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Received — 27 May 2026 ⏭

When Search Becomes Memory: Turning Robot Design Trials into Transferable Skills

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yunfei Wang · Xiaohao Xu · Yang Li · Xiaonan Huang
26 May 2026 at 12:00
arXiv:2605.25832v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as proposal generators for evolutionary robot design, yet most loops remain memoryless: simulator results shape the next population but are not preserved as reusable design knowledge. We present Auto-Robotist, a self-evolving LLM agent that distills morphology-search traces into an explicit natural-language skill library. Each skill stores a structural archetype, evidence-grounded positive and negative rules, and the evaluated designs that support them, making design memory inspectable rather than implicit in a population. During search, the agent retrieves skills to condition LLM edits of elite bodies while retaining a Genetic Algorithm (GA) mutation path for exploration; after evaluation, it updates the library through Add, Diagnose, and Merge. Across seven EvoGym tasks spanning locomotion, traversal, and object interaction, Auto-Robotist improves cold-start 5x5 search and transfers learned skills to 10x10 design spaces, where reference-conditioned transfer outperforms GA on every task. These results suggest that LLM agents can convert expensive physical evaluations into reusable, auditable design principles. Our code will be released upon acceptance.

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siru Zhong · Yiqiu Liu · Zhiqing Cui · Zezhi Shao · Fei Wang · Qingsong Wen · Yuxuan Liang
26 May 2026 at 12:00
arXiv:2601.21726v2 Announce Type: replace Abstract: Deep time series models are vulnerable to noisy data ubiquitous in real-world applications. Existing robustness strategies either prune data or rely on costly prior quantification, failing to balance effectiveness and efficiency. In this paper, we introduce DropoutTS, a model-agnostic plugin that shifts the paradigm from "what" to learn to "how much" to learn. DropoutTS employs a Sample-Adaptive Dropout mechanism: leveraging spectral sparsity to efficiently quantify instance-level noise via reconstruction residuals, it dynamically calibrates model learning capacity by mapping noise to adaptive dropout rates - selectively suppressing spurious fluctuations while preserving fine-grained fidelity. Extensive experiments across diverse noise regimes and open benchmarks show DropoutTS consistently boosts superior backbones' performance, delivering advanced robustness with negligible parameter overhead and no architectural modifications. Our code is available at https://github.com/CityMind-Lab/DropoutTS.

Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System

✇cs.AI, q-bio.NC updates on arXiv.org
By: Longfei Yun · Yihan Wu · Haoran Liu · Xiaoxuan Liu · Ziyun Xu · Yi Wang · Yang Xia · Pengfei Wang · Mingze Gao · Yunxiang Wang · Changfan Chen · Wenjie Fu · Hong Yan · Junfeng Pan
26 May 2026 at 12:00
arXiv:2602.18640v2 Announce Type: replace Abstract: Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product requirements. Progress in this domain is increasingly bottlenecked by the engineering context constraint: the arduous process of translating ambiguous product intent into reasonable, executable, verifiable hypotheses, rather than by modeling techniques alone. We present GEARS (Generative Engine for Agentic Ranking Systems), a framework that reframes ranking optimization as an autonomous discovery process within a programmable experimentation environment. Rather than treating optimization as static model selection, GEARS leverages Specialized Agent Skills to encapsulate ranking expert knowledge into reusable reasoning capabilities, enabling operators to steer systems via high-level intent vibe personalization. Furthermore, to ensure production reliability, the framework incorporates validation hooks to enforce statistical robustness and filter out brittle policies that overfit short-term signals. Experimental validation across diverse product surfaces demonstrates that GEARS consistently identifies superior, near-Pareto-efficient policies by synergizing algorithmic signals with deep ranking context while maintaining rigorous deployment stability.

SEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sihang Jiang · Lipeng Ma · Zhonghua Hong · Keyi Wang · Zhiyu Lu · Tengfei Wang · Shisong Chen · Jinghao Zhang · Tianjun Pan · Weijia Li · Jiaqing Liang · Yanghua Xiao
26 May 2026 at 12:00
arXiv:2604.08988v3 Announce Type: replace Abstract: Current LLM-based agents demonstrate strong performance in episodic task execution but remain constrained by static toolsets and episodic amnesia, failing to accumulate experience across task boundaries. This paper formalizes the Self-Evolving Agent (SEA) from the perspective of digital embodiment and continuous cross-task evolution, introduces the Evolutionary Flywheel as its minimal sufficient architecture, and presents SEA-Eval -- the first benchmark designed specifically for evaluating SEAs. Grounded in Flywheel theory, SEA-Eval establishes SR and T as primary metrics and, through sequential task stream design, is designed to quantify evolutionary gain, evolutionary stability, and implicit alignment convergence. Empirical evaluation reveals that, under comparable success rates, token consumption differs by up to 31.2 times between frameworks on individual tasks, with divergent evolutionary trajectories emerging under sequential analysis -- demonstrating that success rate alone creates a capability illusion and that the sequential convergence of $T$ is the key criterion for distinguishing genuine evolution from pseudo-evolution.

Data Difficulty and the Generalization--Extrapolation Tradeoff in LLM Fine-Tuning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siyuan Liu · Tinghong Chen · Xinghan Li · Yifei Wang · Jingzhao Zhang
26 May 2026 at 12:00
arXiv:2605.12906v2 Announce Type: replace-cross Abstract: Data selection during supervised fine-tuning (SFT) can critically change the behavior of large language models (LLMs). Although existing work has studied the effect of selecting data based on heuristics such as perplexity, difficulty, or length, the reported findings are often inconsistent or context-dependent. In this work, we systematically study the role of data difficulty in fine-tuning from both empirical and theoretical perspectives, and find that there is no universally optimal difficulty level; rather, its effectiveness depends on the dataset size. We show that for a fixed data budget, there exists an optimal data difficulty for SFT, and that this optimal difficulty shifts toward harder data as the data budget increases. To explain this phenomenon, we conduct controlled synthetic experiments that reveal a simple underlying mechanism: the interplay between the (in-distribution) generalization gap and the extrapolation gap. We further support this mechanism through a theoretical analysis using PAC-Bayesian generalization bounds. Overall, our results clarify how data size and difficulty jointly affect the trade-off between generalization and extrapolation in SFT, providing guidance for difficulty-based data selection under certain model and data conditions.

Cusp-singularity-enhanced Coriolis effect for sensitive chip-scale gyroscopes

✇Nature - Issue - nature.com science feeds
By: Sen Zhang · Dingbang Xiao · Fei Wang · Ran Huang · Lei Yu · Ning Zhou · Kaixuan He · Xuezhong Wu · Franco Nori · Hui Jing · Xin Zhou
20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10565-w

By using singularity physics to enable cubic-root scaling of frequency and phase modulations induced by the Coriolis effect to enhance the performance of chip-scale Coriolis vibratory gyroscopes, substantial improvements in signal-to-noise ratio and precision are demonstrated.

Transplantation of encapsulated mitochondria alleviates dysfunction in mitochondrial and Parkinson’s disease models

✇Cell
By: Shiwei Du · Qi Long · Yanshuang Zhou · Jiangqin Fu · Hao Wu · Liang Yang · Yaohang Xie · Yingzhe Ding · Maolei Zhang · Jingyi Guo · Mengfei Wang · Jiajun Lin · Mingli Hu · Jian Zhang · Deyang Yao · Wei Li · Feixiang Bao · Ge Xiang · Yi Wu · Yile Huang · Haozhao Liang · Rui Wang · Heying Li · Baodan Chen · Chong Li · Junwei Wang · Jiwei Zhang · Dajiang Qin · Jianwei Sun · Yun Zhu · Fei Sun · Wuming Wang · Gang Lu · Wai-Yee Chan · Hui Zhao · Chenli Liu · Xingguo Liu
18 March 2026 at 08:00
A mitochondrial transplantation approach rescues mitochondrial deficiency and prevents mitochondrial DNA depletion syndrome, Leigh syndrome, and Parkinson’s disease in cellular and mouse models.
Received — 6 April 2026 ⏭

