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Received — 10 September 2026 ⏭

JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zixiang Chen · Yuheng Lu · Zihao Cheng · Zeming Liu · Jizeng Bai · Ziye Huang · Zhiyin Lin · Zihan Li · Yuhang Guo · Yunhong Wang · Haifeng Wang
10 September 2026 at 12:00
arXiv:2609.10451v1 Announce Type: new Abstract: Real-world GUI usage frequently involves workflows that span multiple devices and platforms, requiring the transfer of intermediate results, maintenance of shared state, and coordination across heterogeneous environments. However, existing GUI benchmarks overwhelmingly evaluate agents on single-device, statically defined tasks, thus leaving such cross-device capabilities largely unexamined, resulting in an overly optimistic assessment of agents' readiness for real-world usage. We introduce JarvisGUI, a dynamic benchmark that evaluates GUI agents on cross-device workflows requiring coordinated interaction across heterogeneous platforms, including Android, Windows, and Ubuntu. Specifically, JarvisGUI formulates GUI tasks as input-output transformations under a lightweight type system, which allows us to automatically compose multi-step, cross-device workflows and dynamically evaluate agent performance within a unified framework. By evaluating agents in virtual environments spanning multiple operating systems, JarvisGUI reveals that state-of-the-art open-source GUI agents struggle with the state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management required for real-world workflows, exposing a critical capability gap invisible to existing benchmarks.

Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lin Shi (Audrey) · Haowei Lin (Audrey) · Zixuan Zhu (Audrey) · Xiaoyue Zhou (Audrey) · Xiang Li (Audrey) · Xiangning Lin (Audrey) · Yaxuan Deng (Audrey) · Han Xu (Audrey) · Yuangang Li (Audrey) · Shanda Li (Audrey) · Zizhao Chen (Audrey) · Hanwen Xing (Audrey) · Harsh Raj (Audrey) · Bo Chen (Audrey) · Quan Shi (Audrey) · Steven Dillmann (Audrey) · Yipeng Gao (Audrey) · Puneesh Khanna (Audrey) · Ruofan Lu (Audrey) · Chao Beyond Zhou (Audrey) · Michael Yang (Audrey) · Robert Zhang (Audrey) · Siyuan Chai (Audrey) · Jiayu Chang (Audrey) · Yizhao Chen (Audrey) · Xiaokun Chen (Audrey) · Yiwei Dai (Audrey) · Wenting Yang (Audrey) · Hange Liu (Audrey) · Minghao Liu (Audrey) · Zihan Wang (Audrey) · Adnan El Assadi (Audrey) · Benedikt Stroebl (Audrey) · E. Kelly Buchanan (Audrey) · Han Meng (Audrey) · Junwei He (Audrey) · Longxuan Yu (Audrey) · Radin Shayanfar (Audrey) · Yukyung Lee (Audrey) · Zhikang Dong (Audrey) · Allen G Hart (Audrey) · Anjiang Wei (Audrey) · Anurag Kashyap (Audrey) · Arpandeep Khatua (Audrey) · Audrey Jixin Zheng (Audrey) · Chengrui Ma (Aud
10 September 2026 at 12:00
arXiv:2609.04298v2 Announce Type: replace Abstract: Evaluating agents on the growing number of agentic benchmarks is challenging because they often require complex environments and agent integrations. We introduce Harbor Adapters, a unified evaluation infrastructure for agentic benchmarks. Our work makes three contributions. First, we develop benchmark adapters that port more than 80 benchmarks to evaluate arbitrary agents, and validate them through rigorous code review and parity experiments. Second, we conduct a large-scale evaluation of 8 models spanning capability tiers across 54 benchmarks; every model is run with Terminus-2 and with one of 3 native harnesses. This enables a broader analysis of agent capabilities and failure modes than was previously possible. Third, we introduce Harbor-Index, a curated set of 82 difficult, diverse, and high-quality tasks spanning 29 benchmarks, refined from the adapted suite through difficulty filtering, AI and human audit, and an audit-and-fix loop. Harbor-Index preserves the challenge and breadth of large-scale agentic evaluations while being affordable to run; no evaluated model-harness configuration exceeds 30% pass rate, and the strongest (GPT-5.5 with Codex) reaches 28.0%. We release the adapters, evaluation results, in-depth analysis, and Harbor-Index as open-source artifacts to support more reliable and comprehensive evaluation of language-model agents.

