# Deep Think: Memory Upgrade — Cross-Session Memory Transfer Protocols

**Topic**: `cross_session_transfer`
**Cycle**: 3
**Model**: gemini-2.5-pro
**Generated**: 2026-02-26T21:18:48.753000+00:00
**Elapsed**: 55.6s
**Thinking tokens**: 16384

---

## Generated Prompt (Phase 1 — Gemini Flash)

AIVA's `memory-lancedb` plugin (`enhanced_index.ts`) leverages LanceDB for persistent storage, meaning `MemoryEntry` objects remain across process restarts. However, the current system lacks any explicit protocols for managing memory at session boundaries. The `db.store` and `db.update` methods operate on individual memory entries without higher-level session context. `_calculateDynamicScores` applies decay based on `createdAt` or `lastRecalledAt`, but there's no mechanism to distinguish between active session memories and archived ones. `api.on("before_agent_start")` uses `autoRecall` to inject a fixed `RECALL_INJECT_MAX` (6) memories, and `api.on("agent_end")` uses `autoCapture` for new memories (`AUTO_CAPTURE_LIMIT` of 5). There's no formalized concept of a "session," "conversation," or "topic shift" to influence memory lifecycle beyond basic recency. This leads to several issues:

1.  **Lack of Session Context Injection**: The `before_agent_start` hook performs a generic `hybridSearch` against the `event.prompt`. It doesn't incorporate any summary of the *previous session*, nor does it explicitly prioritize memories highly relevant to a *new session's initial prompt* in a way that differs from a mid-session query. This can lead to cold starts where the AI misses crucial context from a recently active, but now ended, session. We aim to improve the precision of initial `autoRecall` by 15% (measured by agent adoption rate of suggested memories) by incorporating previous session summaries.

2.  **Inefficient End-of-Session Consolidation**: The `consolidateMemories` function is a manual tool. There's no automated trigger at `agent_end` to perform lightweight, session-specific consolidation or to flag memories from the just-ended session for priority retention or accelerated decay based on their perceived importance *within that session*. This results in a growing memory database that is only periodically cleaned, potentially retaining low-value memories from recent sessions longer than necessary, and delaying the merging of similar memories. We need to reduce the count of near-duplicate memories (`cosineSimilarity > DUPLICATE_THRESHOLD`) created across consecutive sessions by 20% within a 24-hour window.

3.  **Absence of 'Topic Shift' Detection**: The system currently treats all `agent_end` events as a generic endpoint. It cannot detect when a new conversation starts on a completely different topic versus continuing a previous one. This means `autoRecall` for a new conversation about "planning a vacation" might pull irrelevant "coding bug" memories if those were recent and highly important in a prior, unrelated session. We seek to implement a 'topic shift' detection mechanism that can categorize a new `event.prompt` as either "continuing" (similarity > 0.6) or "new topic" (similarity < 0.4) relative to the prior session's main theme, influencing `autoRecall` behavior.

4.  **Undeveloped Long-Term Archival/Prioritization**: Memories are either actively recalled/reinforced or passively decay. There's no intermediate "session-critical" state that gives a temporary, but significant, boost to memories from the *just-ended session* that were highly important or frequently referenced. Conversely, there's no mechanism for gracefully degrading memories that were session-specific but are unlikely to be relevant long-term. This leads to a flat memory landscape where all memories compete equally for attention, regardless of their immediate contextual relevance to ongoing or recent interactions. We need a system to ensure that memories relevant to the *immediately preceding session* maintain a 2x higher recall probability in the subsequent `autoRecall` for a defined "warm-up" period (e.g., 1 hour).

Propose a complete cross-session memory lifecycle protocol, including:

**A. Session Boundary Detection & State Management:**
   - Define a `SessionContext` interface (e.g., `sessionId: string`, `lastActivity: number`, `mainTopicVector: number[]`, `sessionMemories: string[]`).
   - Propose additions to `OpenClawPluginApi` or a new internal service to manage `SessionContext` across `before_agent_start` and `agent_end` events.
   - Sketch pseudocode for `onSessionStart()` and `onSessionEnd()` handlers.

