TFlow enables multi-agent LLMs to collaborate via transient low-rank LoRA perturbations derived from sender activations, yielding up to 8.5 accuracy gains and 83% token reduction versus text-based baselines on Qwen3-4B models.
Neural network diffusion
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DynaDiff uses weight-graph diffusion with a functional consistency loss and dynamics-informed prompting to generate adapted predictors, reporting 10.78% average accuracy gains over baselines while amortizing adaptation cost offline.
KSDiff generates convolutional kernels in kernel space using low-rank core tensor and factor generators with multi-head attention for fast, high-quality pansharpening.
Diffusion models are evolutionary algorithms via a denoising-evolution equivalence, yielding Diffusion Evolution that outperforms mainstream EAs on multi-optima tasks.
citing papers explorer
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Good Agentic Friends Do Not Just Give Verbal Advice: They Can Update Your Weights
TFlow enables multi-agent LLMs to collaborate via transient low-rank LoRA perturbations derived from sender activations, yielding up to 8.5 accuracy gains and 83% token reduction versus text-based baselines on Qwen3-4B models.
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Generative Adaptation of Dynamics to Environmental Shifts via Weight-space Diffusion
DynaDiff uses weight-graph diffusion with a functional consistency loss and dynamics-informed prompting to generate adapted predictors, reporting 10.78% average accuracy gains over baselines while amortizing adaptation cost offline.
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Fast Kernel-Space Diffusion for Remote Sensing Pansharpening
KSDiff generates convolutional kernels in kernel space using low-rank core tensor and factor generators with multi-head attention for fast, high-quality pansharpening.
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Diffusion Models are Evolutionary Algorithms
Diffusion models are evolutionary algorithms via a denoising-evolution equivalence, yielding Diffusion Evolution that outperforms mainstream EAs on multi-optima tasks.