Research that keeps AGI measurable
Synaps AGI is grounded in the INFANT-AGI-X research direction: lifelong learning, sparse routing, complementary memory systems, and edge deployment. Our proprietary implementation is rigorously benchmarked and designed to turn architectural claims into testable, measurable behavior.
INFANT-AGI-X — Proprietary Research
Personalized Artificial General Intelligence via Neuroscience-Inspired Continuous Learning Systems frames the central thesis: AGI needs new architectures, not just bigger static models, and those architectures must support continual, personalized learning on edge devices.
Abstract summary
The paper surveys continual learning and neuroscience-inspired AI, then proposes a system that combines complementary memory, adaptive learning, pruning, sparse coding, and efficient updates to enable lifelong learning on-device. Its goal is personalized AGI that adapts over time without catastrophic forgetting or cloud-only retraining.
Paper-aligned measurements from the implemented system
These numbers summarize the benchmark surfaces documented in the Synaps AGI benchmark and tracker: continual retention, latency, novelty detection, drift, nightly consolidation, and cold-start readiness.
What the research contributes
- A cold-start AGI design that performs useful inference with zero pretraining.
- Multi-tier cognitive memory spanning short-term, long-term, and permanent knowledge for safe retention.
- Intelligent expert routing with dynamic scaling for new domains.
- Bio-inspired consolidation cycles that combine optimization, rehearsal, pruning, and knowledge protection.
- Self-analysis for anomaly detection, drift tracking, and automatic self-improvement.
- Enterprise-grade interfaces including REST endpoints, WebSocket support, and edge-friendly CPU execution.
Synaps AGI extends the research in practice
The live stack adds deployable surfaces and cognitive extensions including advanced planning, theory-of-mind belief tracking, federation, built-in skills, and 100+ REST endpoints with WebSocket support, while staying aligned with the core lifelong-learning thesis.
Four active areas shaping the platform
The paper provides the conceptual frame, and the implementation turns that frame into concrete research surfaces across memory, reasoning, self-improvement, and safety engineering.
Memory Systems
Multi-tier memory, selective promotion, consolidation rehearsal, and protected operational memory are used to keep learning continuous without erasing trusted behavior.
Causal Reasoning
Cause-effect structure, why-style explanations, and counterfactual replay push the stack beyond pattern matching toward explicit reasoning.
Self-Improvement
Novelty, drift, and forgetting signals are measured internally so the system can trigger additional consolidation when cognition becomes unstable.
Safety
Frozen PM, procedural guardrails, and modular auditing keep capability growth aligned with stable operational constraints.