A recent research paper (arXiv:2512.14982) shows that prompt repetition — simply placing a second copy of the entire input immediately after the first — systematically improves the performance of causal large language models on non-reasoning tasks. The improvement occurs without increasing output length and, in most cases, without increasing end-to-end latency.
At first glance, transforming <QUERY> into <QUERY><QUERY> appears to be a prompt optimization trick. In reality, it exposes something more important: an encoding bias in causal LLM architectures. Prompt duplication reveals a structural property of how AI models process information. That bias has direct implications for AI retrieval, citation behavior, and Answer Engine Optimization (AEO).
Prompt Repetition Improves LLM Accuracy: Structural Bias in Causal Models
Prompt Repetition Experiment: Core Insight
Causal LLMs process tokens left to right with an order-sensitive constraint. During prompt encoding, this means past tokens do not incorporate information that appears later in the sequence. Repeating the prompt gives each token a second opportunity to attend to context that appeared earlier, reducing sensitivity to input order without modifying the generation stage.
In other words, prompt repetition can compensate for the inherent architectural asymmetry of causal models and significantly improve task accuracy.
Experimental Setup
Seven widely used models spanning multiple providers (Google’s Gemini 2.0 models, OpenAI’s GPT-4o variants, Anthropic’s Claude 3 models, and Deepseek V3) were tested. Evaluations separated contexts where reasoning was disabled (direct answer tasks) versus enabled (step-by-step prompting).
In practice, the non-reasoning benchmark tasks resemble classification, multiple choice, and factual selection problems. These task types are structurally similar to the majority of AI search and citation decisions.
Key Quantitative Results
When reasoning was disabled, prompt repetition led to statistically significant improvements in 47 out of 70 model-benchmark combinations (McNemar, p < 0.1) with zero statistically significant losses.
Accuracy improvements were larger on tasks that amplify order sensitivity (for example, “options-first” multiple-choice formats, or retrieve a specific item from a list) and smaller but still positive when the question appeared earlier in the prompt structure.
These results support the central claim that prompt repetition reduces order-induced degradation and mitigates the structural encoding bias.
Prompt repetition did not increase the length of generated outputs nor the measured end-to-end latency for non-reasoning tasks. Models preserved identical output formats, enabling direct replacement of baseline prompts.
Variants including three-times repetition showed similar or sometimes superior performance, indicating that the mechanism of duplication rather than simple token expansion accounts for the effect. Importantly, input padding to match token length without duplication did not yield improvements. This confirms that duplication, not raw token count, drives the effect.
For reasoning tasks, however, prompt repetition was neutral to slightly positive, and in rare cases may slightly degrade performance: 5 statistically significant wins, 1 loss, and 22 ties. The rationale behind this is that reasoning prompts implicitly re-encode context, which reduces reliance on a single left-to-right encoding pass and leads to smaller marginal benefit.
Why This Matters for AI Visibility and AEO
Most AI search interactions are non-reasoning tasks. When an LLM selects a brand to cite, chooses a URL to summarize, or retrieves an entity from a comparison list, it is typically performing structured selection rather than multi-step reasoning.
The research demonstrates that prompt duplication reduces order-induced degradation. This implies that for causal models, input structure and positional arrangement influence representational outcomes. If order sensitivity affects benchmark accuracy, it likely affects any task where entity selection, ranking, or retrieval depends on prompt encoding. This structural fragility directly connects to what we define as AI Visibility Consistency, a measurable indicator of how stable brand representation remains across AI systems.
In broader AI retrieval pipelines, entity signals introduced late, inconsistently, or only once may be more vulnerable to representational instability during encoding.
This reframes several assumptions about AI-visible content:
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Information placement is not neutral.
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Order influences internal representation.
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Reinforcement can increase stability without increasing verbosity.
In practical terms, this suggests:
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Critical entity identifiers should not appear only once.
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Definitions should not be deferred to late sections.
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Comparison pages should reinforce entity clarity throughout the document.
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Structural reinforcement may outperform stylistic variation.
Prompt repetition is not the strategic takeaway. It is a controlled demonstration of a broader architectural reality: LLM outputs are inherently fragile to input order.
For AI visibility and AEO strategy, the implication is structural rather than tactical. If encoding bias exists, then content structure can shape how models internalize entities.
