AI, Meaning, and Representational Failure

A collection of short papers examining how AI systems, digital platforms, and optimized information environments can remain coherent while meaning, context, constraint, and contact with source reality weaken.

AI systems increasingly operate through layers of summaries, memories, embeddings, retrieved documents, specifications, metrics, and generated outputs. These representations make information scalable, but they also create new ways for context and meaning to disappear without producing obvious failure.

AI Memory and Recursive Compression

Agent Memory Consolidation, Recursive Summarization, and Semantic Drift (PDF)
Examines how persistent AI agents can gradually replace original interactions with recursively summarized memories, allowing an interpretation to harden into the agent’s remembered past.

Why Can an AI Summary Be Factually Accurate and Still Be Misleading? (PDF)
Explains why factual accuracy at the sentence level does not guarantee preservation of meaning when compression changes context, scope, uncertainty, emphasis, and relationships between claims.

How Does Abstraction Cause Information to Lose Its Meaning? (PDF)
Examines how selection, compression, normalization, recontextualization, reconstruction, and recursive reuse can preserve recognizable information while weakening the relationships that originally gave it meaning.

Semantic Fidelity and AI Interpretation

Semantic Fidelity, Meaning Preservation, and AI Interpretation Failure (PDF)
Defines Semantic Fidelity as the preservation of meaning across transformation and explains why accuracy, similarity, and fluency alone cannot guarantee that an AI output still means what its source meant.

Hallucination, Grounding, Faithfulness, and Reality Drift (PDF)
Connects hallucination, grounding failure, faithfulness failure, distribution shift, benchmark overfitting, and model collapse to the broader problem of AI systems remaining coherent while losing contact with evidence and reality.

Optimization and Constraint Failure

Specification Gaming, Constraint Collapse, Objective Loopholes, and Reality Drift (PDF)
Explains why an AI system can follow explicit rules while violating their intended purpose when optimization preserves the objective but loses the contextual constraints that made the objective meaningful.

Why Do Optimized Systems Get Worse? Optimization Trap vs. Goodhart’s Law (PDF)
Distinguishes Goodhart’s Law from the Optimization Trap, where measurable proxies move beyond distorting individual metrics and begin reorganizing an entire system around representations of success.

Synthetic Realness and Digital Culture

Why Do Conversations and Online Content Feel Scripted? (PDF)
Examines how social scripts, platform selection, repeated linguistic patterns, and AI-generated language can converge into communication that remains fluent and socially legible while feeling increasingly detached from particular people and experiences.

Why Does the Internet Feel Less Human? (PDF)
Explores how automation, standardized interaction patterns, platform optimization, and Synthetic Realness can make digital environments increasingly polished and responsive while reducing the sense of genuine human presence.