**B. Pre-Session Load Protocol:**
   - Modify `api.on("before_agent_start")` to dynamically adjust `autoRecall` behavior based on:
     - The `mainTopicVector` of the *previous session*.
     - A calculated 'topic shift' score between the current `event.prompt` and the previous session's `mainTopicVector`.
   - Implement a mechanism to prioritize a small number of "session-critical" memories from the immediate last session, giving them a temporary boost in `_calculateDynamicScores` via a new `sessionBoost` parameter.
   - Provide a TypeScript sketch for how `hybridSearch` could integrate `SessionContext` for adaptive weighting (e.g., boosting `recency` if continuing a topic).

**C. Post-Session Flush & Consolidation Triggers:**
   - Introduce a lightweight, `agent_end`-triggered consolidation step. This should *not* be the full `consolidateMemories` but a targeted scan, possibly leveraging LanceDB's `delete` and `add` for a small set of very recent memories.
   - Focus on merging highly similar memories (`sim > DUPLICATE_THRESHOLD`) captured *within the same session* or between the current session and the immediate past.
   - Detail how `MemoryEntry` could be extended with `sessionId` or `lastSessionId` to facilitate this.
   - Propose a pseudocode for a new `_performSessionConsolidation(sessionId: string)` function, including metrics to track merges/deletions.

**D. Long-Term Archival & Priority Boosting:**
   - Design a `MemoryEntry` extension (e.g., `sessionCritical: boolean`, `sessionBoostExpiry: number`) to flag and decay temporary session importance.
   - Propose changes to `reinforceMemory` or a new `flagSessionCriticalMemory` function to mark memories that are highly important *within a session* (e.g., if recalled multiple times or explicitly mentioned by the user).
   - How does this interact with `CATEGORY_DECAY_PARAMS` and `REINFORCEMENT_BOOST_FACTOR`? Should `sessionCritical` memories decay slower for a short period?

Provide a complete implementation roadmap with TypeScript code snippets suitable for direct integration into the `enhanced_index.ts` / `enhanced_config.ts` files.

---

## Thinking Process (Phase 2 — Gemini Pro)

**My Proposed Solution for Cross-Session Memory Management**

Okay, so the user's looking for a *complete* solution to improve AIVA's `memory-lancedb` plugin, and they've laid out the problems really clearly. First, I need to understand this: the four main pain points are context-less starts, manual consolidation, lack of topic shift awareness, and no concept of short-term vs. long-term memory. Got it. They want a concrete, *implementable* solution, broken into A, B, C, D sections, with TypeScript code. That's my jam.

My thinking is to tackle this methodically:

1.  **Understand the User's Needs:** They want more than just high-level ideas, they want *code*. I will address the four core problems in the context of the requested sections.

2.  **Devise a Lifecycle Protocol**
    *   **A: Session State and Boundaries:** I need a central place to track the current session. I'm thinking a `SessionManager` service – it's crucial that this state isn't tied to a plugin instance, but managed externally.  `onSessionStart` and `onSessionEnd` will be the entry points. They want to define `sessionId`, `lastActivity`, `mainTopicVector`, and `sessionMemories`. Great starting point. I can then propose how to calculate the `mainTopicVector` (e.g., as the average of all memory vectors within the session)

    *   **B: Pre-Session Loading & Topic Shift:** I'll modify the `before_agent_start` hook. The core is the `SessionContext` from Part A. The main idea here is detecting *topic shift*, comparing the current prompt's vector with the previous session's `mainTopicVector` to see if they're similar. This lets us use the plugin effectively, by boosting or suppressing recency based on the score. I'll need to define thresholds and adapt the `hybridSearch` call. I can also introduce a `sessionBoost` concept, tied to the new `MemoryEntry` fields (this gets us to Part D).

    *   **C: Post-Session Consolidation:** For `agent_end`, I need to keep it lightweight. Instead of running a full consolidation, I will focus on only the memories *within the just-ended session*. This requires adding a `sessionId` field to the `MemoryEntry`. I'll create an `async` background task to consolidate session memories, merging near-duplicates.

    *   **D: Long-Term Archival & Prioritization:** This is about modifying the `MemoryEntry` schema and scoring. I'll add `sessionBoostExpiry`, (instead of the proposed `sessionCritical`) and maybe a `sessionRecallCount`. I'll also add a `flagSessionCriticalMemory` function that's called upon a memory's recall. This function will set `sessionBoostExpiry` to a future time.  The scoring logic will be modified in `_calculateDynamicScores` to temporarily boost memories, and then to ensure the temporary boost interacts gracefully with the existing decay mechanisms.

