Semantic Myopia: Global Coherence in AI

• Semantic Myopia is the narrowing of meaning and loss of global context in AI and human perception when rich sensory input is reduced to local associations.
• It is quantified through metrics like topic drift, domain density, and stickiness ratios, demonstrating significant effects in language and vision–language tasks.
• Interventions, such as explicit semantic planning and holistic multi-aspect alignment, aim to enhance semantic retention and mitigate bias in model outputs.

Semantic myopia refers to the narrowing of meaning and loss of global coherence that occurs in both artificial intelligence systems and human perception when rich sensory or contextual input is reduced to local associations or linguistic labels. This phenomenon manifests across modalities and architectures, leading to significant practical and theoretical challenges in natural language processing, vision–language modeling, and mediated perception.

1. Core Definition and Theoretical Basis

Semantic myopia, sometimes termed topic drift or associative drift, describes the failure of a model or perceptual system to maintain global semantic coherence due to reliance on locally optimal associations. In autoregressive LLMs trained via next-token prediction (NTP), the objective is to maximize $$p_{NTP}(x_t | x_{<t}) = \mathrm{softmax}(W_{\mathrm{token}} h_{t-1})$$ at each step. This local normalization incentivizes selection of the most probable next token given previous context but includes no explicit mechanism for planning or enforcing long-range semantic structure. As a result, generated sequences may wander off-topic, crossing semantic bridges (e.g., via ambiguous tokens like “Constitution”) into incoherent or unintended domains. The same phenomenon appears in vision–language mapping, where reducing rich visual content to minimal captions induces excessive abstraction and the erasure of fine detail, a process analogized as “near-sightedness” in the perceptual-semantic domain (Fofadiya, 3 Dec 2025, Muramoto et al., 2024, Wang et al., 2024).

The myopia is fundamental to decomposed objectives lacking explicit long-horizon constraints. For example, maximizing $\sum_t \log p_{NTP}(x_t | x_{<t})$ provides no global measure of topic continuity, yielding sequences that are syntactically plausible yet semantically adrift.

2. Empirical Manifestations and Metrics

Semantic myopia gives rise to measurable degradations in both generated text and derived representations:

• Topic drift in LLMs, where domain-specific prompts (e.g. medical topics) quickly dissolve into unrelated contexts (e.g. sports, civil rights).
• Domain density and stickiness ratio metrics quantify how many domain-specific terms per 100 tokens persist, and the proportion of content words that remain on-topic.
• In vision–language systems, one-to-one captioning (as in original CLIP) encourages short, general descriptions, introducing bias toward prominent foreground objects and omitting detail or style. Formal measurement employs linguistic-similarity metrics (TF-IDF, WMD, USE, SBERT) and visual-similarity metrics (Histogram Intersection, SIFT match, LPIPS), revealing significant losses in both lexical and structural information during the $I \to s \to I'$ (image–text–image) pipeline (Muramoto et al., 2024).

Table: Example domain stickiness improvements with explicit de-myopization (Fofadiya, 3 Dec 2025).

Domain	Baseline Stickiness	Idea-Gated Stickiness	Change
Science	8.2%	10.3%	+25.6%
Tech	0.8%	1.2%	+50.0%
Finance	5.2%	4.5%	−13%

Qualitative effects include temporal incoherence, stereotype amplification, and misalignment between the intended and actual semantic focus of generated content (Muramoto et al., 2024).

3. Formal and Architectural Interventions

Traditional methods to address myopia include injecting topic vectors via LDA or RNN topic models or using variational autoencoders (VAEs) for latent planning. However, topic-injection methods exhibit topic collapse or lack per-token adaptivity, while VAEs commonly experience posterior collapse, losing the planning signal altogether. Plug-and-play methods such as CTRL, FUDGE, and DExperts perform logit reweighting during decoding but do not provide a tight, differentiable coupling between semantic intent and generation (Fofadiya, 3 Dec 2025).

