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Normal view

Received — 6 April 2026 ⏭

Deciphering functional intra-tumoral heterogeneity in BRAF<sup>V600E</sup>-driven mouse thyroid cancer reveals EMT trajectory and metabolic remodeling

✇Oncogene - Issue - nature.com science feeds
By: Jing Yu · Siyi Shen · Rongkui Luo · Tianyuan Qiu · Jingjing Chen · Wenqing Kong · Ruisen Ma · Ying Yan · Zhoumin Niu · Mei Ma · Zhuoyang Li · Yuting Wu · Yuying Li · Ling Zhang · Hao Ying
4 April 2026 at 08:00

Oncogene, Published online: 04 April 2026; doi:10.1038/s41388-026-03742-8

Deciphering functional intra-tumoral heterogeneity in BRAFV600E-driven mouse thyroid cancer reveals EMT trajectory and metabolic remodeling

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

HoloTrauma 3X Triadic AI Co reasoning for robot assisted emergency maxillofacial reconstruction

✇npj Digital Medicine
By: Qiao Zhou · Yao Liu · Chenfei Wang · Liang Wang · Zhenghan Chen · Yiyi Chen · Chi Yang · Dong Liu · Yuxin Zhang · Jisi Zheng · Xingmei Feng
4 April 2026 at 08:00

npj Digital Medicine, Published online: 04 April 2026; doi:10.1038/s41746-026-02573-x

HoloTrauma 3X Triadic AI Co reasoning for robot assisted emergency maxillofacial reconstruction

Isobavachalcone exerts anti-gastric cancer effects by targeting dihydroorotate dehydrogenase to induce ROS release and activating the STING pathway

✇Omics in Gastric
By: Yong Kuang · Yu Shao · Jia Chen · Chen Dai · Sachiyo Nomura · Fulin Wang · Jingyao Chen · Yulong He · Xing Xiao · Changhua Zhang · Jiahao Feng
3 April 2026 at 18:00

Phytomedicine. 2026 Mar 27;155:158126. doi: 10.1016/j.phymed.2026.158126. Online ahead of print.

ABSTRACT

BACKGROUND: Mitochondrial damage can induce the release of mitochondrial DNA (mtDNA), leading to oxidative stress and activation of immune responses. Targeting mitochondrial dysfunction may thus represent a therapeutic strategy for gastric cancer. Isobavachalcone (IBC), a prenylated chalcone derived from Psoralea corylifolia L., has demonstrated antitumor activity, but its mechanism of action remains unclear, limiting its clinical application.

PURPOSE: This study aimed to investigate the antitumor effects of IBC in gastric cancer and to elucidate the underlying molecular mechanisms, with a focus on mitochondrial damage and immune activation.

STUDY DESIGN: The study combined in vitro and in vivo assays with multi-omics sequencing and network pharmacology to identify IBC's therapeutic target and downstream signaling pathways.

METHODS: Gastric cancer cells and mouse models were treated with IBC to assess its inhibitory effects. Multi-omics approaches and network pharmacology were used to identify potential targets. ROS production, mitochondrial membrane integrity, and immune pathway activation were evaluated via biochemical and molecular assays.

RESULTS: IBC significantly suppresses gastric cancer growth both in vitro and in vivo. Integrated analysis identifies dihydroorotate dehydrogenase (DHODH) as a direct target of IBC. DHODH deficiency can induce mitochondrial membrane remodeling and STING pathway activation. Inhibition of DHODH by IBC induces ROS accumulation, mitochondrial membrane remodeling, and activation of the STING pathway, promoting antitumor immune responses. This study demonstrates that IBC enhances antitumor immunity in gastric cancer through mitochondrial damage-mediated mechanisms.

CONCLUSION: IBC exerts dual antitumor and immunostimulatory effects in gastric cancer by targeting DHODH, inducing mitochondrial damage, and activating the STING pathway, highlighting its promising therapeutic potential in gastric cancer.

