One of the easiest ways to make an AI system fail is to let it remember too much.

Most "context handling" approaches boil down to one of two mistakes:

• Stuff everything into the prompt and hope the model figures out what matters
• Repeatedly re-summarise summaries and accept drift as a fact of life

Both approaches are expensive, opaque, and unstable over long horizons.

There is a better pattern - one that mirrors how biological systems actually scale cognition.

I call it Constrained Fuzzy Context Dragging (CFCD).

---

The Pattern in One Sentence

Constrained Fuzzy Context Dragging is a pattern where probabilistic models propose what is salient, but only deterministic rules are allowed to carry that salience forward to influence future reasoning or generation.

Or more bluntly:

Models may notice. Engineering decides what persists.

This is the same philosophical split as Constrained Fuzziness and Constrained Fuzzy MoM - just applied along the time axis instead of the decision axis.

---

The Core Mistake: Treating Context as Text

LLMs do not "remember". They pattern-match over tokens.

When we treat context as "just more text", we get:

• silent drift
• inconsistent terminology
• runaway cost
• summaries that contradict earlier summaries
• translations that rename the same thing five times
• context becomes an unversioned dependency (nobody knows what changed, but results changed)

Longer context windows do not solve this. They just delay the failure.

The real question is not how much context to keep, but:

What deserves to survive?

---

The CFCD Rule

CFCD enforces a hard separation:

Role	Allowed to be fuzzy?	Allowed to become future constraint?
Salience detection	Yes	No
Context promotion	No	Yes
Generation	Yes	No
Anchor storage (ledger)	No	Yes

Probability can suggest. Determinism decides what gets dragged forward.

Deterministic means: same inputs + same policy ⇒ same anchors. You can still use probabilistic scores; you just run them through fixed promotion/expiry functions (MMR, RRF, vote rules).

CFCD is how you get the benefits of context without paying the price of context.

---

Example 1: Summarisation (Vertical Dragging)

The Naive Approach

• Chunk document
• Summarise each chunk
• Summarise the summaries
• Accept drift

This fails because:

• early errors compound
• emphasis shifts arbitrarily
• contradictions are hidden

CFCD Approach

The key insight: salience detection doesn't require an LLM. You can use deterministic ML (embeddings, TF-IDF, BM25) to find what matters, then constrain the LLM to only see what survived selection.

DocSummarizer implements this:

1. Parse document into segments (sentences, headings, lists, code blocks)
2. Generate embeddings using ONNX sentence-transformers (ML, not LLM)
3. Compute salience scores using:
   • Cosine similarity to document centroid
   • TF-IDF for term importance
   • Position weights (intro/conclusion boost)
   • Content-type adjustments (narrative vs expository)
4. Select top-K using MMR (Maximal Marginal Relevance):
   • Balances relevance (similarity to centroid) with diversity
   • Deterministic: same inputs → same selection
5. LLM synthesises only from selected segments

The LLM never sees the full document. It generates around the anchors, not through them.

// From DocSummarizer: SegmentExtractor.cs
public class SegmentExtractor
{
    private readonly OnnxEmbeddingService _embeddingService;  // ML, not LLM

    public async Task<ExtractionResult> ExtractAsync(string docId, string markdown)
    {
        // 1. Parse document into typed segments
        var segments = ParseToSegments(docId, markdown);

        // 2. For large docs: semantic pre-filtering with multi-anchor approach
        //    - Guaranteed coverage: first/last sentences per section, constraints
        //    - BM25 candidate generation with pseudo-query from TF-IDF terms
        //    - Topic anchors via k-means clustering on sample embeddings
        if (segments.Count > _config.MaxSegmentsToEmbed)
        {
            (segmentsToEmbed, centroid) = await SemanticPreFilterAsync(segments, targetCount);
        }
        else
        {
            await GenerateEmbeddingsAsync(segments);
            centroid = CalculateCentroid(segments);
        }

        // 3. Score by salience using MMR (deterministic)
        //    Salience = lambda * sim(segment, centroid) * position_weight * content_weight
        //             - (1 - lambda) * max_sim(segment, higher_ranked_segments)
        ComputeSalienceScores(segments, centroid, contentType);

