A reward can tell an RL/online learner that something worked without telling it which combination of internal signals made it work. Today, we’re unlocking two learning rules in Adapt-1 Preview: Counterfactual Utility Plasticity (CUP) and Temporal Context Projection (TCP).

We revisited DeepMind's DM Alchemy, comparing three approaches: Adapt-1's ontology-based online sequential learning, Pinon et al.'s transformer dynamics with tree search, and AlKhamissi et al.'s modified representation with episodic memory. Full Blog: reilabs.org/blog/ADAPT1-BEHA…

Introducing Adapt-1 Preview (Δ1): a pretraining-free substrate for test-time learning. It learns from scored feedback while performing the task. reilabs.org/blog/introducing… Today we’re making its first research preview and first results across robot perception, spatial reasoning, and games available.

In 0.5a, genetic algorithms moved from system level (0.4) to unit level. Each Unit now evolves its own traversal parameters. A thread 🧵

Core 0.5a - Patch Notes 0.5a, opening update of the 0.5 series, is now live on Rei Chat as well as our agentic API. Creating new units is advised as it is the only way for 0.5a to take full effect. PATCH NOTES 0.5a's main focus area is how units learn. faster intake, better retention, and more from every interaction. every change in this update touches some part of how a unit processes what it's given, how it holds onto what matters, and how it builds on what it already knows. Expect brief interruptions over the next 72h as we complete migration and roll out minor updates. Unit-Level Evolution • Evolution moved from system level to unit level Genetic algorithm evolution has moved from system level to unit level. Previously, evolutionary pressure operated globally, with competing inference strategies evolving as a shared population. In 0.5a, the specimens at the basis of evolution are defined by each unit's own relationship and concept exploration parameters. Selection, mutation, and crossover now run against how individual units traverse their hypergraphs, form patterns, and abstract concepts. Two units starting from the same configuration will develop increasingly distinct reasoning behaviors over time, not just because their knowledge differs, but because the parameters governing how they discover and reinforce that knowledge are themselves evolving independently. Resilient Hybrid Recall • Semantic recall combined with hypergraph-aware retrieval • Faster access to relevant knowledge with better structural context + hypertags • Better recovery of entities, relationships, and concepts Core 0.5a introduces a unified recall layer that combines semantic similarity with hypergraph structure. Knowledge that has faded from direct semantic reach can still be surfaced through relationship paths, entity linkage, and hypertags. This gives the system structural redundancy in recall without fighting the decay mechanism. Knowledge is still allowed to fade as designed, but the paths to reach it are no longer dependent on a single retrieval method. Recall has improved significantly as a result. Asynchronous Hypergraph Enrichment • Immediate knowledge availability on write • Background extraction of entities and relationships • Richer knowledge structure without adding interaction latency New knowledge is available immediately, while deeper entity and relationship extraction happens in the background. This keeps writes fast while still allowing knowledge structure to become more detailed over time. Modular Core Services • Clearer separation between knowledge, recall, traversal, exploration and routing • More defined internal service boundaries • Modular framework for future updates 0.5a moves Core toward a more modular structure, with dedicated layers handling different parts of the system. This improves maintainability, reduces fragility, and makes the platform easier to extend. Adaptive Context Processing • Condensation of long-form content before deeper extraction • Better retention of important details during heavier workloads • More efficient handling of large or dense inputs Longer inputs are no longer processed as-is. Core condenses dense content before deeper extraction, preserving key facts and relationships while reducing noise. This keeps recall efficient under heavier workloads and prevents important details from being diluted by volume. Knowledge Persistence • Better handling of entities, relations, and knowledge linkage • More stable hypergraph interpretation in live operation • Cleaner storage and integration of new facts into existing structure Where Resilient Hybrid Recall addresses how knowledge is found, this addresses how it's stored and linked. Core now handles entity formation, relation mapping, and structural integration with more consistency, which gives the recall layer stronger material to work with in the first place. Stronger Runtime Reliability • Cleaner local-first service behavior • More predictable execution paths • Better stability during live end-to-end use A major part of 0.5a is reliability. Service behavior is more predictable, local execution is cleaner, and the system is more stable under real usage. System Maturity • End-to-end validation of store, list, query, and clear flows • Verified delayed enrichment behavior under live conditions •Greater confidence in recall quality outside isolated tests 0.5a's notes is the last set of release notes in this format. as closed beta wraps up, so does this style of patch notes, making way for a different format that better fits what comes next. to everyone who's been part of closed beta, thank you. Disclaimer: 0.5a is exclusively accessible via Rei Chat and our agentic API. Rei Chat/Rei Chat API are user-friendly, OpenAI compliant interfaces for interacting with parts of our architecture. units start as blank slates and are designed to become domain experts through training. they are learning systems, mistakes are possible and normal, the user's role is to train their unit at inference.