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Received — 2 April 2026 ⏭

GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Iordanis Fostiropoulos · Muhammad Rafay Azhar · Abdalaziz Sawwan · Boyu Fang · Yuchen Liu · Jiayi Liu · Hanchao Yu · Qi Guo · Jianyu Wang · Fei Liu · Xiangjun Fan
1 April 2026 at 12:00
arXiv:2603.29112v1 Announce Type: new Abstract: We introduce GISTBench, a benchmark for evaluating Large Language Models' (LLMs) ability to understand users from their interaction histories in recommendation systems. Unlike traditional RecSys benchmarks that focus on item prediction accuracy, our benchmark evaluates how well LLMs can extract and verify user interests from engagement data. We propose two novel metric families: Interest Groundedness (IG), decomposed into precision and recall components to separately penalize hallucinated interest categories and reward coverage, and Interest Specificity (IS), which assesses the distinctiveness of verified LLM-predicted user profiles. We release a synthetic dataset constructed on real user interactions on a global short-form video platform. Our dataset contains both implicit and explicit engagement signals and rich textual descriptions. We validate our dataset fidelity against user surveys, and evaluate eight open-weight LLMs spanning 7B to 120B parameters. Our findings reveal performance bottlenecks in current LLMs, particularly their limited ability to accurately count and attribute engagement signals across heterogeneous interaction types.

ASI-Evolve: AI Accelerates AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Weixian Xu · Tiantian Mi · Yixiu Liu · Yang Nan · Zhimeng Zhou · Lyumanshan Ye · Lin Zhang · Yu Qiao · Pengfei Liu
1 April 2026 at 12:00
arXiv:2603.29640v1 Announce Type: new Abstract: Can AI accelerate the development of AI itself? While recent agentic systems have shown strong performance on well-scoped tasks with rapid feedback, it remains unclear whether they can tackle the costly, long-horizon, and weakly supervised research loops that drive real AI progress. We present ASI-Evolve, an agentic framework for AI-for-AI research that closes this loop through a learn-design-experiment-analyze cycle. ASI-Evolve augments standard evolutionary agents with two key components: a cognition base that injects accumulated human priors into each round of exploration, and a dedicated analyzer that distills complex experimental outcomes into reusable insights for future iterations. To our knowledge, ASI-Evolve is the first unified framework to demonstrate AI-driven discovery across three central components of AI development: data, architectures, and learning algorithms. In neural architecture design, it discovered 105 SOTA linear attention architectures, with the best discovered model surpassing DeltaNet by +0.97 points, nearly 3x the gain of recent human-designed improvements. In pretraining data curation, the evolved pipeline improves average benchmark performance by +3.96 points, with gains exceeding 18 points on MMLU. In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +12.5 points on AMC32, +11.67 points on AIME24, and +5.04 points on OlympiadBench. We further provide initial evidence that this AI-for-AI paradigm can transfer beyond the AI stack through experiments in mathematics and biomedicine. Together, these results suggest that ASI-Evolve represents a promising step toward enabling AI to accelerate AI across the foundational stages of development, offering early evidence for the feasibility of closed-loop AI research.

Correction: The multifunctional RNA helicase DDX39A drives glioblastoma progression by modulating WISP1 alternative splicing that induces an immunosuppressive macrophage polarization

✇Oncogene - Issue - nature.com science feeds
By: Yan Zhang · Zhiwei Xue · Naibin Zhang · Yuehua Zhu · YanZhao Wu · Meilin Lv · Zhihan Zhang · Feiyu Mu · Wenchen Xing · Ziyi Tang · Chunjie Wang · Zhiyi Xue · Wenjing Zhou · Xiaofei Liu · Xingang Li · Rolf Bjerkvig · Bin Huang · Mingzhi Han · Jian Wang · Donghai Wang
1 April 2026 at 08:00

Oncogene, Published online: 01 April 2026; doi:10.1038/s41388-026-03756-2

Correction: The multifunctional RNA helicase DDX39A drives glioblastoma progression by modulating WISP1 alternative splicing that induces an immunosuppressive macrophage polarization
Received — 17 March 2026 ⏭