S1P-TREM2 axis protects immunosuppressive neutrophils from ferroptosis to promote tumour progression in hepatocellular carcinoma

✇Omics in Hepatocellular
By: Jun Ma · Tong Wu · Diyu Chen · Can Zhu · Shanhe Huang · Keyi Du · Chuanhui Peng · Junru Chen · Yunhao Chen · Haiyang Xie · Feng Zhang · Jianzhong Chen · Shusen Zheng · Jian Wu
7 September 2026 at 18:00

Gut. 2026 Sep 7:gutjnl-2025-337414. doi: 10.1136/gutjnl-2025-337414. Online ahead of print.

ABSTRACT

BACKGROUND: Neutrophils are increasingly recognised as immunosuppressive drivers of hepatocellular carcinoma (HCC), yet their persistence in the oxidative, lipid-rich tumour microenvironment remains poorly understood.

OBJECTIVE: To elucidate the metabolic and molecular programmes that enable tumour-associated neutrophils (TANs) to resist ferroptosis and sustain immunosuppression in HCC.

DESIGN: We employed human HCC samples, multiple murine HCC models, transcriptomic and lipidomic profiling, genetic loss-of-function systems and therapeutic interventions. Ferroptosis sensitivity, lipid metabolic rewiring and immunological consequences of TANs were systematically evaluated across models and validated in patient datasets and biospecimens.

RESULTS: TANs in human HCC and mouse models exhibit pronounced lipid accumulation and oxidative stress compared with peripheral neutrophils. Multi-omic profiling revealed that TANs are enriched for lipid-binding gene programmes and undergo rewiring towards sphingolipid and unsaturated fatty acid metabolism. We identified triggering receptor expressed on myeloid cells 2 (TREM2) as a key lipid-sensing receptor selectively expressed in TANs. Functional deletion of TREM2 reprogrammed the tumour immune microenvironment, restoring CD8+ T cell activity and suppressing HCC progression. Mechanistically, tumour-derived sphingosine-1-phosphate (S1P) activates TREM2, triggering nuclear factor erythroid 2-related factor 2 (NRF2)-mediated transcription of glutathione peroxidase 4 (GPX4) and solute carrier family 7 member 11 (SLC7A11), thereby promoting ferroptosis resistance. TREM2 expression is transcriptionally induced by granulocyte-macrophage colony-stimulating factor-signal transducer and activator of transcription 3 (GM-CSF-STAT3) signalling. Genetic deletion of TREM2, clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated knockout of sphingosine kinase 1/2 (SPHK1/2) in tumour cells, or pharmacological inhibition of S1P synthesis disrupts this protective lipid-immune circuit, sensitises TANs to ferroptosis and restricts tumour growth. Therapeutically, a peptide-based TREM2 inhibitor reprogrammes TANs, restores CD8+ T cell function and enhances anti-programmed cell death protein 1 (PD-1) immunotherapy efficacy. Clinically, TREM2+ polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) are enriched in HCC tumours, correlate with SPHK1/2 expression and T cell dysfunction and associate with poor patient prognosis.

CONCLUSION: Our study uncovers the S1P-TREM2-NRF2 axis as a critical metabolic-immune circuit that preserves neutrophil survival and immunosuppressive function in HCC. Targeting this lipid-dependent ferroptosis resistance pathway offers a promising therapeutic strategy to overcome immunotherapy resistance in liver cancer.

PMID:42705697 | DOI:10.1136/gutjnl-2025-337414

S1P-TREM2 axis protects immunosuppressive neutrophils from ferroptosis to promote tumour progression in hepatocellular carcinoma

✇(Multiomics OR Omics) AND (Pancreatic)
By: Jun Ma · Tong Wu · Diyu Chen · Can Zhu · Shanhe Huang · Keyi Du · Chuanhui Peng · Junru Chen · Yunhao Chen · Haiyang Xie · Feng Zhang · Jianzhong Chen · Shusen Zheng · Jian Wu
7 September 2026 at 18:00

Gut. 2026 Sep 7:gutjnl-2025-337414. doi: 10.1136/gutjnl-2025-337414. Online ahead of print.

ABSTRACT

BACKGROUND: Neutrophils are increasingly recognised as immunosuppressive drivers of hepatocellular carcinoma (HCC), yet their persistence in the oxidative, lipid-rich tumour microenvironment remains poorly understood.

OBJECTIVE: To elucidate the metabolic and molecular programmes that enable tumour-associated neutrophils (TANs) to resist ferroptosis and sustain immunosuppression in HCC.