        // 4. Return top-K as anchors for synthesis
        var topBySalience = segments
            .OrderByDescending(s => s.SalienceScore)
            .Take(Math.Max(_config.FallbackBucketSize, targetCount))
            .ToList();

        return new ExtractionResult
        {
            AllSegments = segments,
            TopBySalience = topBySalience,
            Centroid = centroid,
            ContentType = contentType
        };
    }

    /// <summary>
    /// Greedy MMR selection: balances relevance with diversity
    /// </summary>
    private void ComputeSalienceScores(List<Segment> segments, float[] centroid, ContentType contentType)
    {
        var candidates = new HashSet<Segment>(segments.Where(s => s.Embedding != null));
        var ranked = new List<Segment>();

        // Pre-compute centroid similarities with content-type adjustments
        foreach (var segment in candidates)
        {
            var baseSim = CosineSimilarity(segment.Embedding!, centroid);
            var contentWeight = ComputeContentTypeWeight(segment, contentType);
            segment.SalienceScore = baseSim * segment.PositionWeight * contentWeight;
        }

        // Greedy MMR: pick best, penalise similar, repeat
        while (candidates.Count > 0)
        {
            var best = candidates
                .Select(c => new {
                    Segment = c,
                    Score = _config.MmrLambda * c.SalienceScore
                          - (1 - _config.MmrLambda) * MaxSimToRanked(c, ranked)
                })
                .OrderByDescending(x => x.Score)
                .First();

            best.Segment.SalienceScore = 1.0 - ((double)ranked.Count / segments.Count);
            ranked.Add(best.Segment);
            candidates.Remove(best.Segment);
        }
    }
}

Adaptive Selection: Context Window Awareness

Because the output is a ranked collection, you can adapt anchor count to the synthesis model at runtime:

public class AdaptiveSynthesizer
{
    public int ComputeAnchorBudget(ModelProfile model, ExtractionResult extraction)
    {
        // Larger context windows → more anchors
        var baseTokenBudget = model.ContextWindow / 4;  // Reserve 25% for output

        // More powerful models → can handle more complex synthesis
        var complexityMultiplier = model.Tier switch
        {
            ModelTier.Small => 0.5,   // tinyllama: fewer, simpler anchors
            ModelTier.Medium => 1.0,  // llama3.2:3b: standard
            ModelTier.Large => 1.5,   // llama3.1:8b+: more context, nuanced synthesis
            _ => 1.0
        };

        var anchorBudget = (int)(baseTokenBudget * complexityMultiplier / _avgTokensPerSegment);

        // Never exceed what we have
        return Math.Min(anchorBudget, extraction.TopBySalience.Count);
    }

    public async Task<Summary> SynthesizeAsync(
        ExtractionResult extraction,
        ModelProfile model)
    {
        var budget = ComputeAnchorBudget(model, extraction);

        // Take top-K from already-ranked segments
        var anchors = extraction.TopBySalience.Take(budget).ToList();

        // Synthesis model only sees what survived selection + adaptation
        return await _llm.SynthesizeAsync(anchors, model);
    }
}

This is still deterministic: same inputs + same model profile → same anchors. The ranking happens once; adaptation is just a slice.

This means you can:

• Use a fast small model with 5 anchors for quick summaries
• Use a powerful model with 50 anchors for comprehensive analysis
• Dynamically adjust mid-pipeline if the first attempt lacks coverage

The key insight: anchors are structure, not prose. The embeddings and MMR are deterministic. The LLM only does the final synthesis, bounded by what survived.

Anchor contract: generation may vary phrasing, but must not contradict anchors. If anchors are insufficient, it must hedge and/or request more evidence.

A concrete anchor representation:

{
  "policyVersion": "cfcd-v1",
  "segments": [
    {"id":"seg-12","text":"Reset requires holding button 10s","salience":0.92},
    {"id":"seg-45","text":"Factory reset clears all settings","salience":0.88}
  ],
  "coverage": "3.2% semantic sample",
  "hedging": "sampled 3% - avoid definitive conclusions"
}

---

Example 2: Translation (Lateral Dragging)

Translation exposes this pattern even more clearly.

