Public articles linked to the same research event.
arXiv Treating an existing archive of interventions and their already-observed outcomes as a fixed offline dataset, the work uses a vision-language model to judge whether an archived outcome matches a natural-language description and uses that judgment as the sole training reward to learn a language-to-intervention mapping for a xenobot, a synthetic multicellular construct with no nervous system; the mapping generalizes to entirely new instructions, reaching 80.0% held-out accuracy on archive data withheld from training versus a 66.7% chance baseline and matching a network trained directly on ground-truth labels.
Treating an existing archive of interventions and their already-observed outcomes as a fixed offline dataset, the work uses a vision-language model to judge whether an archived outcome matches a natural-language description and uses that judgment as the sole training reward to learn a language-to-intervention mapping for a xenobot, a synthetic multicellular construct with no nervous system; the mapping generalizes to entirely new instructions, reaching 80.0% held-out accuracy on archive data withheld from training versus a 66.7% chance baseline and matching a network trained directly on ground-truth labels.
Treating an existing archive of interventions and their already-observed outcomes as a fixed offline dataset, the work uses a vision-language model to judge whether an archived outcome matches a natural-language description and uses that judgment as the sole training reward to learn a language-to-intervention mapping for a xenobot, a synthetic multicellular construct with no nervous system; the mapping generalizes to entirely new instructions, reaching 80.0% held-out accuracy on archive data withheld from training versus a 66.7% chance baseline and matching a network trained directly on ground-truth labels.
Treating an existing archive of interventions and their already-observed outcomes as a fixed offline dataset, the work uses a vision-language model to judge whether an archived outcome matches a natural-language description and uses that judgment as the sole training reward to learn a language-to-intervention mapping for a xenobot, a synthetic multicellular construct with no nervous system; the mapping generalizes to entirely new instructions, reaching 80.0% held-out accuracy on archive data withheld from training versus a 66.7% chance baseline and matching a network trained directly on ground-truth labels.