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Random Attention keeps the prompt and evicts uniformly at random within each attention head, computing no score at all; across four models and six reasoning tasks, it matches the strongest baseline in task performance while delivering 32-43% higher throughput than that method when deployed with vLLM.
It is suggested that inference-time harness design is an important complement to conventional training-time distillation, enabling strong models to transfer cognitive structure to weaker models without retraining.
Mimir is introduced, a neuro-symbolic memory that separates world memory from task memory and dynamically grounds them before each action, substantially outperforming current closed-source models.
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