agenternal

# Agenternal

A personal AI assistant with a **brain-inspired memory system** that forgets, consolidates, reconsolidates, detects contradictions, and abstracts behavioral patterns — capabilities no existing AI memory system (MemGPT, Mem0, Zep, or A-Mem) has shipped in production.

## The Problem

Every AI assistant today has the same memory flaw: it stores everything and retrieves by similarity. This is a filing cabinet, not a brain. As conversations accumulate, the system drowns in redundant facts, conflicting information, and outdated priorities — with no mechanism to resolve any of it.

The human brain solves this with five mechanisms that run simultaneously:

1. **Two-stage consolidation** — fast capture in the hippocampus, slow distillation to the neocortex during sleep
2. **Selective forgetting** — memories decay unless reinforced through spaced retrieval
3. **Reconsolidation** — retrieved memories become temporarily unstable and can be refined by new context
4. **Cognitive dissonance** — contradicting beliefs trigger error signals proportional to the conflict
5. **Hierarchical compression** — raw experience is compressed 2,000,000:1 into schemas and generative models

We implemented all five.

## Memory Architecture

### Three-Layer Hierarchy

```
Raw conversation --> Episodic Memory --> Semantic Memory --> Schema
                    (fast capture)     (distilled facts)   (behavioral patterns)
```

| Layer | Example | Created |
|-------|---------|---------|
| **Episodic** | "On March 15, user said delay the launch because engineering is behind" | After each conversation turn |
| **Semantic** | "User delayed product launch due to engineering delays (March 2026)" | During consolidation (every 6h) |
| **Schema** | "User prioritizes engineering readiness over market timing in launch decisions" | When 3+ semantic memories cluster |

### Memory Strength (Hyperbolic Decay + Spacing Effect)

Every memory has a strength $s \in [0.05,\; 2.0]$ governed by four forces:

**1. Idempotent time decay** — uses a **hyperbolic retention function** (Wixted & Ebbesen 1991; Rubin & Wenzel 1996) rather than the exponential originally proposed by Ebbinghaus (1885). The hyperbolic form has a heavier tail — memories linger longer before vanishing, matching 100+ years of empirical forgetting data:

$$S(m) = 1 + c \cdot (n_m^{\text{spaced}})^p, \quad c = 0.5, \; p = 1.5$$

$$R(m, t) = \frac{1}{1 + k \;\cdot\; \dfrac{\Delta t_m}{S(m)}}, \quad k = \frac{1}{H}$$

$$s_m \leftarrow \max\!\Big(0.05,\;\; s_m^{(0)} \cdot R(m, t)\Big)$$

where $s_m^{(0)}$ is the strength at last access (idempotent base), $H = 10$ days is the half-life at baseline stability, and $n_m^{\text{spaced}}$ is the **spaced** access count — only retrievals with a gap $\geq 12\text{h}$ increment it (Cepeda et al. 2006; Bjork & Bjork 1992). Stability grows as a **power law** of spaced retrievals (Pimsleur 1967), so each spaced retrieval builds proportionally more stability than the last — unlike linear growth where the 10th retrieval adds the same as the 1st.

**2. Spacing-aware retrieval reinforcement** — boost scales with time since last access and diminishes near the ceiling:

$$\alpha(m) = \alpha_{\max} \cdot \underbrace{\left(1 - \frac{s_m}{s_{\max}}\right)}_{\text{diminishing at ceiling}} \cdot \underbrace{\left(1 - e^{-\Delta t_m \,/\, \tau}\right)}_{\text{spacing effect}}$$

$$s_m \leftarrow \min\!\Big(s_{\max},\;\; s_m + \alpha(m)\Big)$$

where $\alpha_{\max} = 0.15$, $s_{\max} = 2.0$, $\tau = 24\text{h}$.

| Scenario | Flat boost (old) | Spacing-aware (new) |
|----------|-----------------|-------------------|
| Recalled 30 sec ago, $s=1.0$ | $+5\% = 1.05$ | $+0.001$ (near zero) |
| Recalled 1 day ago, $s=1.0$ | $+5\% = 1.05$ | $+0.059$ |
| Recalled 7 days ago, $s=1.0$ | $+5\% = 1.05$ | $+0.075$ (near max) |
| Recalled 7 days ago, $s=1.8$ | $+5\% = 1.89$ | $+0.015$ (diminishing) |

