BDH-CQ: IN-CONTEXT LEARNING WITH RECURRENT LATENT REASONING [R]
热度趋势
趋势数据积累中
百分比基于当前可用热度信号，而非评论数或独立用户人数。
BDH-CQ是一种将记忆、适应和推理结合在一起的推理系统。它通过未曾见过的任务演示来更新循环记忆，并通过在高维潜在工作空间中进行迭代计算来解决查询。值得注意的是，BDH-CQ不会将中间推理状态解码成语言。在推理时，模型会持续更新其循环记忆，并在不将中间推理过程口头表达的情况下解决查询。…
We introduce BDH-CQ, a reasoning system that brings these capabilities together. Demonstrations of a previously unseen task update recurrent memory; the query is then solved through iterative computation in a high-dimensional latent workspace. Intermediate reasoning states are not decoded into language. BDH-CQ makes memory, adaptation, and inference part of the same computational fabric. Inputs presented at inference time continuously update the model’s recurrent memory; the model then solves a query through iterative computation in a high-dimensional latent space, without verbalizing its intermediate reasoning. Neither task identifiers nor evaluation-task demonstration pairs participate in training, and no parameters are updated at inference time. A 150M-parameter configuration reaches 29.5% pass@2 on ARC-AGI-1 at a computed $0.00070 per task, breaking through the previously reported cost–accuracy Pareto frontier.