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Received — 13 April 2026 ⏭

Baseline cellular state dictates the molecular impact of KRAS mutant variants in pancreatic cancer cells

✇(Multiomics OR Omics) AND (Pancreatic)
By: Yanixa Quiñones-Avilés · Barbora Salovska · Cassandra S Markham · Yi Di · Benjamin E Turk · Yansheng Liu · Mandar Deepak Muzumdar
10 April 2026 at 18:00

bioRxiv [Preprint]. 2026 Mar 12:2026.03.10.710185. doi: 10.64898/2026.03.10.710185.

ABSTRACT

KRAS is mutated in over 90% of pancreatic ductal adenocarcinomas (PDAC), where hotspot alterations in codons 12, 13, and 61 drive tumor initiation and progression. Although distinct biochemical properties have been described for individual KRAS mutants, whether they generate unique allele-specific signaling programs in PDAC cells remains unresolved. Here, we systematically interrogated the molecular consequences of seven common KRAS mutant variants in reconstituted isogenic, KRAS-deficient PDAC cell lines by integrated transcriptomic, proteomic, and phosphoproteomic profiling. We found that baseline cellular state, rather than allele identity, was the predominant driver of molecular variation. Comparisons with established KRAS reference signatures revealed significant but moderate overlap at the mRNA level and less so at the proteome level. Pathway analyses highlighted interferon response and mitochondrial translation as recurrently altered across alleles, while phosphoproteomic data confirmed robust ERK1/2 activity and suppression of DYRK kinase substrates by mutant KRAS expression. Importantly, no robust allele-specific molecular programs were identified. Together, our study establishes a comprehensive multi-omics resource for KRAS signaling in PDAC and demonstrates that cellular context exerts a stronger influence than allele identity in shaping molecular profiles, with implications for interpreting putative allele-specific signaling dependencies and therapeutic vulnerabilities.

PMID:41959224 | PMC:PMC13060958 | DOI:10.64898/2026.03.10.710185

Received — 4 April 2026 ⏭

Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Gengsheng Li · Tianyu Yang · Junfeng Fang · Mingyang Song · Mao Zheng · Haiyun Guo · Dan Zhang · Jinqiao Wang · Tat-Seng Chua
3 April 2026 at 12:00
arXiv:2604.02288v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training large language models. While Group Relative Policy Optimization (GRPO) is widely adopted, its coarse credit assignment uniformly penalizes failed rollouts, lacking the token-level focus needed to efficiently address specific deviations. Self-Distillation Policy Optimization (SDPO) addresses this by providing denser, more targeted logit-level supervision that facilitates rapid early improvement, yet it frequently collapses during prolonged training. We trace this late-stage instability to two intrinsic flaws: self-distillation on already-correct samples introduces optimization ambiguity, and the self-teacher's signal reliability progressively degrades. To resolve these issues, we propose Sample-Routed Policy Optimization (SRPO), a unified on-policy framework that routes correct samples to GRPO's reward-aligned reinforcement and failed samples to SDPO's targeted logit-level correction. SRPO further incorporates an entropy-aware dynamic weighting mechanism to suppress high-entropy, unreliable distillation targets while emphasizing confident ones. Evaluated across five benchmarks and two model scales, SRPO achieves both the rapid early improvement of SDPO and the long-horizon stability of GRPO. It consistently surpasses the peak performance of both baselines, raising the five-benchmark average on Qwen3-8B by 3.4% over GRPO and 6.3% over SDPO, while simultaneously yielding moderate response lengths and lowering per-step compute cost by up to 17.2%.
Received — 2 April 2026 ⏭

Correction: Integrative multi-omics and machine learning reveals the spatial niche distribution and role of CYP27A1+TAMs in immunotherapy response in non-small cell lung cancer

✇Omics In Lung
By: Qingsheng Liu · Xufeng Liu · Han Zhang · Yuhang Jiang · Ying Shi · Qiuqiao Mu · Yuhao Jing · Daqiang Sun
1 April 2026 at 18:00

Front Immunol. 2026 Mar 16;17:1822612. doi: 10.3389/fimmu.2026.1822612. eCollection 2026.

ABSTRACT

[This corrects the article DOI: 10.3389/fimmu.2026.1782545.].

