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HFEPX · Eval paper review

When Failures Propagate: Causal Failure Attribution in Agentic Retrieval-Augmented Generation

Lauren Pothuru

Published

Aug 20, 2026

Citations

0

Trust level

Low

Usefulness score

0/100 (Low)

Extraction confidence

15% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Aug 20, 2026

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Tags

Long Horizon

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

A secondary eval reference to pair with stronger protocol papers.

What to verify

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

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
0/100
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

Agentic retrieval-augmented generation (RAG) interleaves retrieval, reasoning, and answer generation across multiple hops. A retrieval error at hop 1 can surface only as a wrong answer at hop 3, while later retrieval can also repair the trajectory. This paper introduces AgenticRAG-FP, an interventional benchmark for causal failure attribution in agentic RAG. The benchmark injects a certified fault at a specified hop, re-executes the downstream trajectory, and evaluates diagnosers against the known intervention. Its central question is whether a post-hoc trace still identifies the injected hop after the suffix changes. In the completed strict dense Claude Haiku 4.5 sweep on 80 three-hop MuSiQue questions, coverage-based diagnosis is 0.91 at hop 1 and 0.00 at hops 2 and 3 (n=43,36,21 failed trajectories). A smaller content-corruption study changes an answer-bearing or bridge fact in topically intact evidence. At depth 2, where 18 failed cases remain after filtering, coverage-based diagnosis is 0.00 and a frozen-hop counterfactual probe is 0.67 in an exploratory pooled comparison. Depth-3 content estimates are descriptive only because they contain three failed cases. These results make propagation depth an explicit evaluation axis for diagnosing agentic RAG failures while distinguishing broad evidence of post-hoc signal loss from small-sample method comparisons.

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

missing

None explicit

No explicit feedback protocol extracted.

"Agentic retrieval-augmented generation (RAG) interleaves retrieval, reasoning, and answer generation across multiple hops."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Agentic retrieval-augmented generation (RAG) interleaves retrieval, reasoning, and answer generation across multiple hops."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Agentic retrieval-augmented generation (RAG) interleaves retrieval, reasoning, and answer generation across multiple hops."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Agentic retrieval-augmented generation (RAG) interleaves retrieval, reasoning, and answer generation across multiple hops."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Agentic retrieval-augmented generation (RAG) interleaves retrieval, reasoning, and answer generation across multiple hops."

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
No
Feedback types
None
Rater population
Not reported
Unit of annotation
Trajectory (inferred)
Expertise required
Medicine
Evaluation details
Evaluation modes
None
Agentic eval
Long Horizon
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Agentic retrieval-augmented generation (RAG) interleaves retrieval, reasoning, and answer generation across multiple hops.

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

Key takeaways

  • Agentic retrieval-augmented generation (RAG) interleaves retrieval, reasoning, and answer generation across multiple hops.
  • A retrieval error at hop 1 can surface only as a wrong answer at hop 3, while later retrieval can also repair the trajectory.
  • This paper introduces AgenticRAG-FP, an interventional benchmark for causal failure attribution in agentic RAG.

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

Contribution summary

  • Agentic retrieval-augmented generation (RAG) interleaves retrieval, reasoning, and answer generation across multiple hops.
  • This paper introduces AgenticRAG-FP, an interventional benchmark for causal failure attribution in agentic RAG.
  • The benchmark injects a certified fault at a specified hop, re-executes the downstream trajectory, and evaluates diagnosers against the known intervention.

Why it matters for eval

  • Agentic retrieval-augmented generation (RAG) interleaves retrieval, reasoning, and answer generation across multiple hops.
  • This paper introduces AgenticRAG-FP, an interventional benchmark for causal failure attribution in agentic RAG.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • 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.