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Still Fresh? Evaluating Temporal Drift in Retrieval Benchmarks

Nathan Kuissi, Suraj Subrahmanyan, Nandan Thakur, Jimmy Lin · Mar 4, 2026 · Citations: 0

Automatic Metrics
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Data freshness

Extraction: Fresh

Check recency before relying on this page for active eval decisions. Use stale pages as context and verify against current hub results.

Metadata refreshed

Mar 4, 2026, 7:18 PM

Recent

Extraction refreshed

Mar 14, 2026, 6:20 AM

Fresh

Extraction source

Runtime deterministic fallback

Confidence 0.45

Abstract

Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora. However, temporal changes in technical corpora, such as API deprecations and code reorganizations, can render existing benchmarks stale. In our work, we investigate how temporal corpus drift affects FreshStack, a retrieval benchmark focused on technical domains. We examine two independent corpus snapshots of FreshStack from October 2024 and October 2025 to answer questions about LangChain. Our analysis shows that all but one query posed in 2024 remain fully supported by the 2025 corpus, as relevant documents "migrate" from LangChain to competitor repositories, such as LlamaIndex. Next, we compare the accuracy of retrieval models on both snapshots and observe only minor shifts in model rankings, with overall strong correlation of up to 0.978 Kendall $τ$ at Recall@50. These results suggest that retrieval benchmarks re-judged with evolving temporal corpora can remain reliable for retrieval evaluation. We publicly release all our artifacts at https://github.com/fresh-stack/driftbench.

Low-signal caution for protocol decisions

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • Extraction flags indicate low-signal or possible false-positive protocol mapping.
  • Extraction confidence is 0.45 (below strong-reference threshold).
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HFEPX Relevance Assessment

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A benchmark-and-metrics comparison anchor.

Main weakness

Extraction flags indicate low-signal or possible false-positive protocol mapping.

Trust level

Low

Eval-Fit Score

5/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

HFEPX Fit

Adjacent candidate

Extraction confidence: Low

If you are doing eval pipeline work, start here:

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Field Provenance & Confidence

Each key protocol field shows extraction state, confidence band, and data source so you can decide whether to trust it directly or validate from full text.

Human Feedback Types

missing

None explicit

Confidence: Low Source: Runtime deterministic fallback missing

No explicit feedback protocol extracted.

Evidence snippet: Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora.

Evaluation Modes

partial

Automatic Metrics

Confidence: Low Source: Runtime deterministic fallback evidenced

Includes extracted eval setup.

Evidence snippet: Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora.

Quality Controls

missing

Not reported

Confidence: Low Source: Runtime deterministic fallback missing

No explicit QC controls found.

Evidence snippet: Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora.

Benchmarks / Datasets

partial

Driftbench

Confidence: Low Source: Runtime deterministic fallback evidenced

Useful for quick benchmark comparison.

Evidence snippet: We publicly release all our artifacts at https://github.com/fresh-stack/driftbench.

Reported Metrics

partial

Accuracy, Recall, Recall@50

Confidence: Low Source: Runtime deterministic fallback evidenced

Useful for evaluation criteria comparison.

Evidence snippet: Next, we compare the accuracy of retrieval models on both snapshots and observe only minor shifts in model rankings, with overall strong correlation of up to 0.978 Kendall $τ$ at Recall@50.

Rater Population

missing

Unknown

Confidence: Low Source: Runtime deterministic fallback missing

Rater source not explicitly reported.

Evidence snippet: Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora.

Human Data Lens

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Unknown
  • Unit of annotation: Ranking
  • Expertise required: Coding
  • Extraction source: Runtime deterministic fallback

Evaluation Lens

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Confidence: 0.45
  • Flags: low_signal, possible_false_positive, runtime_fallback_extraction

Protocol And Measurement Signals

Benchmarks / Datasets

Driftbench

Reported Metrics

accuracyrecallrecall@50

Research Brief

Deterministic synthesis

Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora. HFEPX signals include Automatic Metrics with confidence 0.45. Updated from current HFEPX corpus.

Generated Mar 14, 2026, 6:20 AM · Grounded in abstract + metadata only

Key Takeaways

  • Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora.
  • However, temporal changes in technical corpora, such as API deprecations and code reorganizations, can render existing benchmarks stale.

Researcher Actions

  • Treat this as method context, then pivot to protocol-specific HFEPX hubs.
  • Cross-check benchmark overlap: Driftbench.
  • Validate metric comparability (accuracy, recall, recall@50).

Caveats

  • Generated from title, abstract, and extracted metadata only; full-paper implementation details are not parsed.
  • Low-signal flag detected: protocol relevance may be indirect.

Research Summary

Contribution Summary

  • Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora.
  • However, temporal changes in technical corpora, such as API deprecations and code reorganizations, can render existing benchmarks stale.
  • In our work, we investigate how temporal corpus drift affects FreshStack, a retrieval benchmark focused on technical domains.

Why It Matters For Eval

  • Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora.
  • However, temporal changes in technical corpora, such as API deprecations and code reorganizations, can render existing benchmarks stale.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Pass: Benchmark or dataset anchors are present

    Detected: Driftbench

  • Pass: Metric reporting is present

    Detected: accuracy, recall, recall@50

Category-Adjacent Papers (Broader Context)

These papers are nearby in arXiv category and useful for broader context, but not necessarily protocol-matched to this paper.

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