PMID:41918731 | PMC:PMC13033988 | DOI:10.3389/fimmu.2026.1822612

Correction: Integrative multi-omics and machine learning reveals the spatial niche distribution and role of CYP27A1+TAMs in immunotherapy response in non-small cell lung cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Qingsheng Liu · Xufeng Liu · Han Zhang · Yuhang Jiang · Ying Shi · Qiuqiao Mu · Yuhao Jing · Daqiang Sun
1 April 2026 at 18:00

Front Immunol. 2026 Mar 16;17:1822612. doi: 10.3389/fimmu.2026.1822612. eCollection 2026.

ABSTRACT

[This corrects the article DOI: 10.3389/fimmu.2026.1782545.].

PMID:41918731 | PMC:PMC13033988 | DOI:10.3389/fimmu.2026.1822612

Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siyuan Du · Siyi Li · Shuwei Bai · Ang Li · Haolin Li · Mingqing Xiao · Yang Pan · Dongsheng Li · Weidi Xie · Yanfeng Wang · Ya Zhang · Chencheng Zhang · Jiangchao Yao
1 April 2026 at 12:00
arXiv:2603.29176v1 Announce Type: new Abstract: Parkinson's disease (PD) affects over ten million people worldwide. Although temporal interference (TI) and deep brain stimulation (DBS) are promising therapies, inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost. Previous explorations either resort to limited statistical biomarkers that are insufficient to characterize variability, or employ AI-driven methods which is prone to overfitting and opacity. We bridge this gap with a pretraining-finetuning framework to predict outcomes directly from resting-state fMRI. Critically, a generative virtual brain foundation model, pretrained on a collective dataset (2707 subjects, 5621 sessions) to capture universal disorder patterns, was finetuned on PD cohorts receiving TI (n=51) or DBS (n=55) to yield individualized virtual brains with high fidelity to empirical functional connectivity (r=0.935). By constructing counterfactual estimations between pathological and healthy neural states within these personalized models, we predicted clinical responses (TI: AUPR=0.853; DBS: AUPR=0.915), substantially outperforming baselines. External and prospective validations (n=14, n=11) highlight the feasibility of clinical translation. Moreover, our framework provides state-dependent regional patterns linked to response, offering hypothesis-generating mechanistic insights.

Route-Induced Density and Stability (RIDE): Controlled Intervention and Mechanism Analysis of Routing-Style Meta Prompts on LLM Internal States

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dianxing Zhang · Gang Li · Sheng Li
1 April 2026 at 12:00
arXiv:2603.29206v1 Announce Type: new Abstract: Routing is widely used to scale large language models, from Mixture-of-Experts gating to multi-model/tool selection. A common belief is that routing to a task ``expert'' activates sparser internal computation and thus yields more certain and stable outputs (the Sparsity--Certainty Hypothesis). We test this belief by injecting routing-style meta prompts as a textual proxy for routing signals in front of frozen instruction-tuned LLMs. We quantify (C1) internal density via activation sparsity, (C2) domain-keyword attention, and (C3) output stability via predictive entropy and semantic variation. On a RouterEval subset with three instruction-tuned models (Qwen3-8B, Llama-3.1-8B-Instruct, and Mistral-7B-Instruct-v0.2), meta prompts consistently densify early/middle-layer representations rather than increasing sparsity; natural-language expert instructions are often stronger than structured tags. Attention responses are heterogeneous: Qwen/Llama reduce keyword attention, while Mistral reinforces it. Finally, the densification--stability link is weak and appears only in Qwen, with near-zero correlations in Llama and Mistral. We present RIDE as a diagnostic probe for calibrating routing design and uncertainty estimation.
Received — 26 March 2026 ⏭