DESIGN: We employed human HCC samples, multiple murine HCC models, transcriptomic and lipidomic profiling, genetic loss-of-function systems and therapeutic interventions. Ferroptosis sensitivity, lipid metabolic rewiring and immunological consequences of TANs were systematically evaluated across models and validated in patient datasets and biospecimens.

RESULTS: TANs in human HCC and mouse models exhibit pronounced lipid accumulation and oxidative stress compared with peripheral neutrophils. Multi-omic profiling revealed that TANs are enriched for lipid-binding gene programmes and undergo rewiring towards sphingolipid and unsaturated fatty acid metabolism. We identified triggering receptor expressed on myeloid cells 2 (TREM2) as a key lipid-sensing receptor selectively expressed in TANs. Functional deletion of TREM2 reprogrammed the tumour immune microenvironment, restoring CD8+ T cell activity and suppressing HCC progression. Mechanistically, tumour-derived sphingosine-1-phosphate (S1P) activates TREM2, triggering nuclear factor erythroid 2-related factor 2 (NRF2)-mediated transcription of glutathione peroxidase 4 (GPX4) and solute carrier family 7 member 11 (SLC7A11), thereby promoting ferroptosis resistance. TREM2 expression is transcriptionally induced by granulocyte-macrophage colony-stimulating factor-signal transducer and activator of transcription 3 (GM-CSF-STAT3) signalling. Genetic deletion of TREM2, clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated knockout of sphingosine kinase 1/2 (SPHK1/2) in tumour cells, or pharmacological inhibition of S1P synthesis disrupts this protective lipid-immune circuit, sensitises TANs to ferroptosis and restricts tumour growth. Therapeutically, a peptide-based TREM2 inhibitor reprogrammes TANs, restores CD8+ T cell function and enhances anti-programmed cell death protein 1 (PD-1) immunotherapy efficacy. Clinically, TREM2+ polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) are enriched in HCC tumours, correlate with SPHK1/2 expression and T cell dysfunction and associate with poor patient prognosis.

CONCLUSION: Our study uncovers the S1P-TREM2-NRF2 axis as a critical metabolic-immune circuit that preserves neutrophil survival and immunosuppressive function in HCC. Targeting this lipid-dependent ferroptosis resistance pathway offers a promising therapeutic strategy to overcome immunotherapy resistance in liver cancer.

PMID:42705697 | DOI:10.1136/gutjnl-2025-337414

Received — 27 May 2026 ⏭

DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xirui Liu · Sihang Zhou · Yanning Hou · Rong Zhou · Haoyuan Chen · Maolin He · Siwei Wang · Hao Chen · Jian Huang
26 May 2026 at 12:00
arXiv:2605.23939v1 Announce Type: new Abstract: Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different tasks. However, these knowledge types differ fundamentally: reasoning knowledge (e.g., booking a flight requires first searching for routes) is abstract and transferable across websites, while interaction knowledge (e.g., clicking the Search button at a specific coordinate on Site A) depends heavily on page-specific contexts. Existing methods store experiences uniformly. This creates a dilemma: abstract representations lose executability on concrete pages, while concrete representations fail to generalize across domains. This entanglement limits capability accumulation: on new websites, agents either fail to recognize reusable task logic due to surface-level differences or attempt infeasible actions from outdated page structures. To disentangle them, we propose DRIVE, a dual-level skill modeling framework separating historical experience into natural language reasoning skills, which capture transferable task logic, and programmatic interaction skills, grounding abstract actions to executable operations. A scene-aware coordination mechanism adaptively retrieves and invokes these dual-level skills based on task semantics. DRIVE also uses skill-level reflection to identify hierarchy-specific failure modes, enabling targeted skill library expansion and refinement. Experiments across five WebArena domains show DRIVE attains an average task success rate of 52.8%, exceeding the skill-free baseline by 7.3 percentage points. Further ablations show reasoning and interaction skills provide distinct, complementary benefits, supporting separation of transferable task logic from executable page-level operations.

