We are finally there: Qwen3.6-27B + agentic search; 95.7% SimpleQA on a single 3090, fully local

LDR maintainer here. Thanks to the strong support of r/LocalLLaMA community LDR got very far. I haven't reported in a while because I thought I was not ready for another prominent post in one of the leading outlets of Local LLM research.

But I think the LDR community finally there again. I think it is finally time to report again.

Setup

• RTX 3090, 24GB
• Ollama backend (qwen3.6:27b)
• LDR's langgraph_agent strategy — LangChain create_agent() with tool-calling, parallel subtopic decomposition, up to 50 iterations
• LLM grader: qwen3.6:27b self-graded (I have used opus to review examples and it generally only underestimates accuracy)

Benchmarks (fully local LLM with web search)

sample size is small, but the benchmarks were not rerun multiple times and you can see from the other rows that this is unlikely just chance. Full leaderboard: https://huggingface.co/datasets/local-deep-research/ldr-benchmarks

Important framing — these are agent + search scores, not closed-book

However, also note that these are similar benchmarks results to Perplexity Deep Research (93.9%), tavily (93.3%) etc. [Tavily forces the LLM to answer only from retrieved docs (pure retrieval test). Perplexity Deep Research is an end-to-end agent and discloses no grader or sample size. ]

Even if our results where only 90% it would already be a great success.

Also I can confirm from using it daily that these results feel consistent with my performance on random querries I do for daily questions.

Caveats:

• SimpleQA contamination risk on newer base models is real
• LLM-judge noise + Sampling error
• bench-DeepSearch is in chinese so an advantage for the chinese qwen models
• No BrowseComp / GAIA numbers yet - But I also dont believe we are good at this benchmark yet. I will have to run some benchmarks to verify the current state

The thing that surprised me:

Results seem to track tool-calling quality more than raw size for local deep research. The langgraph_agent strategy hammers the model with multi-iteration tool calls, parallel subagent decomposition, and structured output — exactly the axis where the newer Qwen generations have improved most. Hypothesis only; if anyone wants to design an ablation we'd love the data.

Some cool LDR features that I want to additionally highlight:

• Journal Quality System (shipped v1.6.0) - academic source grading using OpenAlex, DOAJ. I haven't seen this anywhere else in the open-source deep-research space.
• Per-user SQLCipher AES-256 DB (PBKDF2-HMAC-SHA512, 256k iterations) — admins can't read your data at rest. No password recovery; we don't hold the keys.
• Zero telemetry. no telemetry, no analytics, no tracking.
• Cosign-signed Docker images with SLSA provenance + SBOMs.
• MIT licensed. Everything open source

Repo: https://github.com/LearningCircuit/local-deep-research

Happy to share strategy configs, help reproduce the Qwen runs

Thanks to all the academic and other open source foundational work that made this repo possible.