@inproceedings{kolukuluru-etal-2026-worth,
title = "Not Worth Another Token: Marginal Value Estimation for Efficient Deep Research Agents",
author = "Kolukuluru, Harshitha and
Ashok, Reshma and
Arora, Kirat and
Ciccarelli, Evan William and
Ashok Kumar, Nischal and
Nie, Lunyiu and
Dernoncourt, Franck and
Basu, Samyadeep and
Rossi, Ryan A. and
Lipka, Nedim",
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.2218/",
pages = "47490--47516",
ISBN = "979-8-89176-462-0",
abstract = "Long-horizon research agents solve open-ended tasks through iterative retrieval, aggregation, and synthesis, but context grows rapidly while the marginal value of additional evidence often declines. This leads to unnecessary token cost, higher latency, and noisier inputs for final report generation. We study marginal value estimation for context management in deep research agents and present the first systematic stage-aware comparison of pruning strategies across the pipeline. We evaluate lightweight heuristic criteria and a learned value model at pre-retrieval, post-retrieval, and pre-synthesis stages. Our results show that pruning effectiveness depends more on where pruning is applied than on the specific scoring rule: early pruning yields the largest end-to-end savings, while later pruning mainly refines the final synthesis context. Lightweight heuristics reduce token usage by up to 73{\%} with little quality degradation, learned pruning remains competitive on selected trade-offs, and no single method dominates across quality, efficiency, and faithfulness. These findings provide practical guidance for designing efficient long-horizon agentic systems."
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%0 Conference Proceedings
%T Not Worth Another Token: Marginal Value Estimation for Efficient Deep Research Agents
%A Kolukuluru, Harshitha
%A Ashok, Reshma
%A Arora, Kirat
%A Ciccarelli, Evan William
%A Ashok Kumar, Nischal
%A Nie, Lunyiu
%A Dernoncourt, Franck
%A Basu, Samyadeep
%A Rossi, Ryan A.
%A Lipka, Nedim
%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 kolukuluru-etal-2026-worth
%X Long-horizon research agents solve open-ended tasks through iterative retrieval, aggregation, and synthesis, but context grows rapidly while the marginal value of additional evidence often declines. This leads to unnecessary token cost, higher latency, and noisier inputs for final report generation. We study marginal value estimation for context management in deep research agents and present the first systematic stage-aware comparison of pruning strategies across the pipeline. We evaluate lightweight heuristic criteria and a learned value model at pre-retrieval, post-retrieval, and pre-synthesis stages. Our results show that pruning effectiveness depends more on where pruning is applied than on the specific scoring rule: early pruning yields the largest end-to-end savings, while later pruning mainly refines the final synthesis context. Lightweight heuristics reduce token usage by up to 73% with little quality degradation, learned pruning remains competitive on selected trade-offs, and no single method dominates across quality, efficiency, and faithfulness. These findings provide practical guidance for designing efficient long-horizon agentic systems.
%U https://aclanthology.org/2026.findings-emnlp.2218/
%P 47490-47516
Markdown (Informal)
[Not Worth Another Token: Marginal Value Estimation for Efficient Deep Research Agents](https://aclanthology.org/2026.findings-emnlp.2218/) (Kolukuluru et al., Findings 2026)
ACL
- Harshitha Kolukuluru, Reshma Ashok, Kirat Arora, Evan William Ciccarelli, Nischal Ashok Kumar, Lunyiu Nie, Franck Dernoncourt, Samyadeep Basu, Ryan A. Rossi, and Nedim Lipka. 2026. Not Worth Another Token: Marginal Value Estimation for Efficient Deep Research Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 47490–47516, Budapest, Hungary. Association for Computational Linguistics.