SortedRL: Accelerating RL Training for LLMs through Online Length-Aware Scheduling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiqi Zhang · Huiqiang Jiang · Xufang Luo · Zhihe Yang · Chengruidong Zhang · Yifei Shen · Dongsheng Li · Yuqing Yang · Lili Qiu · Yang You
25 March 2026 at 12:00
arXiv:2603.23414v1 Announce Type: cross Abstract: Scaling reinforcement learning (RL) has shown strong promise for enhancing the reasoning abilities of large language models (LLMs), particularly in tasks requiring long chain-of-thought generation. However, RL training efficiency is often bottlenecked by the rollout phase, which can account for up to 70% of total training time when generating long trajectories (e.g., 16k tokens), due to slow autoregressive generation and synchronization overhead between rollout and policy updates. We propose SortedRL, an online length-aware scheduling strategy designed to address this bottleneck by improving rollout efficiency and maintaining training stability. SortedRL reorders rollout samples based on output lengths, prioritizing short samples forming groups for early updates. This enables large rollout batches, flexible update batches, and near on-policy micro-curriculum construction simultaneously. To further accelerate the pipeline, SortedRL incorporates a mechanism to control the degree of off-policy training through a cache-based mechanism, and is supported by a dedicated RL infrastructure that manages rollout and update via a stateful controller and rollout buffer. Experiments using LLaMA-3.1-8B and Qwen-2.5-32B on diverse tasks, including logical puzzles, and math challenges like AIME 24, Math 500, and Minerval, show that SortedRL reduces RL training bubble ratios by over 50%, while attaining 3.9% to 18.4% superior performance over baseline given same amount of data.

Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sheng Liu · Long Chen · Zeyun Zhao · Qinglin Gou · Qingyue Wei · Arjun Masurkar · Kevin M. Spiegler · Philip Kuball · Stefania C. Bray · Megan Bernath · Deanna R. Willis · Jiang Bian · Lei Xing · Eric Topol · Kyunghyun Cho · Yu Huang · Ruogu Fang · Narges Razavian · James Zou
25 March 2026 at 12:00
arXiv:2603.21597v2 Announce Type: replace Abstract: Modern clinical practice increasingly depends on reasoning over heterogeneous, evolving, and incomplete patient data. Although recent advances in multimodal foundation models have improved performance on various clinical tasks, most existing models remain static, opaque, and poorly aligned with real-world clinical workflows. We present Cerebra, an interactive multi-agent AI team that coordinates specialized agents for EHR, clinical notes, and medical imaging analysis. These outputs are synthesized into a clinician-facing dashboard that combines visual analytics with a conversational interface, enabling clinicians to interrogate predictions and contextualize risk at the point of care. Cerebra supports privacy-preserving deployment by operating on structured representations and remains robust when modalities are incomplete. We evaluated Cerebra using a massive multi-institutional dataset spanning 3 million patients from four independent healthcare systems. Cerebra consistently outperformed both state-of-the-art single-modality models and large multimodal language model baselines. In dementia risk prediction, it achieved AUROCs up to 0.80, compared with 0.74 for the strongest single-modality model and 0.68 for language model baselines. For dementia diagnosis, it achieved an AUROC of 0.86, and for survival prediction, a C-index of 0.81. In a reader study with experienced physicians, Cerebra significantly improved expert performance, increasing accuracy by 17.5 percentage points in prospective dementia risk estimation. These results demonstrate Cerebra's potential for interpretable, robust decision support in clinical care.

Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBench

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fenfen Lin · Yesheng Liu · Haiyu Xu · Chen Yue · Zheqi He · Mingxuan Zhao · Miguel Hu Chen · Jiakang Liu · JG Yao · Xi Yang
25 March 2026 at 12:00
arXiv:2510.26865v2 Announce Type: replace-cross Abstract: Reading measurement instruments is effortless for humans and requires relatively little domain expertise, yet it remains surprisingly challenging for current vision-language models (VLMs) as we find in preliminary evaluation. In this work, we introduce MeasureBench, a benchmark on visual measurement reading covering both real-world and synthesized images of various types of measurements, along with an extensible pipeline for data synthesis. Our pipeline procedurally generates a specified type of gauge with controllable visual appearance, enabling scalable variation in key details such as pointers, scales, fonts, lighting, and clutter. Evaluation on popular proprietary and open-weight VLMs shows that even the strongest frontier VLMs struggle with measurement reading in general. We have also conducted preliminary experiments with reinforcement finetuning (RFT) over synthetic data, and find a significant improvement on both in-domain synthetic subset and real-world images. Our analysis highlights a fundamental limitation of current VLMs in fine-grained spatial grounding. We hope this resource and our code releases can help future advances on visually grounded numeracy and precise spatial perception of VLMs, bridging the gap between recognizing numbers and measuring the world.

