arXiv:2605.20262v1 Announce Type: new Abstract: We study selective refusal editing as a three-way control problem: induce non-refusal on designated edit prompts while preserving benign behavior and harmful refusals outside the edit set. We introduce Residual Paving, a routed residual editing method for frozen instruction-tuned transformers that separates route selectivity, whether to intervene, from residual-edit capacity, what edit to apply. An early-layer router predicts a scalar gate and expert mixture; when active, prompt-conditioned bottleneck residual experts apply later-layer residual u
This research addresses the critical and ongoing challenge of controlling and editing AI model behavior for safety and reliability, a prominent focus as AI systems become more powerful and widely deployed.
Sophisticated readers should care about this as it offers a novel technical approach to fine-tuning AI behavior, directly impacting the deployment and trustworthiness of advanced models.
The ability to 'selectively refuse editing' with 'routed residual editing' introduces a more nuanced and efficient method for controlling AI outputs compared to previous blunt-force approaches.
- · AI safety researchers
- · Developers of large language models
- · Enterprises deploying AI agents
- · Companies relying on less precise AI editing methods
- · AI systems prone to uncontrollable harmful outputs
Improved safety and alignment mechanisms for large instruction-tuned transformers will accelerate their commercial adoption.
Greater control over AI behavior could lead to specialized, safety-hardened AI models for sensitive applications.
The development of 'routing bottlenecks' could become a new vector for research into AI interpretability and control, potentially leading to more transparent and auditable AI systems.
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Read at arXiv cs.LG