#01Nov 9, 2026
cs.RO
DPGS-SLAM: Diffusion-Prior and Provenance-Aware Monocular Gaussian Splatting SLAM for Dynamic Visual Sensing
Yifan Luo, Takafumi Matsumaru, Vibekananda Dutta and 1 more
This paper presents DPGS-SLAM (Diffusion-Prior and Provenance-Aware Monocular Gaussian Splatting SLAM) for dense mapping in environments containing dynamic objects. Dynamic observations can contaminate camera tracking and scene reconstruction in visual SLAM (simultaneous localization and mapping). Although 3DGS (3D Gaussian splatting) enables efficient dense mapping, existing 3DGS-SLAM systems may exhibit instability when dynamic objects violate their underlying static-scene assumption. Excluding dynamic observations can reduce their influence on tracking and mapping, but may leave occluded static backgrounds missing or weakly constrained. Our proposed DPGS-SLAM is a monocular RGB SLAM framework that supports provisional representations of insufficiently observed static backgrounds through asynchronous diffusion-based background completion and provenance-aware Gaussian mapping. Dynamic regions are identified through optical-flow-based motion discovery and temporally consistent mask propagation by SAM2 (segment anything model 2). For selected keyframes, a fine-tuned diffusion inpainting model generates plausible background appearance in occluded regions, while Metric3D v2 predicts metric depth from the completed images to initialize provisional Gaussian primitives. Provenance labels distinguish generated primitives from observation-supported primitives, excluding generated content from camera-pose estimation and observed-map optimization. When previously occluded backgrounds become visible, eligible generated primitives are removed, and observation-supported primitives are independently initialized from real RGB observations and predicted depth. Experimental results demonstrated competitive camera tracking, with average ATE RMSE values of 1.2 cm on TUM RGB-D and 2.7 cm on BONN RGB-D. On StaDy4D, DPGS-SLAM improved average PSNR by 4.60 dB over WildGS-SLAM using only monocular RGB input.
#02Nov 9, 2026
eess.IV
Leveraging High-Activity Post-Therapeutic I-131 Scans for Deep Learning Reconstruction of Extremely Sparse Planar Images in Differentiated Thyroid Cancer
Boyang Pan, Hang Yang, Jianchen Pan and 4 more
Background: Diagnostic I-131 whole-body planar imaging in differentiated thyroid cancer (DTC) is count-limited by low administered activity, and quality worsens under rapid acquisition. Models trained on such data struggle because their targets lack photon statistics. Purpose: To test whether high-quality post-therapeutic (PT) I-131 scans can serve as superior training references for enhancing sparse diagnostic planar images, using transfer learning and input-image addition. Methods: In this single-center prospective study, 111 patients were enrolled in PT training (n = 19), diagnostic follow-up (FU) training (n = 58), and FU paired test (n = 34) cohorts. Five Restormer-based models were trained: FU_DLx2 on FU labels; PT_DLx2 transfer-learned on PT data; PT_DLx1+ and PT_DLx2+ with input-image addition; PT_DLx6 as a PT-only reference. Test-time processing used two-step denormalization and Poisson re-scaling to preserve noise statistics. Quality was assessed by PSNR, SSIM, and a blinded dual-reader evaluation of all 34 test-cohort patients. Results: PT_DLx2 achieved the highest PSNR (34.4 +/- 3.6 dB) and SSIM (0.81 +/- 0.07). PT_DLx1+ and PT_DLx2+ were comparable (33.8 and 33.3 dB; SSIM 0.80). FU_DLx2 ranked below the unprocessed fast scan (30.3 vs. 32.0 dB; 0.76 vs. 0.79). Subjectively, PT_DLx2+ scored highest in image quality (4.54 +/- 0.60) and diagnostic confidence (4.54 +/- 0.60), surpassing the reference standard (4.19 +/- 0.83 and 4.31 +/- 0.81; P = 0.005 and P = 0.008). FU_DLx2 and PT_DLx6 scored lowest. Conclusion: Count-rich PT scans provide effective training priors for reconstructing extremely sparse images. Input-image addition and cross-domain transfer learning from PT data outperformed direct training on count-limited diagnostic data, supporting 2x scan acceleration with quality matching or exceeding the unaccelerated standard scan for selected PT-derived configurations.