Recent advances directly confront semantic myopia through two principal strategies:

A. Explicit Semantic Planning (Idea-Gated Transformer)

The Idea-Gated Transformer (IGT) introduces a dual-head architecture:

• Token Head: Standard autoregressive next-token predictor.
• Idea Head: Auxiliary head predicting the bag-of-words distribution for upcoming tokens, generating a “Concept Vector.”

A differentiable gating mechanism then prunes the main vocabulary based on the predicted semantic plan:

$$z_{\mathrm{final}}(v) = z_{\mathrm{token}}(v) + \max(\alpha \log(p_{\mathrm{idea}}(v|x_{\leq t})+\epsilon), \beta)$$

This gate suppresses tokens that are semantically off-plan, significantly improving domain retention and stickiness without sacrificing perplexity or n-gram diversity (Fofadiya, 3 Dec 2025).

B. Holistic Multi-Aspect Alignment (Holistic CLIP)

To overcome the “short-caption” myopia, Holistic CLIP generates multiple diverse captions per image (via multi-prompts or multi-VLMs) and processes images through multi-branch encoders, enabling part-to-part contrastive alignment:

• Each class token/branch in the image encoder is mapped to a caption capturing a distinct aspect (main object, background, style).
• Multi-to-multi contrastive losses ($L_{M2M}$) match each branch to its corresponding text embedding. This paradigm substantially enhances image–text retrieval, open-vocabulary classification, and dense visual tasks, demonstrating robust generalization and improved interpretability (Wang et al., 2024).

4. Perceptual and Cognitive Analogues

Semantic myopia is not limited to artificial systems. Prototype experiments with Semantic See-through Goggles provide a phenomenological realization: all real-world input is rendered as text and then reconstructed as images. Across several linguistic and visual similarity measures, this forced semantic mediation preserves high-level content but eliminates fine structure, temporal flow, and identity diversity. Quantitatively, paired condition TF-IDF cosine similarity reaches only ∼0.38, and perceptual metrics such as LPIPS remain high (∼0.67), marking heavy loss of detail (Muramoto et al., 2024).

Qualitative workshop findings highlighted:

• Retained navigational awareness but loss of spatial ambiguity resolution.
• Emergence of stereotype-driven, temporally incoherent reconstructions.
• Realization that semantic mediation, and its attendant myopia, is intrinsic to all acts of labeling, not just AI (Muramoto et al., 2024).

5. Critique, Trade-offs, and Open Challenges

Semantic myopia persists despite dataset scale and model parameter increase, as the decomposed NTP objective is structurally myopic by design. While gating mechanisms and multi-branch architectures reduce topic drift and enhance interpretability, they introduce trade-offs:

• High gating strength can induce repetitive loops, requiring further constraints (e.g. repetition penalties).
• In high-resource or polysemous domains, some rare synonyms and diversity may be suppressed.
• Idea/planning heads—including Concept Vectors—inherit dataset biases and require careful domain-balanced training.

Future directions include:

• Predicting not just surface terms but abstract reasoning steps or entity-aware attributes.
• Integrating reinforcement learning from human feedback (RLHF) to align high-level semantic plans.
• Developing sparse inference kernels for computational efficiency.
• Implementing granular control of semantic reduction, fairness checks, and bias-mitigation techniques at both training and inference stages (Fofadiya, 3 Dec 2025, Muramoto et al., 2024, Wang et al., 2024).

6. Broader Implications and Theoretical Synthesis

Semantic myopia underscores the epistemological limits of meaning-based mediation in both AI and human cognition. Whenever information is funneled through a narrow semantic bottleneck—be it language, architectural abstraction, or categorization—details are irretrievably discarded in favor of generalized, aggregative representation. This effect structures what is preserved (salient objects, domains), and what is omitted (fine detail, minority variation, continuity).

The conceptual framework of “Linguistic/Semantic Virtual Reality,” in which all realities collapsed into a single sentence are equivalent in semantic space, highlights the risk of flattening human and machine experience to stereotype, bias, and inflexible summary (Muramoto et al., 2024).

Awareness of semantic myopia motivates the design of models and systems that can dynamically trade off between abstraction and detail, leveraging multilevel, multi-aspect representations and incorporating explicit mechanisms for global coherence and bias correction.