# Avoiding What Your Teacher Already Knows: Entity Familiarity for Deep-Search Distillation

arXiv: https://arxiv.org/abs/2610.16251 · HTML: https://mlsanity.com/paper/2610.16251/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Knowledge distillation for LLMs, one of 12 directions in Knowledge distillation.
- Builds on 36 of its 46 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Placed in

- Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/)

## Builds on (most cited first, 30 of 36)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
- [DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models](https://mlsanity.com/paper/2512.02556/) (2025)
- [Phi-4 Technical Report](https://mlsanity.com/paper/2412.08905/) (2024)
- [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025)
- [WebSailor: Navigating Super-human Reasoning for Web Agent](https://mlsanity.com/paper/2507.02592/) (2025)
- [BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese](https://mlsanity.com/paper/2504.19314/) (2025)
- [WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization](https://mlsanity.com/paper/2507.15061/) (2025)
- [On-Device Language Models: A Comprehensive Review](https://mlsanity.com/paper/2409.00088/) (2024)
- [SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models](https://mlsanity.com/paper/2506.01062/) (2025)
- [Tongyi DeepResearch Technical Report](https://mlsanity.com/paper/2510.24701/) (2025)
- [TaskCraft: Automated Generation of Agentic Tasks](https://mlsanity.com/paper/2506.10055/) (2025)
- [Distilling LLM Agent into Small Models with Retrieval and Code Tools](https://mlsanity.com/paper/2505.17612/) (2025)
- [Dr. Zero: Self-Evolving Search Agents without Training Data](https://mlsanity.com/paper/2601.07055/) (2026)
- [OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data](https://mlsanity.com/paper/2603.15594/) (2026)
- [OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis](https://mlsanity.com/paper/2603.20278/) (2026)
- [Search-Time Data Contamination](https://mlsanity.com/paper/2508.13180/) (2025)
- [WebLeaper: Empowering Efficiency and Efficacy in WebAgent via Enabling Info-Rich Seeking](https://mlsanity.com/paper/2510.24697/) (2025)
- [Open Data Synthesis For Deep Research](https://mlsanity.com/paper/2509.00375/) (2025)
- [OffSeeker: Online Reinforcement Learning Is Not All You Need for Deep Research Agents](https://mlsanity.com/paper/2601.18467/) (2026)
- [QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks](https://mlsanity.com/paper/2605.24218/) (2026)
- [DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent](https://mlsanity.com/paper/2603.01152/) (2026)
- [Search-Time Contamination in Deep Research Agents: Measuring Performance Inflation in Public Benchmark Evaluation](https://mlsanity.com/paper/2606.05241/) (2026)
- [To Search or Not to Search: Aligning the Decision Boundary of Deep Search Agents via Causal Intervention](https://mlsanity.com/paper/2602.03304/) (2026)
- [FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents](https://mlsanity.com/paper/2606.12087/) (2026)
- [LiveBrowseComp: Are Search Agents Searching, or Just Verifying What They Already Know?](https://mlsanity.com/paper/2605.28721/) (2026)
- [AREX: Towards a Recursively Self-Improving Agent for Deep Research](https://mlsanity.com/paper/2607.21461/) (2026)
- [Synthesizing Agentic Data for Web Agents with Progressive Difficulty Enhancement Mechanisms](https://mlsanity.com/paper/2510.13913/) (2025)
- [EvoBrowseComp: Benchmarking Search Agents on Evolving Knowledge](https://mlsanity.com/paper/2606.13120/) (2026)
- [When Should LLMs Search? Counterfactual Supervision for Search Routing](https://mlsanity.com/paper/2607.05752/) (2026)

## Abstract (arXiv)

Deep-search agents are commonly trained by distilling tool-use trajectories from stronger teacher models. However, structurally complex questions may remain relatively easy for teachers familiar with key entities in the underlying entity graph, resulting in lower search demand and weaker supervision. We refer to this teacher-specific variation in question difficulty as teacher-relative difficulty and investigate entity familiarity as an early predictor. We first introduce Evidence-Verified Entity Familiarity (EVF), which estimates familiarity by verifying a teacher-generated factual brief against online evidence. Across multiple teachers, EVF is significantly correlated with teacher-relative difficulty with AUCs of 0.60--0.70. However, its high cost makes it impractical for constructing entity graphs at scale. To address this limitation, we propose Self-Reported Entity Familiarity (SEF), a lightweight gating signal that directly asks a teacher whether it is familiar with an entity before admitting that entity into the graph. Across multiple teachers, trajectories from SEF-gated synthesis exhibit greater observed search demand and yield higher downstream student accuracy than those from ungated synthesis. Because familiarity is teacher specific, we further study cross-teacher reuse. We screen graphs collected for other teachers using Kimi-K3's SEF judgments and use the retained graphs to produce 5,411 additional trajectories. Combined with 2,200 trajectories from newly constructed, SEF-gated graphs, these data improve Qwen3.8-27B's accuracy from 44.10% to 66.41% on a BrowseComp subset, and achieving state-of-the-art performance among models of comparable size on BrowseComp, SEAL-0, HLE-text, and xbench. Overall, SEF provides a low-cost way to synthesize more informative supervision for deep-search distillation.