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Received — 2 April 2026 ⏭

Tracking vs. Deciding: The Dual-Capability Bottleneck in Searchless Chess Transformers

✇cs.AI, q-bio.NC updates on arXiv.org
By: Quanhao Li · Wei Jiang
1 April 2026 at 12:00
arXiv:2603.29761v1 Announce Type: new Abstract: A human-like chess engine should mimic the style, errors, and consistency of a strong human player rather than maximize playing strength. We show that training from move sequences alone forces a model to learn two capabilities: state tracking, which reconstructs the board from move history, and decision quality, which selects good moves from that reconstructed state. These impose contradictory data requirements: low-rated games provide the diversity needed for tracking, while high-rated games provide the quality signal for decision learning. Removing low-rated data degrades performance. We formalize this tension as a dual-capability bottleneck, P

IMPASTO: Integrating Model-Based Planning with Learned Dynamics Models for Robotic Oil Painting Reproduction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yingke Wang · Hao Li · Yifeng Zhu · Hong-Xing Yu · Ken Goldberg · Li Fei-Fei · Jiajun Wu · Yunzhu Li · Ruohan Zhang
1 April 2026 at 12:00
arXiv:2603.29315v1 Announce Type: cross Abstract: Robotic reproduction of oil paintings using soft brushes and pigments requires force-sensitive control of deformable tools, prediction of brushstroke effects, and multi-step stroke planning, often without human step-by-step demonstrations or faithful simulators. Given only a sequence of target oil painting images, can a robot infer and execute the stroke trajectories, forces, and colors needed to reproduce it? We present IMPASTO, a robotic oil-painting system that integrates learned pixel dynamics models with model-based planning. The dynamics models predict canvas updates from image observations and parameterized stroke actions; a receding-horizon model predictive control optimizer then plans trajectories and forces, while a force-sensitive controller executes strokes on a 7-DoF robot arm. IMPASTO integrates low-level force control, learned dynamics models, and high-level closed-loop planning, learns solely from robot self-play, and approximates human artists' single-stroke datasets and multi-stroke artworks, outperforming baselines in reproduction accuracy. Project website: https://impasto-robopainting.github.io/

MacTok: Robust Continuous Tokenization for Image Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hengyu Zeng · Xin Gao · Guanghao Li · Yuxiang Yan · Jiaoyang Ruan · Junpeng Ma · Haoyu Albert Wang · Jian Pu
1 April 2026 at 12:00
arXiv:2603.29634v1 Announce Type: cross Abstract: Continuous image tokenizers enable efficient visual generation, and those based on variational frameworks can learn smooth, structured latent representations through KL regularization. Yet this often leads to posterior collapse when using fewer tokens, where the encoder fails to encode informative features into the compressed latent space. To address this, we introduce \textbf{MacTok}, a \textbf{M}asked \textbf{A}ugmenting 1D \textbf{C}ontinuous \textbf{Tok}enizer that leverages image masking and representation alignment to prevent collapse while learning compact and robust representations. MacTok applies both random masking to regularize latent learning and DINO-guided semantic masking to emphasize informative regions in images, forcing the model to encode robust semantics from incomplete visual evidence. Combined with global and local representation alignment, MacTok preserves rich discriminative information in a highly compressed 1D latent space, requiring only 64 or 128 tokens. On ImageNet, MacTok achieves a competitive gFID of 1.44 at 256$\times$256 and a state-of-the-art 1.52 at 512$\times$512 with SiT-XL, while reducing token usage by up to 64$\times$. These results confirm that masking and semantic guidance together prevent posterior collapse and achieve efficient, high-fidelity tokenization.

Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jingwei Ni · Yihao Liu · Xinpeng Liu · Yutao Sun · Mengyu Zhou · Pengyu Cheng · Dexin Wang · Erchao Zhao · Xiaoxi Jiang · Guanjun Jiang
1 April 2026 at 12:00
arXiv:2603.25158v3 Announce Type: replace Abstract: Equipping Large Language Model (LLM) agents with domain-specific skills is critical for tackling complex tasks. Yet, manual authoring creates a severe scalability bottleneck. Conversely, automated skill generation often yields fragile or fragmented results because it either relies on shallow parametric knowledge or sequentially overfits to non-generalizable trajectory-local lessons. To overcome this, we introduce Trace2Skill, a framework that mirrors how human experts author skills: by holistically analyzing broad execution experience before distilling it into a single, comprehensive guide. Instead of reacting sequentially to individual trajectories, Trace2Skill dispatches a parallel fleet of sub-agents to analyze a diverse pool of executions. It extracts trajectory-specific lessons and hierarchically consolidates them into a unified, conflict-free skill directory via inductive reasoning. Trace2Skill supports both deepening existing human-written skills and creating new ones from scratch. Experiments in challenging domains, such as spreadsheet, VisionQA and math reasoning, show that Trace2Skill significantly improves upon strong baselines, including Anthropic's official xlsx skills. Crucially, this trajectory-grounded evolution does not merely memorize task instances or model-specific quirks: evolved skills transfer across LLM scales and generalize to OOD settings. For example, skills evolved by Qwen3.5-35B on its own trajectories improved a Qwen3.5-122B agent by up to 57.65 absolute percentage points on WikiTableQuestions. Ultimately, our results demonstrate that complex agent experience can be packaged into highly transferable, declarative skills -- requiring no parameter updates, no external retrieval modules, and utilizing open-source models as small as 35B parameters.