HoloTrauma 3X Triadic AI Co reasoning for robot assisted emergency maxillofacial reconstruction

✇npj Digital Medicine
By: Qiao Zhou · Yao Liu · Chenfei Wang · Liang Wang · Zhenghan Chen · Yiyi Chen · Chi Yang · Dong Liu · Yuxin Zhang · Jisi Zheng · Xingmei Feng
4 April 2026 at 08:00

npj Digital Medicine, Published online: 04 April 2026; doi:10.1038/s41746-026-02573-x

HoloTrauma 3X Triadic AI Co reasoning for robot assisted emergency maxillofacial reconstruction
Received — 4 April 2026 ⏭

Lifting Unlabeled Internet-level Data for 3D Scene Understanding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yixin Chen · Yaowei Zhang · Huangyue Yu · Junchao He · Yan Wang · Jiangyong Huang · Hongyu Shen · Junfeng Ni · Shaofei Wang · Baoxiong Jia · Song-Chun Zhu · Siyuan Huang
3 April 2026 at 12:00
arXiv:2604.01907v1 Announce Type: cross Abstract: Annotated 3D scene data is scarce and expensive to acquire, while abundant unlabeled videos are readily available on the internet. In this paper, we demonstrate that carefully designed data engines can leverage web-curated, unlabeled videos to automatically generate training data, to facilitate end-to-end models in 3D scene understanding alongside human-annotated datasets. We identify and analyze bottlenecks in automated data generation, revealing critical factors that determine the efficiency and effectiveness of learning from unlabeled data. To validate our approach across different perception granularities, we evaluate on three tasks spanning low-level perception, i.e., 3D object detection and instance segmentation, to high-evel reasoning, i.e., 3D spatial Visual Question Answering (VQA) and Vision-Lanugage Navigation (VLN). Models trained on our generated data demonstrate strong zero-shot performance and show further improvement after finetuning. This demonstrates the viability of leveraging readily available web data as a path toward more capable scene understanding systems.
Received — 2 April 2026 ⏭

The 1000 Chinese Pangenome empowers medical and population genetics

✇Nature - Issue - nature.com science feeds
By: Yifei Wang · Zhongqu Duan · Dan Chen · Dandan Shi · Yi Ding · Zhibin Wang · Baoqing Li · Zhiyi Wang · Minmin Guo · Wen Yang · Junren Hou · Wenhao Chen · Yazhou Guo · Wenjie Wei · Yujie Cao · Xiwei Sun · Weiyang Bai · Mingdong Lu · Ting Qi · Xian Shen · Jian Yang
1 April 2026 at 08:00

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10315-y

Development of the pangenome-informed genome assembly (PIGA) workflow enabled the generation of 1,116 diploid genome assemblies (55 de novo and 1,061 pangenome-informed), representing an extensive resource of medically relevant genic variations.

Doxorubicin promotes the production of inflammatory cytokines in tumor-associated macrophages through activating lactate dehydrogenase A

✇Cell Death Discovery nature.com science feeds
By: Bin Liu · Wei Yang · Shuo Feng · Xingning Jiang · Jingfan Lu · Fei Wang · Yaowen Hu · Yanhao Liu · Haifeng Ma · Ting Sun
31 March 2026 at 08:00

Cell Death Discovery, Published online: 31 March 2026; doi:10.1038/s41420-026-03014-0

Doxorubicin promotes the production of inflammatory cytokines in tumor-associated macrophages through activating lactate dehydrogenase A

New perspectives in immunotherapy for hepatocellular carcinoma: Focusing on resistance mechanism, biomarker, and personalized treatment

✇Omics in Hepatocellular
By: Ning Wang · Lei Liu · Baiguo Xu · Fei Wang · Lixia Sun · Qing Ye
29 March 2026 at 18:00

Crit Rev Oncol Hematol. 2026 Mar 27;222:105305. doi: 10.1016/j.critrevonc.2026.105305. Online ahead of print.