The Failure Mode

Translate sentence-by-sentence and you get:

• inconsistent terminology
• renamed entities
• subtle semantic drift

Even with a huge context window, models still "feel free" to vary phrasing.

CFCD Approach

1. Early in the document, extract terminology candidates:
   • proper nouns
   • technical terms
   • repeated phrases
2. Build a term ledger:
   • source → target mapping
   • confidence
   • first-seen location
3. For each sentence:
   • allow fuzzy translation
   • constrain choices using the ledger
   • permit deviation only with strong local evidence
4. Update the ledger deterministically if:
   • a better mapping appears repeatedly
   • or an explicit override is declared

What counts as strong local evidence?

• Term appears in a glossary, table, or definition block (structural cue)
• Explicit user override in configuration
• Repeated within the last W chunks (sliding window) with consistent usage

This is deterministic: structural cues are detected by pattern or parser classification, not model judgment.

Default policy: Δ=0.08 confidence gap, W=50 chunks (tune per domain).

public class TermLedger
{
    private readonly Dictionary<string, TermMapping> _mappings = new();

    public record TermMapping(
        string SourceTerm,
        string TargetTerm,
        float Confidence,
        string FirstSeenChunkId,
        int LastSeenChunkIndex,               // Track recency
        int VotesForCurrentTarget,
        int TotalVotes,                       // Preserve history
        string? ChallengerTarget = null,
        int ChallengerVotes = 0,
        float ChallengerConfidence = 0);

    public void ProposeMapping(string source, string target, float confidence, string chunkId, int chunkIndex)
    {
        source = source.ToLowerInvariant().Trim();

        if (_mappings.TryGetValue(source, out var existing))
        {
            if (existing.TargetTerm == target)
            {
                // Same as current: accumulate votes
                _mappings[source] = existing with
                {
                    Confidence = Math.Max(existing.Confidence, confidence),
                    VotesForCurrentTarget = existing.VotesForCurrentTarget + 1,
                    TotalVotes = existing.TotalVotes + 1,
                    LastSeenChunkIndex = chunkIndex
                };
            }
            else if (existing.ChallengerTarget == target)
            {
                // Same as challenger: accumulate challenger votes
                var newChallengerVotes = existing.ChallengerVotes + 1;
                var newChallengerConfidence = Math.Max(existing.ChallengerConfidence, confidence);

                // Switch if challenger has >= 2 votes AND higher confidence
                if (newChallengerVotes >= 2 && newChallengerConfidence > existing.Confidence)
                {
                    // Swap: old current becomes challenger, challenger becomes current
                    _mappings[source] = existing with
                    {
                        TargetTerm = target,
                        Confidence = newChallengerConfidence,
                        VotesForCurrentTarget = newChallengerVotes,
                        TotalVotes = existing.TotalVotes + 1,
                        LastSeenChunkIndex = chunkIndex,
                        ChallengerTarget = existing.TargetTerm,  // Preserve old mapping as challenger
                        ChallengerVotes = existing.VotesForCurrentTarget,
                        ChallengerConfidence = existing.Confidence
                    };
                }
                else
                {
                    _mappings[source] = existing with
                    {
                        ChallengerVotes = newChallengerVotes,
                        ChallengerConfidence = newChallengerConfidence,
                        TotalVotes = existing.TotalVotes + 1,
                        LastSeenChunkIndex = chunkIndex
                    };
                }
            }
            else if (confidence > existing.ChallengerConfidence)
            {
                // New challenger replaces old challenger
                _mappings[source] = existing with
                {
                    ChallengerTarget = target,
                    ChallengerVotes = 1,
                    ChallengerConfidence = confidence,
                    TotalVotes = existing.TotalVotes + 1,
                    LastSeenChunkIndex = chunkIndex
                };
            }
        }
        else
        {
            _mappings[source] = new TermMapping(
                source, target, confidence, chunkId, chunkIndex,
                VotesForCurrentTarget: 1, TotalVotes: 1);
        }
    }

    public string? GetCanonicalTranslation(string source)
    {
        source = source.ToLowerInvariant().Trim();
        return _mappings.TryGetValue(source, out var mapping)
            ? mapping.TargetTerm
            : null;
    }
}

In practice you also want vote decay (or a sliding window) so early mistakes don't become permanent. The key is that the decay rule is deterministic, not model-decided.

