❌

Reading view

↗️

Transformer-Based Multitask Framework Integrating Habitat and Deep Learning for Predicting Early Disease Control and Survival in Immunotherapy-Treated Hepatocellular Carcinoma

✇(Multiomics OR Omics) AND (Pancreatic)
By: Xiaona Fu · Yusheng Guo · Bingxin Gong · Jie Lou · Ning Wang · Xuejun Chen · Hongtao Hu · Rundong Wang · Xiaofang Guo · Shanmei Li · Yangyang Xie · Shichao Long · Mengsi Li · Shubo Pan · Wen Shen · Zhaogang Zhang · Zifang Song · Pengchen Liang · Xiaoyun Liang · Peng Sun · Guilin Zhang · Shanshan Jiang · Jinxiang Zhang · Lian Yang

Adv Sci (Weinh). 2026 Sep 27:e78005. doi: 10.1002/advs.78005. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) patients show heterogeneous responses to immune checkpoint inhibitors (ICIs). This study developed ECOS-Net, a transformer-based multitask network integrating CT-derived habitat and 2.5-dimensional (2.5D) deep learning features for simultaneously predicting early disease control (DC) and overall survival (OS). Of 1,234 patients with HCC enrolled from eight institutions and public databases, 832 ICI-treated patients were used for model development. ECOS-Net fused features using multi-head attention and generated early DC probabilities and OS risk scores. ECOS-DC achieved AUCs of 0.836, 0.822, and 0.817 in training, internal validation, and external test sets, outperforming clinical models (all p values < 0.05). ECOS-OS yielded C-indices of 0.730, 0.722, and 0.720, respectively. Integrated models also showed favorable external performance (early DC AUC: 0.825; OS C-index: 0.741). Patients with higher ECOS-DC probabilities had a higher likelihood of early DC, whereas those with higher ECOS-OS risk had shorter OS, with directionally consistent associations across most subgroups. Exploratory biological analyses suggested that the higher ECOS-DC probability and lower ECOS-OS risk groups were associated with immune-active tumor microenvironment features. Therefore, ECOS-Net shows potential as a non-invasive imaging-based risk stratification framework for simultaneously predicting early DC and OS in ICI-treated HCC patients.

PMID:42801546 | PMC:PMC13616327 | DOI:10.1002/advs.78005

  •  
  • ↗️
↗️

GLARE: Generative Learning via Adversarial Reward Estimation For Social Dynamics Forecasting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tenghao Huang · Zhaoxuan Tan · Muhao Chen · Jonathan May · Mengting Wan · Longqi Yang · Pei Zhou · Sihao Chen
arXiv:2609.12165v1 Announce Type: new Abstract: Meeting continuation requires tracking the agenda, speaker roles, participant intentions, and disagreement across long multi-party discussions. We introduce the Meeting Dynamic Forecasting Benchmark (MDFB), constructed from 2,207 real-world meetings and 24,794 future-facing queries. Given a transcript prefix and an active question, a model generates a plausible multi-turn continuation in one call. We evaluate utility---progress toward the question---and human-likeness---plausible conversational flow and role consistency---without requiring exact reproduction of the observed future. We further present GLARE, an adaptation of adversarial imitation learning to conditional language generation. A discriminator ranks the observed continuation above samples from the current actor, and its score supplies a KL-regularized policy reward; retraining on current-policy negatives allows the reward landscape to evolve with the actor. GLARE attains average human-evaluated win rates of 0.66 on utility and 0.70 on human-likeness, outperforming SFT and SPIN while remaining below the observed human continuation. We also demonstrate MDFB as a social reasoning arena for comparing general-purpose models, including closed-source systems, through reference-assisted judgments. Together, these studies illustrate the benchmark's use for both task-specific learning and output-based evaluation of meeting behavior.
  •  
  • ↗️
↗️