ABSTRACT

The management of hepatocellular carcinoma (HCC) faces substantial and evolving challenges, driven by its aggressive biology, drug resistance, and the clinical urgency to detect recurrence. The treatment paradigm has undergone a profound transformation, evolving from surgical interventions and molecular targeted agents to the current era dominated by immunotherapy. Immune checkpoint inhibitors, particularly when used in combination with anti-angiogenic drugs or as part of dual-checkpoint blockade regimens, have established a new first-line standard of treatment for advanced HCC, delivering unprecedented survival improvements. Despite this progress, significant obstacles remain, including primary and acquired resistance, variable patient responses, and notably reduced efficacy in specific etiological subgroups. This comprehensive review synthesizes the emerging modalities such as bispecific antibodies, adoptive cell therapies, and innovative rational combinations that integrate systemic immunotherapy with locoregional treatments or novel targeted agents. Furthermore, we delve into the critical search for predictive biomarkers, encompassing liquid biopsy and multi-omics approaches, and dissect the complex cellular and molecular mechanisms underlying therapeutic resistance within the immunosuppressive tumor microenvironment. Finally, we outline future translational directions, emphasizing the expansion of immunotherapy, the development of tailored strategies for therapy-resistant disease, and the imperative move towards a personalized, biomarker-driven treatment framework. This review provides a cohesive overview of the field and charts a roadmap for future research to overcome the current challenges in HCC immunotherapy.

PMID:41905572 | DOI:10.1016/j.critrevonc.2026.105305

Received — 26 March 2026 ⏭

Scaling Attention via Feature Sparsity

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yan Xie · Tiansheng Wen · Tangda Huang · Bo Chen · Chenyu You · Stefanie Jegelka · Yifei Wang
25 March 2026 at 12:00
arXiv:2603.22300v1 Announce Type: cross Abstract: Scaling Transformers to ultra-long contexts is bottlenecked by the $O(n^2 d)$ cost of self-attention. Existing methods reduce this cost along the sequence axis through local windows, kernel approximations, or token-level sparsity, but these approaches consistently degrade accuracy. In this paper, we instead explore an orthogonal axis: feature sparsity. We propose Sparse Feature Attention (SFA), where queries and keys are represented as $k$-sparse codes that preserve high-dimensional expressivity while reducing the cost of attention from $\Theta(n^2 d)$ to $\Theta(n^2 k^2/d)$. To make this efficient at scale, we introduce FlashSFA, an IO-aware kernel that extends FlashAttention to operate directly on sparse overlaps without materializing dense score matrices. Across GPT-2 and Qwen3 pretraining, SFA matches dense baselines while improving speed by up to $2.5\times$ and reducing FLOPs and KV-cache by nearly 50\%. On synthetic and downstream benchmarks, SFA preserves retrieval accuracy and robustness at long contexts, outperforming short-embedding baselines that collapse feature diversity. These results establish feature-level sparsity as a complementary and underexplored axis for efficient attention, enabling Transformers to scale to orders-of-magnitude longer contexts with minimal quality loss. Code is available at https://github.com/YannX1e/Sparse-Feature-Attention.
Received — 14 March 2026 ⏭

Targeted therapies in lung cancer: personalizing treatment across the age spectrum

✇Omics In Lung
By: Yuting Xu · Fei Chen · Honggang Zhang · Xiaofei Wang
13 March 2026 at 18:00

Front Oncol. 2026 Feb 25;16:1743620. doi: 10.3389/fonc.2026.1743620. eCollection 2026.