Agent-Centric Social Trajectory Prediction: A Free Energy Principle Perspective

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yanping Wu · Ji Zhang · Hao Chen · Edmond S. L. Ho · Chongfeng Wei
26 May 2026 at 12:00
arXiv:2605.25748v1 Announce Type: new Abstract: Trajectory prediction methods have demonstrated remarkable capabilities in capturing complex motion patterns. However, existing methods rely on global state assumptions, suffer from insufficient belief inference under partial observability, and lack cognitive behavioral constraints in prediction. These limitations severely compromise both deployment feasibility and physical plausibility in real-world settings. In this work, we propose FEP-Diff, an agent-centric trajectory prediction framework grounded in the Free Energy Principle, aimed at achieving cognitively plausible predictions under realistic constraints. Specifically, a dual-branch spatiotemporal encoder extracts ego-motion dynamics and social interaction cues from local observations. Building upon this, a goal-conditioned belief learner infers multimodal latent belief distributions optimized via a free-energy objective, with a social consistency constraint on the local neighborhood graph to promote cognitive alignment among neighboring agents. Finally, a residual diffusion trajectory generator is conditioned on the learned belief representations with token-level proxy conditioning, producing precise and diverse future predictions. Extensive experiments on five public benchmarks demonstrate that FEP-Diff consistently outperforms state-of-the-art methods under restricted observability. Code: https://anonymous.4open.science/r/FEP-Diff-8876.

Rethinking Federated Unlearning via the Lens of Memorization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaheng Wei · Yanjun Zhang · He Zhang · Leo Yu Zhang · Chao Chen · Kok-Leong Ong · Jun Zhang · Yang Xiang
26 May 2026 at 12:00
arXiv:2605.24545v1 Announce Type: cross Abstract: Federated learning (FL) increasingly needs machine unlearning to comply with privacy regulations. However, existing federated unlearning approaches may overlook the overlapping information between the unlearning and remaining data, leading to ineffective unlearning and unfairness between clients. In this work, we revisit federated unlearning through the lens of memorization. We argue that unlearning should mainly remove the unique memorized information attributable to the data to be forgotten, while preserving overlapping patterns that are also supported by the remaining data. Specifically, we propose Grouped Memorization Evaluation, an example-level metric that separates memorized knowledge from overlapping knowledge. Building on this metric, we introduce Federated Memorization Pruning (FedMemPrune), a pruning-based unlearning approach that resets redundant parameters responsible for memorization. Extensive experiments show that FedMemPrune closely matches retraining-based unlearning baselines while more effectively eliminating memorization than existing federated unlearning algorithms, yielding strong unlearning performance without sacrificing the utility of retained knowledge.

VaaWIT: Visual-Aware Adaptation of Large Language Models for Multilingual Web Image Translation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bo Li · Ronghao Chen · Ningyuan Deng · Huacan Wang · Shaolin Zhu · Lijie Wen
26 May 2026 at 12:00
arXiv:2605.24675v1 Announce Type: cross Abstract: Translating text embedded in Web images is crucial for improving content accessibility and cross-lingual information retrieval, particularly within social media and e-commerce domains. Although Large Vision-Language Models (LVLMs) have advanced multimodal understanding, applying them to Web image translation remains challenging due to the visual representation gap: standard encoders often prioritize high-level semantics over the fine-grained visual details required for recognizing diverse character morphologies. To address this challenge, we propose VaaWIT, an end-to-end framework that adapts Large Language Models for multilingual Web image translation. The framework introduces two key technical contributions: (1) a Dual-Stream Attention Module (DSAM), which facilitates bidirectional interaction between multilingual semantic features and detailed visual representations, thereby synthesizing unified features robust to textual variations; and (2) a Visual-Aware Adapter (VAA), a parameter-efficient fine-tuning strategy that dynamically injects these fused visual cues into the frozen LLM backbone. This design enables the model to align the visual context with linguistic reasoning effectively while minimizing computational costs. Extensive experiments on eight tasks on three public benchmarks demonstrate that VaaWIT significantly outperforms state-of-the-art (SOTA) open-source baselines and achieves competitive performance against proprietary models. These results validate the efficacy of integrating fine-grained visual perception into LLMs for complex Web content analysis.

RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ruize Li · Zhibin Wen · Tao Han · Hao Chen · Fenghua Ling · Wei Zhang · Song Guo · Lei Bai
26 May 2026 at 12:00
arXiv:2605.24945v1 Announce Type: cross Abstract: Accurate evaluation of weather forecasting models is critical for their reliable deployment in real-world applications. However, existing benchmarks predominantly rely on reanalysis products such as ERA5, which are generated through delayed data assimilation and do not reflect the constraints of real-time operational forecasting, thereby resulting in a systematic mismatch between benchmark performance and real-world forecasting. In this work, we introduce RealBench, a next-generation benchmark for AI weather forecasting that emphasizes realistic evaluation under operational conditions. RealBench features a strictly out-of-distribution test set spanning 2025 to eliminate data leakage and capture recent atmospheric regimes. It integrates multiple data sources, including low-latency operational analysis and a large-scale global in-situ observation dataset comprising over 10,000 stations, enabling direct evaluation against real atmospheric measurements. Beyond standard global metrics, RealBench provides a comprehensive evaluation framework for high-impact extreme events, including heatwaves, cold surges, and tropical cyclones, using event-specific metrics that better reflect real-world forecasting priorities. The evaluation results reveal substantial discrepancies between reanalysis-based metrics and real-world performance, particularly concerning extreme events. By highlighting the limitations of existing benchmarks, this work establishes a more faithful and operationally relevant evaluation paradigm, providing a rigorous foundation for advancing next-generation AI weather forecasting systems. The benchmark implementation is available at: https://github.com/lixruize-del/NWP-Benchmark.