Integrated Machine Learning and Multi-Omics Identifies a Novel Molecular Signature for Improving the Prognosis of Hepatocellular Carcinoma

✇Omics in Hepatocellular
By: Zhongxu Wu · Jianguang Xiong · Qisheng Liu · Chengdang Wang · Dan Li · Liuliu Wei · Jian Ding
18 March 2026 at 18:00

J Hepatocell Carcinoma. 2026 Mar 11;13:574690. doi: 10.2147/JHC.S574690. eCollection 2026.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) exhibits significant molecular heterogeneity and complex immune microenvironment, which to some extent limits the accuracy of prognosis assessment and the formulation of individualized treatment strategies. This study aims to identify immune-derived molecular signatures based on multi-omics data and machine learning methods for the prognosis prediction and risk stratification of HCC.

METHODS: Based on weighted gene co-expression network analysis(WGCNA) and differential gene analysis,immune-derived molecular signature (IDMS) were screened in both single-cell and bulk transcriptomes. Prognostic model was constructed by multi-machine learning approachs. Subsequently, we investigated the differences in mutations, biological functions, and immune cell infiltration within the tumor microenvironment between the high- and low-risk groups.In addition, we comprehensively analyzed the drug sensitivity of IDMS and predicted potential drugs.

RESULTS: We identified seven hub genes at the single-cell and bulk transcriptome levels. Based on multiple machine learning, we constructed a prognostic model that demonstrated excellent performance in predicting overall survival for patients with HCC. IDMS -integrated normograms provide a promising and quantitative tool for clinical risk management.Notably, a significant difference in microsatellite instability (MSI) was observed between the high- and low-risk groups. This indicates that patients in the high-risk group might have a better response to immunotherapy. Additionally, we predicted potential drugs targeting to these risk subgroups.

CONCLUSION: Our research developed an IDMS that could serve as an effective tool for patient stratification management and prognosis prediction. This signature could provide a reference for immunotherapy for patients with HCC and improve their prognosis.

PMID:41847219 | PMC:PMC12991065 | DOI:10.2147/JHC.S574690

Received — 14 March 2026 ⏭

MAPK14/SLC7A11/GPX4 axis dysregulation drives podocyte ferroptosis via mediating glycerophospholipid metabolism

✇Cell Death Discovery nature.com science feeds
By: Shi Qiu · Dandan Xie · Sifan Guo · Zhibo Wang · Ying Cai · Xian Wang · Zhencai Hu · Shiwei Wang · Chunsheng Lin · Hong Yao · Qiang Yang · Yu Guan · Qiqi Zhao · Songqi Tang · Wenjie Sun · Yiqiang Xie · Aihua Zhang
11 March 2026 at 08:00

Cell Death Discovery, Published online: 11 March 2026; doi:10.1038/s41420-026-02990-7

MAPK14/SLC7A11/GPX4 axis dysregulation drives podocyte ferroptosis via mediating glycerophospholipid metabolism

PIRA-Bench: A Transition from Reactive GUI Agents to GUI-based Proactive Intent Recommendation Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuxiang Chai · Shunye Tang · Han Xiao · Rui Liu · Hongsheng Li
10 March 2026 at 12:00
arXiv:2603.08013v1 Announce Type: new Abstract: Current Graphical User Interface (GUI) agents operate primarily under a reactive paradigm: a user must provide an explicit instruction for the agent to execute a task. However, an intelligent AI assistant should be proactive, which is capable of anticipating user intentions directly from continuous visual inputs, such as mobile or desktop screenshots, and offering timely recommendations without explicit user prompting. Transitioning to this proactive paradigm presents significant challenges. Real-world screen activity is rarely linear; it consists of long-horizon trajectories fraught with noisy browsing, meaningless actions, and multithreaded task-switching. To address this gap, we introduce PIRA-Bench (Proactive Intent Recommendation Agent Benchmark), a novel benchmark for evaluating multimodal large language models (MLLMs) on continuous, weakly-supervised visual inputs. Unlike reactive datasets, PIRA-Bench features complex trajectories with multiple interleaved intents and noisy segments with various user profile contexts, challenging agents to detect actionable events while fitting to user preferences. Furthermore, we propose the PIRF baseline, a memory-aware, state-tracking framework that empowers general MLLMs to manage multiple task threads and handle misleading visual inputs. PIRA-Bench serves as an initial step toward robust and proactive GUI-based personal assistants.
Received — 11 March 2026 ⏭

