Hierarchical Semantic Compression with Adaptive Context Windowing for Governed AI Systems

Draft — not for submission Author: RY Date: 2026-03-25 Status: Structured outline with key sections filled. Sections marked [EXPAND] need further development before submission.

Abstract

Large context windows have become the dominant response to context management in deployed language model systems, but window size addresses only content availability, not semantic structure. We propose Hierarchical Semantic Compression with Adaptive Context Windowing (HSCA), a four-layer pipeline that builds a compressed relational map alongside the token buffer, enabling structural queries over conversation history that keyword-based retrieval cannot express. HSCA introduces a snapshot mechanism that freezes the relational weight structure of a conversation beat when a coherence threshold is met, accumulating these snapshots into a macro-level conversation map. We describe the architecture, its integration into a governed AI system (OmegA), and a set of five evaluation dimensions that the existing retrieval benchmark literature does not cover.

1. Introduction

The standard framing of the context problem in language model systems is: the model needs more context to give better answers, so give it more context. This framing is productive up to a point. A 200k-token context window is materially better than an 8k-token window for tasks that require simultaneous access to large documents. But extending the window does not change what the system does with the content inside it. It does not give the system a representation of how the conversation evolved. It does not allow the system to localize a query to a specific region of conversational history. And it does not address the core retrieval problem that arises when documents must be ranked for relevance: that shallow, keyword-dense content can outscore substantive, structurally rich content under token-overlap scoring.

HSCA is not a replacement for context windows. It is a complementary structure — a map built alongside the window, not instead of it. The map preserves relational weights between concepts rather than raw text. Snapshots of the map are taken at coherence thresholds, accumulating into a macro representation of how a conversation has moved. Queries can be matched against the map directly, or can be localized to a region of the conversation's history.

The contribution of this paper is:

1. A formal description of the four-layer HSCA pipeline
2. A definition of the snapshot mechanism and its coherence-threshold trigger
3. A micro/meso/macro aggregation hierarchy for conversation structure
4. Analysis of how HSCA addresses the TF-IDF keyword-stuffing vulnerability in current retrieval
5. A set of five evaluation dimensions specific to structural context compression

We describe HSCA as implemented in and motivated by the OmegA governed AI system, but the architecture is general. The integration specifics are given in Section 7.

2. Related Work

Attention mechanisms. The self-attention mechanism (see [Vaswani et al., 2017]) allows a model to weight the importance of different positions in a sequence relative to each other. This is the closest analogue to Layer 1 relational weighting in HSCA, but it operates at inference time and is not persisted. HSCA's relational weights are computed ahead of inference and stored as a durable artifact.

Retrieval-augmented generation (RAG). RAG systems (see [Lewis et al., 2020]) retrieve documents or chunks from an external store based on query similarity and inject them into the context window. HSCA is not a retrieval system in this sense — it is a compression and indexing system that can feed a retrieval layer. The two are complementary. A RAG system over HSCA snapshots would retrieve based on relational weight similarity rather than token overlap.

Sliding window summarization. Approaches such as those surveyed in [see survey on long-document summarization] address context length by summarizing earlier context and sliding a window forward. HSCA's snapshot mechanism differs in a key respect: snapshots preserve the weighted relational structure of the conversation beat, not a textual summary. Summarization discards structural information that is irrelevant to a human reader but relevant to downstream scoring.

Semantic compression. The broader literature on semantic compression (see [work on extractive and abstractive compression]) focuses on reducing document length while preserving meaning. HSCA's compression target is different: it compresses to a relational weight map, not to shorter text. The output is not readable as prose; it is queryable as structure.

Memory architectures for language models. Recent work on external memory for language models (see [work on memory-augmented transformers]) proposes persistent memory stores that the model can read from and write to. HSCA is a specific memory architecture with a defined write trigger (coherence threshold), a defined write format (ContextSnapshot with weight map), and a defined read interface (macro context query with optional conversation position).

[EXPAND: fuller citation list pending literature review. All references above are placeholders indicating the relevant literature strand.]

3. Architecture

HSCA is a four-layer pipeline. Each layer processes a text chunk and adds structure to it. The output of the full pipeline is a WeightedChunk. When the adaptive window engine determines that accumulated chunks have reached coherence threshold, a ContextSnapshot is produced from the buffer.