DeGRe: Dense-supervised Generative Reranking for Recommendation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chaotian Song · Jingyao Zhang · Chenghao Chen · Zisen Sang · Dehai Zhao · Guodong Cao · Boxi Wu · Deng Cai · Jia Jia
26 May 2026 at 12:00
arXiv:2605.25749v1 Announce Type: cross Abstract: In multi-stage recommender systems, reranking optimizes overall utility by capturing intra-list contextual dependencies, yet its central challenge lies in exploring optimal sequences within an exponentially large permutation space. Recent studies have shifted towards end-to-end generative frameworks, which typically leverage list-wise rewards or preference alignment to guide generator training. However, these methods still face two critical issues. First is the heuristic label bias. Existing methods often construct training targets based on simple rules, such as promoting clicked items to the top, while ignoring causal dependencies within the list context. Second is the credit assignment problem. Sparse list-level posterior rewards fail to directly guide intermediate steps in sequence generation, leading to ambiguous optimization directions. To address these issues, we propose DeGRe (Dense-supervised Generative Reranking), a generative reranking framework that bridges the gap between offline exploration and online efficiency through dense supervision. The core of DeGRe lies in its offline-online decoupled design. During the offline phase, we introduce a Lookahead Evaluator based on cumulative regression, which leverages beam search to actively mine high-value lookahead sequences in the unexposed space. During training, we transform the step-wise value estimations from the evaluator into dense supervision signals and distill them into a lightweight Online Generator. This mechanism enables the generator to internalize lookahead planning capabilities, requiring only a single efficient greedy decoding pass during online inference to approximate the global optimum. Experiments demonstrate that DeGRe outperforms baseline models on public benchmarks and industrial datasets. We have successfully deployed DeGRe on Taobao Flash Shopping, significantly improving online recommendations.

Agent Learning via Early Experience

✇cs.AI, q-bio.NC updates on arXiv.org
By: Kai Zhang · Xiangchao Chen · Bo Liu · Tianci Xue · Zeyi Liao · Zhihan Liu · Xiyao Wang · Yuting Ning · Zhaorun Chen · Xiaohan Fu · Jian Xie · Yuxuan Sun · Boyu Gou · Qi Qi · Zihang Meng · Jianwei Yang · Ning Zhang · Xian Li · Ashish Shah · Dat Huynh · Hengduo Li · Zi Yang · Sara Cao · Lawrence Jang · Shuyan Zhou · Jiacheng Zhu · Huan Sun · Jason Weston · Yu Su · Yifan Wu
26 May 2026 at 12:00
arXiv:2510.08558v3 Announce Type: replace Abstract: A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tasks. However, training agents from experience data with reinforcement learning remains difficult in many environments, which either lack verifiable rewards (e.g., websites) or require inefficient long-horizon rollouts (e.g., multi-turn tool use). As a result, most current agents rely on supervised fine-tuning on expert data, which is challenging to scale and generalizes poorly. This limitation stems from the nature of expert demonstrations: they capture only a narrow range of scenarios, and expose the agent to limited environment diversity. We address this limitation with a middle-ground paradigm we call early experience: interaction data generated by the agent's own actions, where the resulting future states serve as supervision without reward signals. Within this paradigm, we study two strategies of using such data: (1) implicit world modeling, which uses collected states to ground the policy in environment dynamics; and (2) self-reflection, where the agent learns from its suboptimal actions to improve reasoning and decision-making. Evaluation across eight diverse environments and multiple model families shows that our approaches consistently improve effectiveness and out-of-domain generalization, highlighting the value of early experience. Moreover, in environments with verifiable rewards, our results provide promising signals that early experience offers a strong foundation for subsequent reinforcement learning, making it a practical bridge between imitation learning and fully experience-driven agents.

