@inproceedings{zhang-etal-2026-masking,
title = "Masking Stale Observations Helps Search Agents {---} Until It Doesn{'}t: A Regime Map and Its Mechanism",
author = "Zhang, Haoxiang and
Xu, Qixin and
Li, Zhuofeng and
Zhang, Lei and
Jiang, Pengcheng and
Zhang, Yu and
McAuley, Julian",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.756/",
pages = "17469--17505",
ISBN = "979-8-89176-459-0",
abstract = "Long-horizon search agents accumulate large amounts of retrieved content across many tool calls, making context-budget efficiency increasingly important. A minimal intervention is to mask stale observations from the context as the trajectory progresses, but it remains unclear when this form of context management helps and why. We study observation masking through a systematic sweep over various agent backbones (4B to 284B parameters) and three retrievers on offline and live-web agentic search benchmarks. We find that the accuracy gain from masking follows an asymmetric inverted-U shape when plotted against the model{'}s accuracy without context management: a plateau under weak retrievers, a peak in the *mismatch regime* (strong retriever $\times$ mid-capacity model), and a sharp collapse when the model is saturated. This pattern reflects the interaction between retriever recall and the model{'}s implicit filtering capacity, rather than either factor in isolation. Mechanistically, masking implements a token-for-turn trade-off: it removes observations the model has largely stopped attending to and pages the agent rarely re-opens. The added turns help when they convert failures into successes, but they fail when masking removes evidence the model would otherwise have used. We therefore reframe context management as a regime-dependent intervention (rather than a default choice), providing a new, holistic viewpoint that unifies the understanding of agentic deep search."
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<abstract>Long-horizon search agents accumulate large amounts of retrieved content across many tool calls, making context-budget efficiency increasingly important. A minimal intervention is to mask stale observations from the context as the trajectory progresses, but it remains unclear when this form of context management helps and why. We study observation masking through a systematic sweep over various agent backbones (4B to 284B parameters) and three retrievers on offline and live-web agentic search benchmarks. We find that the accuracy gain from masking follows an asymmetric inverted-U shape when plotted against the model’s accuracy without context management: a plateau under weak retrievers, a peak in the *mismatch regime* (strong retriever \times mid-capacity model), and a sharp collapse when the model is saturated. This pattern reflects the interaction between retriever recall and the model’s implicit filtering capacity, rather than either factor in isolation. Mechanistically, masking implements a token-for-turn trade-off: it removes observations the model has largely stopped attending to and pages the agent rarely re-opens. The added turns help when they convert failures into successes, but they fail when masking removes evidence the model would otherwise have used. We therefore reframe context management as a regime-dependent intervention (rather than a default choice), providing a new, holistic viewpoint that unifies the understanding of agentic deep search.</abstract>
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%0 Conference Proceedings
%T Masking Stale Observations Helps Search Agents — Until It Doesn’t: A Regime Map and Its Mechanism
%A Zhang, Haoxiang
%A Xu, Qixin
%A Li, Zhuofeng
%A Zhang, Lei
%A Jiang, Pengcheng
%A Zhang, Yu
%A McAuley, Julian
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-etal-2026-masking
%X Long-horizon search agents accumulate large amounts of retrieved content across many tool calls, making context-budget efficiency increasingly important. A minimal intervention is to mask stale observations from the context as the trajectory progresses, but it remains unclear when this form of context management helps and why. We study observation masking through a systematic sweep over various agent backbones (4B to 284B parameters) and three retrievers on offline and live-web agentic search benchmarks. We find that the accuracy gain from masking follows an asymmetric inverted-U shape when plotted against the model’s accuracy without context management: a plateau under weak retrievers, a peak in the *mismatch regime* (strong retriever \times mid-capacity model), and a sharp collapse when the model is saturated. This pattern reflects the interaction between retriever recall and the model’s implicit filtering capacity, rather than either factor in isolation. Mechanistically, masking implements a token-for-turn trade-off: it removes observations the model has largely stopped attending to and pages the agent rarely re-opens. The added turns help when they convert failures into successes, but they fail when masking removes evidence the model would otherwise have used. We therefore reframe context management as a regime-dependent intervention (rather than a default choice), providing a new, holistic viewpoint that unifies the understanding of agentic deep search.
%U https://aclanthology.org/2026.emnlp-main.756/
%P 17469-17505
Markdown (Informal)
[Masking Stale Observations Helps Search Agents — Until It Doesn’t: A Regime Map and Its Mechanism](https://aclanthology.org/2026.emnlp-main.756/) (Zhang et al., EMNLP 2026)
ACL