Use Cases
Agent Memory, without drift.
Preserve semantic identity across long horizons using symbolic representations.
Stable semantic identity over time
Drift-resistant long-term memory
Compact symbolic persistence
Deterministic retrieval behavior
The Problem
Why agent memory drifts
Most agent memory systems rely on continuous embeddings as their semantic base.
Over time, embeddings shift due to context changes, re-embedding cycles, model updates, and numeric instability.
While memory graphs may persist structurally, their semantic grounding drifts.
The Approach
Canonical memory in a symbolic domain
SEMQ introduces a symbolic representation layer that canonicalizes embeddings into a discrete, directional semantic space.
Memory entries are stored and compared in this symbolic domain, decoupled from numeric precision and magnitude.
↻ long-horizon memory loop — retrieval feeds back into future encode/store cycles
optional: decode symbolic code for evaluation
Capabilities
Stable Semantic Identity
Memory entries remain comparable across time.
Drift-Resistant Persistence
Canonicalization reduces semantic drift in repeated cycles.
Compact Long-Horizon Storage
Symbolic representations are inherently smaller to store and transmit.
Deterministic Retrieval
Predictable semantic matching beyond purely continuous embeddings.
Developer Flow
How it works
embed → semq.encode → store(symbolic_code)
query → semq.encode(query) → retrieve(symbolic_code)
optional: semq.decode for evaluation
Evaluation
Evaluation and experiments
Drift behavior, semantic preservation, and storage trade-offs are evaluated under controlled experimental conditions.
View benchmarks →“Long-term agent memory requires a stable semantic representation layer underneath.”