UniToolCall: Unifying Tool-Use Representation, Data, and Evaluation for LLM Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yijuan Liang · Xinghao Chen · Yifan Ge · Ziyi Wu · Hao Wu · Changyu Zeng · Wei Xing · Xiaoyu Shen
26 May 2026 at 12:00
arXiv:2604.11557v2 Announce Type: replace Abstract: Tool-use capability is a fundamental component of LLM agents, enabling them to interact with external systems through structured function calls. However, existing research exhibits inconsistent interaction representations, largely overlooks the structural distribution of tool-use trajectories, and relies on incompatible evaluation benchmarks. We present UniToolCall, a unified framework for tool learning that standardizes the entire pipeline from toolset construction and dataset generation to evaluation. The framework curates a large tool pool of 22k+ tools and constructs a hybrid training corpus of 390k+ instances by combining 10 standardized public datasets with structurally controlled synthetic trajectories. It explicitly models diverse interaction patterns, including single-hop vs. multi-hop and single-turn vs. multi-turn, while capturing both serial and parallel execution structures. To support coherent multi-turn reasoning, we further introduce an Anchor Linkage mechanism that enforces cross-turn dependencies. Furthermore, we convert 7 public benchmarks into a unified Query--Action--Observation--Answer (QAOA) representation with fine-grained evaluation at the function-call, turn, and conversation levels. Experiments show that fine-tuning Qwen3-8B on our dataset substantially improves tool-use performance. Under the distractor-heavy Hybrid-20 setting, achieves 93.0% single-turn Strict Precision, outperforming commercial models including GPT, Gemini, and Claude.

Topology-Driven Transferability Estimation of Medical Foundation Models for Segmentation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaqi Tang · Shaoyang Zhang · Xiaoqi Wang · Jiaying Zhou · Yang Liu · Qingchao Chen
26 May 2026 at 12:00
arXiv:2602.23916v2 Announce Type: replace-cross Abstract: The advent of large-scale self-supervised learning (SSL) has produced a vast zoo of medical foundation models. However, selecting optimal medical foundation models for specific segmentation tasks remains a computational bottleneck. Existing Transferability Estimation (TE) metrics, primarily designed for classification, rely on global statistical assumptions and fail to capture the topological complexity essential for dense prediction. We propose a novel Topology-Driven Transferability Estimation framework that evaluates manifold tractability rather than statistical overlap. Our approach introduces three components: (1) Global Representation Topology Divergence (GRTD), utilizing Minimum Spanning Trees to quantify feature-label structural isomorphism; (2) Local Boundary-Aware Topological Consistency (LBTC), which assesses manifold separability specifically at critical anatomical boundaries; and (3) Task-Adaptive Fusion, which dynamically integrates global and local metrics based on the semantic cardinality of the target task. Validated on the large-scale OpenMind benchmark across diverse anatomical targets and SSL foundation models, our approach significantly outperforms state-of-the-art baselines by around 31% relative improvement in the weighted Kendall metric, providing a robust, training-free proxy for efficient model selection without the cost of fine-tuning. The code will be made publicly available upon acceptance.

De novo design of miniproteins targeting GPCRs

✇Nature - Issue - nature.com science feeds
By: Edin Muratspahić · David Feldman · David E. Kim · Xiangli Qu · Ana-Maria Bratovianu · Paula Rivera-Sánchez · Jan Hendrik Voss · Emil P. T. Hertz · Mads Jeppesen · Federica Dimitri · Kensuke Sakamoto · Amrita Nallathambi · Pia Peceli · Jianjun Cao · Brian P. Cary · Matthew J. Belousoff · Peter Keov · Phuc N. H. Trinh · Qingchao Chen · Yue Ren · Justyn Fine · Sudha Mishra · Annu Dalal · Shachie Sinha · Ramanuj Banerjee · Manisankar Ganguly · Karthik Varappalayam Karuppusamy · Isaac Sappington · Thomas Schlichthaerle · Jason Z. Zhang · Arvind Pillai · Brian Coventry · Ljubica Mihaljević · Magnus Bauer · Susana Vázquez Torres · Amir Motmaen · Gyu Rie Lee · Long Tran · Xinru Wang · Inna Goreshnik · Dionne K. Vafeados · Justin E. Svendsen · Parisa Hosseinzadeh · Nicolai Lindegaard · Matthäus Brandt · Yann Waltenspühl · Kristine Deibler · Lukas Deweid · Anja Bennett · Jendrik Schöppe · Tiantang Dong · Xiaoli Yan · Luke Oostdyk · William Cao · Lakshmi Anantharaman · Johan J. Weisser · Jesper Frank Bastlund · Christoffer Bundgaard · Ayodeji A. Asuni · Justin G. Englis
21 May 2026 at 08:00