daVinci-Env: Open SWE Environment Synthesis at Scale

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dayuan Fu · Shenyu Wu · Yunze Wu · Zerui Peng · Yaxing Huang · Jie Sun · Ji Zeng · Mohan Jiang · Lin Zhang · Yukun Li · Jiarui Hu · Liming Liu · Jinlong Hou · Pengfei Liu
16 March 2026 at 12:00
arXiv:2603.13023v1 Announce Type: cross Abstract: Training capable software engineering (SWE) agents demands large-scale, executable, and verifiable environments that provide dynamic feedback loops for iterative code editing, test execution, and solution refinement. However, existing open-source datasets remain limited in scale and repository diversity, while industrial solutions are opaque with unreleased infrastructure, creating a prohibitive barrier for most academic research groups. We present OpenSWE, the largest fully transparent framework for SWE agent training in Python, comprising 45,320 executable Docker environments spanning over 12.8k repositories, with all Dockerfiles, evaluation scripts, and infrastructure fully open-sourced for reproducibility. OpenSWE is built through a multi-agent synthesis pipeline deployed across a 64-node distributed cluster, automating repository exploration, Dockerfile construction, evaluation script generation, and iterative test analysis. Beyond scale, we propose a quality-centric filtering pipeline that characterizes the inherent difficulty of each environment, filtering out instances that are either unsolvable or insufficiently challenging and retaining only those that maximize learning efficiency. With $891K spent on environment construction and an additional $576K on trajectory sampling and difficulty-aware curation, the entire project represents a total investment of approximately $1.47 million, yielding about 13,000 curated trajectories from roughly 9,000 quality guaranteed environments. Extensive experiments validate OpenSWE's effectiveness: OpenSWE-32B and OpenSWE-72B achieve 62.4% and 66.0% on SWE-bench Verified, establishing SOTA among Qwen2.5 series. Moreover, SWE-focused training yields substantial out-of-domain improvements, including up to 12 points on mathematical reasoning and 5 points on science benchmarks, without degrading factual recall.
Received — 14 March 2026 ⏭

Autophagy-centered regulation of PI3K/Akt/mTOR and MAPK signaling by traditional Chinese medicine in gastric cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Lujia Chen · Guonong He · Hongfei Liu
13 March 2026 at 18:00

Tissue Cell. 2026 Mar 10;101:103409. doi: 10.1016/j.tice.2026.103409. Online ahead of print.

ABSTRACT

Gastric cancer (GC) remains a major global health burden, with high incidence and mortality rates, particularly in East Asia, driven by factors such as Helicobacter pylori infection, dietary risks, and genetic predispositions. Conventional treatments like surgery and chemotherapy are limited by resistance, toxicity, and poor outcomes in advanced stages. The PI3K/Akt/mTOR and MAPK signaling pathways are central to GC pathogenesis, promoting proliferation, survival, metabolic reprogramming, epithelial-mesenchymal transition (EMT), and metastasis through aberrations like PIK3CA mutations, PTEN loss, and KRAS alterations. These pathways exhibit extensive crosstalk, contributing to therapeutic resistance. This review explores the regulatory effects of Traditional Chinese Medicine (TCM) on these pathways in GC, grounded in TCM principles such as Qi deficiency, Damp-Heat, and disharmony of the Spleen and Stomach. Single herbal monomers (e.g., curcumin, berberine, resveratrol) inhibit PI3K/Akt/mTOR by upregulating PTEN and suppressing mTOR, inducing autophagy and apoptosis. Classical herbs like Huangqin and Huanglian modulate Akt and ERK phosphorylation, while compound formulas (e.g., Banxia Xiexin Decoction, Sijunzi Decoction) synergistically target both pathways, reversing EMT and chemoresistance. TCM addresses crosstalk by disrupting feedback loops and reducing inflammation, enhancing efficacy in combination with Western therapies like chemotherapy and immunotherapy. Network pharmacology and multi-omics analyses reveal TCM's multitarget mechanisms, aligning with ZHENG-based personalization. Challenges include research variability, standardization issues, and incomplete mechanistic validation. Future directions emphasize high-quality trials, omics integration, and precision TCM for clinical translation. TCM offers low-toxicity, holistic options for integrative GC management, potentially improving survival and quality of life.

PMID:41825157 | DOI:10.1016/j.tice.2026.103409

Autophagy-centered regulation of PI3K/Akt/mTOR and MAPK signaling by traditional Chinese medicine in gastric cancer

✇Omics in Gastric
By: Lujia Chen · Guonong He · Hongfei Liu
13 March 2026 at 18:00

Tissue Cell. 2026 Mar 10;101:103409. doi: 10.1016/j.tice.2026.103409. Online ahead of print.

