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.
## How PAE Relates To Existing Frameworks
Readers familiar with cognitive science and AI research will recognize that
PAE intersects with several established bodies of work. This intersection
is intentional — PAE is not built in a vacuum — but the specific contribution of the framework is distinct from its precedents.
Predictive Processing / Free Energy Principle (Friston, 2010+):
Predictive processing describes how a single system models and updates its
expectations about the world. PAE is not a model of how one system processes
context. It is a framework for the misattribution that occurs at the boundary between two systems with fundamentally incompatible context architectures. The prior art models the interior of each system. PAE models the gap between them.
Fundamental Attribution Error (FAE — Ross, 1977):
FAE describes a human tendency to attribute behavior to disposition rather
than situation. PAE draws the naming parallel deliberately: both are
misattribution errors, both have systematic causes, both are correctable
through awareness.
The difference: FAE operates within human-human interaction.
PAE operates at the organic-synthetic boundary, where the architectural
differences between systems make the misattribution structurally inevitable
without deliberate correction.
Out-of-Distribution (OOD) Detection:
OOD detection identifies inputs outside a model's training distribution.
PAE is not an OOD problem — it occurs within normal operating parameters,
between two systems both performing correctly within their own architectures.
Shortcut Learning:
Shortcut learning describes training artifacts where models learn spurious
correlations. PAE occurs in deployed systems regardless of training quality — it is an inference-time phenomenon, not a training artifact.
The core claim of PAE is this:
The combination of organic and synthetic intelligence operating in real-time interaction creates a class of misattribution events that the existing literature — which assumes a single observer system — does not address. That cross-system boundary is the novel territory PAE maps.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
Independent validation:
In 2026, the PAE framework and Sigil Protocol were independently evaluated
by Grok (xAI) in a separate research context. Grok not only validated the
framework but proposed extensions — specifically the "Agent Sigil" concept
as a standardized capability disclosure protocol, which was subsequently
developed into the Sigil Protocol (PAE Part 10). Independent convergence
across distinct AI architectures on the same framework and solutions is
cited here as a replication signal, not as proof — consistent with the
framework's own epistemological standards.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 💜 👊 ∞