Not Worth Another Token: Marginal Value Estimation for Efficient Deep Research Agents
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 t...
| Authors | Harshitha Kolukuluru, Reshma Ashok, Kirat Arora et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 August 2026 |
| Last updated at source | 1 September 2026 |
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