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Received — 27 May 2026 ⏭

Selective Test-Time Compute Scaling for Click-Through Rate Prediction via Uncertainty-Triggered Feature Path Exploration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Moyu Zhang · Yun Chen · Yujun Jin · Jinxin Hu · Yu Zhang · Xiaoyi Zeng
26 May 2026 at 12:00
arXiv:2605.24989v1 Announce Type: cross Abstract: Scaling test-time compute has proven highly effective for language models, yet this opportunity remains largely unexplored for industrial Click-Through Rate (CTR) prediction. CTR models suffer from a fundamental asymmetry: feature combinations well-represented in training yield confident predictions, while sparsely observed ones produce unreliable outputs. Existing training-phase solutions such as adaptive gating learn a fixed selection function subject to the same sparsity, offering no per-instance recourse at deployment.We propose UTTSI (Uncertainty-Triggered Test-Time Selective Inference), a training-free model-agnostic framework that scales inference depth proportionally to per-instance uncertainty. A dual-signal estimator combining model logit confidence with a data-level frequency prior distinguishes epistemic uncertainty from aleatoric ambiguity. Every instance undergoes adaptive feature filtering to remove unreliable embeddings; uncertain instances additionally receive stochastic feature-path explorations whose predictions are aggregated via consistency-weighted ensembling. Confident instances bypass exploration entirely, keeping average overhead at approximately $2.8\times$ base model cost with worst-case latency unchanged.Experiments on four datasets with three backbone architectures demonstrate consistent, statistically significant gains over all training-phase baselines. A seven-day online A/B test further confirms a 5.3% relative CTR gain ($p

Multi-omics biomarkers for predicting resistance, hyperprogression, and immune-related toxicity during PD-1/PD-L1 therapy in lung cancer: a literature review

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Na Hu · Ziyue Wang · Ming Liu · Dong Guo · Chenxue Jiang · Yun Chen · Zhenshan Zhang · Menglin Bai · Bo Cheng · Jessica C Hsu · Leilei Wu · Xiujie Sui
25 May 2026 at 18:00

Front Immunol. 2026 May 8;17:1780459. doi: 10.3389/fimmu.2026.1780459. eCollection 2026.

ABSTRACT

Immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) and its ligand programmed death-ligand 1 (PD-L1) have transformed the management of advanced lung cancer, yet most patients experience primary resistance, hyperprogressive disease (HPD), or clinically significant immune-related adverse events (irAEs). Multi-omics technologies now enable integrated interrogation of tumor, microenvironmental, host, and clinical determinants of these divergent outcomes. In this review, we first discuss the biological and clinical foundations of PD-1/PD-L1 blockade in non-small cell and small cell lung cancer, and summarize the spectrum of resistance, HPD, and irAEs observed in trials and real-world practice. We then describe multi-omics study frameworks that connect genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, and microbiome profiling with these outcome phenotypes. Building on this foundation, we synthesize evidence for composite biomarkers of primary and acquired resistance, delineate emerging multi-omics signatures of HPD, and examine host- and tumor-derived multi-omics correlates of organ-specific and systemic irAEs. We further propose an efficacy-risk quadrant framework to guide clinical decision-making when favorable efficacy predictors coexist with elevated risk of severe adverse outcomes, and outline a three-step approach for high-efficacy/high-risk patients: joint probability reporting, multi-omics guided mitigation, and dynamic reassessment. Finally, we evaluate translational strategies that integrate multi-omics scores into baseline risk stratification, dynamic monitoring with attention to technical challenges such as distinguishing true progression from ctDNA pseudoprogression, and biomarker-driven trial design, while assessing the evidence level and translational readiness of candidate assays from retrospective discovery to clinical implementation. A clinical case illustrates how multi-omics can link baseline risk stratification, regimen selection, and longitudinal monitoring into a coherent action plan, while acknowledging that artificial intelligence-driven models remain investigational and real-world application still relies on clinician judgment. Collectively, this review defines how integrated multi-omics biomarkers can be leveraged to predict resistance, HPD, and immune-related toxicity, and to refine patient selection and management during PD-1/PD-L1 therapy in lung cancer.

PMID:42183274 | PMC:PMC13194140 | DOI:10.3389/fimmu.2026.1780459

Multi-omics biomarkers for predicting resistance, hyperprogression, and immune-related toxicity during PD-1/PD-L1 therapy in lung cancer: a literature review

✇Omics In Lung
By: Na Hu · Ziyue Wang · Ming Liu · Dong Guo · Chenxue Jiang · Yun Chen · Zhenshan Zhang · Menglin Bai · Bo Cheng · Jessica C Hsu · Leilei Wu · Xiujie Sui
25 May 2026 at 18:00

Front Immunol. 2026 May 8;17:1780459. doi: 10.3389/fimmu.2026.1780459. eCollection 2026.

