Executable memory for personalized agents: represent a user's memory as typed Python state + constraints an interpreter can run, instead of retrieved text. Code, experiments, and reference implemen…
PreAct: Computer-Using Agents that Get Faster on Repeated Tasks (arXiv:2606.17929). Compile successful runs into verified state-machine programs and replay them 8.5–13× faster with no per-step LLM …
A user's facts become a few rows in a content-addressed memory table — not a rewrite of the model. Paper, code, data, and interactive site for "User as Engram" (arXiv:2606.19172).
AOI: a model-agnostic perception layer giving computer-use agents adaptive video + audio perception with zero retraining, plus DynaCU-Bench (100 dynamic browser tasks). Paper + benchmark + code.
A user's facts become a few rows in a content-addressed memory table — not a rewrite of the model. Paper, code, data, and interactive site for "User as Engram" (arXiv:2606.19172).
Fine-grained computation offload for off-the-shelf servers in tens of lines — paper (arXiv:2607.02630), code, and every measurement. Overlap accelerator offloads (GPU/HSM/inference) with other requests by rerouting through the server's own suspend/resume machinery, plus a zero-edit LD_PRELOAD fiber runtime.
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