PMID:41931998 | DOI:10.1016/j.phymed.2026.158126

Monogenic and Polygenic Risk in Common Liver Diseases: Implications for Clinical Care

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Veeral Ajmera · Daniel Q Huang · Vincent L Chen · K Rajender Reddy
3 April 2026 at 18:00

Gastroenterology. 2026 Apr 1:S0016-5085(26)00312-4. doi: 10.1053/j.gastro.2026.03.020. Online ahead of print.

ABSTRACT

The burden of chronic liver disease is rapidly increasing worldwide, driven primarily by metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic and alcohol-associated liver disease (MetALD), and alcohol-associated liver disease (ALD). Genetic predisposition contributes substantially to variability in disease onset, progression, and outcomes, and recent advances in genomic discovery have brought polygenic risk scores (PRS) and targeted sequencing closer to clinical relevance. This review summarizes the role of genetic testing in clinical hepatology, including monogenic drivers of disease and the growing role of common variants and PRS. Specific populations, including cryptogenic cirrhosis and lean MASLD patients, may be enriched for monogenic drivers of disease. In addition, patients with chronic liver disease may benefit from incorporation of genetic risk scores including PNPLA3, TM6SF2, HSD17B13, and other key variants in determining risk for fibrosis progression and cirrhosis. Across MASLD and ALD, PRS demonstrate modest improvements in predicting fibrosis progression and liver-related events, especially when integrated with clinical risk factors and comorbidities. However, their performance remains limited for population-level screening. Similarly, PRS alone has limited diagnostic accuracy for hepatocellular carcinoma and more complex models with clinical features and multi-omic biomarkers are likely needed. Emerging therapies targeting PNPLA3 and HSD17B13 variants represent a paradigm shift toward genetically informed treatment. Yet challenges remain, including limited ancestral diversity in genomic datasets, pleiotropic effects of variants, cost-effectiveness, and the need for integration with other omics and electronic medical records. As evidence matures, combining genetic risk with clinical and environmental factors may enable more personalized approaches to prognostication and therapy in liver disease.

PMID:41932449 | DOI:10.1053/j.gastro.2026.03.020

Isobavachalcone exerts anti-gastric cancer effects by targeting dihydroorotate dehydrogenase to induce ROS release and activating the STING pathway

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yong Kuang · Yu Shao · Jia Chen · Chen Dai · Sachiyo Nomura · Fulin Wang · Jingyao Chen · Yulong He · Xing Xiao · Changhua Zhang · Jiahao Feng
3 April 2026 at 18:00

Phytomedicine. 2026 Mar 27;155:158126. doi: 10.1016/j.phymed.2026.158126. Online ahead of print.

ABSTRACT

BACKGROUND: Mitochondrial damage can induce the release of mitochondrial DNA (mtDNA), leading to oxidative stress and activation of immune responses. Targeting mitochondrial dysfunction may thus represent a therapeutic strategy for gastric cancer. Isobavachalcone (IBC), a prenylated chalcone derived from Psoralea corylifolia L., has demonstrated antitumor activity, but its mechanism of action remains unclear, limiting its clinical application.

PURPOSE: This study aimed to investigate the antitumor effects of IBC in gastric cancer and to elucidate the underlying molecular mechanisms, with a focus on mitochondrial damage and immune activation.

STUDY DESIGN: The study combined in vitro and in vivo assays with multi-omics sequencing and network pharmacology to identify IBC's therapeutic target and downstream signaling pathways.

METHODS: Gastric cancer cells and mouse models were treated with IBC to assess its inhibitory effects. Multi-omics approaches and network pharmacology were used to identify potential targets. ROS production, mitochondrial membrane integrity, and immune pathway activation were evaluated via biochemical and molecular assays.