Why this matters for debugging: If the translation flips "factory reset" mid-document, you can diff the ledger and see exactly when and why:

chunk-12: "factory reset" → "Werkseinstellungen" (votes: 3, conf: 0.82)
chunk-47: challenger "Zurücksetzen" reaches 2 votes, conf: 0.91
chunk-47: SWITCH - "factory reset" → "Zurücksetzen" (old mapping preserved as challenger)

No guessing. No "the model just decided". Inspectable, deterministic, auditable.

This gives you:

• local fluency (model handles phrasing)
• global consistency (ledger enforces terminology)
• bounded cost (no need to re-translate for consistency)
• auditable decisions (ledger is inspectable)

This is why terminology consistency cannot be solved by "just more context".

---

This Is Not Memory - It Is Constraint Propagation

CFCD does not give the model memory.

It gives the system memory.

The model:

• does not carry state
• does not decide what persists
• does not own context

The system:

• stores anchors
• enforces bounds
• expires stale salience
• controls lifetime explicitly

Expiry rules (deterministic, not model-decided):

• Time-based: anchors expire after T without being referenced
• Use-based: anchors with zero hits over N subsequent chunks decay out
• Contradiction-based: challenger has ≥2 votes AND exceeds current confidence by Δ, within a sliding window of W chunks

That distinction is everything.

flowchart TB
    subgraph Model["Probabilistic Model"]
        M1[Detect Salience]
        M2[Generate Output]
    end

    subgraph System["Deterministic System"]
        S1[Promote Anchors]
        S2[Store in Ledger]
        S3[Enforce on Generation]
        S4[Expire Stale Context]
    end

    M1 --> S1 --> S2
    S2 --> S3 --> M2
    S2 --> S4

    style Model stroke:#f59e0b,stroke-width:2px
    style System stroke:#22c55e,stroke-width:3px

The CFCD Pipeline

Here is the pattern with the ledger as a first-class object:

flowchart LR
    S[Stream: chunks/sentences] --> M[Model proposes salience]
    M --> P[Promotion rules]
    P --> L[Ledger / Anchors]
    L --> G[Generation constrained by anchors]
    G --> O[Output]
    L --> E[Expiry rules]
    E -.-> L

    style L stroke:#22c55e,stroke-width:3px
    style P stroke:#22c55e,stroke-width:2px
    style E stroke:#22c55e,stroke-width:2px
    style M stroke:#f59e0b,stroke-width:2px
    style G stroke:#f59e0b,stroke-width:2px

The green boxes are deterministic. The orange boxes are probabilistic. The ledger sits between them, holding what survived.

What Changes in Practice?

Three shifts:

• You stop persisting prose
• You persist typed anchors
• You treat the ledger as an API, not a prompt

The prompt becomes a view of the ledger, not the ledger itself.

That's the "grok" moment. The ledger is not context. It's a contract.

What Counts as an Anchor?

Anchors are not "important text". They are typed, structured facts that constrain generation:

• Terminology mappings: factory reset → restore factory settings
• Entities and IDs: ProductName, ModelNumber, UserID
• Constraints / invariants: "Reset requires holding button 10s"
• User preferences / style constraints: "Use formal tone", "British spelling"
• Coverage metadata: "3.2% semantic sample, 5 anchors"
• Versioning: policyVersion: "cfcd-v1", anchorSchemaVersion: 2

The point is: anchors are not prose. They are structure. The model writes around them.

---

Biological Alignment

This is an analogy, not a biological claim: the point is selective consolidation, not literal mechanism.

Brains do not replay the entire sensory stream.

