Google Just Validated Our Memory Architecture
How Titans and MIRAS Confirm What We've Been Building
This week, Google published two research papers that sent a clear signal: the approach we've been building for months is exactly where AI memory needs to go.
The papers—Titans and MIRAS—describe an architecture for AI memory that mirrors what we've already implemented in SCMS and Mneme. Multiple memory layers. Forgetting mechanisms. Deep cross-referencing.
This isn't a minor validation. This is Google's research division independently arriving at the same conclusions we reached through practice.
What Google Published
Titans: Multi-Layer Memory Architecture
Titans introduces a three-layer memory system:
| Layer | Function | Human Analog |
|---|---|---|
| Long-term Memory | Learns patterns, connections across thousands of words | Episodic memory |
| Core Attention | Immediate context, decides whether to use long-term | Working memory |
| Persistent Memory | Fixed foundational knowledge from training | Instincts/fundamentals |
Sound familiar? This maps directly to our L0/L1/L2 architecture:
| Titans Layer | SCMS Layer | Function |
|---|---|---|
| Long-term Memory | L1 Validated | Promoted, decay-immune memories |
| Core Attention | L0 Active | Testing ground, subject to decay |
| Persistent Memory | L2 WHY | Anti-patterns, reasoning, foundational knowledge |
MIRAS: Forgetting as Essential Feature
The MIRAS framework identifies four pillars of effective memory systems:
- Memory Architecture — Where and how you store information
- Attentional Bias — What the model prioritizes
- Retention Gate — The forgetting mechanism
- Memory Algorithm — How updates happen
The critical insight from the paper:
"Forgetting is actually just as important as remembering. If you remembered every single detail of every single moment of your life, your brain would be completely overwhelmed and useless. You actually need to filter things down."
This is exactly what SCMS does that competitors don't.
The Competitive Gap Just Widened
Most AI memory systems—Mem0, Claude-mem, and others—use flat vector databases. They save everything the AI thinks is relevant. There's no forgetting mechanism. No layered validation.
Here's how the research validates our approach:
| Feature | Competitors | SCMS/Mneme | Research Says |
|---|---|---|---|
| Memory Layers | 1 (flat) | 3 (L0/L1/L2) | ✅ Multi-layer essential |
| Forgetting | ❌ Manual delete only | ✅ Built-in decay | ✅ Retention gates essential |
| Validation | ❌ Auto-save everything | ✅ User/system validates | ✅ Filtering improves performance |
| Deep Memory | Shallow semantic search | Cross-referenced layers | ✅ Depth beats shallow |
The research shows that flat-storage approaches will hit performance walls. Systems that save everything without forgetting mechanisms become "overwhelmed and useless"—Google's words, not mine.
Triple Sparsity: Our Additional Innovation
Google's Titans uses algorithmic "surprise metrics" to decide what's important. An input that's unexpected gets prioritized for long-term storage.
SCMS goes further. We add a human validation layer on top:
- AI Extraction — The model identifies potentially relevant memories (sparse selection #1)
- User Validation — Human confirms which memories are worth keeping (sparse selection #2)
- Decay Mechanism — Unused memories fade away naturally (sparse selection #3)
This is triple sparsity versus the single sparsity of save-everything approaches.
The result: Our memory banks stay clean. Relevant. High-signal. While competitors accumulate noise that eventually degrades their retrieval quality.
The Deep Memory Advantage
One of the most striking findings from the Titans paper:
"Ablation studies clearly show that the depth of the memory architecture is crucial. Modules with deeper memories consistently achieve lower perplexity."
What does "deep memory" mean in practice?
- Shallow memory: Stores what happened
- Deep memory: Stores what + why + connections
Our L2 layer captures exactly this—the reasoning behind patterns, the failures that taught us what not to do, the anti-patterns that prevent future mistakes.
This isn't just storage. It's understanding.
What This Means Going Forward
For Users
You're using a system that's architecturally aligned with cutting-edge research. The validation workflow, the decay mechanisms, the layered structure—these aren't arbitrary design choices. They're the principles that Google's research says make memory systems actually work.
For Investors
We've been building what the research says is correct. While competitors use flat vector databases that the research shows are deficient, we've already implemented:
- ✅ Multi-layer memory (L0/L1/L2)
- ✅ Forgetting mechanisms (decay)
- ✅ User validation layer (human-in-the-loop)
- ✅ Deep cross-referencing (L2 WHY documentation)
The competitive moat just got deeper.
For the Industry
This research suggests a shift in how AI memory should be architected. The "save everything" approach that dominates current solutions is fundamentally limited. Systems that don't implement forgetting, layering, and validation will hit performance ceilings.
We're ahead of this curve.
The Papers
If you want to dig into the research yourself:
- Titans: Learning to Memorize at Test Time (Behrouz, Zhong, Mirrokni — Google Research, Dec 2024)
- MIRAS: A Unified Framework for Sequence Modeling (Google Research, Dec 2024)
Key quotes to look for:
On multi-layer memory:
"An effective learning system requires distinct yet interconnected memory modules, mirroring the human brain's separation of short-term and long-term memory."
On forgetting:
"To manage the finite capacity of the memory when dealing with extremely long sequences, Titans employs an adaptive weight decay mechanism. This acts as a forgetting gate."
On depth:
"Modules with deeper memories consistently achieve lower perplexity in language modeling."
What We're Building Next
This validation gives us confidence to push further. Our Phase 11 roadmap includes research-aligned enhancements:
- Memory Strength Indicators — Visible confidence scores that decay over time
- Episode Grouping — Memories that belong together, captured together
- Adaptive Decay — Smarter forgetting based on memory capacity and importance
- Deep Cross-Referencing — Auto-detected connections between memories
We're not just aligned with the research. We're building on it.
The Bottom Line
When I started building SCMS, I was solving a practical problem: AI assistants forget everything. I built layered memory, validation workflows, and decay mechanisms because they worked.
Now Google's research team has independently validated that this is the correct architecture. Not just a good approach—the right approach.
The memory systems that will win aren't the ones that store the most. They're the ones that remember wisely.
We've been building that from the start.
Try Mneme at getmneme.com. The SCMS framework is open source at github.com/AIalchemistART/scms-starter-kit.