**3. Evidence-weighted contradiction** (Bayesian likelihood-ratio penalty) — penalty modulated by relative strength of new evidence vs old belief:

$$\beta(s_{\text{old}},\, c_{\text{new}}) = \beta_{\min} + (\beta_{\max} - \beta_{\min}) \cdot e^{-c_{\text{new}} \,/\, s_{\text{old}}}$$

$$s_{\text{old}} \leftarrow \max\!\Big(0.05,\;\; s_{\text{old}} \cdot \beta\Big)$$

where $\beta_{\min} = 0.2$ (harshest), $\beta_{\max} = 0.85$ (mildest), and $c_{\text{new}}$ is derived from the memory's **category** (decisions=1.0, facts=0.9, preferences=0.75, opinions=0.6) rather than LLM self-assessed confidence. Strong beliefs resist weak contradictions; weak beliefs yield to strong evidence. Inspired by Bayesian belief revision (Friston 2010).

**4. Retrieval-induced forgetting** (Anderson, Bjork & Bjork 1994) — when a memory is retrieved, similar-but-non-retrieved competitors are mildly suppressed (3%), sharpening recall over time:

$$\forall\, m_j \notin \text{retrieved}: \quad \text{if } \text{sim}(\mathbf{e}_{m_j}, \mathbf{q}) > 0.7, \quad s_{m_j} \leftarrow s_{m_j} \cdot 0.97$$

Schemas are exempt from suppression. Confirmed across 60+ studies by Murayama et al. (2014).

Memories below $\tau = 0.1$ become invisible to the AI but remain in the database for the user to inspect.

### Consolidation ("Sleep Replay")

A scheduled background daemon runs every $T = 6$ hours:

1. **Clustering**: DBSCAN on cosine distance matrix $D_{ij} = 1 - \cos(\mathbf{e}_i, \mathbf{e}_j)$ with $\varepsilon = 0.35$, `min_samples` $= 3$
2. **Distillation**: Each cluster $C_k$ with $|C_k| \geq 3$ is distilled by Claude Haiku into one semantic memory
3. **Centrality-weighted decay**: Source episodics fade based on distance from cluster centroid — $\gamma_i = 0.5 + 0.4 \cdot (1 - \text{sim}(\mathbf{e}_i, \bar{\mathbf{e}}_{C_k}))$ — central memories fade more, peripheral ones retain unique details
4. **Schema synthesis**: Re-cluster semantics ($\varepsilon = 0.45$), synthesize behavioral patterns from clusters of $\geq 3$
5. **Idempotent decay pass**: Ebbinghaus curve applied to all memories not accessed in 7+ days
6. **Priority snapshots**: Compares current priorities with 30-day-old snapshot, classifies as `deliberate_pivot` | `gradual_drift` | `stable`

### Memory Reconsolidation (Lability Windows)

Based on Nader, Schafe & LeDoux (2000): when memory $m$ is retrieved at time $t_r$, it enters a **labile state** for $W = 6$ hours:

$$m.\text{labile} \leftarrow \text{true}, \quad m.t_{\text{recon}} \leftarrow t_r + W$$

If re-retrieved while already labile, the window **extends**: $m.t_{\text{recon}} \leftarrow \max(m.t_{\text{recon}},\; t_r + W)$. During this window, new conversation context can **passively refine** the memory content without requiring an explicit contradiction. The labile set is capped at $|\mathcal{L}| \leq 10$.

This implements a dual belief-update architecture:

| Pathway | Trigger | Behavior |
|---------|---------|----------|
| **Reconsolidation** | Memory retrieved | Passive refinement: "I prefer async" $\rightarrow$ "I prefer async, except for urgent issues" |
| **Contradiction detection** | Explicit conflict | Evidence-weighted $\beta$ penalty + `superseded_by` link |

Reconsolidation catches **gradual belief drift** that hard contradiction detection would miss. No other production agent memory system implements retrieval-triggered lability windows.

### Contradiction Detection + Decision Ledger

**Two-pass detection:**

- **Real-time** (during extraction): Every new decision or preference is checked against existing memories. Claude Haiku identifies semantic conflicts. Old memory receives evidence-weighted strength penalty.
- **Offline** (during consolidation): Full audit across the memory store for subtle contradictions missed in real-time.

**Decision ledger** — decisions are first-class objects with:

- `decision_text` + `reasoning` + `domain` + `outcome`
- Explicit supersession chains: when a decision is reversed, the old one links to the new one
- The agent can query the ledger by topic or domain to surface prior decisions

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