They:

• detect salience
• consolidate selectively
• inhibit noise
• freeze representations once stable

Language, motor skills, and perception all rely on constrained carry-forward, not raw recall.

CFCD is the same principle, implemented mechanically.

Biological System	CFCD Equivalent
Attention (what to notice)	Salience detection (fuzzy)
Consolidation (what to remember)	Anchor promotion (deterministic)
Inhibition (what to ignore)	Expiration rules (deterministic)
Recall (what to retrieve)	Ledger query (deterministic)

---

Unifying the Patterns

CFMoM and CFCD are the same idea viewed from different angles:

Dimension	Pattern
Single proposer	Constrained Fuzziness
Multiple proposers	Constrained Fuzzy MoM
Long-horizon context	Constrained Fuzzy Context Dragging

All three obey the same rule:

Probability proposes. Determinism persists.

Once you internalise that rule, most "AI system design" stops being mysterious.

flowchart LR
    subgraph CFP["Part 1: Constrained Fuzziness"]
        P1[Single Proposer] --> C1[Constrainer] --> O1[Output]
    end

    subgraph CFMoM["Part 2: Constrained Fuzzy MoM"]
        P2A[Proposer A] --> CS[Consensus Space]
        P2B[Proposer B] --> CS
        P2C[Proposer C] --> CS
        CS --> C2[Coordination Constrainer] --> O2[Output]
    end

    subgraph CFCD["Part 3: Context Dragging"]
        T1[Time T] --> A1[Anchors]
        T2[Time T+1] --> A1
        A1 --> C3[Constrained Generation] --> O3[Output]
    end

    style CFP stroke:#22c55e,stroke-width:2px
    style CFMoM stroke:#3b82f6,stroke-width:2px
    style CFCD stroke:#8b5cf6,stroke-width:2px

---

Why This Is Not Mainstream

Because it:

• reduces compute spend (no re-processing for consistency)
• works with small models (anchors do the heavy lifting)
• looks boring in a demo (no "emergent memory" claims)
• produces fewer dramatic failures (failures are explicit)
• requires real systems thinking (not just prompt engineering)

In other words: it optimises for production, not hype.

---

Anti-Patterns This Replaces

Anti-Pattern	Problem	CFCD Solution
Stuff everything in context	Unbounded cost, attention dilution	Promote only what survives selection
Recursive summarisation	Drift, contradictions	Anchors are structure, not prose
Hope the model remembers	No guarantees	System owns persistence
Per-request full re-processing	Cost scales with history	Ledger persists across requests
Natural language "memory"	Semantic drift	Typed anchors, explicit mappings
One-size-fits-all context	Wastes capacity or starves model	Adaptive slicing from ranked collection

---

Implementation Substrate: Ephemeral

Mostlylucid.Ephemeral is the substrate that makes CFCD practical. If you haven't read the background articles:

• Fire and Don't Quite Forget - the philosophy
• Ephemeral Execution Library - the implementation
• Ephemeral Signals - signal-driven coordination
• Learning LRUs - why bounded memory matters

Ephemeral Primitive	CFCD Role
Signals	Candidate salience (model proposes)
Bounded windows	No unbounded "memory"
Eviction rules	Forgetting is explicit
Sliding expiration	Stale context expires automatically
Signal propagation	Anchors carry forward without mutation

Ephemeral does not "remember". It decides what is allowed to be remembered, and for how long.

That is exactly what CFCD requires.

public class CFCDCoordinator
{
    private readonly SignalSink _sink;
    private readonly TermLedger _ledger;
    private readonly TimeSpan _anchorLifetime = TimeSpan.FromHours(1);

    public void PromoteAnchor(string term, string translation, string chunkId, int chunkIndex)
    {
        _ledger.ProposeMapping(term, translation, 0.8f, chunkId, chunkIndex);

        // Emit signal for observability (key = chunkId for correlation)
        _sink.Raise($"anchor.promoted:{term}", key: chunkId);
    }

    public void ExpireStaleAnchors()
    {
        var cutoff = DateTimeOffset.UtcNow - _anchorLifetime;