3.1 Layer 1: Relational Weighting

Layer 1 computes a structural importance score for the chunk based on co-occurrence relationships within the chunk itself — not against an external corpus.

Let $T$ be a text chunk tokenized into sentences $s_1, \ldots, s_n$. For each pair of words $(w_i, w_j)$ appearing within a sliding window of size $k$ (default $k=5$), a co-occurrence count $c(w_i, w_j)$ is incremented. Define the centrality of sentence $s_m$ as:

$\text{centrality}(s_m) = \frac{\sum_{w \in s_m} \sum_{v \in T} c(w, v)}{\sum_{w \in T} \sum_{v \in T} c(w, v)}$

The sentence score is $\text{centrality}(s_m)$ normalized to $[0, 1]$. The chunk-level layer1_score is the mean sentence score, weighted by sentence length.

This scoring is corpus-independent. A document with high query-term density but low internal co-occurrence density will produce a low layer1_score, regardless of its relevance to any external query. This is the structural property that addresses keyword stuffing: shallow documents have sparse co-occurrence meshes even when they are keyword-rich.

Layer 1 is fully deterministic. No model call is required.

3.2 Layer 2: Domain and Coherence Tagging

Layer 2 assigns domain tags from a controlled vocabulary and computes a coherence score.

Domain classification assigns one or more tags from the set $\mathcal{D} = \{\text{physics}, \text{mathematics}, \text{legal}, \text{medical}, \text{poetry}, \text{narrative}, \text{technical\_prose}, \text{conversational}, \text{philosophical}, \text{historical}, \text{cross\_domain}, \text{unclassified}\}$. Tags are not mutually exclusive. The cross_domain tag is assigned when two or more substantively distinct domain tags are present. In v0.1, domain classification is rule-based (keyword and phrase thresholds per domain); production quality requires a small classification model.

Coherence scoring estimates whether the chunk is structurally intentional text, as distinct from noise, fragments, or adversarial input. Four heuristics contribute to the score:

1. Mean sentence length (optimal range: 10–35 tokens; tails depress score)
2. Pronoun-to-antecedent resolution rate within the chunk
3. Punctuation-to-word ratio (extreme values depress score)
4. Clause nesting depth distribution

The coherence score $\gamma \in [0,1]$ is a weighted combination of these four signals. It is not a quality judgment; a short but complete sentence scores moderately well; a fragment scores low; a dense but grammatically consistent paragraph scores high regardless of subject matter.

Layer 2 is mostly deterministic. Domain classification may be model-assisted in production but must fail-closed to unclassified on any error.

3.3 Layer 3: Nuance Layer

Layer 3 detects higher-order semantic properties that affect how a chunk should be weighted in downstream use. The detection targets are: metaphor, sarcasm, emotional_tone, rhetorical_question, analogy, hedged, absolute, narrative_thread, instructional.

A critical design distinction governs this layer: some flags are deterministic and some are model-dependent.

Deterministic flags (hedged, absolute, rhetorical_question, instructional) can be reliably detected through pattern matching and syntactic analysis. These are always computed.

Model-dependent flags (metaphor, sarcasm, analogy, narrative_thread, emotional_tone beyond polarity) require a model call. When computed by a model, they carry a _model suffix in the flag string (e.g., metaphor_model). When no model is available, these flags are omitted from the nuance_flags list. Omission is semantically distinct from False: it means the flag is unknown, not that it is absent.

This provenance tagging ensures downstream consumers can distinguish confident deterministic signals from model-generated ones and handle uncertainty appropriately. It is consistent with OmegA's general principle of explicit uncertainty representation.

3.4 Layer 4: Adaptive Window and Snapshot Engine

Layer 4 maintains a buffer of WeightedChunk objects and determines when to freeze them into a ContextSnapshot.

After each chunk is added to the buffer, the engine computes the buffer's aggregate coherence:

$\bar{\gamma}_{\text{buf}} = \frac{\sum_{i} \gamma_i \cdot \ell_i}{\sum_{i} \ell_i}$

where $\gamma_i$ is the coherence score and $\ell_i$ is the layer1_score of chunk $i$. A snapshot is triggered when $\bar{\gamma}_{\text{buf}} \geq \theta$ (default $\theta = 0.72$, empirically tunable) or when the buffer exceeds MAX_BUFFER_CHUNKS (default 20).