Incorporating LLM Embeddings for Variation Across the Human Genome

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hongqian Niu · Jordan Bryan · Jacob Williams · Hufeng Zhou · Haoyu Zhang · Xihao Li · Didong Li
1 April 2026 at 12:00
arXiv:2509.20702v2 Announce Type: replace-cross Abstract: Recent advances in large language model (LLM) embeddings have enabled powerful representations for biological data, but most applications to date focus on gene-level information. We present one of the first systematic frameworks to generate genetic variant-level embeddings across the entire human genome. Using curated annotations from FAVOR, ClinVar, and the GWAS Catalog, we construct functional text descriptions for 8.9 billion possible variants and generated embeddings at three scales: 1.5 million HapMap3/MEGA variants, 90 million imputed UK Biobank (UKB) variants, and 9 billion all possible variants. Embeddings were produced using general purpose models including both OpenAI's text-embedding-3-large and the open-source Qwen3-Embedding-0.6B models. Baseline quality control experiments demonstrate high predictive accuracy for variant-level properties, validating the embeddings as structured representations of genomic variation. We further apply them to real-world embedding-augmented genetic risk predictions that demonstrate the performance of using LLM embeddings in polygenic risk score (PRS) style predictions over the UK Biobank cohort data. These resources, publicly available on Hugging Face, provide a foundation for advancing large-scale genomic discovery and precision medicine.

SecureVibeBench: Evaluating Secure Coding Capabilities of Code Agents with Realistic Vulnerability Scenarios

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junkai Chen · Huihui Huang · Yunbo Lyu · Junwen An · Jieke Shi · Chengran Yang · Ting Zhang · Haoye Tian · Yikun Li · Zhenhao Li · Xin Zhou · Xing Hu · David Lo
1 April 2026 at 12:00
arXiv:2509.22097v2 Announce Type: replace-cross Abstract: Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benchmarks have provided valuable insights, but they fail to capture scenarios in which vulnerabilities are actually introduced by human developers, making fair comparisons between humans and agents infeasible. We therefore introduce SecureVibeBench, a benchmark of 105 C/C++ secure coding tasks sourced from 41 projects in OSS-Fuzz for code agents. SecureVibeBench has the following features: (i) realistic task settings that require multi-file edits in large repositories, (ii)~aligned contexts based on real-world open-source vulnerabilities with precisely identified vulnerability introduction points, and (iii) comprehensive evaluation that combines functionality testing and security checking with both static and dynamic oracles. We evaluate 5 popular code agents like OpenHands, supported by 5 LLMs (e.g., Claude sonnet 4.5) on SecureVibeBench. Results show that current agents struggle to produce both correct and secure code, as even the best-performing one, produces merely 23.8\% correct and secure solutions on SecureVibeBench.

Heracles: Bridging Precise Tracking and Generative Synthesis for General Humanoid Control

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zelin Tao · Zeran Su · Peiran Liu · Jingkai Sun · Wenqiang Que · Jiahao Ma · Jialin Yu · Jiahang Cao · Pihai Sun · Hao Liang · Gang Han · Wen Zhao · Zhiyuan Xu · Jian Tang · Qiang Zhang · Yijie Guo
1 April 2026 at 12:00
arXiv:2603.27756v2 Announce Type: replace-cross Abstract: Achieving general-purpose humanoid control requires a delicate balance between the precise execution of commanded motions and the flexible, anthropomorphic adaptability needed to recover from unpredictable environmental perturbations. Current general controllers predominantly formulate motion control as a rigid reference-tracking problem. While effective in nominal conditions, these trackers often exhibit brittle, non-anthropomorphic failure modes under severe disturbances, lacking the generative adaptability inherent to human motor control. To overcome this limitation, we propose Heracles, a novel state-conditioned diffusion middleware that bridges precise motion tracking and generative synthesis. Rather than relying on rigid tracking paradigms or complex explicit mode-switching, Heracles operates as an intermediary layer between high-level reference motions and low-level physics trackers. By conditioning on the robot's real-time state, the diffusion model implicitly adapts its behavior: it approximates an identity map when the state closely aligns with the reference, preserving zero-shot tracking fidelity. Conversely, when encountering significant state deviations, it seamlessly transitions into a generative synthesizer to produce natural, anthropomorphic recovery trajectories. Our framework demonstrates that integrating generative priors into the control loop not only significantly enhances robustness against extreme perturbations but also elevates humanoid control from a rigid tracking paradigm to an open-ended, generative general-purpose architecture.