ABSTRACT

Immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) and its ligand programmed death-ligand 1 (PD-L1) have transformed the management of advanced lung cancer, yet most patients experience primary resistance, hyperprogressive disease (HPD), or clinically significant immune-related adverse events (irAEs). Multi-omics technologies now enable integrated interrogation of tumor, microenvironmental, host, and clinical determinants of these divergent outcomes. In this review, we first discuss the biological and clinical foundations of PD-1/PD-L1 blockade in non-small cell and small cell lung cancer, and summarize the spectrum of resistance, HPD, and irAEs observed in trials and real-world practice. We then describe multi-omics study frameworks that connect genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, and microbiome profiling with these outcome phenotypes. Building on this foundation, we synthesize evidence for composite biomarkers of primary and acquired resistance, delineate emerging multi-omics signatures of HPD, and examine host- and tumor-derived multi-omics correlates of organ-specific and systemic irAEs. We further propose an efficacy-risk quadrant framework to guide clinical decision-making when favorable efficacy predictors coexist with elevated risk of severe adverse outcomes, and outline a three-step approach for high-efficacy/high-risk patients: joint probability reporting, multi-omics guided mitigation, and dynamic reassessment. Finally, we evaluate translational strategies that integrate multi-omics scores into baseline risk stratification, dynamic monitoring with attention to technical challenges such as distinguishing true progression from ctDNA pseudoprogression, and biomarker-driven trial design, while assessing the evidence level and translational readiness of candidate assays from retrospective discovery to clinical implementation. A clinical case illustrates how multi-omics can link baseline risk stratification, regimen selection, and longitudinal monitoring into a coherent action plan, while acknowledging that artificial intelligence-driven models remain investigational and real-world application still relies on clinician judgment. Collectively, this review defines how integrated multi-omics biomarkers can be leveraged to predict resistance, HPD, and immune-related toxicity, and to refine patient selection and management during PD-1/PD-L1 therapy in lung cancer.

PMID:42183274 | PMC:PMC13194140 | DOI:10.3389/fimmu.2026.1780459

Received — 13 April 2026 ⏭

Asymmetric selection of a rice immune module and rebuild of disease resistance

✇Nature - Issue - nature.com science feeds
By: Hui Lin · Fudan Chen · Guanyun Cheng · Bingxiao Yan · Meng Yuan · Jie Qiu · Yiduo Lu · Mingzhe Suo · Ying Chen · Yijie Wang · Kaixuan Cui · Xiangyu Gong · Shasha Liu · Bofan Liu · Jiyun Liu · Jianjun Wang · Rongbai Li · Bizeng Mao · Jianlong Xu · Jong-Seong Jeon · Xuehui Huang · Bin Han · Dong-Lei Yang · Qifei Gao · Haiming Xu · Yiwen Deng · Gongyou Chen · Zuhua He
8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10361-6

Stacking XA48-mediated effector-triggered immunity with XA21-mediated pattern-triggered immunity in Oryza sativa japonica reconstitutes the broad-spectrum resistance from wild rice.
Received — 26 March 2026 ⏭

BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactions

✇cs.AI, q-bio.NC updates on arXiv.org
By: Nan Huo · Xiaohan Xu · Jinyang Li · Per Jacobsson · Shipei Lin · Bowen Qin · Binyuan Hui · Xiaolong Li · Ge Qu · Shuzheng Si · Linheng Han · Edward Alexander · Xintong Zhu · Rui Qin · Ruihan Yu · Yiyao Jin · Feige Zhou · Weihao Zhong · Yun Chen · Hongyu Liu · Chenhao Ma · Fatma Ozcan · Yannis Papakonstantinou · Reynold Cheng
25 March 2026 at 12:00
arXiv:2510.05318v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated remarkable performance on single-turn text-to-SQL tasks, but real-world database applications predominantly require multi-turn interactions to handle ambiguous queries, execution errors, and evolving user requirements. Existing multi-turn benchmarks fall short by treating conversation histories as static context or limiting evaluation to read-only operations, failing to reflect production-grade database assistant challenges. We introduce BIRD-INTERACT, a benchmark that restores this realism through: (1) a comprehensive interaction environment coupling each database with a hierarchical knowledge base, metadata files, and a function-driven user simulator, enabling models to solicit clarifications, retrieve knowledge, and recover from errors without human supervision; (2) two evaluation settings consisting of a pre-defined conversational protocol (c-Interact) and an open-ended agentic setting (a-Interact) where models autonomously decide when to query the user simulator or explore the environment; (3) a challenging task suite covering the full CRUD spectrum for business-intelligence and operational use cases, guarded by executable test cases. Each task features ambiguous and follow-up sub-tasks requiring dynamic interaction. The suite comprises BIRD-INTERACT-FULL (600 tasks, up to 11,796 interactions) for comprehensive performance assessment, and BIRD-INTERACT-LITE (300 tasks with simplified databases) for detailed behavioral analysis and rapid method development. Our empirical results highlight BIRD-INTERACT's difficulty: GPT-5 completes only 8.67% of tasks in c-Interact and 17.00% in a-Interact. Analysis via memory grafting and Interaction Test-time Scaling validates the importance of effective interaction for complex, dynamic text-to-SQL tasks.
Received — 5 March 2026 ⏭