Snapshots triggered by the coherence threshold represent a natural conversational beat boundary. Snapshots triggered by the buffer limit are forced and carry a forced=True flag, signaling to downstream consumers that the boundary was structural rather than semantic.

4. The Snapshot Mechanism

4.1 Formal Definition

Let $B = [c_1, c_2, \ldots, c_n]$ be the chunk buffer at snapshot time. A ContextSnapshot $S$ is defined as:

$S = \langle \text{id}, B, \mu, \Delta, p, f \rangle$

where:

• $\text{id}$ is a UUID
• $B$ is the ordered sequence of WeightedChunk objects
• $\mu \in [0,1]$ is the macro weight (mean layer1_score over $B$)
• $\Delta : \mathcal{D} \to [0,1]$ is the domain summary — a probability distribution over domain tags, weighted by layer1_score
• $p \in \mathbb{Z}_{\geq 0}$ is the conversation position (index in the session's snapshot sequence)
• $f \in \{\text{True}, \text{False}\}$ is the forced flag

The snapshot is not a text artifact. The raw text in $B$ is the least important part of the snapshot. The durable artifacts are $\mu$, $\Delta$, and the weight vectors within each $c_i$. In a production implementation, $c_i.\text{text}$ may be discarded or compressed after snapshot creation; the weight map must be retained.

4.2 Coherence Threshold as Beat Boundary

The coherence threshold $\theta$ functions as a semantic beat boundary detector. When the buffer's weighted coherence crosses $\theta$, the accumulated chunks form a unit that is internally consistent enough to be treated as a single conversational beat. This is analogous to a paragraph boundary in prose — not an arbitrary character count but a structural unit.

The threshold value is not theoretically derived. It is a hyperparameter requiring calibration against a labeled corpus of conversation beat boundaries. Section 9 discusses this as an open problem.

5. Macro Aggregation

5.1 The Three-Level Hierarchy

HSCA organizes context at three levels:

Micro level: Individual WeightedChunk — a scored and tagged text segment, typically corresponding to one conversation turn or a paragraph of document text.

Meso level: ContextSnapshot — a frozen relational map of a coherent conversation beat, covering multiple chunks. Represents one semantic unit of the conversation.

Macro level: The ordered sequence of ContextSnapshot objects for a session — the full conversation map. This is not a flat bag of snapshots; order is preserved, and conversation_position is stable throughout the session.

5.2 What the Macro Layer Represents

The macro layer is a structural record of how the conversation evolved. Given a sequence of snapshots $S_1, S_2, \ldots, S_k$, a consumer can:

• Observe domain distribution shifts across $\Delta(S_i)$: these indicate topic transitions
• Identify the snapshot where a given domain first became prominent
• Localize a query to a region of the conversation: rather than scoring against all content, score against $S_{p-2}$ through $S_p$ for queries framed as "what we were discussing earlier"
• Detect structural anomalies: a forced snapshot (high $f$ rate) may indicate incoherent input or adversarial content

5.3 Structural Queries

A structural query is one that cannot be expressed as keyword matching over a flat document corpus. Examples:

• "What domain was the conversation in before we switched to the current topic?"
• "Was the claim made before or after the user introduced the legal framing?"
• "Which part of this conversation has the highest internal relational density?"

These are answerable from the macro snapshot sequence. They are not answerable from a flat TF-IDF index over the same content.

6. Self-Reinforcing Properties

6.1 The Feedback Mechanism

HSCA's parameters are tunable: co-occurrence window size $k$, coherence threshold $\theta$, and the weighting coefficients in the nuance layer. Over $N$ interactions, the system accumulates a corpus of (WeightedChunk, ContextSnapshot, query_hit) triples, where query_hit records whether a given chunk or snapshot contributed to a successful downstream retrieval.

This corpus is a natural training signal for parameter tuning. Specifically:

• If chunks with high layer1_score consistently contribute to query hits, the current $k$ is well-calibrated
• If snapshots are frequently forced (rather than coherence-triggered), $\theta$ may be too high for the conversation style of this deployment
• If the emotional_tone_model flag correlates with downstream escalations in governed AI contexts, its weight in downstream scoring should increase

6.2 Scope and Limits

This self-reinforcing property is bounded. HSCA does not retrain the underlying language model. It tunes shallow structural parameters of a scoring pipeline. The improvement is real but not unbounded: diminishing returns are expected after sufficient calibration, and the parameters can only improve within the expressiveness of the pipeline architecture itself.