RESULTS: IBC significantly suppresses gastric cancer growth both in vitro and in vivo. Integrated analysis identifies dihydroorotate dehydrogenase (DHODH) as a direct target of IBC. DHODH deficiency can induce mitochondrial membrane remodeling and STING pathway activation. Inhibition of DHODH by IBC induces ROS accumulation, mitochondrial membrane remodeling, and activation of the STING pathway, promoting antitumor immune responses. This study demonstrates that IBC enhances antitumor immunity in gastric cancer through mitochondrial damage-mediated mechanisms.

CONCLUSION: IBC exerts dual antitumor and immunostimulatory effects in gastric cancer by targeting DHODH, inducing mitochondrial damage, and activating the STING pathway, highlighting its promising therapeutic potential in gastric cancer.

PMID:41931998 | DOI:10.1016/j.phymed.2026.158126

Monogenic and Polygenic Risk in Common Liver Diseases: Implications for Clinical Care

✇Omics in Hepatocellular
By: Veeral Ajmera · Daniel Q Huang · Vincent L Chen · K Rajender Reddy
3 April 2026 at 18:00

Gastroenterology. 2026 Apr 1:S0016-5085(26)00312-4. doi: 10.1053/j.gastro.2026.03.020. Online ahead of print.

ABSTRACT

The burden of chronic liver disease is rapidly increasing worldwide, driven primarily by metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic and alcohol-associated liver disease (MetALD), and alcohol-associated liver disease (ALD). Genetic predisposition contributes substantially to variability in disease onset, progression, and outcomes, and recent advances in genomic discovery have brought polygenic risk scores (PRS) and targeted sequencing closer to clinical relevance. This review summarizes the role of genetic testing in clinical hepatology, including monogenic drivers of disease and the growing role of common variants and PRS. Specific populations, including cryptogenic cirrhosis and lean MASLD patients, may be enriched for monogenic drivers of disease. In addition, patients with chronic liver disease may benefit from incorporation of genetic risk scores including PNPLA3, TM6SF2, HSD17B13, and other key variants in determining risk for fibrosis progression and cirrhosis. Across MASLD and ALD, PRS demonstrate modest improvements in predicting fibrosis progression and liver-related events, especially when integrated with clinical risk factors and comorbidities. However, their performance remains limited for population-level screening. Similarly, PRS alone has limited diagnostic accuracy for hepatocellular carcinoma and more complex models with clinical features and multi-omic biomarkers are likely needed. Emerging therapies targeting PNPLA3 and HSD17B13 variants represent a paradigm shift toward genetically informed treatment. Yet challenges remain, including limited ancestral diversity in genomic datasets, pleiotropic effects of variants, cost-effectiveness, and the need for integration with other omics and electronic medical records. As evidence matures, combining genetic risk with clinical and environmental factors may enable more personalized approaches to prognostication and therapy in liver disease.

PMID:41932449 | DOI:10.1053/j.gastro.2026.03.020

<i>TWIST1</i> mediated transcriptional activation of <i>SPON2</i> drives colorectal cancer peritoneal metastasis through stromal cell signaling network

✇Oncogene - Issue - nature.com science feeds
By: Zhuan Zhou · Alessandro La Ferlita · Manoj H. Palavalli · Xiongfeng Chen · Lauren Tyler · Aslam Ejaz · Joal Beane · Patricio M. Polanco · Huocong Huang · Alex C. Kim
3 April 2026 at 08:00

Oncogene, Published online: 03 April 2026; doi:10.1038/s41388-026-03743-7

TWIST1 mediated transcriptional activation of SPON2 drives colorectal cancer peritoneal metastasis through stromal cell signaling network
Received — 4 April 2026 ⏭

