures -- struggles to capture the subtle, non-structural sequence dependencies across domains, leading to poor generalization and high demands on computational resources. To address these shortcomings, we propose \textsc{Taesar}, a \emph{data-centric} framework for \textbf{t}arget-\textbf{a}lign\textbf{e}d \textbf{s}equenti\textbf{a}l \textbf{r}egeneration, which employs a contrastive decoding mechanism to adaptively encode cross-domain context into target-domain sequences. It employs contrastive decoding to encode cross-domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures. Experiments show \textsc{Taesar} outperforms model-centric solutions and generalizes to various sequential models. By generating enriched datasets, \textsc{Taesar} effectively combines the strengths of data- and model-centric paradigms. The code accompanying this paper is available at~ \textcolor{blue}{https://github.com/USTC-StarTeam/Taesar}.

STAT3-mediated transactivation of NOVA2 promotes lung adenocarcinoma metastasis by splicing SMAD4

✇Oncogene - Issue - nature.com science feeds
By: Shengjie Wang · Xin Tong · Runfeng Sun · Xinyi Xia · Donglai Chen · Zhao Wang · Hao Shi · Chenzhuo Wu · Xiaoxiao Guo · Die Hu · Ersuo Jin · Hong-Tao Zhang
1 April 2026 at 08:00

Oncogene, Published online: 01 April 2026; doi:10.1038/s41388-026-03752-6

STAT3-mediated transactivation of NOVA2 promotes lung adenocarcinoma metastasis by splicing SMAD4
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