#03Nov 9, 2026
cs.CL
A generalized transition framework for identifying reduction domains in non-binary constituency parsing
Jungyeul Park, Zihao Huang, Eunkyul Leah Jo
Direct non-binary constituency parsing must identify an ordered sequence of children at each phrase-level reduction. We extend our earlier delimiter-guided bottom-up construction to a common calculus for top-down, in-order, and bottom-up parsing. At reduction, the rightmost delimiter marks the left boundary, the nonterminal marker supplies the mother label, and the completed items in the delimited segment determine the children and arity. We use a stack invariant to prove soundness and completeness and derive the exact length of the static oracle's action sequence. A projection-and-lifting theorem shows that each delimiter-guided system and its canonical counterpart generate the same set of trees under both the structural and grammar-licensed transition relations. In controlled experiments on PTB and CTB, the delimiter-guided parsers achieve similar labeled accuracy to their canonical counterparts across all traversal orders. For bottom-up parsing, delimiter guidance reduces the number of action classes and increases the number of supervised decisions per tree.
#04Nov 9, 2026
cs.SE
Feeling Easier, Working Harder: Cognitive Load in AI-Assisted Code Comprehension
Maryangela White, Maximilian Rose, Matthew Buckman and 3 more
Software developers spend significant time reading and understanding code, increasingly supplemented by generative AI. While these tools aim to reduce the difficulty of reading code, interacting with them introduces a dual-task environment that may impose extraneous cognitive load that is challenging to capture. This study investigates how access to generative AI affects developers' real-time cognitive workload, code comprehension performance, and self-perceived code comprehension strategies. We conducted a 3x2 within-subjects study with 20 novice CS students reviewing Java code snippets of varying lengths with or without AI assistance, followed by an AI-unassisted comprehension quiz. Cognitive workload was continuously captured using a Detection-Response Task (tDRT) during both reading and quiz phases, and compared to post-quiz NASA-TLX and SEQ scores. AI assistance significantly increased objective cognitive workload, raising tDRT reaction times by 11.2%, yet participants' perceived workload remained unchanged. Code comprehension scores were high under both conditions, with no evidence that AI assistance improved comprehension at the difficulty levels evaluated; a ceiling effect limited power to detect benefits on harder tasks. Qualitatively, participants reported trusting the AI conditionally -verifying its output against their own reading for more complex snippets, but directly trusting it for simpler code- and, while many felt their reading process was unchanged, a notable subset shifted toward skimming or reading the AI's explanations in place of the code itself. These findings reveal that developers do not accurately perceive the cognitive resources they expend when managing AI tools. This study provides evidence of added cognitive effort associated with using generative AI, pointing towards a need to account for this dual-task penalty in software engineering tools and education.
#05Nov 9, 2026
cs.CV
Leveraging Complementary Event Frame and Voxel Representations for RGB-Guided Multimodal Pre-training
Wentao Wu, Xiao Wang, Yi Wang and 3 more
In this paper, we propose CM3AE 2.0, a unified RGB Frame and Event-Voxel/-Frame pre-training framework. By jointly learning multimodal spatiotemporal representations and exploiting latent cross-modal dependencies through cross-modal generation, the framework enhances multimodal information interaction and collaborative modeling capabilities. Specifically, we first propose an Event-aware Spatiotemporal Masked Reconstruction (ESMR) strategy to learn unified multimodal spatiotemporal representations. It leverages event responses to guide masking toward dynamically salient regions. Through spatiotemporal masked reconstruction, it learns spatial structures and their temporal variations from continuous RGB-Event observations. In addition, it incorporates multimodal fusion reconstruction and cross-modal contrastive learning to facilitate the modeling of cross-modal complementary information and the learning of shared representations. Building upon this, we further propose a Hierarchical Cross-modal Generation (HCG) strategy to learn latent cross-modal dependencies through visual and language generation. At the visual level, Event-conditioned RGB generation is performed using a pre-trained diffusion model to capture visual associations between the two modalities. At the language level, video-level text description generation based on joint RGB-Event representations establishes connections between multimodal visual features and linguistic semantics, further enriching the learning of cross-modal dependencies. Extensive experiments on four diverse downstream tasks demonstrate that CM3AE 2.0 consistently improves performance, validating the effectiveness and generalization capability of the proposed pre-training framework.