From Heads to Neurons: Causal Attribution and Steering in Multi-Task Vision-Language Models

2026-04-20 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new framework named HONES (Head-Oriented Neuron Explanation & Steering) has been proposed for task-aware neuron attribution and steering in multi-task vision-language models (VLMs). Developed by Qidong Wang, Junjie Hu, and Ming Jiang, HONES addresses limitations in existing neuron-level interpretation methods, which often focus on single tasks and overlook task-dependent information pathways. HONES is a gradient-free framework that ranks feed-forward network (FFN) neurons based on their causal write-in contributions, conditioned on task-relevant attention heads. It further modulates salient neurons using lightweight scaling. Experimental results across four diverse multimodal tasks and two popular VLMs demonstrate that HONES surpasses current methods in identifying task-critical neurons and enhances model performance after steering. The source code for HONES is available on GitHub.

Key takeaway

For research scientists working with multi-task vision-language models, HONES offers a robust, gradient-free method to pinpoint and steer task-critical neurons. You should consider integrating HONES into your VLM development workflow to improve model interpretability and enhance performance across diverse multimodal tasks, especially when existing single-task neuron analyses fall short.

Key insights

HONES improves multi-task VLM interpretation and steering by causally attributing FFN neuron contributions via attention heads.

Principles

• Neuron importance varies by task.
• Contextualize neuron analysis with attention heads.

Method

HONES ranks FFN neurons by causal write-in contributions conditioned on task-relevant attention heads, then modulates salient neurons via lightweight scaling.

In practice

• Apply HONES to identify task-critical VLM neurons.
• Use HONES for performance improvement via neuron steering.

Topics

• Vision-Language Models
• Neuron-Level Interpretation
• Multi-Task Learning
• Causal Attribution
• HONES Framework

Code references

• petergit1/HONES

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.