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• FiLoRA enables explicit control over internal feature reliance in multimodal foundation models without altering the predictive objective.method 70%
• Instruction-conditioned gating in FiLoRA acts as computation-level control signals, allowing natural language instructions to modulate feature reliance.method 70%
• FiLoRA induces consistent and causal shifts in internal computation across text-image and audio-visual benchmarks.result 70%
• FiLoRA selectively amplifies or suppresses core and spurious feature groups without modifying the label space or training objective.result 70%
• FiLoRA yields improved robustness under spurious feature interventions.result 70%
• FiLoRA can be packaged as a cloud-based API for enterprises to adjust feature reliance parameters in their AI systems.market 70%
• Implementing FiLoRA may require tight integration with pre-existing models and datasets.limitation 70%
• FiLoRA utilizes instruction-conditioned low-rank adaptations (LoRA) to control feature reliance.technical 70%

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