Infeasibility Aware Large Language Models for Combinatorial Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yakun Wang · Min Chen · Zeguan Wu · Junyu Liu · Sitao Zhang · Zhenwen Shao
3 April 2026 at 12:00
arXiv:2604.01455v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly explored for NP-hard combinatorial optimization problems, but most existing methods emphasize feasible-instance solution generation and do not explicitly address infeasibility detection. We propose an infeasibility-aware framework that combines certifiable dataset construction, supervised fine-tuning, and LLM-assisted downstream search. For the minor-embedding problem, we introduce a new mathematical programming formulation together with provable zero-phase infeasibility screening, which enables scalable construction of training instances labeled either as feasible with structured certificates or as certifiably infeasible. Using training data generated through this exact optimization pipeline, we show that an 8B-parameter LLM can be fine-tuned to jointly perform solution generation and infeasibility detection. We further utilize LLM outputs as warm starts for downstream local search, providing a practical way to accelerate optimization even when the LLM outputs are imperfect. Experiments show that our fine-tuned model improves overall accuracy by up to 30\% over GPT-5.2; meanwhile LLM-guided warm starts provide up to $2\times$ speedup compared with starting from scratch in downstream local search.

LLM Agents as Social Scientists: A Human-AI Collaborative Platform for Social Science Automation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lei Wang · Yuanzi Li · Jinchao Wu · Heyang Gao · Xiaohe Bo · Xu Chen · Ji-Rong Wen
3 April 2026 at 12:00
arXiv:2604.01520v1 Announce Type: new Abstract: Traditional social science research often requires designing complex experiments across vast methodological spaces and depends on real human participants, making it labor-intensive, costly, and difficult to scale. Here we present S-Researcher, an LLM-agent-based platform that assists researchers in conducting social science research more efficiently and at greater scale by "siliconizing" both the research process and the participant pool. To build S-Researcher, we first develop YuLan-OneSim, a large-scale social simulation system designed around three core requirements: generality via auto-programming from natural language to executable scenarios, scalability via a distributed architecture supporting up to 100,000 concurrent agents, and reliability via feedback-driven LLM fine-tuning. Leveraging this system, S-Researcher supports researchers in designing social experiments, simulating human behavior with LLM agents, analyzing results, and generating reports, forming a complete human-AI collaborative research loop in which researchers retain oversight and intervention at every stage. We operationalize LLM simulation research paradigms into three canonical reasoning modes (induction, deduction, and abduction) and validate S-Researcher through systematic case studies: inductive reproduction of cultural dynamics consistent with Axelrod's theory, deductive testing of competing hypotheses on teacher attention validated against survey data, and abductive identification of a cooperation mechanism in public goods games confirmed by human experiments. S-Researcher establishes a new human--AI collaborative paradigm for social science, in which computational simulation augments human researchers to accelerate discovery across the full spectrum of social inquiry.

CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ao Qu · Han Zheng · Zijian Zhou · Yihao Yan · Yihong Tang · Shao Yong Ong · Fenglu Hong · Kaichen Zhou · Chonghe Jiang · Minwei Kong · Jiacheng Zhu · Xuan Jiang · Sirui Li · Cathy Wu · Bryan Kian Hsiang Low · Jinhua Zhao · Paul Pu Liang
3 April 2026 at 12:00
arXiv:2604.01658v1 Announce Type: new Abstract: Large language model (LLM)-based evolution is a promising approach for open-ended discovery, where progress requires sustained search and knowledge accumulation. Existing methods still rely heavily on fixed heuristics and hard-coded exploration rules, which limit the autonomy of LLM agents. We present CORAL, the first framework for autonomous multi-agent evolution on open-ended problems. CORAL replaces rigid control with long-running agents that explore, reflect, and collaborate through shared persistent memory, asynchronous multi-agent execution, and heartbeat-based interventions. It also provides practical safeguards, including isolated workspaces, evaluator separation, resource management, and agent session and health management. Evaluated on diverse mathematical, algorithmic, and systems optimization tasks, CORAL sets new state-of-the-art results on 10 tasks, achieving 3-10 times higher improvement rates with far fewer evaluations than fixed evolutionary search baselines across tasks. On Anthropic's kernel engineering task, four co-evolving agents improve the best known score from 1363 to 1103 cycles. Mechanistic analyses further show how these gains arise from knowledge reuse and multi-agent exploration and communication. Together, these results suggest that greater agent autonomy and multi-agent evolution can substantially improve open-ended discovery. Code is available at https://github.com/Human-Agent-Society/CORAL.