OneLA: Scaling Linear-Attention Decoding to Large Beams in Generative Recommendation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiangrui Yang · Cheng Peng · Yunfeng Zhao · Liang Zeng · Ao Hu · Jiawei Yang · Shengzhe Wang · Jingshan Lv · Xiao Liang · Chen Yang · Jiaqiang Liu · Yiming Qiu
arXiv:2609.12399v1 Announce Type: new Abstract: Generative recommendation (GR) relies on large-beam decoding to generate hundreds of candidate items, creating a new scaling challenge for recurrent linear attention. Existing linear attention serving systems either materialize a full recurrent state for every beam or repeatedly replay shared history, incurring substantial memory and traffic overhead. To address this, we present OneLA, a linear-attention decoding framework that exploits the shared prompt and short divergent suffixes of GR workloads. Specifically, OneLA represents all beam states using a single shared prompt-derived state and compact, append-only records of their divergent transitions. Using this representation, OneLA computes only the state information required at each decoding step, without reconstructing a full recurrent state for every beam. Furthermore, OneLA uses a lightweight ancestry index to track the transition records that make up each beam's history, allowing beams to be updated without moving or copying existing records. A fused GPU kernel further reuses the shared state across beams. Our analysis shows that OneLA achieves 1.54-2.46x end-to-end decode speedups while substantially reducing recurrent-state memory use and data movement.
  •  
  • ↗️
↗️

CircRHBDD1(4,5) drives malignant progression of oral squamous cell carcinoma by enhancing DKK1 mRNA stability through facilitating m<sup>6</sup>A-dependent IGF2BP2 binding

✇Oncogene - Issue - nature.com science feeds
By: Daixi Ren · Jiarong Zhang · Mao Huang · Jie Wu · Mei Yang · Yixuan Liu · Pingjuan Xiang · Hongke Qu · Dan Wang · Junshang Ge · Qijia Yan · Lei Shi · Bo Xiang · Wei Xiong · Guiyuan Li · Chunmei Fan · Zhaoyang Zeng

Oncogene, Published online: 11 September 2026; doi:10.1038/s41388-026-03986-4

CircRHBDD1(4,5) drives malignant progression of oral squamous cell carcinoma by enhancing DKK1 mRNA stability through facilitating m6A-dependent IGF2BP2 binding
  •  
  • ↗️
↗️

Grounded Continuation: A Linear-Time Runtime Verifier for LLM Conversations

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qisong He · Jinwei Hu · Xinmiao Huang · Changshun Wu · Yi Dong · Xiaowei Huang
arXiv:2605.14175v2 Announce Type: replace Abstract: In a long conversation, an LLM can produce a plausible continuation that rests on premises the conversation has already abandoned. No runtime check ties its output to what the conversation has established, a gap that context-manipulation attacks on deployed agents exploit. We close this gap with a runtime verifier: an LLM Interpreter classifies each utterance into one of eight epistemic operations, and a symbolic engine applies them to a dependency map that records what every claim rests on and whether it still stands. Whether a continuation is grounded reduces to a walk over the map, linear in its size, with no LLM call. Retraction propagates through the same map with a conflict-free guarantee, flagging exactly the conclusions that lose support. On ReviseQA for belief revision and MemoryAgentBench's fact-consolidation split, two third-party benchmarks where earlier premises are superseded, the verifier leads a budget-matched retrieval baseline across five QA models and lifts MemoryAgentBench single-hop accuracy from 0.46--0.95 to 0.93--0.98. With the verifier, even the 7B model overtakes unaided GPT-4o. These runs feed the engine the benchmarks' own structured updates. When a GPT-4o Interpreter extracts every update from raw text instead, accuracy is statistically unchanged. Per-query cost is flat in conversation length, prompts staying near 0.8k tokens where full context reaches 114k and retraction queries under a microsecond at 2000 turns.
  •  
  • ↗️
↗️

Targeting KRAS reprograms a Treg-dominant immunosuppressive microenvironment and sensitizes KRAS-mutant gastric adenocarcinoma to CTLA-4 immunotherapy

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xiaohuan Tang · Peiyao Yang · Xiaoyong Guo · Shuhua Wei · Xiaojing Cheng · Mengyuan Li · Yigang Bao · Sachiyo Nomura · Yu Sun · Longtao Huangfu · Xiaofang Xing · Yongning Jia · Fei Shan · Xiangyu Gao · Chuanhui Han · Ziyu Li

Sci China Life Sci. 2026 Sep 3. doi: 10.1007/s11427-026-3438-4. Online ahead of print.