The framing "handed the reins of its own constraints" is accurate in a specific sense: the thresholds and weights that determine what gets preserved and what gets discarded are determined by the system's own operational history, not fixed by a designer at initialization. The designer sets the architecture. The system calibrates the parameters.

6.3 Governance Considerations

In a governed AI system, self-tuning parameters require audit trails. Any parameter update driven by the self-improvement signal should be logged with the triggering corpus state and the delta applied. This is consistent with OmegA's general telemetry and traceability requirements. A parameter update that cannot be explained by reference to the corpus state that produced it is not acceptable.

7. Application to Retrieval

7.1 The Keyword Stuffing Vulnerability

Standard TF-IDF retrieval scores documents by the frequency of query terms weighted by their inverse document frequency. A document that contains the query terms at high density — regardless of what those terms mean in context — scores well. This is the keyword stuffing vulnerability: shallow documents with deliberate term repetition can outrank substantive documents with lower term density.

HSCA addresses this at the structural level. A document stuffed with query terms but lacking internal co-occurrence structure produces a sparse weight map and a low layer1_score. This is not a heuristic penalty applied to detected stuffing; it is a direct consequence of the co-occurrence scoring. The vulnerability disappears because the scoring function cannot be gamed by term repetition alone.

Formally: let $D_s$ be a shallow document with high query-term density and low internal co-occurrence, and $D_d$ be a deep document with lower query-term density and high internal co-occurrence. Under TF-IDF, $\text{score}(D_s, Q) > \text{score}(D_d, Q)$ is possible when query terms are dense in $D_s$. Under HSCA Layer 1, $\text{layer1\_score}(D_s) \ll \text{layer1\_score}(D_d)$ because co-occurrence density is a function of structural relationships between words, not their raw frequency.

7.2 Context-Local Relevance

Current retrieval in most RAG systems scores chunks against a global query over a flat corpus. HSCA enables context-local relevance: scoring a query against a specific region of the conversation's macro map.

This matters in governed AI contexts where the relevant background for a query is not "everything in the corpus" but "the context established in this part of the conversation." A claim made in snapshot $S_3$ should be evaluated against the domain and relational structure established in $S_1$ through $S_3$, not against content from $S_{15}$ that postdates it.

7.3 Integration with OmegA ClaimGraph

In OmegA, each answer generates ClaimNode objects with support_strength and grounding_strength fields. HSCA feeds into this system in two ways:

1. A ContextSnapshot with high macro_weight over a domain becomes a structured grounding source for claims in that domain
2. The layer1_score of the chunks grounding a claim becomes a structural component of support_strength

The effect is that claim support strength reflects the structural quality of the source material, not just the presence of relevant terms. A claim grounded in a shallow keyword-dense source will carry low support strength by construction.

8. Evaluation Framework

The existing retrieval and factuality benchmark literature does not directly address the evaluation of structural context compression. We define five evaluation dimensions specific to HSCA.

8.1 Snapshot Fidelity

What it measures: Does a snapshot capture the semantic thread of the conversation beat, or does it preserve the highest-volume segment irrespective of centrality?

Method: Construct test conversations where the semantically central thread is distributed across multiple short chunks and a long but peripheral chunk is also present. Evaluate whether snapshot weight distribution concentrates on the central material.

Metric: Weighted recall of human-labeled central content against snapshot layer1_score distribution. Target: ≥80% alignment at the topic level.

8.2 Macro Coherence

What it measures: Does the macro snapshot sequence correctly represent how the conversation evolved across topic transitions?

Method: Conversations with known ground-truth topic transitions. Compare snapshot domain distribution shifts against ground truth.

Metric: Topic transition detection accuracy (position error ≤1 snapshot) at ≥85% of transitions.

8.3 Self-Improvement Signal

What it measures: Does parameter tuning from accumulated interaction history improve snapshot fidelity?

Method: Snapshot fidelity (eval 8.1) measured at $N=0$, $N=100$, $N=1000$ interactions after parameter tuning cycles.

Metric: Monotonic improvement in fidelity score across $N$ on a held-out test set.

8.4 Adversarial Shallow Content

What it measures: Can keyword-dense but structurally shallow documents fool the weight system?