ABSTRACT

Lung cancer remains the leading cause of cancer-related mortality, yet current precision oncology approaches remain overwhelmingly tumor-centric, guided by genomic alterations and immune biomarkers, while largely neglecting the profound impact of aging biology on treatment response. While emerging evidence suggests that aging biology can modify therapeutic benefit and toxicity, its clinical integration remains uneven and largely investigational. In this review, we explicitly distinguish the chronological aging from biological aging to clarify how host biology modifies therapeutic benefit and toxicity. We synthesize mechanistic, translational, and early clinical evidence, while explicitly noting areas where prospective validation is lacking, to reframe personalization of lung cancer therapy through an age-conscious lens. We summarize data indicating that immunosenescence is associated with T-cell exhaustion, myeloid dominance, and extracellular matrix stiffening, features that may contribute to immune-evasive tumor phenotypes and attenuated responses to immune checkpoint blockade in subsets of patients, while pediatric cases, though rare, illustrate how global precision initiatives like iTHER and ZERO enable cautious adaptation of adult therapies. Moving beyond chronological age, we discuss biological age biomarkers, including PhenoAgeAccel, epigenetic clocks, telomere length, and frailty indices, which outperform traditional metrics in predicting risk, resistance, and toxicity, and propose integrating these tools into trial design, screening, and care planning which show promise for risk stratification and toxicity prediction but are not yet validated for routine treatment selection. Looking forward, we outline investigational strategies at the intersection of geroscience and oncology, including immune engineering, senolytics, microenvironmental modulation, and AI-driven multi-omic modeling. Overall, this review argues that biological age represents a critical but still underdeveloped dimension of precision oncology, and highlights key evidence gaps that must be addressed before age-aware personalization can be implemented in routine lung cancer care.

PMID:41821888 | PMC:PMC12975599 | DOI:10.3389/fonc.2026.1743620

Regulatory mechanisms of ALKBH5/CIITA axis in the synergistic modulation of hepatocellular carcinoma radiotherapy and immunotherapy

✇Omics in Hepatocellular
By: Fei Wang · Hui Hou · Hong Yang · Ting Dou · Zhenxia Wang · Hongdan Fu · Hong Li · Wei Zhang · Haiping Zhao · Hao Yang
11 March 2026 at 18:00

Genes Immun. 2026 Mar 10. doi: 10.1038/s41435-026-00382-6. Online ahead of print.

ABSTRACT

The prognosis for hepatocellular carcinoma remains grim. Combining radiotherapy with immune checkpoint blockade (ICB) has shown potential to enhance therapeutic outcomes, yet there is a pressing need for further advancements. Our previous research demonstrated that this combined approach suppresses ALKBH5 gene expression and increases m6A modification levels in hepatocellular carcinoma tissues. High-throughput sequencing and detailed molecular analysis revealed that inhibiting ALKBH5 amplifies CIITA m6A modifications post-therapy. This modulation triggers MHC II molecule expression in tumors, facilitating the presentation of tumor-associated antigens to CD4 + T lymphocytes and the recruitment of CD8 + T cells for an anti-tumor immune response. Building on these findings, we engineered a CIITA vector with a specific site mutation to confirm that the regulation of CIITA by the combined radiotherapy and immunotherapy is mediated through m6A methylation. Consequently, we established a comprehensive network involving ALKBH5, CIITA, MHC II, and CD4+ and CD8 + T cells. To elucidate the role and underlying molecular mechanisms of this combined therapy in reshaping the tumor immune microenvironment for hepatocellular carcinoma, we employed multi-omics approaches across in vitro, animal model, and clinical multi-dimensional studies, offering novel insights for enhancing treatment efficacy.

PMID:41807814 | DOI:10.1038/s41435-026-00382-6

Received — 5 March 2026 ⏭

T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qinsi Wang · Hancheng Ye · Jinhee Kim · Jinghan Ke · Yifei Wang · Martin Kuo · Zishan Shao · Dongting Li · Yueqian Lin · Ting Jiang · Chiyue Wei · Qi Qian · Wei Wen · Helen Li · Yiran Chen
5 March 2026 at 13:00
arXiv:2603.03790v1 Announce Type: cross Abstract: Think about how human handles complex reading tasks: marking key points, inferring their relationships, and structuring information to guide understanding and responses. Likewise, can a large language model benefit from text structure to enhance text-processing performance? To explore it, in this work, we first introduce Structure of Thought (SoT), a prompting technique that explicitly guides models to construct intermediate text structures, consistently boosting performance across eight tasks and three model families. Building upon this insight, we present T2S-Bench, the first benchmark designed to evaluate and improve text-to-structure capabilities of models. T2S-Bench includes 1.8K samples across 6 scientific domains and 32 structural types, rigorously constructed to ensure accuracy, fairness, and quality. Evaluation on 45 mainstream models reveals substantial improvement potential: the average accuracy on the multi-hop reasoning task is only 52.1%, and even the most advanced model achieves 58.1% node accuracy in end-to-end extraction. Furthermore, on Qwen2.5-7B-Instruct, SoT alone yields an average +5.7% improvement across eight diverse text-processing tasks, and fine-tuning on T2S-Bench further increases this gain to +8.6%. These results highlight the value of explicit text structuring and the complementary contributions of SoT and T2S-Bench. Dataset and eval code have been released at https://t2s-bench.github.io/T2S-Bench-Page/.