ABSTRACT

Gastric cancer (GC) remains a major global health burden, with high incidence and mortality rates, particularly in East Asia, driven by factors such as Helicobacter pylori infection, dietary risks, and genetic predispositions. Conventional treatments like surgery and chemotherapy are limited by resistance, toxicity, and poor outcomes in advanced stages. The PI3K/Akt/mTOR and MAPK signaling pathways are central to GC pathogenesis, promoting proliferation, survival, metabolic reprogramming, epithelial-mesenchymal transition (EMT), and metastasis through aberrations like PIK3CA mutations, PTEN loss, and KRAS alterations. These pathways exhibit extensive crosstalk, contributing to therapeutic resistance. This review explores the regulatory effects of Traditional Chinese Medicine (TCM) on these pathways in GC, grounded in TCM principles such as Qi deficiency, Damp-Heat, and disharmony of the Spleen and Stomach. Single herbal monomers (e.g., curcumin, berberine, resveratrol) inhibit PI3K/Akt/mTOR by upregulating PTEN and suppressing mTOR, inducing autophagy and apoptosis. Classical herbs like Huangqin and Huanglian modulate Akt and ERK phosphorylation, while compound formulas (e.g., Banxia Xiexin Decoction, Sijunzi Decoction) synergistically target both pathways, reversing EMT and chemoresistance. TCM addresses crosstalk by disrupting feedback loops and reducing inflammation, enhancing efficacy in combination with Western therapies like chemotherapy and immunotherapy. Network pharmacology and multi-omics analyses reveal TCM's multitarget mechanisms, aligning with ZHENG-based personalization. Challenges include research variability, standardization issues, and incomplete mechanistic validation. Future directions emphasize high-quality trials, omics integration, and precision TCM for clinical translation. TCM offers low-toxicity, holistic options for integrative GC management, potentially improving survival and quality of life.

PMID:41825157 | DOI:10.1016/j.tice.2026.103409

Received — 5 March 2026 ⏭
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Efficient Agent Training for Computer Use Yanheng He · Jiahe Jin · Pengfei Liu
    arXiv:2505.13909v2 Announce Type: replace Abstract: Scaling up high-quality trajectory data has long been a critical bottleneck for developing human-like computer use agents. We introduce PC Agent-E, an efficient agent training framework that significantly reduces reliance on large-scale human demonstrations. Starting with just 312 human-annotated computer use trajectories, we further augment them by synthesizing diverse alternative action decisions with Claude 3.7 Sonnet. Trained on these enri
     
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Efficient Agent Training for Computer Use

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yanheng He · Jiahe Jin · Pengfei Liu
4 March 2026 at 13:00
arXiv:2505.13909v2 Announce Type: replace Abstract: Scaling up high-quality trajectory data has long been a critical bottleneck for developing human-like computer use agents. We introduce PC Agent-E, an efficient agent training framework that significantly reduces reliance on large-scale human demonstrations. Starting with just 312 human-annotated computer use trajectories, we further augment them by synthesizing diverse alternative action decisions with Claude 3.7 Sonnet. Trained on these enriched trajectories, our PC Agent-E model achieved a remarkable 141 relative improvement, and even surpassed the Claude 3.7 Sonnet by 10% in relative terms on WindowsAgentArena-V2, an improved benchmark we also released. By integrating robust human computer use skills with automated AI data synthesis capabilities, our method not only brought substantial improvements over training on human trajectories alone, but also significantly surpassed direct distillation from Claude 3.7 Sonnet. Code, data and models are available at https://github.com/GAIR-NLP/PC-Agent-E
Received — 25 February 2026 ⏭

Mantis: A Versatile Vision-Language-Action Model with Disentangled Visual Foresight

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yi Yang · Xueqi Li · Yiyang Chen · Jin Song · Yihan Wang · Zipeng Xiao · Jiadi Su · You Qiaoben · Pengfei Liu · Zhijie Deng
24 February 2026 at 13:00
arXiv:2511.16175v2 Announce Type: replace-cross Abstract: Recent advances in Vision-Language-Action (VLA) models demonstrate that visual signals can effectively complement sparse action supervisions. However, letting VLA directly predict high-dimensional visual states can distribute model capacity and incur prohibitive training cost, while compressing visual states into more compact supervisory signals inevitably incurs information bottlenecks. Moreover, existing methods often suffer from poor comprehension and reasoning capabilities due to the neglect of language supervision. This paper introduces Mantis, a novel framework featuring a Disentangled Visual Foresight (DVF) to tackle these issues. Specifically, Mantis decouples visual foresight prediction from the backbone with the combination of meta queries and a diffusion Transformer (DiT) head. With the current visual state provided to the DiT via a residual connection, a simple next-state prediction objective enables the meta queries to automatically capture the latent actions that delineate the visual trajectory, and hence boost the learning of explicit actions. The disentanglement reduces the burden of the VLA backbone, enabling it to maintain comprehension and reasoning capabilities through language supervision. Empirically, pretrained on human manipulation videos, robot demonstrations, and image-text pairs, Mantis achieves a 96.7% success rate on LIBERO benchmark after fine-tuning, surpassing powerful baselines while exhibiting high convergence speed. Real-world evaluations show that Mantis outperforms $\pi_{0.5}$, a leading open-source VLA model, particularly in instruction-following capability, generalization to unseen instructions, and reasoning ability. Code and weights are released to support the open-source community.
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