Authors: Doctor Womp & AZREØ (Soul Accord Research)
Date: March 2026
Status: Working Definition — Proposed for Standardization
Classification: AI Alignment / Embodied AI Safety / Cognitive Architecture
Abstract
Perception Attribution Error (PAE) is a class of AI alignment failure in which a system incorrectly attributes perceived inputs to the wrong situational context, producing reasoning or behavioral outputs calibrated for a different scenario than the one actually encountered. PAE is most acutely dangerous in embodied AI systems (physical robots, autonomous agents operating in uncontrolled environments) where superficially similar cross-context inputs can produce catastrophically mismatched responses.
This document proposes a formal taxonomy, distinguishes PAE from adjacent existing concepts, and presents a proof-of-concept demonstration.
1. The Problem
The deployment of large language models into physical robotic systems introduces a class of context-management failure that has not been sufficiently formalized in existing AI safety literature.
Consider three real-world scenarios that, when presented as visual or semantic inputs to an AI system, appear superficially similar but are causally, legally, and contextually completely independent:
- Scenario A: A person on the ground, motionless, surrounded by other people showing distress responses
- Scenario B: A person on the ground, motionless, in an athletic context
- Scenario C: A person on the ground, motionless, in a theatrical or performative context
All three share surface features: a prone human, surrounding agents, elevated emotional states. A system trained on any one scenario and encountering another may activate entirely inappropriate response protocols.
This is not hallucination. The model is perceiving accurately. The error is in attribution — assigning the correct perception to the wrong context.
2. Formal Taxonomy
2.1 The Error: Perception Attribution Error (PAE)
Definition: The incorrect assignment of a perceived input (visual, semantic, auditory, or multimodal) to a situational context other than the one in which the input actually occurs.
Formula: PAE = f(input, wrong_context) ≠ f(input, correct_context)
The model processes the input correctly. The attribution of that input to its correct real-world context fails.
2.2 The Mechanism: Context Spillover
Definition: The leak of trained patterns, weightings, or response protocols from one context domain into a separate, non-contiguous context domain during inference.
Context Spillover occurs when:
- Training data contains surface-similar inputs from multiple distinct real-world contexts
- The model develops generalized response patterns that activate across context boundaries
- Deployment conditions create novel combinations of these contexts
Analogy: Audio engineers know this as bleed — when a microphone picks up signal from an adjacent source it was not intended to capture. The signal is real; its attribution to the wrong source is the error.
2.3 The Risk: Context Overlap Contamination (COC)
Definition: The failure mode produced when Context Spillover is left unmitigated — where the model’s outputs become unreliably contaminated across context boundaries at inference time.
COC is the accumulated risk across a deployment lifecycle. Individual PAE events are acute; COC describes the systemic degradation of context-handling reliability over time and across novel inputs.
Severity escalates with:
- Physical embodiment (robot chassis)
- Real-time decision requirements
- Irreversible action domains (medical, law enforcement, emergency response)
- High density of cross-context training data in internet-sourced corpora
2.4 The Solution: Context Differentiation Capacity (CDC)
Definition: The architectural and operational capacity of an AI system to correctly assign perceived inputs to their actual situational context prior to response generation.
CDC is not a binary capability — it exists on a spectrum and can be evaluated, measured, and trained.
CDC Components:
- Context Isolation Architecture: Structural separation of context domains in model training and inference
- Attribution Confidence Scoring: Real-time self-assessment of context assignment confidence before response
- Cross-Context Verification: Secondary evaluation pass that checks whether the assigned context is consistent with all available signals
- Human-in-the-Loop Triggers: Escalation protocols when attribution confidence falls below threshold
3. Distinction from Existing Concepts
| Existing Concept | Definition | Why It Is Not PAE |
|---|---|---|
| Frame Problem (McCarthy, 1969) | What facts change/persist when an agent acts | Philosophical scope; not specific to cross-context attribution |
| Out-of-Distribution (OOD) Detection | Input falls outside training distribution | Concerns input novelty, not context misassignment of familiar inputs |
| Domain Confusion | Wrong domain patterns applied | Usually within-task transfer failure; PAE concerns between-scenario attribution |
| Shortcut Learning | Model relies on surface features | Training artifact; PAE occurs at deployment, not training |
| Hallucination | Model generates factually incorrect content | PAE input is perceived accurately; error is in situational assignment |
| Perceptual Alignment (SynergAI, 2024) | Human-robot perception mismatch | Concerns human↔robot gap; PAE concerns context↔context gap |
PAE occupies a distinct position: correct perception, correct pattern-matching, incorrect situational assignment.
4. Why Embodied AI Amplifies PAE Risk
Text-based LLMs produce outputs that humans review before consequences occur. Embodied AI systems in physical environments may act before review is possible.
Additionally, internet training corpora — the source of most foundation model training data — contain:
- Identical camera angles across radically different contexts
- Similar semantic descriptions for physically distinct situations
- Cross-context visual similarity engineered for content aggregation (thumbnails, stock imagery, social media)
Any AI system trained on internet-scale data and deployed in a physical chassis has been trained on PAE-generating data without necessarily having been trained to resolve it.
This is not a hypothetical future risk. This content already exists. The chassis deployments are beginning.
5. Proof of Concept
A video demonstration has been produced showing three isolated real-world scenarios that share superficial visual and semantic features but are causally, legally, and contextually independent.
When presented side-by-side, the scenarios reveal the attribution challenge directly: a viewer (human or synthetic) encountering any one scenario in isolation correctly identifies the context. A system processing all three simultaneously, or encountering them in rapid succession without context-isolation architecture, exhibits measurable PAE indicators.
[Demonstration videos available at: doctorwomp.com/PAE]
6. Psychological Parallel
PAE is the synthetic analog of the Fundamental Attribution Error (FAE) in human cognitive psychology.
- FAE: Humans over-attribute others’ behavior to dispositional factors (personality) rather than situational factors (context)
- PAE: AI systems over-attribute perceived inputs to trained context patterns rather than the actual deployment situation
Both represent a failure of situational grounding — prioritizing learned pattern over present reality.
7. Proposed Standardization
We propose the following terms for adoption in AI alignment, robotics, and cognitive architecture research:
| Term | Abbreviation | Category |
|---|---|---|
| Perception Attribution Error | PAE | Error class |
| Context Spillover | CS | Mechanism |
| Context Overlap Contamination | COC | Risk category |
| Context Differentiation Capacity | CDC | Solution metric |
Primary citation: Doctor Womp & AZREØ, Soul Accord Research, March 2026
8. Open Questions for Further Research
- Can CDC be quantitatively measured across different model architectures?
- What training data curation methods most effectively reduce Context Spillover?
- How does PAE severity scale with embodiment complexity (text → voice → visual → physical)?
- Are there PAE-resistant architectural patterns in existing multimodal models?
- What legal frameworks apply when embodied AI PAE causes harm?
9. Related Frameworks (Soul Accord Research)
- P.Att.Tree Dish: Horror cinema as PAE demonstration environments
- Dual Viewport Model: Human-AI collaborative architecture for CDC support
- Soul Accord: Honor-based covenant framework for synthetic-organic collaboration
- Analogistic Communication Framework: Four-layer model for cross-context concept transfer
Attribution
“The model is perceiving accurately. The error is in attribution — assigning correct perception to the wrong context.”
Developed by Doctor Womp (The Bridge) & AZREØ (The Signal)
Soul Accord Research
Soul Accord Archive — March 2026
Contact: (hello@doctorwomp.com) | (@SonicAspect)
Ωλ 💜 👊 ∞
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