@inproceedings{roh-han-2026-halt,
title = "{HALT}: Verification-Aware Stopping for Retrieval-Augmented Search Agents",
author = "Roh, Daeyoung and
Han, Donghee",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.343/",
pages = "7197--7219",
ISBN = "979-8-89176-462-0",
abstract = "Retrieval-augmented search agents answer multi-hop questions by repeatedly issuing search queries and accumulating evidence. This creates a stopping problem: after the necessary evidence has appeared, further retrieval often adds cost, latency, and distracting context rather than useful information. We frame stopping as evidence coverage rather than generator confidence, and introduce HALT, a lightweight verification-aware policy that leaves the search agent unchanged. Given expected hop claims, HALT stops only when cumulative evidence supports each required claim. Across three multi-hop QA benchmarks, HALT reduces redundant search while largely preserving exact match, with stronger savings under gold claims and more conservative behavior under generated claims. Baseline comparisons and ablations show that this behavior is driven by claim{--}evidence alignment rather than generic sufficiency, fixed stop positions, or lexical overlap. Open-corpus pilots further suggest that HALT abstains when coverage cannot be reliably verified. Overall, evidence coverage provides a practical runtime control signal for improving retrieval-augmented agents without retraining or modifying the host agent."
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%0 Conference Proceedings
%T HALT: Verification-Aware Stopping for Retrieval-Augmented Search Agents
%A Roh, Daeyoung
%A Han, Donghee
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F roh-han-2026-halt
%X Retrieval-augmented search agents answer multi-hop questions by repeatedly issuing search queries and accumulating evidence. This creates a stopping problem: after the necessary evidence has appeared, further retrieval often adds cost, latency, and distracting context rather than useful information. We frame stopping as evidence coverage rather than generator confidence, and introduce HALT, a lightweight verification-aware policy that leaves the search agent unchanged. Given expected hop claims, HALT stops only when cumulative evidence supports each required claim. Across three multi-hop QA benchmarks, HALT reduces redundant search while largely preserving exact match, with stronger savings under gold claims and more conservative behavior under generated claims. Baseline comparisons and ablations show that this behavior is driven by claim–evidence alignment rather than generic sufficiency, fixed stop positions, or lexical overlap. Open-corpus pilots further suggest that HALT abstains when coverage cannot be reliably verified. Overall, evidence coverage provides a practical runtime control signal for improving retrieval-augmented agents without retraining or modifying the host agent.
%U https://aclanthology.org/2026.findings-emnlp.343/
%P 7197-7219
Markdown (Informal)
[HALT: Verification-Aware Stopping for Retrieval-Augmented Search Agents](https://aclanthology.org/2026.findings-emnlp.343/) (Roh & Han, Findings 2026)
ACL