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HFEPX · Eval paper review

When Retrieval Partially Conflicts with Skills: Understanding Procedural Hallucination in Language Models

Guanqun Jiang, Pengfei Cao, Kang Liu, Jun Zhao

Published

Oct 19, 2026

Citations

0

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 19, 2026

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General Pairwise Preference

Should you rely on this paper?

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

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
40/100
Adjacent candidate

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

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Language models increasingly use skills that provide reusable instructions and procedures for task execution. After a relevant skill has been selected, the model may need to apply its governing procedure together with retrieved task context. When the retrieved context is useful but contains cues that violate the skill, the model must retain compatible information while rejecting the conflicting cues. We study failures in which the model adopts such cues and produces a retrieval-supported but skill-invalid answer, which we call procedural hallucination. We introduce SourceClash, a matched benchmark with 1,271 bundles and 6,355 prompts across ten procedural domains. Each bundle fixes the task, skill specification, and answer space while varying the relation between the skill and retrieval. Across nine models, valid-answer rates average 25.7% under conflicting retrieval, compared with 78.2% and 72.8% under two supportive retrieval settings. Mechanistic analysis of open-weight models shows that the answer-preference difference typically reaches its maximum shortly after generation begins. Residual replacement moves the model toward valid answers, while attention decomposition attributes most of the repair to value vectors. Intervention at the identified value pathway also improves final answer validity. These results characterize how partly conflicting retrieval changes skill-guided generation.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

partial

Pairwise Preference

Directly usable for protocol triage.

"Language models increasingly use skills that provide reusable instructions and procedures for task execution."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Language models increasingly use skills that provide reusable instructions and procedures for task execution."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Language models increasingly use skills that provide reusable instructions and procedures for task execution."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Language models increasingly use skills that provide reusable instructions and procedures for task execution."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Language models increasingly use skills that provide reusable instructions and procedures for task execution."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Pairwise Preference
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Language models increasingly use skills that provide reusable instructions and procedures for task execution.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Language models increasingly use skills that provide reusable instructions and procedures for task execution.
  • After a relevant skill has been selected, the model may need to apply its governing procedure together with retrieved task context.
  • When the retrieved context is useful but contains cues that violate the skill, the model must retain compatible information while rejecting the conflicting cues.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Pairwise preference evaluation

Contribution summary

  • We introduce SourceClash, a matched benchmark with 1,271 bundles and 6,355 prompts across ten procedural domains.
  • Across nine models, valid-answer rates average 25.7% under conflicting retrieval, compared with 78.2% and 72.8% under two supportive retrieval settings.
  • Mechanistic analysis of open-weight models shows that the answer-preference difference typically reaches its maximum shortly after generation begins.

Why it matters for eval

  • We introduce SourceClash, a matched benchmark with 1,271 bundles and 6,355 prompts across ten procedural domains.
  • Mechanistic analysis of open-weight models shows that the answer-preference difference typically reaches its maximum shortly after generation begins.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Pairwise Preference

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.