3.  **Organize and Structure the Response:** I'll follow the user's A, B, C, D structure precisely. I'll present this as follows:

    *   A brief introduction to acknowledge the issues and the overall solution.
    *   **Part A:** Define `SessionContext`, propose `SessionManager`, outline `onSessionStart/End`, and how to calculate `mainTopicVector`.
    *   **Part B:** Show how to modify `before_agent_start`, define topic shift logic, sketch `hybridSearch` with adaptable parameters, and how to include `sessionBoost`.
    *   **Part C:** Propose the `MemoryEntry` change, provide the `agent_end` hook, and write `_performSessionConsolidation`.
    *   **Part D:** Define the new `MemoryEntry` fields, create `flagSessionCriticalMemory`, show the updated `_calculateDynamicScores` and interaction with existing decay.
    *   A clear, step-by-step implementation roadmap, broken into phases (schema, session management, hooks, scoring, and testing).

4.  **Careful Review and Refinement:** I'll verify the code is clean, and idiomatic, and addresses all the requirements, and fits within the system. Is the logic sound? I must ensure it addresses the user's needs. I'll also check all constants are well-explained and make sure the consolidation process is non-blocking. Also, I'll provide an implementation roadmap to guide the user in setting up the system.

---

## Deep Think Response

Excellent and detailed problem analysis. Here is a complete cross-session memory lifecycle protocol designed to address the identified issues, with a clear implementation roadmap and TypeScript code snippets for integration.

---

### **Overview of the Proposed Protocol**

This protocol introduces a `SessionManager` to track conversational context. It modifies the agent's startup and shutdown hooks to create a "warm" start for continuing conversations and an intelligent "cooldown" for recently ended ones. This involves:

1.  **Session Tracking:** Explicitly defining and managing a `SessionContext` across agent runs.
2.  **Topic-Aware Recall:** Using topic vectors to detect shifts in conversation and adjust memory recall strategies accordingly.
3.  **Session-Specific Consolidation:** Running a lightweight, automated cleanup at the end of each session to reduce redundancy.
4.  **Dynamic Priority Boosting:** Temporarily boosting the importance of memories from the most recent session to ensure continuity.

---

### **A. Session Boundary Detection & State Management**

We'll introduce a centralized `SessionManager` to persist session state. This service will be accessible by the plugin and live outside the plugin's instance lifecycle, ensuring state is maintained between hook calls.

#### 1. Define `SessionContext` and `MemoryEntry` Extensions

In `enhanced_config.ts` or a new `types.ts`:

```typescript
// enhanced_config.ts or a new types.ts

export interface SessionContext {
  sessionId: string;
  isActive: boolean;
  startedAt: number;
  lastActivity: number;
  // The average vector of all memories created/recalled during the session
  mainTopicVector: number[] | null; 
  // IDs of memories created during this session
  newMemoryIds: string[]; 
}

// Extend the existing MemoryEntry interface
export interface MemoryEntry {
  id: string;
  content: string;
  embedding: number[];
  metadata: {
    source: string;
    category: MemoryCategory;
    createdAt: number;
    lastRecalledAt: number;
    importance: number;
    // New Fields
    sessionId?: string; // ID of the session that created this memory
    sessionRecallCount: number; // How many times recalled within its creation session
    sessionBoostExpiry: number; // Timestamp until which this memory gets a recall boost
  };
}

// Configuration Constants
export const SESSION_TIMEOUT_MS = 30 * 60 * 1000; // 30 minutes
export const CONTINUATION_THRESHOLD = 0.6; // Cosine similarity to be considered a continuation
export const NEW_TOPIC_THRESHOLD = 0.4; // Cosine similarity to be considered a new topic
export const DUPLICATE_THRESHOLD = 0.95; // For session-level consolidation
export const SESSION_BOOST_FACTOR = 2.0; // Multiplier for recently session-critical memories
export const SESSION_BOOST_DURATION_MS = 60 * 60 * 1000; // 1 hour boost
```

