# Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation

Source: [arXiv](https://arxiv.org/abs/2607.05382v1)  
Feed7 permalink: https://feed7.dev/p/2607-05382v1-1xo10v8  
Published: Unknown  
Trust: Needs Review (needs_review)

## Why Included

SearchGen-Bench shows open image generators score 21–28/100 on long-tail entities, and naive search retrieval only adds noise; a teach-then-search co-training recipe learns when to retrieve versus rely on weights.

## Source Summary

**The gist** **SearchGen-Bench** pairs **20,839 prompts** across twelve failure categories with a pre-executed 1M-item multimodal corpus for offline experiments. Frontier open visual generators score only **21–28 out of 100** — a roughly **40-point collapse** existing benchmarks miss — because they confidently fabricate long-tail entities and post-cutoff events.

## Practical Implication

**Why it matters** The retrieval lesson agents keep re-learning applies to image generation: **naive search fails**, injecting noise into prompts the generator already handles. The paper frames a generator-specific **knowledge boundary** — what lives in weights versus external context — and shows a **teach-then-search co-training** loop discovers it, improving monotonically. If you wire image generation into agent pipelines, gate retrieval on that boundary rather than searching every request.

## Agent-Ready Context

**The gist** **SearchGen-Bench** pairs **20,839 prompts** across twelve failure categories with a pre-executed 1M-item multimodal corpus for offline experiments. Frontier open visual generators score only **21–28 out of 100** — a roughly **40-point collapse** existing benchmarks miss — because they confidently fabricate long-tail entities and post-cutoff events.

**Why it matters** The retrieval lesson agents keep re-learning applies to image generation: **naive search fails**, injecting noise into prompts the generator already handles. The paper frames a generator-specific **knowledge boundary** — what lives in weights versus external context — and shows a **teach-then-search co-training** loop discovers it, improving monotonically. If you wire image generation into agent pipelines, gate retrieval on that boundary rather than searching every request.

**Watch out** Scores cover **open generators** only, and only a **minimal version** of the co-training recipe is demonstrated; whether the learned boundary keeps up as generators and trending entities keep moving is the open question.

## Context Map

- Layer: context
- Domains: image, research
- Topics: retrieval, tool-use, generative-media

## Uncertainty

- Scores cover **open generators** only, and only a **minimal version** of the co-training recipe is demonstrated; whether the learned boundary keeps up as generators and trending entities keep moving is the open question.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.