        // Ledger entries older than cutoff without recent usage are removed
        // This is deterministic, not model-decided
        _ledger.ExpireOlderThan(cutoff);
        _sink.Raise("anchors.expired");
    }
}

---

Implementation Sketch: Document Translation

Bringing it together for a complete document translation pipeline:

public class CFCDTranslationPipeline
{
    private readonly ILlmService _llm;
    private readonly TermLedger _ledger;
    private readonly SignalSink _sink;
    private readonly int _seedChunks = 3;  // K: configurable seed window

    public async Task<TranslatedDocument> TranslateAsync(
        Document source,
        string targetLanguage)
    {
        var chunks = Chunker.Chunk(source, maxTokens: 500).ToList();
        var translated = new List<TranslatedChunk>();

        // Phase 1: Extract terminology candidates (first pass, fuzzy)
        // Seed from first K chunks: front-loads terminology, avoids churn later
        for (var i = 0; i < Math.Min(_seedChunks, chunks.Count); i++)
        {
            var chunk = chunks[i];
            var candidates = await _llm.ExtractTerminologyAsync(chunk);
            foreach (var term in candidates)
            {
                _ledger.ProposeMapping(
                    term.Source,
                    term.ProposedTranslation,
                    term.Confidence,
                    chunkId: chunk.Id,
                    chunkIndex: i);
            }
        }

        _sink.Raise("terminology.seeded");

        // Phase 2: Translate with ledger constraints
        for (var i = 0; i < chunks.Count; i++)
        {
            var chunk = chunks[i];

            // Get canonical translations for terms in this chunk
            var constraints = ExtractConstraints(chunk, _ledger);

            // Translate with constraints (model must respect ledger)
            var result = await _llm.TranslateWithConstraintsAsync(
                chunk,
                targetLanguage,
                constraints);

            // Update ledger with any new terms (deterministic rules)
            foreach (var newTerm in result.NewTerminology)
            {
                if (ShouldPromote(newTerm, _ledger))
                {
                    _ledger.ProposeMapping(
                        newTerm.Source,
                        newTerm.Target,
                        newTerm.Confidence,
                        chunkId: chunk.Id,
                        chunkIndex: i);

                    _sink.Raise($"anchor.promoted:{newTerm.Source}");
                }
            }

            translated.Add(result.Chunk);
        }

        return new TranslatedDocument(translated, _ledger.GetAllMappings());
    }

    private bool ShouldPromote(TermCandidate term, TermLedger ledger)
    {
        // Deterministic rules, not model judgment:
        // - Proper nouns always promoted
        // - Technical terms promoted if repeated
        // - Common words never promoted
        return term.Type == TermType.ProperNoun ||
               (term.Type == TermType.Technical && term.OccurrenceCount >= 2);
    }
}

---

Closing Thought

The mistake most AI systems make is assuming intelligence improves by remembering more.

In practice, intelligence scales by deciding what to never have to think about again.

CFCD is how you do that without lying to yourself.

---

The Series

Part	Pattern	Axis
1	Constrained Fuzziness	Single component
2	Constrained Fuzzy MoM	Multiple components
3	Constrained Fuzzy Context Dragging (this article)	Time / memory
4	Image Summarizer	Practical implementation

All three patterns share the same invariant: probabilistic components propose; deterministic systems persist.

The Ten Commandments of LLM Use codify these rules. The DiSE architecture implements them for code evolution. Bot Detection implements them for request classification. DocSummarizer implements them for document retrieval. ImageSummarizer implements them for image analysis.

Different domains. Same pattern. Same rule. DoomSummarizer implements context dragging for parallel long-form document generation - running summaries, negative prompts, entity continuity, and drift detection maintain coherence across concurrent LLM calls.

---

Next: Practical Implementation

Part 4 is now available: Image Summarizer : A Constrained Fuzzy Image RAG Engine. It implements all three patterns in a wave-based image analysis pipeline—ColorWave computes facts, VisionLlmWave proposes, ImageLedger accumulates salient features, and deterministic selection constrains the output. Clone it from github.com/scottgal/lucidrag.