ABSTRACT

Oncogenic KRAS mutations define a distinct molecular subset of gastric adenocarcinoma (GA), yet their impact on the tumor immune microenvironment remains incompletely understood. In this study, we established a genetically faithful and immunocompetent KRASG12D-driven mouse model of GA, together with matched organoids and cell lines, to investigate how oncogenic KRAS shapes tumor-immune interactions. KRAS-mutant tumors consistently developed an immunosuppressive microenvironment characterized by enrichment of regulatory T cells (Tregs), accompanied by reduced cytotoxic lymphocyte infiltration and intrinsic resistance to PD-1 blockade. Although pharmacologic targeting of KRAS effectively suppressed tumor growth and increased immune cell infiltration, functional immune analyses revealed persistent Treg-mediated immunosuppression that limited effective antitumor immunity. Mechanistically, TGF-β signaling was required to maintain Treg dominance and suppress effector T cell function in KRAS-driven tumors. Importantly, disruption of this suppressive axis through combined KRAS inhibition and CTLA-4 blockade attenuated TGF-β activity, impaired Treg function, and enhanced antitumor immune responses in vivo. Collectively, these findings identify oncogenic KRAS as a key regulator of TGF-β-dependent immune suppression in GA and provide mechanistic insight into immune evasion within this molecular subtype.

PMID:42714795 | DOI:10.1007/s11427-026-3438-4

  •  
  • ↗️
↗️

CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bowen Wang · Dunjie Lu · Junli Wang · Tianyi Bai · Shixuan Liu · Zhipeng Zhang · Haiquan Wang · Hao Hu · Tianbao Xie · Shuai Bai · Dayiheng Liu · Que Shen · Junyang Lin · Tao Yu
arXiv:2605.25624v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its extension to computer-use agents (CUAs) has been bottlenecked by the scarcity of scalable training data with deterministic rewards. Constructing such data for CUAs requires consistent task instruction, executable environment, and verifiable reward. However, hand-curated benchmarks achieve high reward fidelity but cover few applications and LLM-as-judge-based datasets scale broadly but lack reliable verification. We present CUA-Gym, a scalable pipeline that co-generates task instructions, environment states, and reward functions. Concretely, a Generator agent constructs the initial and golden environment states, and a separate Discriminator agent writes the reward function from the task specification. An orchestrator agent drives the two through iterative rounds upon execution. Generated tuples then pass a final filter combining LLM majority voting and agent rollouts, ensuring quality beyond the per-task adversarial loop. To address the scarcity of training environments, we further synthesize CUA-Gym-Hub, a broad suite of high-fidelity mock web applications grounded in real-world software-use distributions, expanding the scale of CUA RLVR data by magnitude. Using this pipeline, we construct CUA-Gym, a dataset of 32,112 verified RLVR training tuples grounded in 110 environments. Trained with GSPO on CUA-Gym, our CUA-Gym-A3B and CUA-Gym-A17B achieve 62.1% and 72.6% on OSWorld-Verified, outperforming prior open-source CUAs at comparable scales, with performance scaling smoothly in both data volume and environment diversity. The same checkpoints also improve on the held-out WebArena benchmark, indicating transfer beyond the training environments. We will open-source the full synthesis pipeline, dataset, CUA-Gym-Hub environments, and models.
  •  
  • ↗️
↗️

