CRINN: Teaching an AI to Make Vector Search Lightning-Fast

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“My vector database is getting sluggish—can anything be done without a PhD in performance engineering?”
“Is there a way to let software tune itself?”
“Once my model is trained, can I still squeeze out more speed?”

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If you have asked any of these questions, this post explains a practical path forward.
We will walk through 「CRINN」—a framework that uses 「contrastive reinforcement learning」 to accelerate 「approximate nearest-neighbor search (ANNS)」 by 10 %–85 %, without touching a line of hand-tuned assembly.

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1. Why ANNS Matters More Every Day

Real-world job	Why ANNS is the bottleneck
Retrieval-augmented generation (RAG)	Must fetch the most relevant passages from millions of vectors in milliseconds.
AI customer-support agents	Need to map a user’s question to the best knowledge-base article instantly.
Music or product recommendations	Must find the closest taste neighbors for every user request.

Traditional workflow: an engineer profiles, tweaks parameters, and rewrites code for weeks.
CRINN workflow: a large language model (LLM) guided by reinforcement learning iteratively produces faster code in hours.

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2. What CRINN Does in One Sentence

CRINN treats 「execution speed」 as a reward signal and asks an LLM to keep rewriting an ANNS library until it is both faster and still accurate.

2.1 Cast of Characters

Component	Role
Large language model	Proposes new code versions for each ANNS module.
Reinforcement learner	Measures actual speed, assigns a scalar reward, and updates the model.
GLASS baseline	Supplies the initial HNSW implementation that CRINN improves upon.

2.2 Three-Stage Optimization Order

1. 「Graph Construction」 – decides how data points connect.
2. 「Search」 – decides where to start and how to walk the graph.
3. 「Refinement」 – applies low-level tricks such as prefetching and early termination.

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3. Eight Speed Tricks the AI Taught Itself

Below are the exact improvements CRINN discovered, expressed in plain language and side-by-side code snippets.

3.1 Graph Construction Insights

Idea	Old Habit	CRINN Change	Benefit
「Adaptive ef」	Fixed number of neighbors (ef_construction)	Scale the neighbor budget based on target recall	No wasted work
「Multi-level prefetch」	Always prefetch 5 edges	Prefetch depth adapts to neighbor density	Fewer cache misses
「Multiple entry points」	Begin every search from one global node	Start up to 9 diverse entry points in parallel	Covers more of the graph quickly

Code Example: Adaptive ef

// Before: rigid budget
size_t ef = ef_construction;   // always constant

// After: dynamic budget
if (target_recall > critical_threshold)
    dynamic_ef = ef_search * (1.0 + recall_excess * 14.5);
else
    dynamic_ef = ef_search;

Code Example: Multi-level Prefetch

// Before: fixed window
for (int j = 0; j < min(5, size); ++j)
    computer.prefetch(neighbors[j], 1);

// After: depth adapts from 24 to 48 based on runtime data
prefetch_depth = min(adaptive_depth, size);

Code Example: Multiple Entry Points

// Before: single start
start_node = enterpoint_node;
results = search(start_node, query);

// After: up to 9 diverse starts
for (node : strategic_entrypoints)
    if (distance_to_others(node) > threshold)
        entry_points.add(node);
for (ep : entry_points)
    results.merge(search(ep, query));

3.2 Search-Phase Insights

Idea	Old Habit	CRINN Change	Benefit
「Tiered entry selection」	One entry node	Primary, secondary, tertiary entry points	Better quality/cost trade-off
「Batch processing + adaptive prefetch」	Prefetch one neighbor at a time	Group edges into batches and prefetch ahead	Cache-friendly
「Early convergence detection」	Exhaust candidate pool	Stop when no improvement detected	Saves CPU cycles

Code Example: Tiered Entry Selection

// Before
initialize_search(single_entry_point);

// After
add_entry(primary_entry_point);
if (search_budget > threshold_1)
    add_entry(secondary_entry_point);
if (search_budget > threshold_2)
    add_entry(tertiary_entry_point);

Code Example: Batch Prefetch

// Before
for i in range(prefetch_count):
    prefetch(neighbor[i]);

// After
prefetch_size = prefetch_count * batch_factor;
for i in range(adaptive_prefetch_size):
    prefetch(neighbor[i]);
    if (processing_node[j])
        prefetch(neighbor[j + prefetch_size]);  // look-ahead

Code Example: Early Termination

// Before: exhaust list
while (has_candidates())
    process_neighbor();

// After: stop early
no_improvement_count = 0;
while (has_candidates()) {
    improvements = process_neighbor();
    if (improvements == 0 && check_convergence(++no_improvement_count))
        break;
}

