@inproceedings{pothuru-2026-failures,
title = "When Failures Propagate: Causal Failure Attribution in Agentic Retrieval-Augmented Generation",
author = "Pothuru, Lauren",
editor = "Kamalloo, Ehsan and
Gontier, Nicolas and
Rozanov, Nikolai and
Hashemzadeh, Maryam and
Arora, Akhil",
booktitle = "Proceedings of the Second Workshop for {RE}search on Agent Language Models ({REALM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.realm-1.46/",
pages = "675--686",
ISBN = "979-8-89176-485-9",
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 site. Its central question is whether a post-hoc trace still identifies the injected hop after the suffix changes. In the dense Claude Haiku 4.5 structural sweep on 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), but it predicts hop 1 in 96{\%} of failures, an earliest-hop rule; at hops 2 and 3, an LLM judge and frozen-hop repair localize only 0.25 to 0.48 of failures. In larger content-corruption runs, depth-2 exact-hop accuracy is 0.00 for coverage, 0.49 for an LLM judge, 0.59 for frozen-hop repair, and 0.14 for suffix regeneration (n=51); the judge and frozen-hop repair do not significantly differ (exact McNemar p=0.33)."
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<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 site. Its central question is whether a post-hoc trace still identifies the injected hop after the suffix changes. In the dense Claude Haiku 4.5 structural sweep on 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), but it predicts hop 1 in 96% of failures, an earliest-hop rule; at hops 2 and 3, an LLM judge and frozen-hop repair localize only 0.25 to 0.48 of failures. In larger content-corruption runs, depth-2 exact-hop accuracy is 0.00 for coverage, 0.49 for an LLM judge, 0.59 for frozen-hop repair, and 0.14 for suffix regeneration (n=51); the judge and frozen-hop repair do not significantly differ (exact McNemar p=0.33).</abstract>
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%0 Conference Proceedings
%T When Failures Propagate: Causal Failure Attribution in Agentic Retrieval-Augmented Generation
%A Pothuru, Lauren
%Y Kamalloo, Ehsan
%Y Gontier, Nicolas
%Y Rozanov, Nikolai
%Y Hashemzadeh, Maryam
%Y Arora, Akhil
%S Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-485-9
%F pothuru-2026-failures
%X 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 site. Its central question is whether a post-hoc trace still identifies the injected hop after the suffix changes. In the dense Claude Haiku 4.5 structural sweep on 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), but it predicts hop 1 in 96% of failures, an earliest-hop rule; at hops 2 and 3, an LLM judge and frozen-hop repair localize only 0.25 to 0.48 of failures. In larger content-corruption runs, depth-2 exact-hop accuracy is 0.00 for coverage, 0.49 for an LLM judge, 0.59 for frozen-hop repair, and 0.14 for suffix regeneration (n=51); the judge and frozen-hop repair do not significantly differ (exact McNemar p=0.33).
%U https://aclanthology.org/2026.realm-1.46/
%P 675-686
Markdown (Informal)
[When Failures Propagate: Causal Failure Attribution in Agentic Retrieval-Augmented Generation](https://aclanthology.org/2026.realm-1.46/) (Pothuru, REALM 2026)
ACL