ContextBudget: Budget-Aware Context Management for Long-Horizon Search Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yong Wu · YanZhao Zheng · TianZe Xu · ZhenTao Zhang · YuanQiang Yu · JiHuai Zhu · Chao Ma · BinBin Lin · BaoHua Dong · HangCheng Zhu · RuoHui Huang · Gang Yu
3 April 2026 at 12:00
arXiv:2604.01664v1 Announce Type: new Abstract: LLM-based agents show strong potential for long-horizon reasoning, yet their context size is limited by deployment factors (e.g., memory, latency, and cost), yielding a constrained context budget. As interaction histories grow, this induces a trade-off between retaining past information and staying within the context limit. To address this challenge, we propose Budget-Aware Context Management (BACM), which formulates context management as a sequential decision problem with a context budget constraint. It enables agents to assess the available budget before incorporating new observations and decide when and how much of the interaction history to compress. We further develop BACM-RL, an end-to-end curriculum-based reinforcement learning approach that learns compression strategies under varying context budgets. Experiments on compositional multi-objective QA and long-horizon web browsing benchmarks show that BACM-RL consistently outperforms prior methods across model scales and task complexities, achieving over $1.6\times$ gains over strong baselines in high-complexity settings, while maintaining strong advantages as budgets shrink, where most methods exhibit a downward performance trend.

Hierarchical Memory Orchestration for Personalized Persistent Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junming Liu · Yifei Sun · Weihua Cheng · Haodong Lei · Yuqi Li · Yirong Chen · Ding Wang
3 April 2026 at 12:00
arXiv:2604.01670v1 Announce Type: new Abstract: While long-term memory is essential for intelligent agents to maintain consistent historical awareness, the accumulation of extensive interaction data often leads to performance bottlenecks. Naive storage expansion increases retrieval noise and computational latency, overwhelming the reasoning capacity of models deployed on constrained personal devices. To address this, we propose Hierarchical Memory Orchestration (HMO), a framework that organizes interaction history into a three-tiered directory driven by user-centric contextual relevance. Our system maintains a compact primary cache, coupling recent and pivotal memories with an evolving user profile to ensure agent reasoning remains aligned with individual behavioral traits. This primary cache is complemented by a high-priority secondary layer, both of which are managed within a global archive of the full interaction history. Crucially, the user persona dictates memory redistribution across this hierarchy, promoting records mapped to long-term patterns toward more active tiers while relegating less relevant information. This targeted orchestration surfaces historical knowledge precisely when needed while maintaining a lean and efficient active search space. Evaluations on multiple benchmarks achieve state-of-the-art performance. Real-world deployments in ecosystems like OpenClaw demonstrate that HMO significantly enhances agent fluidity and personalization.

Can Heterogeneous Language Models Be Fused?

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shilian Chen · Jie Zhou · Qin Chen · Wen Wu · Xin Li · Qi Feng · Liang He
3 April 2026 at 12:00
arXiv:2604.01674v1 Announce Type: new Abstract: Model merging aims to integrate multiple expert models into a single model that inherits their complementary strengths without incurring the inference-time cost of ensembling. Recent progress has shown that merging can be highly effective when all source models are \emph{homogeneous}, i.e., derived from the same pretrained backbone and therefore share aligned parameter coordinates or compatible task vectors. Yet this assumption is increasingly unrealistic in open model ecosystems, where useful experts are often built on different families such as Llama, Qwen, and Mistral. In such \emph{heterogeneous} settings, direct weight-space fusion becomes ill-posed due to architectural mismatch, latent basis misalignment, and amplified cross-source conflict. We address this problem with \texttt{HeteroFusion} for heterogeneous language model fusion, which consists of two key components: topology-based alignment that transfers knowledge across heterogeneous backbones by matching functional module structures instead of raw tensor coordinates, and conflict-aware denoising that suppresses incompatible or noisy transfer signals during fusion. We further provide analytical justification showing that preserving the target adapter basis while predicting structured updates leads to a stable and well-conditioned transfer process. Across heterogeneous transfer, multi-source fusion, noisy-source robustness, and cross-family generalization settings, \texttt{HeteroFusion} consistently outperforms strong merging, fusion, and ensemble baselines.

EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hanrong Zhang · Shicheng Fan · Henry Peng Zou · Yankai Chen · Zhenting Wang · Jiayu Zhou · Chengze Li · Wei-Chieh Huang · Yifei Yao · Kening Zheng · Xue Liu · Xiaoxiao Li · Philip S. Yu
3 April 2026 at 12:00
arXiv:2604.01687v1 Announce Type: new Abstract: Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address. A tool is a single, self-contained function, whereas a skill is a structured bundle of interdependent multi-file artifacts. Currently, skill generation is not only label-intensive due to manual authoring, but also may suffer from human--machine cognitive misalignment, which can lead to degraded agent performance, as evidenced by evaluations on SkillsBench. Therefore, we aim to enable agents to autonomously generate skills. However, existing self-evolving methods designed for tools cannot be directly applied to skills due to their increased complexity. To address these issues, we propose EvoSkills, a self-evolving skills framework that enables agents to autonomously construct complex, multi-file skill packages. Specifically, EvoSkills couples a Skill Generator that iteratively refines skills with a Surrogate Verifier that co-evolves to provide informative and actionable feedback without access to ground-truth test content. On SkillsBench, EvoSkills achieves the highest pass rate among five baselines on both Claude Code and Codex, and also exhibits strong generalization capabilities to six additional LLMs.

Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianhao Shi · Yang Zhang · Xiaoyan Zhao · Fengbin Zhu · Chenyi Lei · Han Li · Wenwu Ou · Yang Song · Yongdong Zhang · Fuli Feng
3 April 2026 at 12:00
arXiv:2604.01690v1 Announce Type: new Abstract: The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a rigorous examination of its behavioral and distributional implications. Leveraging a comprehensive longitudinal dataset comprising tens of millions of users from a leading Chinese video-sharing platform, this study elucidated the distinct creation and consumption behaviors characterizing AIGC versus Human-Generated Content (HGC). We identified a prevalent scale-over-preference dynamic, wherein AIGC creators achieve aggregate engagement comparable to HGC creators through high-volume production, despite a marked consumer preference for HGC. Deeper analysis uncovered the ability of the algorithmic content distribution mechanism in moderating these competing interests regarding AIGC. These findings advocated for the implementation of AIGC-sensitive distribution algorithms and precise governance frameworks to ensure the long-term health of the online content platforms.

LiteInception: A Lightweight and Interpretable Deep Learning Framework for General Aviation Fault Diagnosis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhihuan Wei · Xinhang Chen · Danyang Han · Yang Hu · Jie Liu · Xuewen Miao · Guijiang Li
3 April 2026 at 12:00
arXiv:2604.01725v1 Announce Type: new Abstract: General aviation fault diagnosis and efficient maintenance are critical to flight safety; however, deploying deep learning models on resource-constrained edge devices poses dual challenges in computational capacity and interpretability. This paper proposes LiteInception--a lightweight interpretable fault diagnosis framework designed for edge deployment. The framework adopts a two-stage cascaded architecture aligned with standard maintenance workflows: Stage 1 performs high-recall fault detection, and Stage 2 conducts fine-grained fault classification on anomalous samples, thereby decoupling optimization objectives and enabling on-demand allocation of computational resources. For model compression, a multi-method fusion strategy based on mutual information, gradient analysis, and SE attention weights is proposed to reduce the input sensor channels from 23 to 15, and a 1+1 branch LiteInception architecture is introduced that compresses InceptionTime parameters by 70%, accelerates CPU inference by over 8x, with less than 3% F1 loss. Furthermore, knowledge distillation is introduced as a precision-recall regulation mechanism, enabling the same lightweight model to adapt to different scenarios--such as safety-critical and auxiliary diagnosis--by switching training strategies. Finally, a dual-layer interpretability framework integrating four attribution methods is constructed, providing traceable evidence chains of "which sensor x which time period." Experiments on the NGAFID dataset demonstrate a fault detection accuracy of 81.92% with 83.24% recall, and a fault identification accuracy of 77.00%, validating the framework's favorable balance among efficiency, accuracy, and interpretability.