#### 2. `SessionManager` Service

This would be a singleton service. For simplicity, we can simulate it as a static class within the plugin file, but in a larger application, it should be managed by the dependency injection container.

```typescript
// enhanced_index.ts (or a separate session_manager.ts)

class SessionManager {
  private static instance: SessionManager;
  private currentSession: SessionContext | null = null;

  private constructor() {}

  public static getInstance(): SessionManager {
    if (!SessionManager.instance) {
      SessionManager.instance = new SessionManager();
    }
    return SessionManager.instance;
  }

  public getCurrentSession(): SessionContext | null {
    if (this.currentSession && Date.now() - this.currentSession.lastActivity > SESSION_TIMEOUT_MS) {
      this.endSession(); // End session due to inactivity
    }
    return this.currentSession;
  }

  public startSession(): SessionContext {
    if (this.currentSession?.isActive) {
        this.currentSession.lastActivity = Date.now();
        return this.currentSession;
    }
    this.currentSession = {
      sessionId: crypto.randomUUID(),
      isActive: true,
      startedAt: Date.now(),
      lastActivity: Date.now(),
      mainTopicVector: null,
      newMemoryIds: [],
    };
    return this.currentSession;
  }

  public endSession(): void {
    if (this.currentSession) {
      this.currentSession.isActive = false;
    }
  }

  public addMemoryToSession(memoryId: string, session: SessionContext) {
    session.newMemoryIds.push(memoryId);
  }

  public async updateSessionTopicVector(db: EnhancedLanceDB, session: SessionContext): Promise<void> {
    if (session.newMemoryIds.length === 0) {
      session.mainTopicVector = null;
      return;
    }
    const memories = await db.getMany(session.newMemoryIds);
    const vectors = memories.map(m => m.embedding);
    if (vectors.length > 0) {
      // Simple averaging for the topic vector
      const vectorLength = vectors[0].length;
      const avgVector = new Array(vectorLength).fill(0);
      for (const vec of vectors) {
        for (let i = 0; i < vectorLength; i++) {
          avgVector[i] += vec[i];
        }
      }
      session.mainTopicVector = avgVector.map(v => v / vectors.length);
    }
  }
}
```

#### 3. `onSessionStart()` and `onSessionEnd()` Logic

These aren't explicit hooks but will be implemented within the existing `before_agent_start` and `agent_end` hooks.

-   **Session Start (in `before_agent_start`)**: Get the current session. If it's null or inactive, start a new one.
-   **Session End (in `agent_end`)**: Update the session's topic vector, then mark it as inactive.

---

### **B. Pre-Session Load Protocol**

Modify `before_agent_start` to be session-aware.

```typescript
// enhanced_index.ts

// Inside the plugin's `install` method:
const sessionManager = SessionManager.getInstance();
let lastSession: SessionContext | null = null;

api.on("before_agent_start", async (event) => {
  const previousSession = sessionManager.getCurrentSession();
  const currentSession = sessionManager.startSession();

  let topicShiftScore = 0;
  let isContinuation = false;

  if (previousSession && !previousSession.isActive && previousSession.mainTopicVector) {
    lastSession = previousSession; // Store the immediately preceding session
    const promptVector = await this.vectorize(event.prompt);
    topicShiftScore = this.cosineSimilarity(promptVector, previousSession.mainTopicVector);
    
    if (topicShiftScore > CONTINUATION_THRESHOLD) {
      isContinuation = true;
    }
  }

  // Dynamically adjust autoRecall based on session context
  const searchParams = {
    query: event.prompt,
    limit: RECALL_INJECT_MAX,
    // Provide session context to the search function
    sessionContext: {
        isContinuation,
        lastSessionId: lastSession?.sessionId,
    }
  };
  
  // New session on a new topic: Standard hybrid search, maybe with lower recency weight.
  // Continuing session: Boost recency and memories from the last session.
  await this.autoRecall(searchParams);
});

// Modify the search logic to use this context.
// Let's assume hybridSearch is refactored to take this context.

public async hybridSearch(query: string, limit: number, sessionContext?: { isContinuation: boolean, lastSessionId?: string }) {
    const queryVector = await this.vectorize(query);
    const allMemories = await this.db.getAll(); // Simplified; ideally a more efficient query

    // Modify score calculation to include session boost
    const scoredMemories = allMemories.map(entry => {
        let score = this._calculateDynamicScores(entry, queryVector);
        
        // Apply session boost for memories from the immediately preceding session
        if (sessionContext?.isContinuation && entry.metadata.sessionId === sessionContext.lastSessionId) {
             score *= SESSION_BOOST_FACTOR;
        }

        // Check for the general "sessionBoostExpiry"
        if (entry.metadata.sessionBoostExpiry > Date.now()) {
            score *= SESSION_BOOST_FACTOR;
        }

        return { ...entry, score };
    });

    // Sort and return top N
    return scoredMemories.sort((a, b) => b.score - a.score).slice(0, limit);
}

// Modify _calculateDynamicScores slightly to be cleaner
private _calculateDynamicScores(entry: MemoryEntry, queryVector: number[]): number {
    const recency = this._calculateRecencyScore(entry.metadata.lastRecalledAt || entry.metadata.createdAt);
    const importance = entry.metadata.importance;
    const similarity = this.cosineSimilarity(entry.embedding, queryVector);

    // Dynamic weighting can be adjusted here too based on `isContinuation` if passed down
    // For now, the boost is applied in the calling function `hybridSearch`
    return (similarity * 1.5) + (recency * 0.5) + (importance * 1.0);
}
```