VORL-EXPLORE: A Hybrid Learning Planning Approach to Multi-Robot Exploration in Dynamic Environments

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ning Liu · Sen Shen · Zheng Li · Sheng Liu · Dongkun Han · Shangke Lyu · Thomas Braunl
10 March 2026 at 12:00
arXiv:2603.07973v1 Announce Type: cross Abstract: Hierarchical multi-robot exploration commonly decouples frontier allocation from local navigation, which can make the system brittle in dense and dynamic environments. Because the allocator lacks direct awareness of execution difficulty, robots may cluster at bottlenecks, trigger oscillatory replanning, and generate redundant coverage. We propose VORL-EXPLORE, a hybrid learning and planning framework that addresses this limitation through execution fidelity, a shared estimate of local navigability that couples task allocation with motion execution. This fidelity signal is incorporated into a fidelity-coupled Voronoi objective with inter-robot repulsion to reduce contention before it emerges. It also drives a risk-aware adaptive arbitration mechanism between global A* guidance and a reactive reinforcement learning policy, balancing long-range efficiency with safe interaction in confined spaces. The framework further supports online self-supervised recalibration of the fidelity model using pseudo-labels derived from recent progress and safety outcomes, enabling adaptation to non-stationary obstacles without manual risk tuning. We evaluate this capability separately in a dedicated severe-traffic ablation. Extensive experiments in randomized grids and a Gazebo factory scenario show high success rates, shorter path length, lower overlap, and robust collision avoidance. The source code will be made publicly available upon acceptance.

DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yang Zhou · Hao Shao · Letian Wang · Zhuofan Zong · Hongsheng Li · Steven L. Waslander
10 March 2026 at 12:00
arXiv:2601.01528v2 Announce Type: replace-cross Abstract: Video generation models, as one form of world models, have emerged as one of the most exciting frontiers in AI, promising agents the ability to imagine the future by modeling the temporal evolution of complex scenes. In autonomous driving, this vision gives rise to driving world models: generative simulators that imagine ego and agent futures, enabling scalable simulation, safe testing of corner cases, and rich synthetic data generation. Yet, despite fast-growing research activity, the field lacks a rigorous benchmark to measure progress and guide priorities. Existing evaluations remain limited: generic video metrics overlook safety-critical imaging factors; trajectory plausibility is rarely quantified; temporal and agent-level consistency is neglected; and controllability with respect to ego conditioning is ignored. Moreover, current datasets fail to cover the diversity of conditions required for real-world deployment. To address these gaps, we present DrivingGen, the first comprehensive benchmark for generative driving world models. DrivingGen combines a diverse evaluation dataset curated from both driving datasets and internet-scale video sources, spanning varied weather, time of day, geographic regions, and complex maneuvers, with a suite of new metrics that jointly assess visual realism, trajectory plausibility, temporal coherence, and controllability. Benchmarking 14 state-of-the-art models reveals clear trade-offs: general models look better but break physics, while driving-specific ones capture motion realistically but lag in visual quality. DrivingGen offers a unified evaluation framework to foster reliable, controllable, and deployable driving world models, enabling scalable simulation, planning, and data-driven decision-making.
Received — 5 March 2026 ⏭

Graph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modeling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaqi Lv · Qingfeng Du · Yu Zhang · Yongqi Han · Sheng Li
5 March 2026 at 13:00
arXiv:2603.03662v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have emerged as a powerful framework for processing graph-structured data. However, conventional GNNs and their variants are inherently limited by the homophily assumption, leading to degradation in performance on heterophilic graphs. Although substantial efforts have been made to mitigate this issue, they remain constrained by the message-passing paradigm, which is inherently rooted in homophily. In this paper, a detailed analysis of how the underlying label autocorrelation of the homophily assumption introduces bias into GNNs is presented. We innovatively leverage a negative feedback mechanism to correct the bias and propose Graph Negative Feedback Bias Correction (GNFBC), a simple yet effective framework that is independent of any specific aggregation strategy. Specifically, we introduce a negative feedback loss that penalizes the sensitivity of predictions to label autocorrelation. Furthermore, we incorporate the output of graph-agnostic models as a feedback term, leveraging independent node feature information to counteract correlation-induced bias guided by Dirichlet energy. GNFBC can be seamlessly integrated into existing GNN architectures, improving overall performance with comparable computational and memory overhead.

