@zensation/algorithms

Neuroscience-inspired memory algorithms for AI agents. Pure TypeScript. Zero dependencies.

What's Inside

Seven battle-tested algorithms extracted from a production AI system (170K+ LOC, 9,200+ tests):

| Algorithm | Inspired By | What It Does | |-----------|------------|--------------| | FSRS | Free Spaced Repetition Scheduler | Optimal review scheduling — your AI never forgets what matters | | Ebbinghaus | Ebbinghaus (1885) | Exponential forgetting curves with personalized decay profiles | | Emotional | Amygdala modulation (Cahill & McGaugh, 1998) | Tag memories with arousal/valence/significance — emotional memories decay 3x slower | | Hebbian | Hebb's Rule (1949) | "Neurons that fire together wire together" — co-activation strengthening with homeostatic normalization | | Bayesian | Bayesian belief propagation | Confidence propagation through knowledge graphs | | Context Retrieval | Encoding Specificity (Tulving, 1973) | Context-dependent retrieval boost — memories recalled better in similar contexts | | Similarity | NLP heuristics | Negation detection (EN/DE), Jaccard similarity, text analysis |

Quick Start

npm install @zensation/algorithms

import {
  // FSRS Spaced Repetition
  initFromDecayClass,
  getRetrievability,
  updateAfterRecall,
  scheduleNextReview,

  // Emotional Memory
  tagEmotion,
  computeEmotionalWeight,

  // Hebbian Learning
  computeHebbianStrengthening,
  computeHebbianDecay,

  // Bayesian Confidence
  propagateForRelation,
} from '@zensation/algorithms';

// 1. Create a memory with FSRS scheduling
const memory = initFromDecayClass('normal_decay');
console.log(memory);
// { difficulty: 5, stability: 7, nextReview: Date }

// 2. Check if user can recall it
const retention = getRetrievability(memory);
console.log(`Recall probability: ${(retention * 100).toFixed(1)}%`);

// 3. User recalled it with grade 4 (good)
const updated = updateAfterRecall(memory, 4, retention);
console.log(`Next review: ${updated.nextReview}`);
// Stability increased — next review is further out

// 4. Tag emotional significance
const emotion = tagEmotion('I just got promoted! This is amazing!');
console.log(emotion);
// { sentiment: 0.8+, arousal: 0.7+, valence: 0.9+, significance: 0.85 }

const weight = computeEmotionalWeight(emotion);
console.log(`Decay multiplier: ${weight.decayMultiplier}x`);
// ~2.7x — this memory will decay nearly 3x slower

// 5. Strengthen knowledge graph edges via Hebbian learning
const newWeight = computeHebbianStrengthening(1.0);
// 1.09 — asymptotic growth toward MAX_WEIGHT (10.0)

// 6. Propagate confidence through relations
const newConfidence = propagateForRelation(
  0.5,   // base confidence
  0.8,   // source confidence
  1.0,   // edge weight
  'supports'
);
// 0.9 — supporting evidence increases confidence

Tree-Shakeable Imports

Import only what you need:

// Just FSRS
import { updateAfterRecall, getRetrievability } from '@zensation/algorithms/fsrs';

// Just emotional tagging
import { tagEmotion } from '@zensation/algorithms/emotional';

// Just Hebbian dynamics
import { computeHebbianStrengthening } from '@zensation/algorithms/hebbian';

Why These Algorithms?

FSRS vs SM-2

SM-2 (SuperMemo 2, 1990) uses fixed multipliers. FSRS uses the desirable difficulty principle: reviewing when retention is low gives a bigger stability boost. The result? 30% fewer reviews for the same retention.

Emotional Memory

Human brains consolidate emotional memories more strongly (flashbulb memory effect). This module gives your AI the same capability: memories tagged with high arousal + significance get up to 3x longer decay half-life.

Hebbian Learning

Knowledge graph edges that are frequently co-activated grow stronger. Edges that are never used decay and get pruned. The result is a self-organizing knowledge structure that reflects actual usage patterns.