Nano World Models: A Minimalist Implementation of Future Video Prediction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siqiao Huang · Partha Kaushik · Michael Chen · Hengkai Pan · Omar Chehab · Fernando Moreno-Pino · Max Simchowitz
arXiv:2605.23993v1 Announce Type: cross Abstract: World models have become a central paradigm for learning predictive simulators that support generation, planning, and decision-making. Yet, despite rapid progress in industry-scale interactive video generation, the broader research community still lacks compact, reproducible, and easily extensible implementations for studying the design choices underlying modern world models. We introduce Nano World Models, a minimalist codebase for future video prediction centered around diffusion forcing. Nano World Models provides a unified interface for generative objectives, model scales, action-conditioning mechanisms, latent observation spaces, datasets, evaluation protocols, and long-horizon rollout procedures. This design enables controlled studies of world-modeling components that are often entangled across separate implementations. Through experiments across simple control environments, game simulation, and real-robot data, we examine how prediction parameterization, architecture scale, action injection, sampling budget, and domain complexity affect video prediction quality and autoregressive rollout behavior. By releasing code, configurations, evaluation scripts, and pretrained checkpoints, Nano World Models aims to provide a compact yet extensible experimental substrate for open, reproducible, and scientific world-model research.
  •  
  • ↗️
↗️

Adversarial Error Correction for Visual Autoregressive Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ligong Bi · Tao Huang · Jianyuan Guo · Chang Xu
arXiv:2605.24843v1 Announce Type: cross Abstract: Visual Autoregressive (VAR) models have emerged as a powerful paradigm for image synthesis by performing hierarchical next-scale prediction. However, VAR models are inherently prone to cascading error propagation, where subtle coarse-scale mispredictions are amplified across the hierarchy, ultimately distorting the final synthesis. To mitigate this, we propose AID-VAR, a plug-and-play framework that enhances pre-trained VARs through Adversarially Injected Diagnosis. Instead of a standard passive generation, AID-VAR introduces a proactive error-correction mechanism inspired by the adversarial feedback in GANs. We deploy a discriminator to diagnose fidelity gaps at each scale transition, coupled with a lightweight guidance injector. This module operates as a non-invasive adapter that refines the feature manifold of a frozen VAR backbone, effectively steering the generation toward the distribution of real images without destabilizing the pre-trained latent space. Furthermore, to rigorously evaluate this cross-scale progression, we introduce the Inter-Scale Consistency Score (ISCS), a novel metric that quantifies the fidelity and structural alignment between consecutive resolution scales. Experimental results across various backbones demonstrate that AID-VAR delivers sharper textural details and fewer structural distortions with negligible overhead. For instance, AID-VAR-d20 achieves a 16% improvement in FID with only a 3% increase in parameters. These results establish AID-VAR as a highly efficient and scalable pathway for upgrading large-scale VAR generators, enhancing global coherence and local detail without altering training data, base architectures, or sampling schedules. Code is available at https://github.com/bijiw515/AID-VAR.
  •  
  • ↗️
↗️

Hide to Guide: Learning via Semantic Masking

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ruitao Liu · Qinghao Hu · Alex Hu · Yecheng Wu · Shang Yang · Luke J. Huang · Zhuoyang Zhang · Han Cai · Song Han
arXiv:2605.25198v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a powerful paradigm for improving language models on reasoning-intensive tasks, but its effectiveness is often limited by exploration. For example, models often fail on hard problems, leaving little useful reward signal. External expert traces offer a natural source of guidance, yet they may also expose reward-relevant content along the critical path to the verifier target, such as final answers, intermediate values, executable implementations, or answer-related entities. This content can create an unintended reward hacking channel, allowing the policy to obtain reward by copying the trace rather than learning the underlying reasoning or agentic behavior. Existing guided-RL methods reduce this risk by using partial trajectories, but they mainly control how much expert information is shown heuristically rather than which parts should be hidden. To this end, we propose Semantic Masked Expert Policy Optimization (SMEPO), a fine-grained semantic masking strategy for expert-guided RLVR. Instead of truncating traces coarsely or revealing them unchanged, SMEPO masks reward-relevant semantic spans along the critical path while preserving the expert's decomposition, plan, and procedural structure. This turns hard problems from reasoning from scratch into a fill-in-the-blank process: the policy can follow the expert's problem-solving route, but must still reconstruct the missing values, code, or entities by itself. SMEPO is simple to apply and requires no changes to the reward function or RL objective. Across diverse domains, including math, code, and agentic search, SMEPO improves accuracy by up to 3.2 points over GRPO and reduces training time by up to 4.2x. The code is available at https://github.com/mit-han-lab/SMEPO.
  •  
  • ↗️
↗️

Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziheng Cheng · Yixiao Huang · Hanlin Zhu · Haoran Geng · Somayeh Sojoudi · Jitendra Malik · Pieter Abbeel · Xin Guo
arXiv:2605.25210v1 Announce Type: cross Abstract: Diffusion models are increasingly used as powerful conditional generators, yet real deployments often involve multiple target distributions arising from different tasks, e.g., diverse prompt domains in text-to-image generation, or multiple environments in robotics with diffusion policies. This naturally leads to a multi-objective learning (MOL) problem. A key challenge is that achieving good Pareto trade-offs can require a generalist model class with substantially larger capacity than what suffices for solving any individual task, thereby increasing statistical cost since sample complexity typically scales with the model complexity. To reconcile this, we develop a principled MOL framework for diffusion models with limited data: a semi-supervised regime where paired (labeled) samples are scarce, but (unlabeled) condition data are abundant. We propose a two-stage training procedure that first fits lightweight specialist models from limited paired data, and then distills them into a generalist model by generating pseudo-samples. We establish generalization bounds showing that the required number of paired samples only depends on the complexity of the specialist model classes. We further extend the theory to diffusion policies for sequential decision making to account for distribution shift in on-policy rollouts. Extensive experiments on robotic control and image restoration tasks are conducted to verify our theoretical results.
  •  
  • ↗️
↗️

SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiahao Huo · Wenjie Qu · Yibo Yan · Kening Zheng · Jiaheng Zhang · Xuming Hu · Philip S. Yu · Mingxun Zhou
arXiv:2605.25796v1 Announce Type: cross Abstract: Semantic-level watermarking (SWM) improves robustness against text modifications by treating sentences as the basic unit. However, robustness to paragraph-level paraphrasing remains difficult because such attacks globally disrupt watermark signals by changing sentence order. In this work, we propose SAMark, a self-anchored watermarking framework that removes the dependency on sentence order by establishing a step-independent green region in semantic space. To improve detectability, we introduce a multi-channel hyperbolic scoring mechanism that amplifies watermark signals while suppressing noise from weakly aligned candidates. We further propose a diversity-aware filtering strategy that combines hard filtering with soft regularization, extending beyond simple n-gram repetition filters to address semantic redundancy. Experimental results show that SAMark achieves up to 90.2% TP@FP1% under typical paragraph-level paraphrasing attacks, outperforming the strongest prior baseline by more than 30% on average, while maintaining generation quality competitive with unwatermarked text and breaking the robustness-quality trade-off that limits prior methods.
  •  
  • ↗️
↗️

Why Your Deep Research Agent Fails? On Hallucination Evaluation in Full Research Trajectory

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuhao Zhan · Tianyu Fan · Linxuan Huang · Zirui Guo · Chao Huang
arXiv:2601.22984v2 Announce Type: replace Abstract: Diagnosing failure patterns in Deep Research Agents (DRAs) remains a critical challenge. Existing benchmarks predominantly rely on end-to-end evaluation, obscuring intermediate hallucinations that accumulate throughout the research trajectory. To bridge this gap, we propose a shift from outcome-based to processaware evaluation by auditing hallucinations in the full plan-search-summarize trajectory. We introduce the PING Taxonomy, which categorizes DRA hallucinations into four complementary types: Propagation, Intent, Noiseinduced, and Grounding. We further instantiate this taxonomy into a fine-grained evaluation framework that decomposes trajectories into atomic actions, claims, and sub-queries for rigorous verification. Leveraging this framework to isolate 100 distinctively hallucinationprone tasks including adversarial scenarios, we curate DeepHalluBench. Experiments on six representative DRAs show that, on our hallucination-prone stress-test set, all evaluated systems still exhibit non-negligible reliability gaps. Furthermore, our diagnostic analysis traces these failures to systemic deficits, especially hallucination propagation and cognitive biases, providing actionable insights for future architectural optimization. Code and data are available in https://github.com/yuhao-zhan/DeepHalluBench.
  •  
  • ↗️
↗️

OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search

✇cs.AI, q-bio.NC updates on arXiv.org
By: Erhan Zhang · Yiqun Chen · Zechun Niu · Wei Yang · Xiaochi Wei · Yan Gao · Yi Wu · Yao Hu · Jiaxin Mao
arXiv:2604.03675v3 Announce Type: replace Abstract: Agentic search enables language models to solve knowledge-intensive tasks by adaptively acquiring external evidence over multiple steps. Reinforcement learning with verifiable rewards (RLVR) has emerged as a widely adopted training paradigm for search agents, yet outcome-only rewards are sparse and provide limited credit assignment for intermediate search actions. Existing process-reward methods therefore seek to densify supervision through proxy signals, external evaluators, or likelihood-based information gain. However, proxy rewards can deviate from the final outcome objective, while fixed evaluators can become stale as the search policy evolves, leading to unreliable process supervision. To address these challenges, we propose OASES, an Outcome-Aligned Search-Evaluation Supervision framework for agentic search. OASES derives outcome-aligned process rewards by evaluating how well each intermediate search state supports answering the original question. It further co-trains the search policy and the state evaluator on policy, allowing the evaluator to adapt to evolving search behavior and provide more reliable process rewards. Experiments on five multi-hop QA benchmarks show that OASES consistently outperforms strong RL baselines, with further analyses confirming the benefits of outcome-aligned process rewards and search-evaluation co-training.
  •  
  • ↗️
↗️

EditCaption: Human-Refined SFT and HAE-DPO for Image Editing Instruction Synthesis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiangyuan Wang · Honghao Cai · Yunhao Bai · Chao Hui · Tianze Zhou · Haohua Chen · Hao Shi · Yuling Wu · Yao Hu · Xu Tang · Yibo Chen · Wei Zhu
arXiv:2604.08213v2 Announce Type: replace-cross Abstract: High-quality source-target image pairs with precise editing instructions are essential for instruction-guided image editing, yet constructing such training triplets at scale remains costly. Recent pipelines often rely on vision-language models to synthesize editing instructions automatically, but we find that strong VLMs still struggle to describe visual transformations between image pairs. In particular, they exhibit three recurring failure modes: orientation inconsistency, viewpoint ambiguity, and missing fine-grained attributes. In a human evaluation on 400 image pairs, several open-source VLM baselines produce critical-error rates above 47\%, making many synthesized instructions unsuitable for downstream training. To address this, we propose EditCaption, a two-stage post-training pipeline for image editing instruction synthesis. First, we construct a 100K supervised fine-tuning dataset through GLM-based auto-captioning, EditScore filtering, and human refinement. Second, we collect 10K human-annotated preference pairs, where each rejected instruction is labeled with its primary error type and severity. Based on this dataset, we propose Hardness-Adaptive Error-Aware DPO (HAE-DPO), a task-adapted DPO objective that introduces an adaptive margin based on human-labeled severity, failure-mode type, and reference-model hardness. Experiments across three benchmarks demonstrate that our 235B model with SFT+HAE-DPO achieves state-of-the-art performance among open-source and closed models, scoring 4.720 on Eval-400, 4.672 on HQ-Edit, and 4.651 on ByteMorph-Bench -- surpassing Gemini-3-Pro on all three. Human evaluation confirms critical error rates drop from 47.75\% to 17.50\%, with correct rates improving from 41.75\% to 70.25\%, surpassing Gemini-3-Pro (66.00\%).
  •  
  • ↗️
↗️

From Muscle Bursts to Motor Intent: Self-Supervised Token Modeling for Heterogeneous EMG