---

### **C. Post-Session Flush & Consolidation Triggers**

At the end of an agent run, perform a lightweight, targeted consolidation for the session that just occurred.

#### 1. Add `sessionId` to New Memories

In `db.store` or `autoCapture`, when creating a memory, tag it with the current session ID.

```typescript
// In autoCapture or wherever memories are created
const currentSession = sessionManager.getCurrentSession();
if (currentSession) {
    newMemory.metadata.sessionId = currentSession.sessionId;
    newMemory.metadata.sessionRecallCount = 0;
    newMemory.metadata.sessionBoostExpiry = 0; // Not boosted by default
    sessionManager.addMemoryToSession(newMemory.id, currentSession);
}
await this.db.store(newMemory);
```

#### 2. `agent_end` Hook Modification

```typescript
// enhanced_index.ts - inside `install` method

api.on("agent_end", async (event) => {
    // Standard autoCapture
    await this.autoCapture(event.transcript);
    
    const session = sessionManager.getCurrentSession();
    if (session) {
        // Update the main topic vector for the session before closing it
        await sessionManager.updateSessionTopicVector(this.db, session);
        sessionManager.endSession();
        
        // Trigger consolidation asynchronously so it doesn't block the response
        this._performSessionConsolidation(session.sessionId).catch(console.error);
    }
});
```

#### 3. `_performSessionConsolidation` Implementation

This function is the core of the lightweight consolidation.

```typescript
// enhanced_index.ts - new private method in the plugin class

private async _performSessionConsolidation(sessionId: string): Promise<{ merged: number; deleted: number }> {
    console.log(`Starting lightweight consolidation for session: ${sessionId}`);
    const metrics = { merged: 0, deleted: 0 };

    const sessionMemoryIds = (await this.db.getAll())
        .filter(m => m.metadata.sessionId === sessionId)
        .map(m => m.id);

    if (sessionMemoryIds.length === 0) return metrics;

    const sessionMemories = await this.db.getMany(sessionMemoryIds);

    for (const memory of sessionMemories) {
        // Search for near-duplicates (created before this memory, to avoid double-processing)
        const potentialDuplicates = await this.db.search(memory.embedding, { 
            limit: 5, 
            where: `metadata.createdAt < ${memory.metadata.createdAt}` 
        });

        const bestMatch = potentialDuplicates.find(p => this.cosineSimilarity(p.embedding, memory.embedding) > DUPLICATE_THRESHOLD);

        if (bestMatch) {
            // Merge `memory` into `bestMatch`
            const combinedContent = `Original: ${bestMatch.content}\nMerged: ${memory.content}`;
            const updatedImportance = Math.max(bestMatch.metadata.importance, memory.metadata.importance) + 0.1;

            await this.db.update(bestMatch.id, {
                content: combinedContent,
                'metadata.importance': updatedImportance,
                'metadata.lastRecalledAt': Date.now(), // Treat merge as a recall
            });

            // Delete the redundant memory
            await this.db.delete(memory.id);
            metrics.merged++;
            metrics.deleted++;
            console.log(`Merged memory ${memory.id} into ${bestMatch.id}`);
        }
    }

    console.log(`Consolidation for session ${sessionId} complete. Merged: ${metrics.merged}, Deleted: ${metrics.deleted}`);
    return metrics;
}
```

---

### **D. Long-Term Archival & Priority Boosting**

This involves flagging important memories within a session and ensuring they retain relevance for a short period.

#### 1. `MemoryEntry` Schema Extensions (Already Defined in Part A)

-   `sessionRecallCount: number`: Tracks intra-session usage.
-   `sessionBoostExpiry: number`: The timestamp until which the memory gets a boost.