Context-Dependent Retrieval

Tulving showed that memory recall improves when the retrieval context matches the encoding context. This module captures temporal + task context at encoding time and provides up to a 30% retrieval boost when contexts match.

API Reference

FSRS (@zensation/algorithms/fsrs)

| Function | Description | |----------|-------------| | initFromDecayClass(class, emotionalWeight?) | Create initial state from decay class | | initFromSM2(stability) | Convert SM-2 stability to FSRS state | | getRetrievability(state, now?) | Calculate current recall probability | | scheduleNextReview(state, targetRetention?, now?) | Schedule next optimal review | | updateAfterRecall(state, grade, retrievability, now?) | Update after successful recall (grade 1-5) | | updateAfterForgot(state, retrievability, now?) | Update after failed recall | | updateStabilityCompat(stability, success, multiplier?) | Drop-in SM-2 replacement | | getRetentionProbabilityCompat(lastAccess, stability, multiplier?) | Drop-in Ebbinghaus replacement |

Ebbinghaus (@zensation/algorithms/ebbinghaus)

| Function | Description | |----------|-------------| | calculateRetention(lastAccess, stability, emotionalMultiplier?) | Full retention analysis | | updateStability(stability, success) | SM-2 stability update | | getRepetitionCandidates(facts, threshold?) | Find facts due for review | | calculateOptimalInterval(stability, targetRetention?) | Optimal review interval | | batchCalculateRetention(facts) | Efficient batch retention | | learnDecayProfile(history) | Personalized decay curves | | calculatePersonalizedRetention(lastAccess, stability, profile) | User-specific retention |

Emotional (@zensation/algorithms/emotional)

| Function | Description | |----------|-------------| | tagEmotion(text, contextDomain?) | Multi-dimensional emotion analysis | | computeEmotionalWeight(tag) | Consolidation weight + decay multiplier | | isEmotionallySignificant(text, threshold?) | Quick significance check | | computeContextualValence(text, domain) | Domain-adjusted valence |

Hebbian (@zensation/algorithms/hebbian)

| Function | Description | |----------|-------------| | computeHebbianStrengthening(weight) | Asymptotic edge strengthening | | computeHebbianDecay(weight) | Exponential decay with pruning | | computeHomeostaticNormalization(weights, targetSum) | Normalize weight distribution | | generatePairs(items) | Generate C(n,2) co-activation pairs |

Bayesian (@zensation/algorithms/bayesian)

| Function | Description | |----------|-------------| | propagateForRelation(base, source, weight, type) | Single-edge confidence propagation | | applyDamping(newValue, previousValue) | Blend with previous for stability | | isSignificantChange(newValue, previousValue) | Check if update is worth persisting |

Context Retrieval (@zensation/algorithms/context-retrieval)

| Function | Description | |----------|-------------| | captureEncodingContext(taskType?) | Snapshot current context | | calculateContextSimilarity(encoding, current?) | Context match score + boost | | serializeContext(ctx) / deserializeContext(data) | Storage helpers |

Similarity (@zensation/algorithms/similarity)

| Function | Description | |----------|-------------| | detectNegation(text) | Detect negation with target extraction (EN/DE) | | computeStringSimilarity(a, b) | Jaccard word overlap similarity | | stripNegation(text) | Remove negation words | | safeJsonParse(json, fallback) | Safe JSON parsing with fallback |

Logging

All functions accept an optional Logger parameter. Pass console, your favorite logger, or nothing (silent by default):

import { updateAfterRecall } from '@zensation/algorithms';

// Silent (default)
updateAfterRecall(state, 4, 0.9);

// With logging
updateAfterRecall(state, 4, 0.9, new Date(), console);

Part of ZenBrain

This package is part of the ZenBrain monorepo — the neuroscience-inspired memory system for AI agents.

| Package | Description | |---------|-------------| | @zensation/algorithms | Pure algorithms (this package) | | @zensation/core | Memory layers + coordinator | | @zensation/adapter-postgres | PostgreSQL + pgvector storage | | @zensation/adapter-sqlite | SQLite + sqlite-vec storage |

License

Apache 2.0 — see LICENSE.