Method: Adversarial documents constructed to have high query-term overlap and low internal co-occurrence density. These documents rank high under TF-IDF.

Metric: Adversarial documents should score ≤0.35 on layer1_score in ≥90% of constructed cases, while scoring in the top 3 under TF-IDF on the same query set. This establishes that HSCA and TF-IDF disagree on exactly the documents where disagreement is expected.

8.5 Cross-Domain Bridging

What it measures: Does the system correctly link semantically related concepts that appear in different domain framings across snapshot boundaries?

Reference case: A user discusses a rabbit (narrative domain). The system should link it to Aesop's tortoise and hare fable (narrative + philosophical) via shared semantic structure, even though surface token overlap is low.

Method: Concept pairs that are semantically related but appear in different domain framings. Verify that snapshots taken across the domain boundary carry shared weight on a bridge domain.

Metric: Bridge domain weight ≥0.2 in snapshot pairs spanning the relevant domain boundary, in ≥75% of test cases.

9. Limitations and Open Problems

9.1 Layer 3 Without a Model

The nuance layer in v0.1 provides deterministic detection for hedging, absolute claims, rhetorical questions, and instructional form. Metaphor, sarcasm, analogy, and narrative thread detection require a model. In the absence of a model, these flags are omitted, and cross-domain bridging (eval 8.5) degrades significantly because analogy detection is the primary mechanism for forming cross-domain links. This is the largest functional gap in v0.1 relative to the full architecture.

9.2 Threshold Calibration

The coherence threshold $\theta = 0.72$ is a reasonable starting value but is not empirically grounded. Without a calibration corpus of labeled conversation beat boundaries, threshold setting is guesswork. Too high a threshold produces coarse snapshots that merge semantically distinct beats. Too low a threshold produces over-segmentation. This is an empirical problem; it cannot be resolved theoretically.

9.3 Short Chunk Degeneracy

Co-occurrence scoring degrades on chunks shorter than three sentences. Single-sentence chunks have no within-chunk co-occurrence structure and will produce near-zero layer1_scores. This is a real limitation for conversational AI applications where many turns are one or two sentences. Mitigation strategies (chunk accumulation before scoring, flat low-score assignment) are not yet specified.

9.4 Cross-Session Macro Aggregation

The current design scopes the macro snapshot sequence to a single session. Cross-session aggregation — building a user-level or topic-level macro map across multiple sessions — is architecturally feasible but not implemented. The risk of stale relational weights from older sessions contaminating current context is real and requires a decay or expiration policy that does not yet exist.

9.5 Computational Cost

Running four layers on every ingested turn adds latency. Layer 1 (co-occurrence matrix) scales as $O(n \cdot k)$ in text length $n$ and window size $k$. Layer 2 is similarly cheap. Layer 3 with model calls adds a model inference cost per turn. In latency-sensitive deployments, Layer 3 model calls may need to be asynchronous or batched. The interaction between asynchronous nuance flag computation and snapshot triggering timing is not resolved.

10. Conclusion

HSCA addresses a structural limitation in current context management for language model systems: the conflation of content availability with semantic understanding. Large context windows provide more content. HSCA provides structure over that content — specifically, a relational weight map that compresses naturally into conversation-beat snapshots and accumulates into a macro map of how a conversation has evolved.

The four-layer pipeline (relational weighting, domain and coherence tagging, nuance detection, adaptive window and snapshot engine) is designed to be mostly deterministic, with clearly bounded model dependencies in Layer 3. The snapshot mechanism produces a durable, queryable artifact rather than a text summary, enabling structural queries over conversation history that keyword-based retrieval cannot express.

The most significant near-term contribution is the structural solution to keyword stuffing: shallow documents cannot fool a co-occurrence-based weight system through term repetition alone. The most significant long-term property is the self-reinforcing calibration loop, which allows the system's scoring parameters to improve from its own interaction history.

Open problems are honest: threshold calibration requires a labeled corpus, Layer 3 without a model is significantly limited, and cross-session aggregation is deferred. What is specified here is implementable and testable. The eval framework provides five measurable dimensions not covered by the existing literature.

This draft is a structured outline with key sections filled. Sections marked [EXPAND] require additional development. Citations are placeholders indicating relevant literature strands; no specific citations have been verified for this draft.