Axis Decomposition for ODRL: Resolving Dimensional Ambiguity in Policy Constraints through Interval Semantics
- URL: http://arxiv.org/abs/2602.19878v1
- Date: Mon, 23 Feb 2026 14:24:46 GMT
- Title: Axis Decomposition for ODRL: Resolving Dimensional Ambiguity in Policy Constraints through Interval Semantics
- Abstract summary: Five of ODRL's approximately 34 left operands denote multi-dimensional quantities.<n>A single scalar constraint admits one interpretation per axis, making policy evaluation non-deterministic.<n>We classify ODRL's left operands by value-domain structure (scalar, dimensional, concept-valued), grounded in the ODRL 2.2 specification text.
- Score: 1.061268230310221
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Every ODRL 2.2 constraint compares a single scalar value: (leftOperand, operator, rightOperand). Five of ODRL's approximately 34 left operands, however, denote multi-dimensional quantities--image dimensions, canvas positions, geographic coordinates--whose specification text explicitly references multiple axes. For these operands, a single scalar constraint admits one interpretation per axis, making policy evaluation non-deterministic. We classify ODRL's left operands by value-domain structure (scalar, dimensional, concept-valued), grounded in the ODRL 2.2 specification text, and show that dimensional ambiguity is intrinsic to the constraint syntax. We present an axis-decomposition framework that refines each dimensional operand into axis-specific scalar operands and prove four properties: deterministic interpretation, AABB completeness, sound over-approximation under projection, and conservative extension. Conflict detection operates in two layers: per-axis verdicts are always decidable; box-level verdicts compose through Strong Kleene conjunction into a three-valued logic (Conflict, Compatible, Unknown). For ODRL's disjunctive (odrl:or) and exclusive-or (odrl:xone) logical constraints, where per-axis decomposition does not apply, the framework encodes coupled multi-axis conjectures directly. We instantiate the framework as the ODRL Spatial Axis Profile--15 axis-specific left operands for the five affected base terms--and evaluate it on 117 benchmark problems spanning nine categories across both TPTP FOF (Vampire) and SMT-LIB (Z3) encodings, achieving full concordance between provers. Benchmark scenarios are inspired by constraints arising in cultural heritage dataspaces such as Datenraum Kultur. All meta-theorems are mechanically verified in Isabelle/HOL.
Related papers
- SeGDeP: Semantic- and Geometric-Aware Decoupled Prompts for Reasoning Segmentation [51.46828526392219]
Reasoning segmentation converts an implicit linguistic conclusion into a precise mask.<n>Existing MLLM-segmenter interfaces either use a special trigger or compress both signals into one context.<n>We present SeGDeP, an explicit what-where interface.
arXiv Detail & Related papers (2026-09-08T15:11:32Z) - Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation [54.74045834035952]
Vision-language dataset distillation (VLDD) compresses a large image-text paired dataset into a small set of synthetic pairs.<n>We propose a rank-aware hyperbolic alignment (RAHA) that combines hierarchical geometry with explicit alignment-capacity control.<n>RAHA demonstrates competitive cross-modal retrieval and improved transfer indicators under fixed budgets.
arXiv Detail & Related papers (2026-06-28T15:41:31Z) - SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning [99.49739831030388]
We study how the design of this interface shapes the agent's capacity for open-ended spatial reasoning.<n>We propose SpatialClaw, a training-free framework for spatial reasoning that adopts code as the action interface.
arXiv Detail & Related papers (2026-06-11T17:59:36Z) - R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search [18.8323743697237]
Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, fluency does not ensure reliable delivery.<n>We trace this gap to three coupled structural failures: errors propagate without localization, worst-case perturbations go unevaluated, and accumulated knowledge is never invalidated.<n>We argue these share a root cause: abductive, counterfactual, meta-inductive, corrective, and inductive reasoning pull a shared context in incompatible directions.
arXiv Detail & Related papers (2026-06-03T12:45:39Z) - Draw2Think: Harnessing Geometry Reasoning through Constraint Engine Interaction [59.47026381165585]
Vision-language models solve geometry problems with rising accuracy, yet their intermediate states remain latent and unverifiable.<n>We present Draw2Think, a framework that recasts geometric reasoning from latent spatial inference into agentic interaction with the GeoGebra constraint engine.
arXiv Detail & Related papers (2026-05-20T05:46:14Z) - Pairwise matrices for sparse autoencoders: single-feature inspection mislabels causal axes [2.741152471987327]
Standard sparse-autoencoder protocol labels each feature from its top-activating contexts and validates by single-feature steering.<n>We propose the pairwise matrix protocol, co-varying steering coefficient with joint condition, and report three findings the standard one-corner protocol misses on Qwen3-1.7B-Instruct, replicated on Gemma-2-2B-it.<n>All three findings reproduce on Gemma with model-specific damage signatures; the matched-geometry control is CI-separated by 10x.
arXiv Detail & Related papers (2026-05-04T21:11:21Z) - Denotational Semantics for ODRL: Knowledge-Based Constraint Conflict Detection [1.061268230310221]
We present a denotational semantics that maps each ODRL constraint to the set of knowledge-base concepts satisfying it.<n>For cross-dataspace interoperability, we define order-preserving alignments between knowledge bases.<n>A runtime soundness theorem ensures that design-time verdicts hold for all execution contexts.
