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Paper Citation Record · LEDGER

IFIR: A Comprehensive Benchmark for Evaluating Instruction-Following in Expert-Domain Information Retrieval

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.04644.

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

pith.paper-citation-record.v1
2503.04644 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:59:01.906434Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:30:17.158940Z

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 ba70b736-7f69-4b87-9c4a-8a97cdc60592 · inbound

LLMs are Also Effective Embedding Models: An In-depth Overview cites this paper.

LLMs are Also Effective Embedding Models: An In-depth Overview IFIR: A Comprehensive Benchmark for Evaluating Instruction-Following in Expert-Domain Information Retrieval

Reference 156

Resolution
unresolved
no resolver link, observed 2026-08-11T13:59:01.906434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:59:01.906434Z digest=sha256:c2c395f92c9f70b27ce2965237f7ebbda6dd8f87b0665fe563f4daae312ca03a

Observation 13820063-f277-4a7c-b6af-54393ee93e2c · inbound

Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective cites this paper.

Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective IFIR: A Comprehensive Benchmark for Evaluating Instruction-Following in Expert-Domain Information Retrieval

Reference 32

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:30:17.193560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-07T15:30:15.544995Z digest=sha256:fbc5cf10201428450a05baafd84caf5989aaa946525a7b502f480c644f5aa444