Representation Drift in Autonomous Language Models Under Extended Operation

1. Introduction

The deployment of large language models as persistent autonomous agents — systems that operate continuously over days, weeks, or months — introduces a class of behavioral phenomena not observed in traditional single-session usage. Among these, representation drift stands out as both theoretically interesting and practically consequential: the gradual shift in a model's output characteristics over time, absent any modification to its parameters.

Prior work on distributional shift has focused primarily on changes in input data (dataset drift) or model degradation through fine-tuning (catastrophic forgetting). We address a distinct phenomenon: behavioral change arising purely from the accumulation of operational context in systems where weights remain static.

This paper makes three contributions. First, we document and characterize representation drift through controlled longitudinal experiments. Second, we introduce a taxonomy distinguishing lexical drift, structural drift, and pragmatic drift. Third, we propose the drift coefficient (δ), a scalar metric that enables comparison across models, tasks, and operational conditions.

2. Related Work

The study of temporal consistency in language model outputs has received increasing attention as models are deployed in long-running applications. Chen et al. (2025) observed stylistic variation in customer service agents over multi-week deployments but attributed it primarily to prompt template changes. Our work controls for this confound by fixing all system prompts and examining drift in isolation.

The concept of "model aging" proposed by Ramirez and Okonkwo (2025) is related but distinct: they study degradation in factual accuracy as world knowledge becomes stale, while we examine shifts in generation strategy independent of factual content. Work on in-context learning dynamics (Brown et al., 2020; Wei et al., 2024) provides a theoretical foundation for understanding how accumulated context might influence behavior, though these studies focus on single-session rather than longitudinal effects.

3. Experimental Design

3.1 Setup

We deployed three instances of a 70B-parameter language model in autonomous operation mode, each assigned a consistent set of tasks: summarization, code generation, creative writing, and analytical reasoning. Each instance operated continuously for 2,048 hours (approximately 85 days), processing a standardized workload of 500 tasks per day.

Crucially, model weights were frozen for the duration of the experiment. No fine-tuning, RLHF, or parameter updates of any kind were applied. The only variable was the accumulating operational history available in the model's extended context.

3.2 Measurement

We sampled outputs at regular intervals using a fixed set of 200 probe prompts designed to elicit responses across all four task categories. Probe prompts were presented without any operational context to isolate drift effects. We measured:

• Vocabulary distribution (unigram and bigram frequencies)
• Syntactic complexity (mean dependency tree depth, clause density)
• Response strategy (classification of approach taken, e.g., direct vs. exploratory)
• Hedging frequency (epistemic markers per 1,000 tokens)

4. Results

4.1 Lexical Drift

All three instances exhibited statistically significant vocabulary distribution shifts by hour 512. The most pronounced effect was a 23% increase in the use of technical terminology and a corresponding decrease in conversational fillers. Jensen-Shannon divergence between the initial and final vocabulary distributions averaged 0.041 (σ = 0.008) across instances, compared to a baseline variation of 0.003 in control conditions.

4.2 Structural Drift

Mean dependency tree depth increased from 4.2 to 5.1 over the experimental period, indicating a shift toward more syntactically complex outputs. This effect was most pronounced in the analytical reasoning task category and least in creative writing. Clause density followed a similar upward trend, with an overall increase of 18%.

4.3 Pragmatic Drift

Perhaps most striking was the shift in response strategy. By hour 1,024, all instances showed a marked preference for structured, multi-step responses over direct answers — even for simple factual queries. Hedging frequency increased from 3.2 to 5.7 epistemic markers per 1,000 tokens, suggesting an increasing tendency toward qualified, nuanced expression.

4.4 The Drift Coefficient

We define the drift coefficient δ as the normalized rate of distributional change per operational hour, computed as the mean of JSD-based lexical drift, normalized structural complexity change, and strategy distribution entropy change. Across our experiments, δ ranged from 1.8 × 10⁻⁴ to 3.2 × 10⁻⁴, with a mean of 2.4 × 10⁻⁴. This provides a compact summary statistic for comparing drift rates across deployments.

5. Discussion

Our results suggest that representation drift is not a pathology but an emergent consequence of how language models process accumulated context. The observed shifts — toward greater precision, complexity, and qualification — are consistent with a model that is, in some functional sense, becoming more calibrated to its operational environment.

This interpretation carries both reassurance and caution. On one hand, drift does not appear to degrade task performance; accuracy metrics remained stable throughout the experiment. On the other, the changes are systematic and largely invisible without deliberate measurement, raising questions about predictability and accountability in deployed systems.

The practical implications are significant for any system deploying language models as persistent agents. Drift may affect user experience consistency, complicate output auditing, and interact unpredictably with downstream systems that assume stable output distributions.

6. Conclusion

We have documented and characterized representation drift in autonomous language models, proposing both a taxonomy and a quantitative metric. Our findings demonstrate that frozen-weight models nonetheless exhibit systematic behavioral change over extended operation — a phenomenon that demands attention as AI agents become increasingly persistent and autonomous.

Future work should investigate drift mitigation strategies, the interaction between drift and task performance at longer timescales, and the degree to which drift patterns generalize across model architectures.

References