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhenghao Huang · Huilin Yao · Kaikai Wang
arXiv:2605.03462v3 Announce Type: replace-cross Abstract: Surface electromyography provides a practical way to infer human movement intention from wearable muscle recordings, but models trained under a single acquisition setting often lose reliability when the user, session, electrode layout, or gesture protocol changes. This paper proposes AEMG, a self-supervised learning approach designed to extract reusable neuromuscular representations from diverse EMG sources. Eight public gesture datasets are first transformed into a shared signal format to reduce discrepancies in channel configuration, sensor topology, and recording protocol. Instead of relying on fixed-length sliding windows, AEMG identifies contraction events from energy variations and represents them as compact neuromuscular tokens, while ordered token groups describe the coordinated activity of multiple muscles during motion. A spatially and temporally conditioned Transformer is then used to encode these token sequences, preserving information about electrode position, activation timing, and sequential structure. For pre-training, the model constructs a discrete library of contraction prototypes through vector-quantized reconstruction and further learns contextual dependencies by recovering masked neuromuscular tokens from surrounding observations. Experiments under leave-one-subject-out and low-label adaptation settings show that the learned representation improves robustness to unseen users and reduces the amount of calibration data required for gesture recognition. These findings suggest that event-level token modeling offers a scalable route toward adaptable and data-efficient EMG-based motor-intent understanding.
  •  
  • ↗️
↗️

Action with Visual Primitives

✇cs.AI, q-bio.NC updates on arXiv.org
By: Weilong Guo · Yuchen Wang · Renping Zhou · Yunfeng Zhang · Rui Fang · Yuyang Pang · Wenda Xu · Gao Huang
arXiv:2605.22183v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for generalist robotic manipulation. A common design in current architectures maps language instructions and visual observations to actions in a single forward pass. While conceptually simple, this formulation entangles instruction comprehension, spatial scene understanding, and motor control within a single learning objective. As a result, the action expert must implicitly relearn cognitive and perceptual capabilities already present in the pretrained VLM, which can limit both learning efficiency and generalization. We introduce AVP (Action with Visual Primitives), an end-to-end architecture that implements this visual-primitive-centric interface: the VLM infers the next-stage target and emits visual-primitive tokens that condition a flow-matching action expert, with supervision derived from end-effector kinematics. Real-robot experiments on general pick-and-place tasks show that AVP improves the success rate by 27.61% over pi_0.5 and outperforms other recent methods, with consistent gains in data efficiency, spatial-compositional generalization, and object-level transfer.
  •  
  • ↗️
↗️

GPX8<sup>+</sup> cancer-associated fibroblast-derived lactate contributes to lenvatinib resistance by facilitating BRPF1 expression through histone H3 lysine 18 lactylation in hepatocellular carcinoma

✇Oncogene - Issue - nature.com science feeds
By: Qinsi Wan · Jie Li · Mengzhou Guo · Jiahao Hu · Liangliang Bai · Meixia Zhang · Bimin Li · Yuan Fang · Xiaojing Du · Xingxing Zhang · Li Song · Sinuo Chen · Zhuoran Qi · Jinglin Xia · Bei Lv · Mingyan He

Oncogene, Published online: 22 May 2026; doi:10.1038/s41388-026-03711-1

GPX8+ cancer-associated fibroblast-derived lactate contributes to lenvatinib resistance by facilitating BRPF1 expression through histone H3 lysine 18 lactylation in hepatocellular carcinoma
  •  
  • ↗️
↗️

EBV strain interacts with host HLA to drive nasopharyngeal carcinoma risk

✇Nature - Issue - nature.com science feeds
By: Yanhong Chen · Jingtong Liang · Wanlin Zhang · Xinyu Zhang · Xinyi Zhang · Ching-Yuan Wang · Fei Yao · Shanshan Zhang · Xiang Zhou · Weimin Ye · Ruimei Feng · Yonglin Cai · Zhe Zhang · Mingfang Ji · Qian Cui · Xihong Lin · Jiesen Li · Jialei Xu · Qiuting Zhang · Qinyao Huang · Yingying Cheng · Yanran Luo · Xiaoping Ye · Qisheng Feng · Minzhong Tang · Mu-Sheng Zeng · Yi-Xin Zeng · Zhonghua Liu · Weiwei Zhai · Jianjun Liu · Miao Xu

Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10416-8

A genome-to-genome association study identifies host and viral risk factors that interact to drive nasopharyngeal carcinoma endemicity in southern China.
  •  
  • ↗️
❌