Nature, Published online: 21 May 2026; doi:10.1038/s41586-026-10656-8

De novo design of miniproteins targeting GPCRs
Received — 8 April 2026 ⏭

FeynmanBench: Benchmarking Multimodal LLMs on Diagrammatic Physics Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zeyu Wang · Xiaogang Li · Peiyao Xiao · Qinhao Kong · Ben Wang · Chengliang Xu · Zichao Chen · Bing Zhao · Hu Wei
7 April 2026 at 12:00
arXiv:2604.03893v1 Announce Type: new Abstract: Breakthroughs in frontier theory often depend on the combination of concrete diagrammatic notations with rigorous logic. While multimodal large language models (MLLMs) show promise in general scientific tasks, current benchmarks often focus on local information extraction rather than the global structural logic inherent in formal scientific notations. In this work, we introduce FeynmanBench, the first benchmark centered on Feynman diagram tasks. It is designed to evaluate AI's capacity for multistep diagrammatic reasoning, which requires satisfying conservation laws and symmetry constraints, identifying graph topology, converting between diagrammatic and algebraic representations, and constructing scattering amplitudes under specific conventions and gauges. To support large-scale and reproducible evaluation, we developed an automated pipeline producing diverse Feynman diagrams along with verifiable topological annotations and amplitude results. Our database spans the electromagnetic, weak, and strong interactions of the Standard Model, encompasses over 100 distinct types and includes more than 2000 tasks. Experiments on state-of-the-art MLLMs reveal systematic failure modes, including unstable enforcement of physical constraints and violations of global topological conditions, highlighting the need for physics-grounded benchmarks for visual reasoning over scientific notation. FeynmanBench provides a logically rigorous test of whether AI can effectively engage in scientific discovery, particularly within theoretical physics.

Determined by User Needs: A Salient Object Detection Rationale Beyond Conventional Visual Stimuli

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chenglizhao Chen · Shujian Zhang · Luming Li · Wenfeng Song · Shuai Li
7 April 2026 at 12:00
arXiv:2604.03526v1 Announce Type: cross Abstract: Existing \textbf{s}alient \textbf{o}bject \textbf{d}etection (SOD) methods adopt a \textbf{passive} visual stimulus-based rationale--objects with the strongest visual stimuli are perceived as the user's primary focus (i.e., salient objects). They ignore the decisive role of users' \textbf{proactive needs} in segmenting salient objects--if a user has a need before seeing an image, the user's salient objects align with their needs, e.g., if a user's need is ``white apple'', when this user sees an image, the user's primary focus is on the ``white apple'' or ``the most white apple-like'' objects in the image. Such an oversight not only \textbf{fails to satisfy users}, but also \textbf{limits the development of downstream tasks}. For instance, in salient object ranking tasks, focusing solely on visual stimuli-based salient objects is insufficient for conducting an analysis of fine-grained relationships between users' viewing order (usually determined by user's needs) and scenes, which may result in wrong ranking results. Clearly, it is essential to detect salient objects based on user needs. Thus, we advocate a \textbf{User} \textbf{S}alient \textbf{O}bject \textbf{D}etection (UserSOD) task, which focuses on \textbf{detecting salient objects align with users' proactive needs when user have needs}. The main challenge for this new task is the lack of datasets for model training and testing.

Discrete Prototypical Memories for Federated Time Series Foundation Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Liwei Deng · Qingxiang Liu · Xinhe Niu · Shengchao Chen · Sheng Sun · Yuankai Wu · Guodong Long · Yuxuan Liang
7 April 2026 at 12:00
arXiv:2604.04475v1 Announce Type: cross Abstract: Leveraging Large Language Models (LLMs) as federated learning (FL)-based time series foundation models offers a promising way to transfer the generalization capabilities of LLMs to time series data while preserving access to private data. However, the semantic misalignment between time-series data and the text-centric latent space of existing LLMs often leads to degraded performance. Meanwhile, the parameter-sharing mechanism in existing FL methods model heterogeneous cross-domain time-series data into a unified continuous latent space, which contradicts the fact that time-series semantics frequently manifest as discrete and recurring regimes. To address these limitations, we propose \textsc{FeDPM}, a federated framework for time-series foundation models based on discrete prototypical memories. Specifically, we learn local prototypical memory priors for intra-domain time-series data. We then align cross-domain memories to promote a unified discrete latent space and introduce a domain-specific memory update mechanism to balance shared and personalized prototypical knowledge. Extensive experiments demonstrate the efficiency and effectiveness of \textsc{FeDPM}. The code is publicly available at https://anonymous.4open.science/r/FedUnit-64D1.
Received — 6 April 2026 ⏭

