Dialectics for Artificial Intelligence

• URL: http://arxiv.org/abs/2512.17373v2
• Date: Tue, 23 Dec 2025 08:23:39 GMT
• Title: Dialectics for Artificial Intelligence
• Authors: Zhengmian Hu,
• Abstract summary: Can artificial intelligence discover, from raw experience and without human supervision, concepts that humans have discovered?<n>One challenge is that conceptual boundaries can shift, split, and merge as inquiry progresses.<n>We propose an algorithmic-information viewpoint that treats a concept as an information object defined only through its structural relation to an agent's total experience.
• Score: 24.816514958011442
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Can artificial intelligence discover, from raw experience and without human supervision, concepts that humans have discovered? One challenge is that human concepts themselves are fluid: conceptual boundaries can shift, split, and merge as inquiry progresses (e.g., Pluto is no longer considered a planet). To make progress, we need a definition of "concept" that is not merely a dictionary label, but a structure that can be revised, compared, and aligned across agents. We propose an algorithmic-information viewpoint that treats a concept as an information object defined only through its structural relation to an agent's total experience. The core constraint is determination: a set of parts forms a reversible consistency relation if any missing part is recoverable from the others (up to the standard logarithmic slack in Kolmogorov-style identities). This reversibility prevents "concepts" from floating free of experience and turns concept existence into a checkable structural claim. To judge whether a decomposition is natural, we define excess information, measuring the redundancy overhead introduced by splitting experience into multiple separately described parts. On top of these definitions, we formulate dialectics as an optimization dynamics: as new patches of information appear (or become contested), competing concepts bid to explain them via shorter conditional descriptions, driving systematic expansion, contraction, splitting, and merging. Finally, we formalize low-cost concept transmission and multi-agent alignment using small grounds/seeds that allow another agent to reconstruct the same concept under a shared protocol, making communication a concrete compute-bits trade-off.

Related papers

• Insight: Interpretable Semantic Hierarchies in Vision-Language Encoders [52.94006363830628]
  Language-aligned vision foundation models perform strongly across diverse downstream tasks.<n>Recent works decompose these representations into human-interpretable concepts, but provide poor spatial grounding and are limited to image classification tasks.<n>We propose Insight, a language-aligned concept foundation model that provides fine-grained concepts, which are human-interpretable and spatially grounded in the input image.
  arXiv  Detail & Related papers  (2026-01-20T09:57:26Z)
• Interpretation as Linear Transformation: A Cognitive-Geometric Model of Belief and Meaning [0.0]
  I show how belief distortion, motivational drift, counterfactual evaluation, and the limits of mutual understanding arise from purely algebraic constraints.<n>I argue that this cognitive-geometric perspective clarifies the boundaries of influence in both human and artificial systems.
  arXiv  Detail & Related papers  (2025-12-10T17:13:01Z)
• A Geometric Unification of Concept Learning with Concept Cones [58.70836885177496]
  Two traditions of interpretability have evolved side by side but seldom spoken to each other: Concept Bottleneck Models (CBMs) and Sparse Autoencoders (SAEs)<n>We show that both paradigms instantiate the same geometric structure.<n>CBMs provide human-defined reference geometries, while SAEs can be evaluated by how well their learned cones approximate or contain those of CBMs.
  arXiv  Detail & Related papers  (2025-12-08T09:51:46Z)
• A Complexity-Based Theory of Compositionality [53.025566128892066]
  In AI, compositional representations can enable a powerful form of out-of-distribution generalization.<n>Here, we propose a definition, which we call representational compositionality, that accounts for and extends our intuitions about compositionality.<n>We show how it unifies disparate intuitions from across the literature in both AI and cognitive science.
  arXiv  Detail & Related papers  (2024-10-18T18:37:27Z)
• ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance [90.57708419344007]
  We present **ClassDiffusion**, a technique that leverages a **semantic preservation loss** to explicitly regulate the concept space when learning a new concept.<n>Although simple, this approach effectively prevents semantic drift during the fine-tuning process of the target concepts.
  arXiv  Detail & Related papers  (2024-05-27T17:50:10Z)
• Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning [22.201878275784246]
  Focus in Explainable AI is shifting from explanations defined in terms of low-level elements, such as input features, to explanations encoded in terms of interpretable concepts learned from data. How to reliably acquire such concepts is, however, still fundamentally unclear. We propose a mathematical framework for acquiring interpretable representations suitable for both post-hoc explainers and concept-based neural networks.
  arXiv  Detail & Related papers  (2023-09-14T14:26:20Z)
• A Geometric Notion of Causal Probing [85.49839090913515]
  The linear subspace hypothesis states that, in a language model's representation space, all information about a concept such as verbal number is encoded in a linear subspace.<n>We give a set of intrinsic criteria which characterize an ideal linear concept subspace.<n>We find that, for at least one concept across two languages models, the concept subspace can be used to manipulate the concept value of the generated word with precision.
  arXiv  Detail & Related papers  (2023-07-27T17:57:57Z)
• When are Post-hoc Conceptual Explanations Identifiable? [18.85180188353977]
  When no human concept labels are available, concept discovery methods search trained embedding spaces for interpretable concepts. We argue that concept discovery should be identifiable, meaning that a number of known concepts can be provably recovered to guarantee reliability of the explanations. Our results highlight the strict conditions under which reliable concept discovery without human labels can be guaranteed.
  arXiv  Detail & Related papers  (2022-06-28T10:21:17Z)
• Automatic Concept Extraction for Concept Bottleneck-based Video Classification [58.11884357803544]
  We present an automatic Concept Discovery and Extraction module that rigorously composes a necessary and sufficient set of concept abstractions for concept-based video classification. Our method elicits inherent complex concept abstractions in natural language to generalize concept-bottleneck methods to complex tasks.
  arXiv  Detail & Related papers  (2022-06-21T06:22:35Z)

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.