Quantum-Inspired Fine-Tuning for Few-Shot AIGC Detection via Phase-Structured Reparameterization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Kaiyang Xing · Han Fang · Zhaoyun Chen · Zhonghui Li · Yang Yang · Weiming Zhang · Guoping Guo
4 March 2026 at 13:00
arXiv:2603.02281v1 Announce Type: cross Abstract: Recent studies show that quantum neural networks (QNNs) generalize well in few-shot regimes. To extend this advantage to large-scale tasks, we propose Q-LoRA, a quantum-enhanced fine-tuning scheme that integrates lightweight QNNs into the low-rank adaptation (LoRA) adapter. Applied to AI-generated content (AIGC) detection, Q-LoRA consistently outperforms standard LoRA under few-shot settings. We analyze the source of this improvement and identify two possible structural inductive biases from QNNs: (i) phase-aware representations, which encode richer information across orthogonal amplitude-phase components, and (ii) norm-constrained transformations, which stabilize optimization via inherent orthogonality. However, Q-LoRA incurs non-trivial overhead due to quantum simulation. Motivated by our analysis, we further introduce H-LoRA, a fully classical variant that applies the Hilbert transform within the LoRA adapter to retain similar phase structure and constraints. Experiments on few-shot AIGC detection show that both Q-LoRA and H-LoRA outperform standard LoRA by over 5% accuracy, with H-LoRA achieving comparable accuracy at significantly lower cost in this task.

Contextual Drag: How Errors in the Context Affect LLM Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yun Cheng · Xingyu Zhu · Haoyu Zhao · Sanjeev Arora
4 March 2026 at 13:00
arXiv:2602.04288v2 Announce Type: replace-cross Abstract: Central to many self-improvement pipelines for large language models (LLMs) is the assumption that models can improve by reflecting on past mistakes. We study a phenomenon termed contextual drag: the presence of failed attempts in the context biases subsequent generations toward structurally similar errors. Across evaluations of 11 proprietary and open-weight models on 8 reasoning tasks, contextual drag induces 10-20% performance drops, and iterative self-refinement in models with severe contextual drag can collapse into self-deterioration. Structural analysis using tree edit distance reveals that subsequent reasoning trajectories inherit structurally similar error patterns from the context. We demonstrate that neither external feedback nor successful self-verification suffices to eliminate this effect. While mitigation strategies such as fallback-behavior fine-tuning and context denoising yield partial improvements, they fail to fully restore baseline performance, positioning contextual drag as a persistent failure mode in current reasoning architectures.
Received — 25 February 2026 ⏭

BioLM-Score: Language-Prior Conditioned Probabilistic Geometric Potentials for Protein-Ligand Scoring

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhangfan Yang · Baoyun Chen · Dong Xu · Jia Wang · Ruibin Bai · Junkai Ji · Zexuan Zhu
24 February 2026 at 13:00
arXiv:2602.18476v1 Announce Type: cross Abstract: Protein-ligand scoring is a central component of structure-based drug design, underpinning molecular docking, virtual screening, and pose optimization. Conventional physics-based energy functions are often computationally expensive, limiting their utility in large-scale screening. In contrast, deep learning-based scoring models offer improved computational efficiency but frequently suffer from limited cross-target generalization and poor interpretability, which restrict their practical applicability. Here we present BioLM-Score, a simple yet generalizable protein-ligand scoring model that couples geometric modeling with representation learning. Specifically, it employs modality-specific and structure-aware encoders for proteins and ligands, each augmented with biomolecular language models to enrich structural and chemical representations. Subsequently, these representations are integrated through a mixture density network to predict multimodal interatomic distance distributions, from which statistically grounded likelihood-based scores are derived. Evaluations on the CASF-2016 benchmark demonstrate that BioLM-Score achieves significant improvements across docking, scoring, ranking, and screening tasks. Moreover, the proposed scoring function serves as an effective optimization objective for guiding docking protocols and conformational search. In summary, BioLM-Score provides a principled and practical alternative to existing scoring functions, combining efficiency, generalization, and interpretability for structure-based drug discovery.

