arXiv (All)
open access
How Calibration Content Shapes Attention-Based Reranking
Attention-based rerankers score documents by aggregating query-to-document attention and subtracting a null-query calibration pass to remove positional and structural bias. Although widely used, this calibration assumes that the null pass removes irrelevant signal from each document. We show that mo
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- RidgeRank: Efficient Visual Document Reranking via Score Fusion and a Shallow Linear Readout#1119265
- Equal Ranking Quality, Different Decisions: Measuring and Reducing Order Dependence in LLM Scorers#1117563
- Preserving What Matters: Semantic Scaffolds Beyond Saturation in Summarization Evaluation#1037286
- Which Reranking Conclusions Survive the Answer Interface? A Prospective Finite-Orbit Audit#1062150
- GSS-Attention Framework: Non-Collapsing Attention, Rule Underdetermination, and Global Non-Individuation in Interpretive Systems#732261
Record · ID 1011486
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive