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Received — 27 May 2026 ⏭

Dynamic Dual-Granularity Skill Bank for Agentic RL

✇cs.AI, q-bio.NC updates on arXiv.org
By: Songjun Tu · Chengdong Xu · Qichao Zhang · Yaocheng Zhang · Xiangyuan Lan · Linjing Li · Dong Li · Dongbin Zhao
26 May 2026 at 12:00
arXiv:2603.28716v2 Announce Type: replace Abstract: Agentic RL can benefit substantially from reusable experience, yet existing skill-based methods mainly extract trajectory-level guidance and often lack principled mechanisms for maintaining an evolving skill memory. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills for high-level guidance and step skills for fine-grained decision support and error correction. D2Skill jointly trains the policy and skill bank through paired baseline and skill-injected rollouts under the same policy, using their performance gap to derive hindsight utility signals for both skill updating and policy optimization. Built entirely from training-time experience, the skill bank is continuously expanded through reflection and maintained with utility-aware retrieval and pruning. Experiments on ALFWorld, WebShop, and Search-Augmented QA tasks show that D2Skill substantially improves performance over skill-free baselines across models of different scales. Further ablations and analyses show that both dual-granularity skill modeling and dynamic skill maintenance are critical to these gains, while the learned skills exhibit higher utility, transfer across evaluation settings, and introduce only modest training overhead.

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.

JAEGER: Joint 3D Audio-Visual Grounding and Reasoning in Simulated Physical Environments

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhan Liu · Changli Tang · Yuxin Wang · Zhiyuan Zhu · Youjun Chen · Yiwen Shao · Tianzi Wang · Lei Ke · Zengrui Jin · Chao Zhang
26 May 2026 at 12:00
arXiv:2602.18527v2 Announce Type: replace-cross Abstract: Current audio-visual large language models (AV-LLMs) are predominantly restricted to 2D perception, relying on RGB video and monaural audio. This design choice introduces a fundamental dimensionality mismatch that precludes reliable source localization and spatial reasoning in complex 3D environments. We address this limitation by presenting JAEGER, a framework that extends AV-LLMs to 3D space, to enable joint spatial grounding and reasoning through the integration of RGB-D observations and multi-channel first-order ambisonics. A core contribution of our work is the neural intensity vector (Neural IV), a learned spatial audio representation that encodes robust directional cues to enhance direction-of-arrival estimation, even in adverse acoustic scenarios with overlapping sources. To facilitate large-scale training and systematic evaluation, we propose SpatialSceneQA, a benchmark of 61k instruction-tuning samples curated from simulated physical environments. Extensive experiments demonstrate that our approach consistently surpasses 2D-centric baselines across diverse spatial perception and reasoning tasks, underscoring the necessity of explicit 3D modelling for advancing AI in physical environments. Our source code, pre-trained model checkpoints, and datasets are available at https://github.com/liuzhan22/JAEGER.

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.

FDX1 as a predictive biomarker and therapeutic target for lymph node metastasis in gastric cancer

✇Omics in Gastric
By: Tianhao Zhang · Jianqiu Liu · Peng Duan · Zikang Li · Zhixin Huang · Zeyu Zhao · Yang Cheng · Zhi Liang · Jianhui Chen · Meilan Cai · Jinping Ma
10 May 2026 at 18:00

Clin Exp Med. 2026 May 10. doi: 10.1007/s10238-026-02160-0. Online ahead of print.

ABSTRACT

The prognostic values of cuproptosis-related genes (CRGs) in gastric cancer with lymph node metastasis (GCLM), especially in the tumor immune microenvironment (TIME), remain unclear. We analyzed the expression, mutation, immunity, drug sensitivity, and prognostic value of CRGs in GCLM using TCGA and GEO cohorts. Consensus clustering was performed to identify CRG subtypes, with differences characterized by multi-omics analysis. A CRG-based prognostic risk score and immune score were constructed for individualized assessment, and the role of CRGs was validated through in vitro and in vivo experiments. Consensus clustering revealed that CRGs were significantly enriched in biological processes related to mitosis and energy metabolism, as well as in immune-related and cancer-associated pathways. Four distinct CRG subtypes were identified, showing marked differences in expression profiles, prognosis, genetic alterations, TIME, and chemotherapeutic drug sensitivity. We developed an exploratory CRG-based prognostic risk score for preliminary individualized assessment, and the functional relevance of CRGs in GCLM was further validated through in vitro experiments. Among these, FDX1, LIAS, DLAT, MTF1, and GLS were identified as key determinants of overall survival in patients with GCLM, with FDX1 emerging as a potential independent prognostic factor. Notably, upregulation of FDX1 significantly suppressed lymph node metastasis of gastric cancer cells in a mouse popliteal lymph node metastasis model. Our data uncovers FDX1 might be a potential favorable prognostic factors in GCLM patients. These findings may improve our understanding of CRGs in GCLM and provide new in-sights for assessing prognosis and developing more effective treatment strategies.

PMID:42107026 | DOI:10.1007/s10238-026-02160-0

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