Not All Tokens See Equally: Perception-Grounded Policy Optimization for Large Vision-Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zekai Ye · Qiming Li · Xiaocheng Feng · Ruihan Chen · Ziming Li · Haoyu Ren · Kun Chen · Dandan Tu · Bing Qin
3 April 2026 at 12:00
arXiv:2604.01840v1 Announce Type: new Abstract: While Reinforcement Learning from Verifiable Rewards (RLVR) has advanced reasoning in Large Vision-Language Models (LVLMs), prevailing frameworks suffer from a foundational methodological flaw: by distributing identical advantages across all generated tokens, these methods inherently dilute the learning signals essential for optimizing the critical, visually-grounded steps of multimodal reasoning. To bridge this gap, we formulate \textit{Token Visual Dependency}, quantifying the causal information gain of visual inputs via the Kullback-Leibler (KL) divergence between visual-conditioned and text-only predictive distributions. Revealing that this dependency is highly sparse and semantically pivotal, we introduce Perception-Grounded Policy Optimization (PGPO), which is a novel fine-grained credit assignment framework that dynamically reshapes advantages at the token level. Through a threshold-gated, mass-conserving mechanism, PGPO actively amplifies learning signals for visually-dependent tokens while suppressing gradient noise from linguistic priors. Extensive experiments based on the Qwen2.5-VL series across seven challenging multimodal reasoning benchmarks demonstrate that PGPO boosts models by 18.7% on average. Both theoretical and empirical analyses confirm that PGPO effectively reduces gradient variance, prevents training collapse, and acts as a potent regularizer for robust, perception-grounded multimodal reasoning. Code will be published on https://github.com/Yzk1114/PGPO.

SenseMath: Do LLMs Have Number Sense? Evaluating Shortcut Use, Judgment, and Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haomin Zhuang · Xiangqi Wang · Yili Shen · Ying Cheng · Xiangliang Zhang
3 April 2026 at 12:00
arXiv:2604.01988v1 Announce Type: new Abstract: Large language models often default to step-by-step computation even when efficient numerical shortcuts are available. This raises a basic question: do they exhibit number sense in a human-like behavioral sense, i.e., the ability to recognize numerical structure, apply shortcuts when appropriate, and avoid them when they are not? We introduce SenseMath, a controlled benchmark for evaluating structure-sensitive numerical reasoning in LLMs. SenseMath contains 4,800 items spanning eight shortcut categories and four digit scales, with matched strong-shortcut, weak-shortcut, and control variants. It supports three evaluation settings of increasing cognitive demand: Shortcut Use (whether models can apply shortcuts on shortcut-amenable problems); Applicability Judgment (whether they can recognize when a shortcut is appropriate or misleading); and Problem Generation (whether they can generate new problem items that correctly admit a given type of shortcut). Our evaluation across five LLMs, ranging from GPT-4o-mini to Llama-3.1-8B, shows a consistent pattern: when explicitly prompted, models readily adopt shortcut strategies and achieve substantial accuracy gains on shortcut-amenable items (up to 15%), yet under standard chain-of-thought prompting they spontaneously employ such strategies in fewer than 40% of cases, even when they demonstrably possess the requisite capability. Moreover, this competence is confined to the Use level; models systematically over-generalise shortcuts to problems where they do not apply, and fail to generate valid shortcut-bearing problems from scratch. Together, these results suggest that current LLMs exhibit procedural shortcut fluency without the structural understanding of when and why shortcuts work that underlies human number sense.
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