This paper argues that the dominant metaphor for LLM failure, hallucinations, misdiagnoses the real problem. Language models do not primarily fail by inventing false facts, but by undergoing fidelity decay, the gradual erosion of meaning across recursive transformations. Even when outputs remain accurate and coherent, nuance, metaphor, intent, and contextual ground steadily degrade. The paper proposes a unified framework for measuring this collapse through four interrelated dynamics, lexical decay, semantic drift, ground erosion, and semantic noise, and sketches how each can be operationalized into concrete benchmarks. The central claim is that accuracy alone is an insufficient evaluation target. Without explicit fidelity metrics, AI systems risk becoming fluent yet hollow, technically correct while culturally and semantically impoverished.

> The contribution of this work lies in its move from critique to measurement. It proposes concrete methods: recursive summarization chains, metaphor stress-tests, resonance surveys, and noise-infused retrieval experiments. These allow researchers to track how meaning erodes over time. By integrating these methods, it outlines a pathway toward fidelity-centered benchmarks that complement existing accuracy metrics.

To me, starting to solve the problem by meticulously measuring it, is a sign of a good solution.

True title: Measuring Fidelity Decay: A Framework for Semantic Drift and Collapse

Getting a 403 when I try to read. Anyone have a backup link?

This is slop

ofc not, they "bungee jump"

:p

Hallucinations that have certain characteristics and boundaries are still hallucinations. This is happening because learning models are doing pattern matching, so to put it briefly anything that fits may work and end up in the output.

Being able to admit the flaws and limitations of a technology is often critical to advancing adoption. Unfortunately, producers of currently popular learning model based technologies are more interested in speculation and growth and speculative growth than genuinely robust operation. This paper is a symptom of a larger problem that is contributing to the bubble pop, downturn, or "AI winter" that we are collectively heading toward.

This is so short and empty sorry, the author would be well placed to try to ground their work in a modicum of empiricism, the puffed-up style here makes things a bit hard to read. I do not know if this is slop it's getting harder to guess, and some actual humans have been writing like this long before LLMs. Still, what is the actual finding being presented here?

This paper perfectly articulates the problem I spent the last year solving. The shift from "hallucination" to "fidelity decay" is the correct mental model for agent stability.

I built an open source framework called SAFi that implements the "Fidelity Meter" concept mentioned in section 4. It treats the LLM as a stochastic component in a control loop. It calculates a rolling "Alignment State" (using an Exponential Moving Average) and measures "Drift" as the vector distance from that state.

The paper discusses "Ground Erosion" where the model loses its hierarchy of values. In my system, the "Spirit" module detects this erosion and injects negative feedback to steer the agent back to the baseline. I recently red-teamed this against 845 adversarial attacks and it maintained fidelity 99.6% of the time.

It is cool to see the theoretical framework catching up to what is necessary in engineering practice.

Repo link: https://github.com/jnamaya/SAFi