Compositional Diffusion with Guided Search for Long-Horizon Planning

• URL: http://arxiv.org/abs/2601.00126v2
• Date: Mon, 05 Jan 2026 15:30:54 GMT
• Title: Compositional Diffusion with Guided Search for Long-Horizon Planning
• Abstract summary: We propose Compositional Diffusion with Guided Search (CDGS) for compositional generative models.<n>Our method explores diverse combinations of local modes through population-based sampling, prunes infeasible candidates using likelihood-based filtering, and enforces global consistency through iterative resampling.<n>The approach generalizes across domains, enabling coherent text-guided panoramic images and long videos through effective local-to-global message passing.
• Score: 25.850015543620128
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Generative models have emerged as powerful tools for planning, with compositional approaches offering particular promise for modeling long-horizon task distributions by composing together local, modular generative models. This compositional paradigm spans diverse domains, from multi-step manipulation planning to panoramic image synthesis to long video generation. However, compositional generative models face a critical challenge: when local distributions are multimodal, existing composition methods average incompatible modes, producing plans that are neither locally feasible nor globally coherent. We propose Compositional Diffusion with Guided Search (CDGS), which addresses this mode averaging problem by embedding search directly within the diffusion denoising process. Our method explores diverse combinations of local modes through population-based sampling, prunes infeasible candidates using likelihood-based filtering, and enforces global consistency through iterative resampling between overlapping segments. CDGS matches oracle performance on seven robot manipulation tasks, outperforming baselines that lack compositionality or require long-horizon training data. The approach generalizes across domains, enabling coherent text-guided panoramic images and long videos through effective local-to-global message passing. More details: https://cdgsearch.github.io/

Related papers

• Plan First, Diffuse Later: Extrinsic Graph Guidance for Long-Horizon Diffusion Planning [44.52185127499181]
  Compositional diffusion models offer a promising route to long-horizon planning.<n>But enforcing local behavior over long chains is often insufficient for a coherent global structure to emerge.<n>Recent works tackle this limitation through intrinsic search, which explores multiple paths during the denoising process.
  arXiv  Detail & Related papers  (2026-05-16T07:53:04Z)
• Refining Compositional Diffusion for Reliable Long-Horizon Planning [31.40044829548484]
  We propose a training-free guidance method that steers compositional sampling toward high-density, globally coherent plans.<n> Experiments on challenging long-horizon tasks from OGBench, including locomotion, object manipulation, and pixel-based observations, demonstrate that RCD consistently outperforms existing methods.
  arXiv  Detail & Related papers  (2026-05-04T18:44:19Z)
• Mitigating Long-Tail Bias via Prompt-Controlled Diffusion Augmentation [36.94429692322632]
  We present a prompt-controlled diffusion augmentation framework that synthesizes paired label--image samples with explicit control of both domain and semantic composition.<n>We show gains concentrated on minority classes and improved Urban and Rural generalization, demonstrating controllable augmentation as a practical mechanism to mitigate long-tail bias in remote-sensing segmentation.
  arXiv  Detail & Related papers  (2026-02-04T16:49:16Z)
• Sequence Pathfinder for Multi-Agent Pickup and Delivery in the Warehouse [10.576983033957953]
  Multi-Agent Pickup and Delivery (MAPD) is a challenging extension of Multi-Agent Path Finding (MAPF)<n> Communication learning can alleviate the lack of global information but introduce high computational complexity due to point-to-point communication.<n>We propose the Sequential Pathfinder (SePar) to achieve implicit information exchange, reducing decision-making complexity from exponential to linear.
  arXiv  Detail & Related papers  (2025-09-28T09:48:13Z)
• MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection [64.29452783056253]
  The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia.<n>Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored.<n>We propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities.
  arXiv  Detail & Related papers  (2024-09-15T13:08:59Z)
• Coherent and Multi-modality Image Inpainting via Latent Space Optimization [61.99406669027195]
  PILOT (intextbfPainting vtextbfIa textbfLatent textbfOptextbfTimization) is an optimization approach grounded on a novel textitsemantic centralization and textitbackground preservation loss. Our method searches latent spaces capable of generating inpainted regions that exhibit high fidelity to user-provided prompts while maintaining coherence with the background.
  arXiv  Detail & Related papers  (2024-07-10T19:58:04Z)
• Multi-view Aggregation Network for Dichotomous Image Segmentation [76.75904424539543]
  Dichotomous Image (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images. Existing methods rely on tedious multiple encoder-decoder streams and stages to gradually complete the global localization and local refinement. Inspired by it, we model DIS as a multi-view object perception problem and provide a parsimonious multi-view aggregation network (MVANet) Experiments on the popular DIS-5K dataset show that our MVANet significantly outperforms state-of-the-art methods in both accuracy and speed.
  arXiv  Detail & Related papers  (2024-04-11T03:00:00Z)
• Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models [54.1843419649895]
  We propose a solution based on denoising diffusion probabilistic models (DDPMs) Our motivation for choosing diffusion models over other generative models comes from the flexible internal structure of diffusion models. Our method can unite multiple diffusion models trained on multiple sub-tasks and conquer the combined task.
  arXiv  Detail & Related papers  (2022-12-01T18:59:55Z)
• Learning Continuous Implicit Representation for Near-Periodic Patterns [41.70435860192222]
  Near-Periodic Patterns (NPP) are ubiquitous in man-made scenes and are composed of tiled motifs with appearance differences caused by lighting, defects, or design elements. representing NPP is challenging because it needs to maintain global consistency while preserving local variations. We learn a neural representation using a coordinate-based with single image optimization.
  arXiv  Detail & Related papers  (2022-08-25T18:01:04Z)
• Style Curriculum Learning for Robust Medical Image Segmentation [62.02435329931057]
  Deep segmentation models often degrade due to distribution shifts in image intensities between the training and test data sets. We propose a novel framework to ensure robust segmentation in the presence of such distribution shifts.
  arXiv  Detail & Related papers  (2021-08-01T08:56:24Z)
• Global Aggregation then Local Distribution for Scene Parsing [99.1095068574454]
  We show that our approach can be modularized as an end-to-end trainable block and easily plugged into existing semantic segmentation networks. Our approach allows us to build new state of the art on major semantic segmentation benchmarks including Cityscapes, ADE20K, Pascal Context, Camvid and COCO-stuff.
  arXiv  Detail & Related papers  (2021-07-28T03:46:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.