MAC-AutoML

MAC-AutoML / SpecEyes

Public

This is the official implementation of our paper "SpecEyes: Accelerating Agentic Multimodal LLMs via Speculative Perception and Planning"

32
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100% credibility
Found Mar 25, 2026 at 32 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Research evaluation toolkit for testing faster reasoning in AI models that analyze images and use tools like zooming.

How It Works

1
🔍 Discover SpecEyes

You find this project while looking for ways to make AI picture helpers think faster without losing smarts.

2
🛠️ Prepare your setup

Follow simple steps to get your computer ready with the needed helpers.

3
📥 Gather test pictures

Download collections of images with questions to challenge your AI.

4
🤖 Pick your AI duo

Choose a quick thinker for easy questions and a powerful one for tough ones.

5
Run the race
✅
Easy win

Quick AI answers directly and correctly.

🔄
Team up

Quick AI passes to powerful one for deeper thinking.

6
👨‍⚖️ Judge the answers

Use a smart checker to score accuracy across all tests.

🏆 Celebrate speedups

Enjoy 2x faster AI thinking with almost no accuracy drop, ready to share your results.

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AI-Generated Review

What is SpecEyes?

SpecEyes accelerates agentic multimodal LLMs by using a lightweight vision-language model to quickly screen images and questions, then applying answer separability gating to deliver fast answers or defer to a stronger tool-using model. This official implementation provides Python evaluation scripts for benchmarks like V*, HR-Bench, and POPE, plus judge scripts and confidence analysis tools via official GitHub Actions and CLI. Developers get speedup metrics and JSONL results without rebuilding from scratch.

Why is it gaining traction?

It tackles sequential tool-use bottlenecks in VLMs, enabling agent-level speculation that skips full loops for simple queries—yielding measured latency drops on real benchmarks. The hook is plug-and-play evals with batched inference, vLLM judge endpoints, and ablation scripts for thresholds like logit gaps, making it easy to test accelerating gains over baselines.

Who should use this?

ML engineers benchmarking VLMs on visual reasoning tasks, like high-res counting or object localization in HR-Bench. Researchers tuning speculative decoding for production agentic pipelines, or teams integrating QwenVL/DeepEyes/Thyme who need quick confidence-based routing.

Verdict

Solid prototype for accelerating multimodal agents, with clear README setup and Apache 2.0 license, but 32 stars and 1.0% credibility score signal early maturity—lacks broad tests or community polish. Grab it for experiments if you're in VLM eval.

(178 words)

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