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.
W2t: Lora weights already know what they can do.arXiv preprint arXiv:2603.15990, 2026a
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DNG-Encoder represents NN weights as dynamic graphs to preserve sequential inference and powers INR2JLS, which raises INR classification accuracy by ~10% on CIFAR-100-INR.
Four changes to Activation Oracle training yield marginal capability gains but better practical quality, plus an open-sourced evaluation suite AObench.
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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Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
DNG-Encoder represents NN weights as dynamic graphs to preserve sequential inference and powers INR2JLS, which raises INR classification accuracy by ~10% on CIFAR-100-INR.
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Building Better Activation Oracles
Four changes to Activation Oracle training yield marginal capability gains but better practical quality, plus an open-sourced evaluation suite AObench.