Prompt2Effect is a weight-driven hypernetwork that synthesizes LoRA adapters for I2V models from prompts and base weights via SVD parameterization, matching fine-tuned quality at 3.3s inference instead of 56 GPU hours.
ArXiv:2503.24354 [cs]
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
P2F generates low-rank parameter increments for LLM fingerprinting directly from textual descriptions in a single forward pass.
ReLoRA reduces time-to-readiness for LoRA adapters on updated LLMs by up to 8.9x through adaptive Bayesian initialization and scheduled regularization while improving accuracy by up to 4.6%.
SOLAR introduces a self-optimizing agent using meta-learning on model weights and RL-driven strategy discovery for lifelong adaptation in LLMs, claiming superior performance on reasoning tasks across domains.
citing papers explorer
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Prompt2Effect: Training-Free Image-to-Video Model Specialization via LoRA Generation
Prompt2Effect is a weight-driven hypernetwork that synthesizes LoRA adapters for I2V models from prompts and base weights via SVD parameterization, matching fine-tuned quality at 3.3s inference instead of 56 GPU hours.
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Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation
P2F generates low-rank parameter increments for LLM fingerprinting directly from textual descriptions in a single forward pass.
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ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services
ReLoRA reduces time-to-readiness for LoRA adapters on updated LLMs by up to 8.9x through adaptive Bayesian initialization and scheduled regularization while improving accuracy by up to 4.6%.
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SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation
SOLAR introduces a self-optimizing agent using meta-learning on model weights and RL-driven strategy discovery for lifelong adaptation in LLMs, claiming superior performance on reasoning tasks across domains.