Navigating the Conceptual Multiverse

• URL: http://arxiv.org/abs/2604.17815v2
• Date: Tue, 21 Apr 2026 20:39:49 GMT
• Title: Navigating the Conceptual Multiverse
• Abstract summary: We build and evaluate the conceptual multiverse, an interactive system that represents conceptual decisions.<n>We develop a general verification framework that enforces properties of good decision structures.<n>Across three calibrated domains, the conceptual multiverse helped participants develop a working map of a problem.
• Score: 8.506067537110214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When language models answer open-ended problems, they implicitly make hidden decisions that shape their outputs, leaving users with uncontextualized answers rather than a working map of the problem; drawing on multiverse analysis from statistics, we build and evaluate the conceptual multiverse, an interactive system that represents conceptual decisions such as how to frame a question or what to value as a space users can transparently inspect, intervenably change, and check against principled domain reasoning; for this structure to be worth navigating rather than misleading, it must be rigorous and checkable against domain reasoning norms, so we develop a general verification framework that enforces properties of good decision structures like unambiguity and completeness calibrated by expert-level reasoning; across three domains, the conceptual multiverse helped participants develop a working map of the problem, with philosophy students rewriting essays with sharper framings and reversed theses, alignment annotators moving from surface preferences to reasoning about user intent and harm, and poets identifying compositional patterns that clarified their taste.

Related papers

• RoboPIN: Grounded Embodied Reasoning via Pinned Chain-of-Thought [50.36951914894845]
  Embodied reasoning requires models to perceive task-relevant objects and spaces in physical environments.<n>Current vision-language models rely on text-only or coordinate-augmented chain-of-thought.<n>We propose Pinned Chain-of-Thought, a structured reasoning paradigm that pins every reasoning step to visual evidence.
  arXiv  Detail & Related papers  (2026-06-14T11:30:42Z)
• Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts [58.86896791968143]
  Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks.<n>We identify a puzzling phenomenon termed Seeing but Not Thinking: models accurately perceive image content yet fail in subsequent reasoning.<n>We propose the Routing Distraction hypothesis: when processing visual inputs, the routing mechanism fails to adequately activate task-relevant reasoning experts.
  arXiv  Detail & Related papers  (2026-04-09T17:59:44Z)
• Which Concepts to Forget and How to Refuse? Decomposing Concepts for Continual Unlearning in Large Vision-Language Models [35.42746849116121]
  Continual unlearning poses the challenge of enabling large vision-language models to selectively refuse specific image-instruction pairs in response to sequential deletion requests.<n>We propose a novel continual unlearning framework that grounds refusal behavior in fine-grained descriptions of visual and textual concepts from deletion targets.
  arXiv  Detail & Related papers  (2026-03-23T02:07:40Z)
• Deconstructing Multimodal Mathematical Reasoning: Towards a Unified Perception-Alignment-Reasoning Paradigm [82.57296908195654]
  Multimodal Mathematical Reasoning (MMR) has attracted increasing attention for its capability to solve mathematical problems that involve both textual and visual modalities.<n>Current models still face significant challenges in real-world visual math tasks.
  arXiv  Detail & Related papers  (2026-03-09T12:11:00Z)
• Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflicts [74.47786985522762]
  We identify a critical failure mode termed textual inertia, where models tend to blindly adhere to the erroneous text while neglecting conflicting visual evidence.<n>We propose the LogicGraph Perturbation Protocol that structurally injects perturbations into the reasoning chains of diverse LMMs.<n>Results reveal that models successfully self-correct in less than 10% of cases and predominantly succumb to blind textual error propagation.
  arXiv  Detail & Related papers  (2026-01-07T16:39:34Z)
• Figure It Out: Improve the Frontier of Reasoning with Executable Visual States [53.77871196174248]
  Complex reasoning problems often involve implicit spatial and geometric relationships that are not explicitly encoded in text.<n>We introduce FIGR, which integrates executable visual construction into multi-turn reasoning via end-to-end reinforcement learning.<n>Experiments on eight challenging mathematical benchmarks demonstrate that FIGR outperforms strong text-only chain-of-thought baselines.
  arXiv  Detail & Related papers  (2025-12-30T15:39:11Z)
• Artemis: Structured Visual Reasoning for Perception Policy Learning [64.57381337070616]
  Empirical observations indicate that purely linguistic intermediate reasoning often reduces performance on perception tasks.<n>We introduce Artemis, a perception-policy learning framework that performs structured proposal-based reasoning.
  arXiv  Detail & Related papers  (2025-12-01T18:45:30Z)
• Transparent Visual Reasoning via Object-Centric Agent Collaboration [24.503547825977023]
  Ocean is an inherently interpretable framework built on object-centric representations and a transparent multi-agent reasoning process.<n>We benchmark it against standard visual classifiers and popular posthoc explanation tools like GradCAM and LIME.<n>Our results demonstrate competitive performance with respect to state-of-the-art black-box models with a faithful reasoning process.
  arXiv  Detail & Related papers  (2025-09-28T09:06:52Z)
• A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs [3.2228025627337864]
  This paper introduces a structured evaluation framework to dissect the perception-reasoning interface in Vision-Language Models (VLMs)<n>We propose three distinct evaluation paradigms, mirroring human problem-solving strategies.<n>Applying this framework, we demonstrate that CA, leveraging powerful language models for reasoning over rich, independently generated descriptions, achieves new state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2025-01-23T12:42:42Z)
• Divide and Conquer: Answering Questions with Object Factorization and Compositional Reasoning [30.392986232906107]
  We propose an integral framework consisting of a principled object factorization method and a novel neural module network. Our factorization method decomposes objects based on their key characteristics, and automatically derives prototypes that represent a wide range of objects. With these prototypes encoding important semantics, the proposed network then correlates objects by measuring their similarity on a common semantic space. It is capable of answering questions with diverse objects regardless of their availability during training, and overcoming the issues of biased question-answer distributions.
  arXiv  Detail & Related papers  (2023-03-18T19:37:28Z)
• Visual Superordinate Abstraction for Robust Concept Learning [80.15940996821541]
  Concept learning constructs visual representations that are connected to linguistic semantics. We ascribe the bottleneck to a failure of exploring the intrinsic semantic hierarchy of visual concepts. We propose a visual superordinate abstraction framework for explicitly modeling semantic-aware visual subspaces.
  arXiv  Detail & Related papers  (2022-05-28T14:27:38Z)

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.