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ARXIV:2606.02737 · INFORMATION RETRIEVAL · SUBMITTED 03 JUN · 20:33 UTC · FRESHNESS FRESH
ARXIV:2606.02737INFORMATION RETRIEVALSUBMITTED 03 JUN · 20:33 UTCFRESHNESS FRESHAndrianos Michail · Elias Schuhmacher · Juri Opitz · Simon Clematide · Rico Sennrich · arXiv
An inference-time attention calibration method to reduce positional bias in dense retrieval without retraining, improving fairness across languages and domains.
Opportunity summary
Pain An inference-time attention calibration method to reduce positional bias in dense retrieval without retraining, improving fairness across languages and domains.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence partial
An inference-time attention calibration method to reduce positional bias in dense retrieval without retraining, improving fairness across languages and domains. We ask whether this bias can be reduced at inference time, without retraining and…
Dense retrieval models exhibit positional bias: retrieval effectiveness degrades when relevant information appears later in a passage (Zeng et al., 2025). We ask whether this bias can be reduced at inference time, without retraining…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. A single configuration (B=128, lambda=0.5, 50% layer depth) improves the harmonic mean of nDCG@10 across positional groups on FineWeb-PosQ for all three models without…
Information Retrieval moved forward this cycle; last verified June 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
An inference-time attention calibration method to reduce positional bias in dense retrieval without retraining, improving fairness across languages and domains.
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10.48550/arXiv.2606.02737An inference-time attention calibration method to reduce positional bias in dense retrieval without retraining, improving fairness across languages and domains.
Abstract
Dense retrieval models exhibit positional bias: retrieval effectiveness degrades when relevant information appears later in a passage (Zeng et al., 2025). We ask whether this bias can be reduced at inference time, without retraining and without sacrificing overall retrieval effectiveness. To this end, we adapt inference-time attention calibration (Schuhmacher et al., 2026) to downstream retrieval and extend it with a strength coefficient lambda that interpolates between the original and fully calibrated attention distributions. Across three embedding models on SQuAD-PosQ and FineWeb-PosQ, we examine how basket size, calibrated layer set, and strength affect the trade-off between positional fairness and retrieval effectiveness, finding that partial calibration frequently outperforms full calibration. A single configuration (B=128, lambda=0.5, 50% layer depth) improves the harmonic mean of nDCG@10 across positional groups on FineWeb-PosQ for all three models without per-model tuning, and applies to both <s>-pooled and last-token-pooled architectures. This default configuration transfers without modification to PosIR, which spans 10 languages and 31 domains, reducing the Position Sensitivity Index in all 16 length-quartile x model x retrieval-setting combinations, while preserving or improving aggregate nDCG@10. We release our extended codebase at https://github.com/impresso/fair-sentence-transformers
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Proof status
partial0 refs; 4 sources; 67% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
An inference-time attention calibration method to reduce positional bias in dense retrieval without retraining, improving fairness across languages and domains. We ask whether this bias can be reduced at inference time, without retraining and without sacrificing overall retrieva...
METHOD
Dense retrieval models exhibit positional bias: retrieval effectiveness degrades when relevant information appears later in a passage (Zeng et al., 2025). We ask whether this bias can be reduced at inference time, without retraining and without sacrificing overall retrieval effe...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. A single configuration (B=128, lambda=0.5, 50% layer depth) improves the harmonic mean of nDCG@10 across positional groups on FineWeb-PosQ for all three models without per-model tuning, and applies to bot...
WHY NOW
Information Retrieval moved forward this cycle; last verified June 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
{"file name": "input.pdf", "number of pages": 13, "author": "Andrianos Michail; Elias Schuhmacher; Juri Opitz; Simon Clematide; Rico Sennrich"
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An inference-time attention calibration method to reduce positional bias in dense retrieval without retraining, improving fairness across languages and domains.
Segment
Information Retrieval
Adoption evidence
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Commercial read
7.0/10 public viability
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2/3 checks · 67%
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reason
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proof status
unverified
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confidence low
next verification path
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fresh
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Build readiness
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passport absent
fresh
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Artifact maturity
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fresh
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Technical feasibility
partial
Current read
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Run minimal reproduction from the Build Passport prototype path.
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Evidence
0 references, 4 sources, 67% evidence coverage.
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missing
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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