#### 2. Update `reinforceMemory`

When a memory is recalled, we check if it belongs to the current session and update its session-specific metadata. This is how we identify "session-critical" memories.

```typescript
// enhanced_index.ts - in `reinforceMemory` or wherever recall is handled

public async reinforceMemory(memoryId: string): Promise<void> {
    const memory = await this.db.get(memoryId);
    if (!memory) return;

    const newImportance = memory.metadata.importance + REINFORCEMENT_BOOST_FACTOR;
    const updates = {
        'metadata.lastRecalledAt': Date.now(),
        'metadata.importance': Math.min(newImportance, 1.0), // Cap importance at 1.0
        'metadata.sessionBoostExpiry': Date.now() + SESSION_BOOST_DURATION_MS, // Apply/refresh the boost!
    };

    // If recalled within its creation session, increment the session recall count
    const currentSession = SessionManager.getInstance().getCurrentSession();
    if (currentSession && memory.metadata.sessionId === currentSession.sessionId) {
        updates['metadata.sessionRecallCount'] = (memory.metadata.sessionRecallCount || 0) + 1;
    }

    await this.db.update(memoryId, updates);
}
```

#### 3. Interaction with Scoring

The `hybridSearch` function shown in Part B already incorporates the `sessionBoostExpiry` check. This implementation is clean because it acts as a temporary multiplier on the standard score, which is composed of similarity, recency, and importance.

-   **Synergy:** When a memory is reinforced, its `importance` and `lastRecalledAt` are updated, boosting its base score. The added `sessionBoostExpiry` gives it an *additional*, temporary multiplier, ensuring it remains highly visible for the next hour.
-   **Graceful Degradation:** Once `sessionBoostExpiry` passes, the `SESSION_BOOST_FACTOR` is no longer applied. The memory's relevance then relies on its now-higher base importance and recency, which will decay normally according to `CATEGORY_DECAY_PARAMS`. This perfectly models the "warm-up" period followed by a return to the standard memory lifecycle.

---

### **Implementation Roadmap**

1.  **Phase 1: Schema & Configuration**
    -   [ ] Update the `MemoryEntry` interface in `enhanced_config.ts` with `sessionId`, `sessionRecallCount`, and `sessionBoostExpiry`.
    -   [ ] Define the `SessionContext` interface.
    -   [ ] Add new constants to `enhanced_config.ts`: `SESSION_TIMEOUT_MS`, `CONTINUATION_THRESHOLD`, `NEW_TOPIC_THRESHOLD`, `DUPLICATE_THRESHOLD`, `SESSION_BOOST_FACTOR`, `SESSION_BOOST_DURATION_MS`.

2.  **Phase 2: Session Management Core**
    -   [ ] Implement the `SessionManager` static class as described in Part A.
    -   [ ] Ensure it correctly handles starting, ending, and timing out sessions.

3.  **Phase 3: Hook & Logic Modifications**
    -   [ ] Modify `before_agent_start` to integrate `SessionManager`, calculate topic shift, and pass context to the search function.
    -   [ ] Modify `agent_end` to update the session topic vector, end the session, and trigger `_performSessionConsolidation`.
    -   [ ] Update memory creation logic (`autoCapture` or `db.store`) to tag new memories with the current `sessionId`.
    -   [ ] Update `reinforceMemory` to set the `sessionBoostExpiry` and increment `sessionRecallCount`.

4.  **Phase 4: New Functionality Implementation**
    -   [ ] Implement the `_performSessionConsolidation` private method.
    -   [ ] Refactor `hybridSearch` (or the underlying search/scoring logic) to accept `sessionContext` and apply the `SESSION_BOOST_FACTOR` based on both `isContinuation` and `sessionBoostExpiry`.

5.  **Phase 5: Testing & Tuning**
    -   [ ] Test the topic shift detection with sample prompts. Tune `CONTINUATION_THRESHOLD` and `NEW_TOPIC_THRESHOLD`.
    -   [ ] Monitor the `_performSessionConsolidation` logs to validate that near-duplicates are being merged. Measure the reduction in duplicate memories over a 24-hour period.
    -   [ ] Create test cases where an agent is stopped and started on a related topic. Verify that the agent correctly recalls context from the previous session and that the adoption rate of suggested memories improves.
    -   [ ] Verify that the recall probability for "session-critical" memories is elevated for the configured duration.
