Medicine

MAM-CLIP: Vision-Language Pretraining on Mammography Atlases for BI-RADS Classification

Halil Ibrahim Gulluk, Olivier Gevaert

Featured May 29, 2026

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AI-generated analysis — This is SciGrove's AI interpretation of the paper, not peer-reviewed content. Always refer to the original paper.

Simply

A new method teaches AI models to think just enough: it makes them concise when they're right and confident, but encourages deeper thinking when they're wrong, balancing speed and accuracy.

In depth

The paper introduces Conditional Entropy Shaping (CES), a framework that dynamically adjusts an LLM's reasoning depth. It achieves this by penalizing high-entropy tokens on correct reasoning paths to encourage conciseness, while rewarding them on incorrect paths to stimulate deeper exploration and error correction, thereby balancing accuracy and efficiency.

Key Takeaways

1
CES implements a conditional bidirectional policy that adapts an LLM's exploratory behavior based on the correctness of its reasoning path.
2
The framework significantly improves the accuracy-efficiency trade-off in LLMs, achieving higher accuracy with reduced response lengths on mathematical benchmarks.
3
CES enables LLMs to develop an adaptive reasoning strategy, allowing them to think concisely for simple problems and explore more deeply for difficult ones.

Conceptual Flow

HIGH LEVEL

Methodology: Adaptive Thinking for LLMs

The AI checks if its thinking path is right or wrong, then decides to either shorten its thoughts or explore new ideas.

AI's Thinking Path

Is Path Correct?

Adjust Thinking Style

Concise Answer

Deeper Exploration

Results: Smarter, Faster Problem Solving

This smart thinking makes the AI solve problems more accurately and with fewer words, especially for math.

Old AI Performance

New AI Performance

Compare Results

Higher Accuracy

Shorter Answers