3.3 Refinement-Stage Insights

Idea	Old Habit	CRINN Change	Benefit
「Adaptive memory prefetch」	Traverse edges one by one	Prefetch future edges based on pattern	Lower latency
「Pre-computed edge metadata」	Count edges at runtime	Store counts and pattern scores in advance	Removes redundant work

Code Example: Adaptive Prefetch in Refinement

// Before
for each edge v in node_edges:
    if (distance(v) < best_distance)
        update best_node;

// After
if (should_prefetch)
    prefetch(edges[0]);
for i, edge v in node_edges {
    prefetch(edges[i + lookahead]);  // prefetch ahead
    if (distance(v) < best_distance)
        update best_node;
}

Code Example: Pre-computed Metadata

// Before
count = 0;
for each edge in node:
    if (edge != -1) count++;

// After
metadata = get_precomputed_metadata(level, node);
edge_count = metadata.count;
pattern_score = metadata.intelligence_score;
if (pattern_score > threshold)
    apply_pattern_optimization();

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4. Benchmark Results: How Much Faster Is It?

All figures are gathered on the 「ann-benchmarks」 suite without hardware-specific tuning.

4.1 Overall Win-Loss Table

Dataset	Distance Type	CRINN Rank
GIST-960	Euclidean	「#1」
MNIST-784	Euclidean	「#1」
GloVe-25	Angular	「#1」
SIFT-128	Euclidean	Tied #1
GloVe-100	Angular	Slight loss at 0.95 recall
NYTimes-256	Angular	Noticeable loss (see FAQ)

4.2 Detailed Speed-Ups

Dataset	Recall 0.9	Recall 0.99	Peak improvement
MNIST-784	24.8 k QPS	17.5 k QPS	「+85 %」
GIST-960	4.3 k QPS	1.1 k QPS	「+73 %」
SIFT-128	36.9 k QPS	13.0 k QPS	「+25 %」

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QPS = queries per second; higher is faster.

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4.3 Stage-by-Stage Contribution

Stage	Extra speed	Cumulative
Graph construction	+22 %	+22 %
Search	+18 %	+40 %
Refinement	+10 %	+50 %

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5. Quick-Start Guide (Linux)

5.1 System Dependencies

sudo apt-get update
sudo apt-get install -y build-essential git python3 python3-distutils python3-venv
pip3 install numpy pybind11

5.2 Build from Source

git clone https://github.com/deepreinforce-ai/CRINN.git
cd CRINN
bash build.sh

5.3 Run the Demo

python examples/main.py

The script downloads the SIFT-1 M dataset, runs a default benchmark, and prints QPS-vs-recall curves to your terminal.

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6. Frequently Asked Questions

6.1 Why is NYTimes-256 slower?

The reinforcement learner was rewarded only on 「Euclidean」 distance in this release. Angular-distance patterns therefore received less training signal. Future versions will include 「joint Euclidean + angular rewards」.

6.2 Hardware requirements?

Any x86-64 CPU with 「AVX2」 (most machines from 2015 onward). GPU is 「not」 required.

6.3 Can I swap in my own ANNS library?

Yes. Split your codebase into the three modules described above, follow the 「prompt template」 in the paper, and CRINN will optimize them in sequence.

6.4 Will accuracy drop?

No. The reward function only counts points inside the 「0.85–0.95 recall band」 and discards any submission that misses the reference recall.

6.5 Training time?

Roughly 「6 hours」 on a single A100 for all three modules using the SIFT-1 M dataset. The resulting code generalizes to the other five datasets without retraining.

6.6 How do I verify correctness?

Run python examples/validate.py. It cross-checks every result against the original GLASS implementation.

6.7 Roadmap?

• Add 「ParlayANN」 as an alternative starting point.
• Train on 「both Euclidean and angular distances」.
• Release 「Python bindings」 for easier integration.

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7. Citation

@article{deepreinforce2025crinn,
  title={CRINN: Contrastive Reinforcement Learning for Approximate Nearest Neighbor Search},
  author={Li, Xiaoya and Sun, Xiaofei and Wang, Albert and Chris, Shum and Li, Jiwei},
  journal={arXiv preprint arXiv:2508.02091},
  year={2025}
}

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8. Closing Thoughts

CRINN demonstrates that 「LLMs plus reinforcement learning」 can automate the painstaking work once reserved for human performance experts.
You no longer need to master cache-line alignment or SIMD intrinsics to shave milliseconds off vector search. Download the code, run the benchmark, and apply the same idea to your own retrieval stack.
May your queries be swift and your recalls accurate.