#01Nov 5, 2026
cs.RO
MessyNav: Zero-Shot Navigation via Manipulation in Messy Environments
Kenneth Llontop, Cherie Ho, Dylan Zhou and 4 more
Real-world environments are messy, with paths often blocked by everyday objects like chairs and toys, requiring robots to perform interactive navigation by manipulating obstacles to clear a path. Existing methods rely on extensive simulation-based learning or per-scene instructions specifying which objects to avoid or manipulate. They struggle to generalize across novel objects, diverse environments, and varied contexts such as a cart of fragile glassware that should be avoided while an empty one can be pushed. We formalize this problem as open-world interactive navigation and present MessyNav, a zero-shot framework based on vision-language models (VLMs). At its core, MessyNav performs explicit VLM-based affordance reasoning before task planning to decide whether to swipe, pick up, or avoid an object and produce a high-level plan. Evaluated on a mobile manipulator across real-world indoor and outdoor environments, MessyNav improves success rate by 27 percentage points (pp) over the strongest baseline in real-robot trials and achieves statistically significant gains in a larger-scale offline evaluation, without task-specific training.
#02Nov 5, 2026
cs.CV
MIKU: Material Invariance and Knowledge Unification for PBR Generation
Yajie Bao, Jiaye Fu, Youtian Lin and 5 more
High-quality material generation for physically based rendering (PBR) remains challenging because datasets and synthesis pipelines lack standardized representations. Differences in material definitions, rendering conventions, and parameterizations lead to unstable and physically implausible results. We propose MIKU, a unified framework for standardized PBR asset representation and physically consistent material generation across diverse 3D content domains. Its core is a unified processing pipeline that standardizes the workflow from data preprocessing to final texture synthesis, maintaining consistent geometry and texture representations. A normalization scheme harmonizes heterogeneous material parameterizations in a common representation space, enabling robust cross-dataset training and inference. Building on this formulation, we train a novel physically aware diffusion model for PBR texture generation within a two-stage multi-view synthesis framework. First, geometry-aware albedo generation synthesizes multi-view albedo maps under explicit geometric guidance to ensure cross-view appearance consistency. Second, metallic-roughness generation uses the diffusion model to predict physically plausible roughness and metallic maps conditioned on the generated albedo and unified representation. Trained end-to-end on standardized data, the model learns to generate materials that respect category-level material priors and semantic part boundaries. By decoupling representation standardization from appearance synthesis, MIKU substantially improves material generation stability and physical consistency. Extensive experiments demonstrate state-of-the-art texture quality and semantic consistency across object parts, advancing automated material generation for 3D content creation.
#03Nov 5, 2026
cs.SD
FFMT: Frequency-Factorized Multi-Rate Tokenization for Music-to-Dance Generation
Qirong Guo, Ruijie Li, Zeyan Li and 3 more
Dance motion combines slowly varying body configurations with rapid local movements. We investigate how explicit temporal-frequency decomposition can guide the temporal resolution and codebook capacity of discrete motion representations. We introduce frequency-factorized motion tokenization (FFMT), which separates root-relative motion into a low-frequency choreographic scaffold and a complementary high-frequency residual. FFMT uses a lower token rate and a smaller codebook for the low-frequency component, and a higher token rate and a larger codebook for the high-frequency residual. This representation further induces a plan-then-articulate autoregressive factorization: low-frequency tokens establish coarse choreography, while high-frequency tokens realize local motion details conditioned on the generated plan and music. Experiments on AIST++ and FineDance show strong distributional fidelity while preserving realistic diversity and music--motion synchronization. Ablations consistently support frequency factorization, asymmetric temporal allocation, and low-to-high conditioning.
#04Nov 5, 2026
math.OC
From What Is Used to What Is Missing: Rethinking Dynamic Rank in LoRA
Peng Xue, Guodong Fan, Guang-Yong Chen and 1 more
Parameter-efficient fine-tuning with LoRA is governed by rank, which controls parameter budget and locally accessible task updates. Existing dynamic-rank methods mainly focus on capacity allocation, leaving unresolved whether the current adaptation space is sufficient for the task. We characterize the first-order information boundary of standard factor gradients, showing that their unobservable subspace coincides with the normal space of the low-rank manifold. Thus, factor gradients alone cannot distinguish under-optimization within the current space from task-relevant directions beyond its first-order reach. This motivates a geometric view of rank evolution, where tangent information optimizes existing degrees of freedom and the normal spectrum quantifies the value of new ones. Under a local quadratic model, we show that leading normal singular directions yield the locally optimal rank expansion, with spectral energy determining its gain. Building on this characterization, we propose GEAR, which turns the tangent--normal decomposition into rank evolution through tangent-space optimization, matrix-free normal-spectrum estimation for rank expansion, and task-induced geometric initialization. Across language and vision benchmarks, GEAR achieves the best average performance in all evaluated settings with substantially fewer trainable parameters. On LLaMA3-8B, it raises the average commonsense score from 86.61 to 88.45 while reducing trainable parameters from 42.0M to 14.4M.
#05Nov 5, 2026
cs.LG
AtlasULP: Domain-aware Universal Link Prediction via Relation Atlas
Yujing Liu, Yixin Liu, Yu Zheng and 4 more
Link prediction (LP) is a widely applied task in graph learning. Conventional LP approaches typically follow a dataset-specific paradigm, requiring independent training for each graph and incurring high computational and maintenance costs in large-scale applications. Motivated by these limitations, recent work explores Universal Link Prediction (ULP), aiming to enable training-free inference on arbitrary unseen graphs. However, existing methods primarily rely on subgraph sampling strategies to construct universal representations, which are unable to capture higher-order information due to the exponential growth of higher-order neighborhoods. As a result, they often under-perform on graphs where long-range signals are essential, such as biological networks. Moreover, their prediction models are typically domain-agnostic and lack the ability to adapt to different graph domains, leading to limited generalization. To address these challenges, we propose an Atlas-based Universal Link Prediction framework (AtlasULP), which introduces the relation atlas to capture structural associations between node pairs from both global and local perspectives, enabling a comprehensive characterization of connectivity signals across diverse graph domains. Building upon this representation, we develop a domain-guided prompting ULP model, which generates domain-aware prompt tokens from contextual structures and performs prompt-guided in-context prediction for adaptive link inference. Extensive experiments demonstrate that AtlasULP consistently outperforms state-of-the-art methods across diverse graph domains.