Interaction Locality in Hierarchical Recursive Reasoning

• URL: http://arxiv.org/abs/2605.20784v1
• Date: Wed, 20 May 2026 06:25:49 GMT
• Title: Interaction Locality in Hierarchical Recursive Reasoning
• Abstract summary: spatial reasoning requires both location-bound computation and location-invariant structure.<n>We propose interaction locality, a framework for measuring whether information flow stays within nearby cells or semantic segments, or crosses them.<n>We instantiate the framework with sparse-autoencoder feature ablations and finite-noise activation patching, with structural Jacobian and attention checks reported in the appendix.
• Score: 6.961253535504979
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatial reasoning requires both location-bound computation and location-invariant structure: agents must make local moves while preserving route, object, or constraint-level plans. We propose interaction locality, a task-geometry-aware framework for measuring whether information flow stays within nearby cells or semantic segments, or crosses them. We instantiate the framework with sparse-autoencoder feature ablations and finite-noise activation patching, with structural Jacobian and attention checks reported in the appendix, and apply it to HRM and TRM, two compact hierarchical and recursive reasoning models, on Maze-Hard, Sudoku Extreme, and ARC-AGI. Across these models, activation patching gives the clearest architectural fingerprint: high-level recurrent states tend to write information within nearby cells or same-segment units, while repeated recursive updates accumulate these local writes into broader solution structure. This pattern holds across maze paths, Sudoku constraints, and ARC-AGI object neighborhoods, with the strongest concentration in TRM. To test whether interaction locality extends beyond toy-yet-challenging grid benchmarks, we also apply it to MTU3D, a large-scale embodied 3D scene-grounding model. In this MTU3D setting, causal spatial locality appears primarily at the transition where visual scene features are handed to the downstream grounding module, rather than uniformly throughout the visual encoder. This contrast suggests that the local-to-global handoff observed in HRM and TRM is tied to explicit recursive reasoning dynamics, while embodied 3D models may concentrate causal spatial structure at module boundaries. Interaction locality turns the intuitive local-execution/global-planning story into a reproducible measurement framework for recursive and embodied spatial reasoning.

Related papers

• EgoHieraLoc: A Cortically Inspired Hierarchical Segmentation-Guided Framework for Egocentric Visual Query Localization [9.547789251222838]
  Visual query localization (VQL) aims to retrieve and re-localize a queried object in egocentric videos.<n>Human vision handles ambiguity through a hierarchical process.<n>We propose textbfEgoHieraLoc, a unified framework for VQL-2D and VQL-3D.
  arXiv  Detail & Related papers  (2026-08-10T14:31:01Z)
• Pair2Scene: Learning Local Object Relations for Procedural Scene Generation [10.247549170637418]
  Pair2Scene is a novel procedural generation framework that integrates learned local rules with scene hierarchies and physics-based algorithms.<n>We show that our framework outperforms existing methods in generating complex environments that go beyond training data.
  arXiv  Detail & Related papers  (2026-04-13T17:59:55Z)
• Multi-session Localization and Mapping Exploiting Topological Information [1.3316173544577008]
  We propose a novel multi-session framework that builds on map-based localization.<n>Our approach incorporates a topology-informed, uncertainty-aware decision-making mechanism.<n>We validate our method on sequences overlapping from datasets and demonstrate its effectiveness in a real-world mine-like environment.
  arXiv  Detail & Related papers  (2026-02-19T10:17:46Z)
• Seeing the Unseen: Mask-Driven Positional Encoding and Strip-Convolution Context Modeling for Cross-View Object Geo-Localization [8.559240391514063]
  Cross-view object geo-localization enables high-precision object localization through cross-view matching.<n>Existing methods rely on keypoint-based positional encoding, which captures only 2D coordinates while neglecting object shape information.<n>We propose a mask-based positional encoding scheme that leverages segmentation masks to capture both spatial coordinates and object silhouettes.<n>We present EDGeo, an end-to-end framework for robust cross-view object geo-localization.
  arXiv  Detail & Related papers  (2025-10-23T06:07:07Z)
• Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition [63.55828203989405]
  We introduce a novel Topology-Aware Modeling (TAM) framework for Sim2Real UDA on object point clouds.<n>Our approach mitigates the domain gap by leveraging global spatial topology, characterized by low-level, high-frequency 3D structures.<n>We propose an advanced self-training strategy that combines cross-domain contrastive learning with self-training.
  arXiv  Detail & Related papers  (2025-06-26T11:53:59Z)
• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• Hierarchical Temporal Context Learning for Camera-based Semantic Scene Completion [57.232688209606515]
  We present HTCL, a novel Temporal Temporal Context Learning paradigm for improving camera-based semantic scene completion. Our method ranks $1st$ on the Semantic KITTI benchmark and even surpasses LiDAR-based methods in terms of mIoU.
  arXiv  Detail & Related papers  (2024-07-02T09:11:17Z)
• Hierarchical Spatio-Temporal Representation Learning for Gait Recognition [6.877671230651998]
  Gait recognition is a biometric technique that identifies individuals by their unique walking styles. We propose a hierarchical-temporal representation learning framework for extracting gait features from coarse to fine. Our method outperforms the state-of-the-art while maintaining a reasonable balance between model accuracy and complexity.
  arXiv  Detail & Related papers  (2023-07-19T09:30:00Z)
• LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers [60.51925353387151]
  We propose a novel module named Local Context Propagation (LCP) to exploit the message passing between neighboring local regions. We use the overlap points of adjacent local regions as intermediaries, then re-weight the features of these shared points from different local regions before passing them to the next layers. The proposed method is applicable to different tasks and outperforms various transformer-based methods in benchmarks including 3D shape classification and dense prediction tasks.
  arXiv  Detail & Related papers  (2022-10-23T15:43:01Z)
• Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes [82.4186966781934]
  We introduce a simple, efficient, and effective two-stage detector, termed as Ret3D. At the core of Ret3D is the utilization of novel intra-frame and inter-frame relation modules. With negligible extra overhead, Ret3D achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-08-18T03:48:58Z)
• 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.