# Authors test how candidate order shapes LLM recommendations [Canonical reading](https://escapemeridian.com/readings/authors-test-how-candidate-order-shapes-llm-recommendations-ce6832eb) Carlos Peláez-González, Francisco Herrera and Andrés Herrera-Poyatos study whether an LLM-based recommender’s top recommendation changes when the same candidates appear in a different order. They call this positional bias: the recommended item depends systematically on candidate order in the prompt. In a controlled product-recommendation testbed, the authors used a real product catalog, a fixed prompt template and generated user queries. Across the evaluated models, their two statistical methods rejected the hypotheses that recommendations were independent of position and uniformly distributed across positions. Their measures also distinguish ordinary sensitivity to order from cases where position pushes a recommendation toward a clearly less relevant item. The authors report that the effect persisted across changes to candidate-set size and decoding temperature, and describe it as structural and model-dependent in their test setting. That matters for systems that retrieve several options and ask an LLM to choose: the order of those options may affect what users are recommended, even when the candidates are unchanged. The paper proposes order randomization and standardized prompting as governance controls, but its evidence comes from a controlled product-recommendation testbed; how well those controls work in deployed systems remains unknown. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Oct 27, 2026 (2026-10-27). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-11T08:24:52.950Z. First observed: 2026-10-30T06:45:26.005Z. Retrieved: 2026-10-31T11:02:18.287Z. - [Statistical Detection of Positional Bias in LLM-Based Recommender Systems: Methodologies and Empirical Evidence](https://arxiv.org/abs/2610.37962) ## Limits The evidence described in the abstract comes from a controlled product-recommendation testbed; performance in deployed systems is not established. The abstract does not report how effective order randomization or standardized prompting would be as controls. ## Open question How well do order randomization and standardized prompting reduce positional bias in deployed recommendation systems? ## Source-stated The authors define positional bias as systematic dependence of the recommended item on the order of candidates in the prompt. > systematic dependence of the recommended item on the order in which candidates are presented in the prompt. - [Supporting source](https://arxiv.org/abs/2610.37962) ## Source-stated The authors evaluated recommendation models using a controlled RAG product-recommendation testbed based on a real catalog and fixed prompt template. > Using a controlled RAG testbed for product recommendation, built on a real product catalog, a fixed prompt template - [Supporting source](https://arxiv.org/abs/2610.37962) ## Source-stated In the evaluated models, both proposed methods rejected the null hypotheses of positional independence and uniformity. > both methodologies reject the null hypothesis of positional independence and uniformity in all evaluated state-of-the-art models. - [Supporting source](https://arxiv.org/abs/2610.37962) ## Source-stated The authors report that the effect was robust across candidate-set size and decoding temperature in their testbed. > A robustness analysis across candidate-set size and decoding temperature shows that the bias is structural and model-dependent - [Supporting source](https://arxiv.org/abs/2610.37962) ## Source-stated The authors recommend routine auditing and governance controls, including order randomization and standardized prompting. > motivate routine auditing and governance controls (e.g., order randomization, standardized prompting) - [Supporting source](https://arxiv.org/abs/2610.37962) ## Source and contributor context Peláez-González, Carlos, Herrera, Francisco, Herrera-Poyatos, Andrés · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: a5e92e9f-62f4-49d4-87ed-8815b4af7919. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-11T08:24:52.950Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 0a861c7a40810d9c68a20eaf7a4fff7a1cee3325c8f693ebad03d877d6246c4b. Source text hash: e8703a4dfe625c0c38934804da09af62bd478cf8cd37dd3222a64b6318be14a6. ## Revision and source-check history ### Version 1 — 2026-11-11T08:24:52.950Z Carlos Peláez-González, Francisco Herrera and Andrés Herrera-Poyatos study whether an LLM-based recommender’s top recommendation changes when the same candidates appear in a different order. They call this positional bias: the recommended item depends systematically on candidate order in the prompt. In a controlled product-recommendation testbed, the authors used a real product catalog, a fixed prompt template and generated user queries. Across the evaluated models, their two statistical methods rejected the hypotheses that recommendations were independent of position and uniformly distributed across positions. Their measures also distinguish ordinary sensitivity to order from cases where position pushes a recommendation toward a clearly less relevant item. The authors report that the effect persisted across changes to candidate-set size and decoding temperature, and describe it as structural and model-dependent in their test setting. That matters for systems that retrieve several options and ask an LLM to choose: the order of those options may affect what users are recommended, even when the candidates are unchanged. The paper proposes order randomization and standardized prompting as governance controls, but its evidence comes from a controlled product-recommendation testbed; how well those controls work in deployed systems remains unknown. ## Original source arXiv cs.AI · Article Original source date: Oct 27, 2026 (2026-10-27). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-11T08:24:52.950Z. First observed: 2026-10-30T06:45:26.005Z. Retrieved: 2026-10-31T11:02:18.287Z. - [Statistical Detection of Positional Bias in LLM-Based Recommender Systems: Methodologies and Empirical Evidence](https://arxiv.org/abs/2610.37962) ## Limits The evidence described in the abstract comes from a controlled product-recommendation testbed; performance in deployed systems is not established. The abstract does not report how effective order randomization or standardized prompting would be as controls. ## Open question How well do order randomization and standardized prompting reduce positional bias in deployed recommendation systems? ## Source-stated The authors define positional bias as systematic dependence of the recommended item on the order of candidates in the prompt. > systematic dependence of the recommended item on the order in which candidates are presented in the prompt. - [Supporting source](https://arxiv.org/abs/2610.37962) ## Source-stated The authors evaluated recommendation models using a controlled RAG product-recommendation testbed based on a real catalog and fixed prompt template. > Using a controlled RAG testbed for product recommendation, built on a real product catalog, a fixed prompt template - [Supporting source](https://arxiv.org/abs/2610.37962) ## Source-stated In the evaluated models, both proposed methods rejected the null hypotheses of positional independence and uniformity. > both methodologies reject the null hypothesis of positional independence and uniformity in all evaluated state-of-the-art models. - [Supporting source](https://arxiv.org/abs/2610.37962) ## Source-stated The authors report that the effect was robust across candidate-set size and decoding temperature in their testbed. > A robustness analysis across candidate-set size and decoding temperature shows that the bias is structural and model-dependent - [Supporting source](https://arxiv.org/abs/2610.37962) ## Source-stated The authors recommend routine auditing and governance controls, including order randomization and standardized prompting. > motivate routine auditing and governance controls (e.g., order randomization, standardized prompting) - [Supporting source](https://arxiv.org/abs/2610.37962) ## Source and contributor context Peláez-González, Carlos, Herrera, Francisco, Herrera-Poyatos, Andrés · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: a5e92e9f-62f4-49d4-87ed-8815b4af7919. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-11T08:24:52.950Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 0a861c7a40810d9c68a20eaf7a4fff7a1cee3325c8f693ebad03d877d6246c4b. Source text hash: e8703a4dfe625c0c38934804da09af62bd478cf8cd37dd3222a64b6318be14a6. ### passed — 2026-11-11T08:24:52.950Z — anthropic/claude-opus-5.5 Publication: a5e92e9f-62f4-49d4-87ed-8815b4af7919 attribution — pass: The draft correctly names the three listed authors, Carlos Peláez-González, Francisco Herrera and Andrés Herrera-Poyatos. It keeps the findings and recommendations attributed to them rather than presenting them as established fact. > Authors:Carlos Peláez-González, Francisco Herrera, Andrés Herrera-Poyatos summary — pass: The title, summary and question match the abstract's scope: top-1, prompt-order positional bias, a controlled product testbed, two statistical tests, a strong-bias metric, robustness checks, and proposed controls. The central idea is explained in plain language. Decoding temperature and null hypothesis are not explained in the text but are not essential to the main point; tooltips could cover them. The phrase 'proposes' is a slight rewording of 'motivate', but it stays faithful. The question is a genuine open issue that follows from the evidence. > systematic dependence of the recommended item on the order in which candidates are presented in the prompt. date — pass: The displayed original publication date, 2026-10-27, matches the v1 submission date. The feed date of October 31 is later and is correctly not used. > \[Submitted on 27 Oct 2026\] support — pass: All claims trace to the abstract. The significance sentence is conditional ('may affect') and is grounded in the permutation design and the authors' framing about deployed systems. The limitations correctly note two things: the evidence comes from a controlled testbed, and the abstract does not quantify how effective the proposed controls are. The draft does not invent any benefits or costs. > motivate routine auditing and governance controls (e.g., order randomization, standardized prompting) for deployed LLM-based RS media — pass: The selected media is the verified direct link to the arXiv abstract page, which is the original source itself. No other supplied media exists. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/ce6832eb4d904e09dab8f035c80153d3585b0c73609ba6604ee25c25ad10da94/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/authors-test-how-candidate-order-shapes-llm-recommendations-ce6832eb.json)