Spliceosomal component SNRPE drives cell proliferation by regulating CTP synthase 1 mRNA splicing in ovarian cancer

✇Oncogene - Issue - nature.com science feeds
By: Yingying Pu · Zhongshao Chen · Qianqian Gao · Yanling Liu · Ning Yang · Yingwei Li · Beihua Kong
4 April 2026 at 08:00

Oncogene, Published online: 04 April 2026; doi:10.1038/s41388-026-03764-2

Spliceosomal component SNRPE drives cell proliferation by regulating CTP synthase 1 mRNA splicing in ovarian cancer
Received — 4 April 2026 ⏭

The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinlei Yu · Zhangquan Chen · Yongbo He · Tianyu Fu · Cheng Yang · Chengming Xu · Yue Ma · Xiaobin Hu · Zhe Cao · Jie Xu · Guibin Zhang · Jiale Tao · Jiayi Zhang · Siyuan Ma · Kaituo Feng · Haojie Huang · Youxing Li · Ronghao Chen · Huacan Wang · Chenglin Wu · Zikun Su · Xiaogang Xu · Kelu Yao · Kun Wang · Chen Gao · Yue Liao · Ruqi Huang · Tao Jin · Cheng Tan · Jiangning Zhang · Wenqi Ren · Yanwei Fu · Yong Liu · Yu Wang · Xiangyu Yue · Yu-Gang Jiang · Shuicheng Yan
3 April 2026 at 12:00
arXiv:2604.02029v1 Announce Type: new Abstract: Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an increasing body of work shows that many critical internal processes are more naturally carried out in continuous latent space than in human-readable verbal traces. This shift is driven by the structural limitations of explicit-space computation, including linguistic redundancy, discretization bottlenecks, sequential inefficiency, and semantic loss. This survey aims to provide a unified and up-to-date landscape of latent space in language-based models. We organize the survey into five sequential perspectives: Foundation, Evolution, Mechanism, Ability, and Outlook. We begin by delineating the scope of latent space, distinguishing it from explicit or verbal space and from the latent spaces commonly studied in generative visual models. We then trace the field's evolution from early exploratory efforts to the current large-scale expansion. To organize the technical landscape, we examine existing work through the complementary lenses of mechanism and ability. From the perspective of Mechanism, we identify four major lines of development: Architecture, Representation, Computation, and Optimization. From the perspective of Ability, we show how latent space supports a broad capability spectrum spanning Reasoning, Planning, Modeling, Perception, Memory, Collaboration, and Embodiment. Beyond consolidation, we discuss the key open challenges, and outline promising directions for future research. We hope this survey serves not only as a reference for existing work, but also as a foundation for understanding latent space as a general computational and systems paradigm for next-generation intelligence.

DR-LoRA: Dynamic Rank LoRA for Fine-Tuning Mixture-of-Experts Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guanzhi Deng · Bo Li · Ronghao Chen · Xiujin Liu · Zhuo Han · Huacan Wang · Lijie Wen · Linqi Song
3 April 2026 at 12:00
arXiv:2601.04823v5 Announce Type: replace Abstract: Mixture-of-Experts (MoE) has become a prominent paradigm for scaling Large Language Models (LLMs). Parameter-efficient fine-tuning methods, such as LoRA, are widely adopted to adapt pretrained MoE LLMs to downstream tasks. However, existing approaches typically assign identical LoRA ranks to all expert modules, ignoring the heterogeneous specialization of pretrained experts. This uniform allocation leads to a resource mismatch: task-relevant experts are under-provisioned, while less relevant ones receive redundant parameters. To address this, we propose DR-LoRA, a Dynamic Rank LoRA framework for fine-tuning pretrained MoE models. Specifically, DR-LoRA initializes all expert LoRA modules with a small active rank and uses an expert saliency score, which combines routing frequency and gradient-based rank importance, to identify which experts would benefit most from additional capacity. It then periodically expands the active ranks of the task-critical expert LoRA, progressively constructing a heterogeneous rank distribution tailored to the target task. Experiments on three MoE models across six tasks show that DR-LoRA consistently outperforms LoRA and other strong baselines, demonstrating that task-adaptive heterogeneous rank allocation is an effective strategy to improve active capacity utilization in MoE fine-tuning.
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