MapReduce LoRA: Advancing the Pareto Front in Multi-Preference Optimization for Generative Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chieh-Yun Chen · Zhonghao Wang · Qi Chen · Zhifan Ye · Min Shi · Yue Zhao · Yinan Zhao · Hui Qu · Wei-An Lin · Yiru Shen · Ajinkya Kale · Irfan Essa · Humphrey Shi
24 February 2026 at 13:00
arXiv:2511.20629v4 Announce Type: replace-cross Abstract: Reinforcement learning from human feedback (RLHF) with reward models has advanced alignment of generative models to human aesthetic and perceptual preferences. However, jointly optimizing multiple rewards often incurs an alignment tax, improving one dimension while degrading others. To address this, we introduce two complementary methods: MapReduce LoRA and Reward-aware Token Embedding (RaTE). MapReduce LoRA trains preference-specific LoRA experts in parallel and iteratively merges them to refine a shared base model; RaTE learns reward-specific token embeddings that compose at inference for flexible preference control. Experiments on Text-to-Image generation (Stable Diffusion 3.5 Medium and FLUX.1-dev) show improvements of 36.1%, 4.6%, and 55.7%, and 32.7%, 4.3%, and 67.1% on GenEval, PickScore, and OCR, respectively. On Text-to-Video generation (HunyuanVideo), visual and motion quality improve by 48.1% and 90.0%, respectively. On the language task, Helpful Assistant, with Llama-2 7B, helpful and harmless improve by 43.4% and 136.7%, respectively. Our framework sets a new state-of-the-art multi-preference alignment recipe across modalities.
Received — 17 February 2026 ⏭

FlowHOI: Flow-based Semantics-Grounded Generation of Hand-Object Interactions for Dexterous Robot Manipulation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Huajian Zeng · Lingyun Chen · Jiaqi Yang · Yuantai Zhang · Fan Shi · Peidong Liu · Xingxing Zuo
17 February 2026 at 13:00
arXiv:2602.13444v1 Announce Type: cross Abstract: Recent vision-language-action (VLA) models can generate plausible end-effector motions, yet they often fail in long-horizon, contact-rich tasks because the underlying hand-object interaction (HOI) structure is not explicitly represented. An embodiment-agnostic interaction representation that captures this structure would make manipulation behaviors easier to validate and transfer across robots. We propose FlowHOI, a two-stage flow-matching framework that generates semantically grounded, temporally coherent HOI sequences, comprising hand poses, object poses, and hand-object contact states, conditioned on an egocentric observation, a language instruction, and a 3D Gaussian splatting (3DGS) scene reconstruction. We decouple geometry-centric grasping from semantics-centric manipulation, conditioning the latter on compact 3D scene tokens and employing a motion-text alignment loss to semantically ground the generated interactions in both the physical scene layout and the language instruction. To address the scarcity of high-fidelity HOI supervision, we introduce a reconstruction pipeline that recovers aligned hand-object trajectories and meshes from large-scale egocentric videos, yielding an HOI prior for robust generation. Across the GRAB and HOT3D benchmarks, FlowHOI achieves the highest action recognition accuracy and a 1.7$\times$ higher physics simulation success rate than the strongest diffusion-based baseline, while delivering a 40$\times$ inference speedup. We further demonstrate real-robot execution on four dexterous manipulation tasks, illustrating the feasibility of retargeting generated HOI representations to real-robot execution pipelines.

Enhancing Delta Compression in LLMs via SVD-based Quantization Error Minimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Boya Xiong · Shuo Wang · Weifeng Ge · Guanhua Chen · Yun Chen
17 February 2026 at 13:00
arXiv:2506.11087v3 Announce Type: replace-cross Abstract: Supervised Fine-Tuning (SFT) empowers Large Language Models (LLMs) with exceptional performance on specialized tasks, but it yields dense, high-dimensional delta parameters that pose severe storage and distribution challenges. Singular Value Decomposition (SVD)-based compression offers a compact representation for such delta parameters, but existing methods adopt heuristic quantization without clarifying underlying mechanisms, leading to poor generalizability. In this work, we propose PrinMix, a rigorous SVD-based framework that models quantization as an optimization problem, grounding the design in mathematical mechanisms. We first theoretically derive quantization error and identify a key singular-value-dominated scaling mechanism, which mathematically proves the necessity of mix-precision quantization. We then model the quantization scheme as a 0/1 Integer Linear Programming (ILP) problem, which yields optimal bit-budget-constrained solutions without empirical assumptions. Furthermore, PrinMix integrates a Reconstruction Target Correction (RTC) method to compensate for errors from the $\mathbf{V}$-then-$\mathbf{U}$ sequential quantization process. Extensive experiments confirm PrinMix performs well: for 7B LLMs, PrinMix outperforms SOTA Delta-CoMe on challenging benchmarks by 22.3% on AIME2024 and 6.1% on GQA.
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