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Paper Citation Record · LEDGER

Re-Ranking Step by Step: Investigating Pre-Filtering for Re-Ranking with Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.18740.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2406.18740 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:02:16.880939Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-23T20:58:26.588584Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c0d90904-d13b-48f3-ac41-65f8c6cab60c · inbound

Human-LLM Compound System for Scientific Ideation through Facet Recombination and Novelty Evaluation cites this paper.

Human-LLM Compound System for Scientific Ideation through Facet Recombination and Novelty Evaluation Re-Ranking Step by Step: Investigating Pre-Filtering for Re-Ranking with Large Language Models

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-05-23T20:58:26.591529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T20:58:01.255342Z digest=sha256:383f3c6a4471b01dac7f7fbec958da650ec0912e1eccb011842a9e9b6cb21e98

Observation f3795405-1c28-4365-a453-0d000cbb2ce9 · inbound

ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual Attribution cites this paper.

ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual Attribution Re-Ranking Step by Step: Investigating Pre-Filtering for Re-Ranking with Large Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-09T17:02:16.880939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T17:02:16.880939Z digest=sha256:45725d750ae5af82b2ad66a4663e104356ece2aef5ffee71b5549a39bd450946

Observation 947c7355-6292-40e5-9ea9-791044a2ace9 · inbound

Likert or Not: LLM Absolute Relevance Judgments on Fine-Grained Ordinal Scales cites this paper.

Likert or Not: LLM Absolute Relevance Judgments on Fine-Grained Ordinal Scales Re-Ranking Step by Step: Investigating Pre-Filtering for Re-Ranking with Large Language Models

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T14:20:44.222237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:20:44.222237Z digest=sha256:f1450341c3fe1d4d7e6997293d81349bcdb9c7df600bfff1959adb0beeb53cdd

Observation 389001a9-951a-4331-9860-8222ced42cba · inbound

Approximating the universal thermal climate index using sparse regression with orthogonal polynomials cites this paper.

Approximating the universal thermal climate index using sparse regression with orthogonal polynomials Re-Ranking Step by Step: Investigating Pre-Filtering for Re-Ranking with Large Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-05T20:04:42.985554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:04:42.985554Z digest=sha256:59300b9e82759882097b8d233ca2200a108c113baa2305ee066c0ca6d681f42d

Observation a6f52fb4-6cf8-466d-a230-ac08d7848cfb · inbound

100x Cost & Latency Reduction: Performance Analysis of AI Query Approximation using Lightweight Proxy Models cites this paper.

100x Cost & Latency Reduction: Performance Analysis of AI Query Approximation using Lightweight Proxy Models Re-Ranking Step by Step: Investigating Pre-Filtering for Re-Ranking with Large Language Models

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-15T09:29:54.000707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T09:27:34.198587Z digest=sha256:8934c97d4c3c950dd5fbbcf76cc18f3f9de8a109da42236bdc175d6d530fed91