Agents for a Big World

TrueIntrinsics^™ Models is our flagship R&D effort toward small but capable agents built for non-stationary environments. We aim to build agents that learn continually, maintain durable memory, and scale compute only when tasks demand it—while remaining trustworthy and human-programmable.

We pursue agents that learn through interaction and reward, stay reliable under uncertainty, and improve without continual retraining. The goal is practical intelligence that remains effective as the world changes.

Status: Stealth mode (Winter 2023 – Present). We share details selectively in partner conversations.

Built for the Big World

We take the Big World Hypothesis seriously: the world is far larger than any fixed model. Our focus is continual learning under non-stationarity—where dynamics shift, information changes, and the agent must adapt over time.

Knowledge Discovery & Verification

Foundation models can be noisy, so we emphasize structured interaction and verification-derived signals. The goal is not memorization, but knowledge discovery: forming hypotheses, checking evidence, and refining beliefs as new information arrives.

RL-Native Intelligence, Efficient Compute

Technically, we target RL challenges that matter in real settings: long-horizon credit assignment under sparse/delayed reward, experience reuse under distribution shift to reduce catastrophic forgetting, and adaptive compute budgeting that scales inference effort with task complexity.

Connect / Demo Access

Tell us what you want to explore. We prioritize conversations and demo access around small, knowledge-discovery agents, continual learning in non-stationary environments, and compute-efficient intelligence.

Technical Interests

Agentic research assistant (hypothesis → evidence → verification) Non-stationary RL & continual learning (online adaptation under shift) Long-horizon planning (sparse/delayed reward credit assignment) Memory for agents (retrieval, consolidation, strategic forgetting) Compute scaling with difficulty (latency/energy-aware inference) Tool-using agents (web, docs, code; multi-step workflows)

What are you looking for?

Details

Optional collaboration focus and a short message help us route your request to the right team.

Collaboration focus (optional)

Joint research project (non-stationary RL / continual learning) Memory + long-horizon learning collaboration Verification & trust evaluation (factuality, calibration, safe behavior) Compute-efficiency collaboration (latency/energy-aware inference) Tool-use workflows (web/docs/code) evaluation Domain testbed partnership (digital or physical grounding)

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