Scoped research. Real directions.
Our research focuses on the specific hard problems that QIA and PayQIA surface in production: context compression, knowledge graph construction, multi-agent coordination, and memory-system safety. Every finding is labeled by its verification status.
Labeling policyAll quantitative claims and benchmarks on this page are labeled as unverified, pre-launch, or internal projections unless independently validated. We carry this standard over from PayQIA's own benchmark disclosures.
Context Compression & Retrieval Efficiency
How much of a conversation context can be safely compressed without losing signal? QIA's context reduction techniques are the core of its efficiency advantage. We're researching adaptive summarization models that preserve semantically-critical facts while discarding noise.
Internal projection
Up to 92% context reduction vs. raw passthroughunverified
Approach
Recursive compression with confidence-scored fact retention
Status
Pre-launch, unverified — full benchmark methodology in progress
Related product
Q-RAG / QIA EngineKnowledge Graph Entity Resolution & Memory Decay
How should memories link, merge, and decay over time in a persistent knowledge graph? This is the architectural foundation of QIA Graph — automatic entity resolution that connects related facts while smart decay models prune stale or irrelevant information without human intervention.
Key question
When should a memory decay vs. be reinforced by new context?
Approach
Graph-theoretic entity linking with probabilistic decay scoring
Status
Active — early design validated in QIA Graph production layer
Related product
QIA GraphMulti-Agent Shared Memory & Swarm Coordination
As agent networks grow in complexity, a shared memory layer becomes the coordination primitive. This research direction explores how multiple autonomous agents can write to, read from, and negotiate over a common QIA-managed context graph — without conflicts, staleness, or information leakage between agents.
Roadmap
Targeted for product implementation 2027+
Open problem
Conflict resolution when two agents hold contradictory memory states
Status
Early stage — theoretical framework, no production deployment yet
Related product
Future QIA productMemory-System Safety & Privacy-Preserving Retrieval
Memory infrastructure introduces novel risks: tenant data bleed, retrieval of sensitive context in multi-user deployments, and the right to have data truly forgotten (not just flagged). This is Haqikos' scoped equivalent of AI safety research — focused specifically on memory system guarantees rather than general model alignment.
Key property
Zero-knowledge encryption — PayQIA cannot read your stored data
Research question
How do we guarantee retrieval isolation across tenants in a shared vector store?
Compliance
SOC 2 Type II (in progress) · GDPR-aligned decay/deletion guarantees
Related product
Trust & ComplianceInternal Technical Reports
Internal reports that have not been peer-reviewed or independently verified. Published in the interest of transparency about our methodology and findings-to-date.
Context Compression Benchmarks: Preliminary Methodology and Results
“Initial findings on recursive context compression techniques applied to long-session LLM interactions. We measure compression ratio and accuracy retention across contexts of 1M+ tokens. All results are internal, pre-launch, and unverified by independent parties.”
Evaluating Multi-Agent Coordination on Sandboxed Workspace Directory Trees
“A benchmarking paradigm for evaluating multi-agent systems executing file edits, testing loops, and dependency resolutions on non-contiguous codebases. Framing for future swarm-coordination research.”