Voaige is an AI research lab translating insights from cognitive and systems neuroscience into computational principles for reasoning, memory, and search.
We are engineering these principles into LLMs under the constraint of building state-of-the-art,
commercially deployable systems.
A video of under a minute, without sound, in two parts, with the agent's trajectory under it throughout as an axis of numbered steps. First, an agent works through a task one step at a time. Every step opens onto several possible future states, and the model's learned priors guide which one to pursue. At the first two steps the priors clearly favor one path and the agent takes it. At the third the paths waver and none stands out, so cognition, which coordinates search and decides when and where to intervene, steps in: the learned priors narrow the directions, episodic memory recalls a similar past episode, and explicit search explores the remaining paths and checks the answer. Second, explicit search solves hard problems. On the same task, standard inference, where fixed model priors guide every step, takes the favored path at the third step too. It is wrong, and nothing checks it, so the run drifts off course. Test Time Cognition (System 2 thinking), deliberate search where it matters, searches at steps 3 and 5 and reaches the goal.
Intelligence is the process of searching among possible actions, explanations, and solutions in pursuit of a goal. It involves generating and evaluating candidates, validating them against evidence, and pursuing promising paths.
Memory is the retention and retrieval of experience that allows previous computation to be reused. It can guide and narrow search, resume it from a useful earlier point, or supply a known solution that bypasses explicit search.
Cognition brings learned knowledge, memory, and exploration together to decide where to search, how to evaluate possibilities, and when to continue, change direction, or stop.
Learning is the process by which experience improves a system’s ability to search. It can improve the quality and reliability of solutions, expand the range of problems the system can solve, and reduce the time and computation required.
From hypothesis to system
Higher intelligence at lower cost,
keeping the same model weights and agent harness.
The TTC inference layer is what the agent interacts with. The model weights sit inside it, and TTC directs the inference without changing the model weights or the agent harness. It is the site of inference-time computation: the cognitive architecture that governs how reasoning unfolds between input and output.
Explore Test Time CognitionOur Research Applied
Test Time Cognition achieves SOTA performance.
TTC = Test Time Cognition
Test Time Cognition achieves higher performance at lower cost.
TTC = Test Time Cognition
Test Time Cognition achieves higher performance at lower cost.
TTC = Test Time Cognition
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