Improving Diffusion Planners by Self-Supervised Action Gating with Energies

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuan Lu · Dongqi Han · Yansen Wang · Dongsheng Li
4 March 2026 at 13:00
arXiv:2603.02650v1 Announce Type: cross Abstract: Diffusion planners are a strong approach for offline reinforcement learning, but they can fail when value-guided selection favours trajectories that score well yet are locally inconsistent with the environment dynamics, resulting in brittle execution. We propose Self-supervised Action Gating with Energies (SAGE), an inference-time re-ranking method that penalises dynamically inconsistent plans using a latent consistency signal. SAGE trains a Joint-Embedding Predictive Architecture (JEPA) encoder on offline state sequences and an action-conditioned latent predictor for short horizon transitions. At test time, SAGE assigns each sampled candidate an energy given by its latent prediction error and combines this feasibility score with value estimates to select actions. SAGE can integrate into existing diffusion planning pipelines that can sample trajectories and select actions via value scoring; it requires no environment rollouts and no policy re-training. Across locomotion, navigation, and manipulation benchmarks, SAGE improves the performance and robustness of diffusion planners.

Multi-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaqi Lv · Qingfeng Du · Yu Zhang · Yongqi Han · Sheng Li
4 March 2026 at 13:00
arXiv:2603.03106v1 Announce Type: cross Abstract: Graph fraud detection (GFD) is crucial for identifying fraudulent behavior within graphs, benefiting various domains such as financial networks and social media. Existing methods based on graph neural networks (GNNs) have succeeded considerably due to their effective expressive capacity for graph-structured data. However, the inherent inductive bias of GNNs, including the homogeneity assumption and the limited global modeling ability, hinder the effectiveness of these models. To address these challenges, we propose Multi-scale Neighborhood Awareness Transformer (MANDATE), which alleviates the inherent inductive bias of GNNs. Specifically, we design a multi-scale positional encoding strategy to encode the positional information of various distances from the central node. By incorporating it with the self-attention mechanism, the global modeling ability can be enhanced significantly. Meanwhile, we design different embedding strategies for homophilic and heterophilic connections. This mitigates the homophily distribution differences between benign and fraudulent nodes. Moreover, an embedding fusion strategy is designed for multi-relation graphs, which alleviates the distribution bias caused by different relationships. Experiments on three fraud detection datasets demonstrate the superiority of MANDATE.

Geometry-Guided Reinforcement Learning for Multi-view Consistent 3D Scene Editing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiyuan Wang · Chunyu Lin · Lei Sun · Zhi Cao · Yuyang Yin · Lang Nie · Zhenlong Yuan · Xiangxiang Chu · Yunchao Wei · Kang Liao · Guosheng Lin
4 March 2026 at 13:00
arXiv:2603.03143v1 Announce Type: cross Abstract: Leveraging the priors of 2D diffusion models for 3D editing has emerged as a promising paradigm. However, maintaining multi-view consistency in edited results remains challenging, and the extreme scarcity of 3D-consistent editing paired data renders supervised fine-tuning (SFT), the most effective training strategy for editing tasks, infeasible. In this paper, we observe that, while generating multi-view consistent 3D content is highly challenging, verifying 3D consistency is tractable, naturally positioning reinforcement learning (RL) as a feasible solution. Motivated by this, we propose \textbf{RL3DEdit}, a single-pass framework driven by RL optimization with novel rewards derived from the 3D foundation model, VGGT. Specifically, we leverage VGGT's robust priors learned from massive real-world data, feed the edited images, and utilize the output confidence maps and pose estimation errors as reward signals, effectively anchoring the 2D editing priors onto a 3D-consistent manifold via RL. Extensive experiments demonstrate that RL3DEdit achieves stable multi-view consistency and outperforms state-of-the-art methods in editing quality with high efficiency. To promote the development of 3D editing, we will release the code and model.
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