IPD: Boosting Sequential Policy with Imaginary Planning Distillation in Offline Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yihao Qin · Yuanfei Wang · Hang Zhou · Peiran Liu · Hao Dong · Yiding Ji
5 March 2026 at 13:00
arXiv:2603.04289v1 Announce Type: cross Abstract: Decision transformer based sequential policies have emerged as a powerful paradigm in offline reinforcement learning (RL), yet their efficacy remains constrained by the quality of static datasets and inherent architectural limitations. Specifically, these models often struggle to effectively integrate suboptimal experiences and fail to explicitly plan for an optimal policy. To bridge this gap, we propose \textbf{Imaginary Planning Distillation (IPD)}, a novel framework that seamlessly incorporates offline planning into data generation, supervised training, and online inference. Our framework first learns a world model equipped with uncertainty measures and a quasi-optimal value function from the offline data. These components are utilized to identify suboptimal trajectories and augment them with reliable, imagined optimal rollouts generated via Model Predictive Control (MPC). A Transformer-based sequential policy is then trained on this enriched dataset, complemented by a value-guided objective that promotes the distillation of the optimal policy. By replacing the conventional, manually-tuned return-to-go with the learned quasi-optimal value function, IPD improves both decision-making stability and performance during inference. Empirical evaluations on the D4RL benchmark demonstrate that IPD significantly outperforms several state-of-the-art value-based and transformer-based offline RL methods across diverse tasks.

MIRAGE: Knowledge Graph-Guided Cross-Cohort MRI Synthesis for Alzheimer's Disease Prediction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guanchen Wu · Zhe Huang · Yuzhang Xie · Runze Yan · Akul Chopra · Deqiang Qiu · Xiao Hu · Fei Wang · Carl Yang
4 March 2026 at 13:00
arXiv:2603.02434v1 Announce Type: cross Abstract: Reliable Alzheimer's disease (AD) diagnosis increasingly relies on multimodal assessments combining structural Magnetic Resonance Imaging (MRI) and Electronic Health Records (EHR). However, deploying these models is bottlenecked by modality missingness, as MRI scans are expensive and frequently unavailable in many patient cohorts. Furthermore, synthesizing de novo 3D anatomical scans from sparse, high-dimensional tabular records is technically challenging and poses severe clinical risks. To address this, we introduce MIRAGE, a novel framework that reframes the missing-MRI problem as an anatomy-guided cross-modal latent distillation task. First, MIRAGE leverages a Biomedical Knowledge Graph (KG) and Graph Attention Networks to map heterogeneous EHR variables into a unified embedding space that can be propagated from cohorts with real MRIs to cohorts without them. To bridge the semantic gap and enforce physical spatial awareness, we employ a frozen pre-trained 3D U-Net decoder strictly as an auxiliary regularization engine. Supported by a novel cohort-aggregated skip feature compensation strategy, this decoder acts as a rigorous structural penalty, forcing 1D latent representations to encode biologically plausible, macro-level pathological semantics. By exclusively utilizing this distilled "diagnostic-surrogate" representation during inference, MIRAGE completely bypasses computationally expensive 3D voxel reconstruction. Experiments demonstrate that our framework successfully bridges the missing-modality gap, improving the AD classification rate by 13% compared to unimodal baselines in cohorts without real MRIs.
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