SAGE: Steerable Agentic Data Generation for Deep Search with Execution Feedback
Metadata
| Field | Content | |-------|---------| | Title | SAGE: Steerable Agentic Data Generation for Deep Search with Execution Feedback | | Authors | Fangyuan Xu, Rujun Han, Yanfei Chen, Zifeng Wang, I-Hung Hsu, Jun Yan, et al. (10 authors total) | | Published | January 2026 | | Source | https://arxiv.org/abs/2601.18202 | | Type | Academic paper | | Category | Deep Research |
One-line Summary
This work ("SAGE: Steerable Agentic Data Generation for Deep Search with Execution…") targets the problem of generating steerable, verifiable training data for agentic deep search, using execution feedback to validate synthesized agent trajectories.
Background and Motivation
Agentic search systems face persistent challenges around efficiency, scalability, and understanding user intent. Traditional pipelines separate retrieval, ranking, and generation, which struggles to meet the LLM-era demands of natural-language interaction, multi-hop reasoning, and up-to-date knowledge.
SAGE addresses the data bottleneck for such systems: training deep-search agents requires trajectories demonstrating when to retrieve, what queries to issue, which tools to call, and how to synthesize results. The core idea is to make this data generation steerable (controllable in the behaviors covered) and grounded in execution feedback (quality verified against actual task execution outcomes rather than human judgment alone).
Relevance to Search / Rec / Personalization
The forum post situates SAGE within the broader agentic-search landscape:
- Architecture: Cascaded retrieve + rerank + generate remains mainstream, but the agentic paradigm makes "retrieval count and policy" itself a learnable object.
- Data: High-quality instruction data and session logs matter as much as models; synthetic data must guard against knowledge leakage and distribution shift — which is exactly the gap execution-feedback-based generation like SAGE aims to close.
- Evaluation: Offline metrics increasingly diverge from online satisfaction; task success rate, citation accuracy, and multi-hop reasoning chain integrity are becoming process-level metrics.
- Deployment: Latency, cost, explainability, and safety are hard constraints for production systems.
- Researchers: Reproduce the core comparisons; check whether statistical significance and compute costs are reported.
- Engineers: Extract pluggable modules (planner, retriever, reranker) and estimate integration cost with existing stacks.
- Product managers: Focus on user-perceivable gains (latency, answer trustworthiness, multi-turn consistency) rather than offline nDCG alone.
- A Comprehensive Survey of Deep Research (arXiv:2506.12594)
- A Survey of LLM-based Deep Search Agents (arXiv:2508.05668)
- AgentIR: Reasoning-Aware Retrieval for Deep Research Agents (arXiv:2603.04384)
- Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning (arXiv:2502.04644)
Engineering Checklist Highlights
| Concern | Suggestion | |---------|-----------| | Data | PII scrubbing, versioned/rollback-able embedding indexes | | Latency | Cascading with early stopping, caching hot queries, async reranking | | Quality | Interleaving experiments, human audits, citation verification | | Safety | Source whitelisting, poisoning detection, output filtering | | Cost | Small-model routing, distillation, hybrid sparse+dense retrieval |
Who Should Read This
Note on Fidelity
This forum entry is largely an index/awesome-list item with templated commentary; several sections (exact numbers, full abstract) were placeholders. For quantitative claims, consult the original paper PDF at arXiv:2601.18202.