Targeting FTO shows therapeutic potential in esophageal squamous cell carcinoma by modulating microRNA biogenesis

✇Oncogene - Issue - nature.com science feeds
By: Wei Zhou · Chunyan Wang · Changhao Li · Pengxiang Chen · Yuchen Liu · Hongyuan Mao · Pengfei Zhu · Yuxiao Gong · Yufeng Cheng · Lin Zhang
1 April 2026 at 08:00

Oncogene, Published online: 01 April 2026; doi:10.1038/s41388-026-03754-4

Targeting FTO shows therapeutic potential in esophageal squamous cell carcinoma by modulating microRNA biogenesis

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

Multi-omics analysis reveals AR as a potential prognostic factor and immune-related therapeutic target in gastric cancer

✇Omics in Gastric
By: Tianru Ben · Zonghao Liu · Shitong Cheng · Yanlu Bai · Linlin Gao · Jingya Zhang · Ruixuan Wang · Nan Xia
27 March 2026 at 18:00

Biochem Biophys Rep. 2026 Mar 16;46:102537. doi: 10.1016/j.bbrep.2026.102537. eCollection 2026 Jun.

ABSTRACT

BACKGROUND: Although studies have shown that the androgen receptor (AR) is associated with tumor progression and malignant regulation, its role in the tumor immune microenvironment and predictive value for prognosis and immunotherapy response in various cancer types have not been systematically analyzed.

METHODS: In this paper, multi-omics techniques was used to analyze AR comprehensively.

RESULTS: A comprehensive pan-cancer analysis revealed that the AR was expressed in a variety of tumors, especially as a risk factor for poor prognosis in gastric cancer. In addition, gene set enrichment analysis showed that the AR promotes cell proliferation and tumor cell invasion and regulates anti-tumor response. Immune score, immune cell infiltration, and anticancer immune cycle analysis showed that high AR levels were correlated with low infiltration of CD4+ T cells and NKT cells, high infiltration of Th2 cells and MDSCs, negatively correlated with antigen-presenting molecules, and positively correlated with various immune-negative regulatory molecules. Single-cell sequencing highlighted the heterogeneous expression of ARs in different cell types, particularly in epithelial cells, where high AR levels were associated with the enhanced activity of tumor-promoting pathways.

CONCLUSIONS: In conclusion, this study highlights the potential of the AR as a novel biomarker for gastric cancer prognosis and immunotherapy efficacy, expanding its applicability in the development of new antitumor drugs.

PMID:41890218 | PMC:PMC13014673 | DOI:10.1016/j.bbrep.2026.102537

SVNeoPP: A Workflow for Structural-Variant-Derived Neoantigen Prediction and Prioritization Using Multi-Omics Data

✇Omics in Hepatocellular
By: Wanyang An · Xiaoxiu Tan · Zhenhao Liu · Li Zou · Manman Lu · Lu Xie
27 March 2026 at 18:00

Biology (Basel). 2026 Mar 19;15(6):492. doi: 10.3390/biology15060492.

ABSTRACT

BACKGROUND: Tumor neoantigens are key targets for personalized vaccines and T-cell therapies, yet most pipelines focus on neoantigens derived from SNV/small indel and often yield a limited number of high-quality candidates. SVs are prevalent in tumors and can generate novel chimeric sequences and neopeptides, making them a promising additional source of neoantigens. However, SV-derived neoantigen prediction remains challenging due to breakpoint uncertainty, isoform-dependent coding inference, and limited integration of multi-dimensional evidence and reproducibility.

METHODS: We developed SVNeoPP (Structural Variant Neoantigen Prediction and Prioritization), an end-to-end workflow for SV-derived neoantigen analysis. SVNeoPP takes WGS and RNA-seq as inputs, performs SV calling and annotation, and reconstructs altered transcripts and coding sequences in a traceable, isoform-aware manner to generate candidate peptides. Candidates are prescreened by integrating antigen-processing features with HLA binding prediction, and then hierarchically filtered and prioritized based on transcript expression, LC-MS/MS proteomics evidence, immunogenicity predictions, and sequence similarity to experimentally validated neoantigen databases. SVNeoPP is implemented in Snakemake to enable modular extension, checkpoint-based restarts, and end-to-end reproducibility.

RESULTS: Using a hepatocellular carcinoma (HCC) multi-omics dataset as a proof of concept, we demonstrated the performance of SVNeoPP and obtained a high-priority shortlist of candidate peptides. Compared with other methods, SVNeoPP substantially expanded the candidate search space for SV-derived neoantigens and showed more favorable distributions of antigen-processing and HLA binding features.

CONCLUSIONS: SVNeoPP provides a reusable, traceable, and interpretable multi-dimensional evidence-driven framework for SV-derived neoantigens. As a complementary module to SNV/small-indel pipelines, it broadens the neoantigen candidate repertoire and generates ranked candidates with interpretable evidence to facilitate downstream prioritization and decision-making.

PMID:41892252 | PMC:PMC13024079 | DOI:10.3390/biology15060492

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