Cross-Family Universality of Behavioral Axes via Anchor-Projected Representations

Researchers introduce an anchor-projection framework that enables behavioral directions to transfer across different large language model families by mapping their diverse hidden representations into a shared coordinate space. The approach achieves high cross-model alignment (0.83 ten-way detection accuracy) without fine-tuning, demonstrating that interpretability and control mechanisms can be standardized across architecturally different models.

This research addresses a fundamental challenge in AI interpretability: large language models from different developers use incompatible architectures, making it difficult to understand or transfer behavioral controls across model families. The anchor-projection framework solves this by establishing a universal coordinate system where hidden representations from Llama, Qwen, Mistral, and Phi models converge into shared behavioral directions. Rather than extracting model-specific control mechanisms, researchers can now identify canonical directions in this shared space and reconstruct them for any target model.

The technical achievement reflects broader progress in mechanistic interpretability, where researchers increasingly map internal model behaviors to understand decision-making processes. Previous work focused on single-model analysis or required expensive retraining; this framework requires only anchor activations from reference points, making it practical for rapid deployment. The robustness across the LQMP cluster suggests these model families develop similar internal structures despite different training procedures and tokenizers.

For the AI industry, this has significant implications. Model developers and safety researchers can now establish standardized control mechanisms across competing implementations, simplifying safety audits and behavioral steering. The finding that two source models suffice for approximating transferable directions reduces computational overhead. However, the framework's applicability beyond the tested cluster remains unclear—whether it extends to entirely different architectures or closed-source models requires further investigation.

Looking ahead, this work could enable faster safety certification processes and cross-model behavior standardization, while raising questions about whether such universal structures represent fundamental properties of language intelligence or artifacts of current training paradigms.

• →An anchor-projection framework enables behavioral directions to transfer across different LLM families without fine-tuning or target-specific extraction
• →Same-axis directions align tightly across Llama-Qwen-Mistral-Phi models, achieving 0.83 ten-way detection accuracy in shared coordinate space
• →Only two source models and small anchor pools suffice to approximate transferable directions, reducing computational requirements
• →Canonical steering achieves refusal-rate shifts up to +0.46% under distribution shift, demonstrating practical controllability across models
• →The framework reveals representation-level transfer robustness, suggesting model families develop similar internal structures despite architectural differences