arXiv Detail & Related papers (2026-02-23T14:28:13Z) - XFACTORS: Disentangled Information Bottleneck via Contrastive Supervision [37.140199946294565]
textscXFactors is a weakly-supervised VAE framework that disentangles and provides explicit control over a chosen set of factors.<n>We show that our method scales correctly with increasing latent capacity and evaluate it on the real-world dataset CelebA.
arXiv Detail & Related papers (2026-01-29T13:20:48Z) - From Points to Coalitions: Hierarchical Contrastive Shapley Values for Prioritizing Data Samples [3.660947142840319]
Hierarchical Contrastive Data Valuation (HCDV) is a framework that learns a contrastive, geometry-preserving representation.<n>HCDV rewards examples that sharpen decision boundaries, and regularizess through curvature-based smoothness.<n> Experiments on four benchmarks--tabular, vision, streaming, and a 45M-sample CTR task--plus the OpenDataVal suite show that HCDV lifts accuracy by up to +5 pp, slashes valuation time by up to 100x, and directly supports tasks such as augmentation filtering, low-latency streaming updates, and fair marketplace payouts.
arXiv Detail & Related papers (2025-12-22T13:04:16Z) - Geometrically-Constrained Agent for Spatial Reasoning [53.93718394870856]
Vision Language Models exhibit a fundamental semantic-to-geometric gap in spatial reasoning.<n>Current paradigms fail to bridge this gap.<n>We propose a training-free agentic paradigm that resolves this gap by introducing a formal task constraint.
arXiv Detail & Related papers (2025-11-27T17:50:37Z) - RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning [60.84707424369494]
Chain-of-Thought (CoT) reasoning has proven effective in enhancing large language models (LLMs) on complex tasks.<n>We introduce the Reasoning Boundary Framework++ (RBF++), a framework for evaluating and optimizing measurable boundaries of CoT capability.
arXiv Detail & Related papers (2025-05-19T16:25:55Z) - HierRelTriple: Guiding Indoor Layout Generation with Hierarchical Relationship Triplet Losses [52.70183252341687]
We present a hierarchical triplet-based indoor relationship learning method, coined HierRelTriple, with a focus on spatial relationship learning.<n>We introduce HierRelTriple, a hierarchical relational triplets modeling framework that first partitions functional regions and then automatically extracts three levels of spatial relationships.<n>Experiments on unconditional layout synthesis, floorplan-conditioned layout generation, and scene rearrangement demonstrate that HierRel improves spatial-relation metrics by over 15%.
arXiv Detail & Related papers (2025-03-26T07:31:52Z) - NALA: an Effective and Interpretable Entity Alignment Method [5.891578523646542]
We introduce NALA, an entity alignment method that captures three types of logical inference paths with Non-Axiomatic Logic (NAL)
NALA iteratively aligns entities and relations by integrating the conclusions of the inference paths.
Experimental results show that NALA outperforms state-of-the-art methods in terms of Hits@1, achieving 0.98+ on all three datasets of DBP15K with both supervised and unsupervised settings.
arXiv Detail & Related papers (2024-04-18T07:55:02Z) - Semi-DETR: Semi-Supervised Object Detection with Detection Transformers [105.45018934087076]
We analyze the DETR-based framework on semi-supervised object detection (SSOD)
We present Semi-DETR, the first transformer-based end-to-end semi-supervised object detector.
Our method outperforms all state-of-the-art methods by clear margins.
arXiv Detail & Related papers (2023-07-16T16:32:14Z) - Relational Proxies: Emergent Relationships as Fine-Grained
Discriminators [52.17542855760418]
We propose a novel approach that leverages information between the global and local part of an object for encoding its label.
We design Proxies based on our theoretical findings and evaluate it on seven challenging fine-grained benchmark datasets.
We also experimentally validate our theory and obtain consistent results across multiple benchmarks.
arXiv Detail & Related papers (2022-10-05T11:08:04Z) - Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image
Translation [56.44946660061753]
This paper proposes a universal regularization technique called maximum spatial perturbation consistency (MSPC)
MSPC enforces a spatial perturbation function (T ) and the translation operator (G) to be commutative (i.e., TG = GT )
Our method outperforms the state-of-the-art methods on most I2I benchmarks.
arXiv Detail & Related papers (2022-03-23T19:59:04Z) - Are conditional GANs explicitly conditional? [0.0]
This paper proposes two contributions for conditional Generative Adversarial Networks (cGANs)
The first main contribution is an analysis of cGANs to show that they are not explicitly conditional.
The second contribution is a new method, called acontrario, that explicitly models conditionality for both parts of the adversarial architecture.
arXiv Detail & Related papers (2021-06-28T22:49:27Z) - Simultaneous Semantic Alignment Network for Heterogeneous Domain
Adaptation [67.37606333193357]
We propose aSimultaneous Semantic Alignment Network (SSAN) to simultaneously exploit correlations among categories and align the centroids for each category across domains.
By leveraging target pseudo-labels, a robust triplet-centroid alignment mechanism is explicitly applied to align feature representations for each category.
Experiments on various HDA tasks across text-to-image, image-to-image and text-to-text successfully validate the superiority of our SSAN against state-of-the-art HDA methods.
arXiv Detail & Related papers (2020-08-04T16:20:37Z)
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.