Semantic Compression in Structured Intelligence Computing

Community Article Published December 4, 2025

From Raw Streams to Goal-Aware Meaning Flows Draft v0.1 — Non-normative supplement to SI-Core / SI-NOS / SIM/SIS / SCP specs

This document is non-normative. It explains how to think about and use semantic compression in a Structured Intelligence Computing (SIC) stack. Normative contracts live in the SIM/SIS design docs, SCP spec, SI-Core / SI-NOS design, and the evaluation packs.

1. Why semantic compression exists at all

Modern AI systems drown in data:

sensors stream millions of events,
logs collect everything “just in case,”
models read raw text/images that are mostly redundant.

Naively, you can:

compress bytes (gzip, codecs),
downsample (fewer samples per second),
truncate (keep last N events).

These tricks help with bandwidth and storage, but they do not answer a deeper question:

What is the minimum meaning we need to move, so that the system can still make good decisions?

In SIC, the unit of interest is not “bytes sent” but goal-relevant structure:

Which parts of the world matter for flood safety, fairness, stability?
Which details can be discarded without harming goals?
How do we prove that our compression choices are safe and reversible enough?

That is what semantic compression is for.

2. Raw vs semantic: a working mental model

Think in terms of two channels:

Raw channel — unstructured or lightly structured data:
- sensor tick streams,
- verbose logs,
- full images, audio, traces.
Semantic channel — compact, structured summaries of what matters:
- aggregates (“average water level per segment per minute”),
- events (“threshold crossed in sector 12”),
- hypotheses (“possible leak near station X, confidence 0.84”),
- frames (“flood-risk state vector for next 6h”).

A few simple quantities:

$B_{\text{raw}}$ : bits per time unit in the raw channel.
$B_{\text{sem}}$ : bits per time unit in the semantic channel.
Semantic compression ratio:
```
R_s = B_raw / B_sem   (R_s ≥ 1)
```
$U_{\text{full}}$ : decision utility if you had access to all raw data.
$U_{\text{sem}}$ : decision utility when you only use semantic summaries.
Utility loss:
```
ε = U_full - U_sem
```

A semantic compression scheme is “good” when:

$R_{s}$ is high (you saved bandwidth), and
$\varepsilon$ is small (you did not cripple decisions).

In goal-native terms (see Goal-Native Algorithms):

Good semantic compression means: you preserve enough structure that your Goal Contribution Scores (GCS) stay almost the same, while you move far fewer bits.

3. The semantic compression stack

In SIC, “semantic compression” is not a single box. It is a stack of cooperating components:

SCE — Semantic Compression Engine (software/algorithmic).
SIM / SIS — semantic memory interfaces.
SCP — Semantic Communication Protocol.
SPU / SIPU / SI-GSPU — optional hardware / accelerator support.

Very roughly:

World → Raw Streams → SCE → SIM/SIS → SCP → Network → SIM/SIS → Consumers

3.1 SCE — Semantic Compression Engine

The Semantic Compression Engine is where raw-ish data becomes structured summaries.

Responsibilities:

ingest raw or lightly structured inputs (events, logs, vectors),
produce semantic units with:
- explicit type (what it represents),
- scope (where/when it applies),
- confidence / coverage metadata,
- links to backing raw data when needed,
obey goal-aware constraints on $R_{s}$ and $\varepsilon$ .

A non-normative Python-flavoured interface:

class SemanticCompressionEngine:
    def compress(self, stream, context) -> list[SemanticUnit]:
        """Turn raw-ish events into semantic units.
        context includes goals, risk level, and ε-budgets.
        """

    def adjust_policy(self, goals, risk_profile):
        """Retune compression strategy based on goals and risk.
        For example, lower ε for safety-critical streams.
        """

The key point: compression is not just about size. It is a controlled transformation, aware of:

goals (what will these semantics be used for?),
risk (how dangerous is under-observation here?),
GCS budgets (how much utility loss is acceptable?).

3.1.1 Example: A minimal SCE implementation

Here is a small, non-optimized sketch of an SCE for a single numeric sensor stream. It:

aggregates events into windows,
emits either state_summary or threshold_event semantic units,
tightens or relaxes its policy based on a simple risk profile.

class CompressionPolicy:
    def __init__(self, variance_threshold: float, confidence_min: float):
        self.variance_threshold = variance_threshold
        self.confidence_min = confidence_min

    @classmethod
    def default(cls) -> "CompressionPolicy":
        return cls(variance_threshold=0.5, confidence_min=0.75)


class MinimalSCE(SemanticCompressionEngine):
    def __init__(self, goal_constraints):
        self.goal_constraints = goal_constraints
        self.policy = CompressionPolicy.default()

    def compress(self, stream, context):
        """stream: iterable of Event(value, timestamp, meta)"""
        units = []
        window = []

        for event in stream:
            window.append(event)
            if len(window) >= context.window_size:
                mean_val = sum(e.value for e in window) / len(window)
                variance = sum((e.value - mean_val) ** 2 for e in window) / len(window)

                if self._is_threshold_crossing(window, context.threshold):
                    units.append(SemanticUnit(
                        type="threshold_event",
                        payload={"mean": mean_val},
                        confidence=self._confidence_from_variance(variance),
                    ))
                elif variance > self.policy.variance_threshold:
                    units.append(SemanticUnit(
                        type="state_summary",
                        payload={"mean": mean_val, "variance": variance},
                        confidence=self._confidence_from_variance(variance),
                    ))
                # else: window is too boring; drop it

                window = []  # start next window

        return units

    def adjust_policy(self, goals, risk_profile):
        """Tighten or relax compression based on risk profile."""
        if risk_profile.level == "HIGH":
            # For high-risk periods, keep more information and demand higher confidence.
            self.policy.variance_threshold *= 0.5
            self.policy.confidence_min = max(self.policy.confidence_min, 0.9)
        else:
            # For low-risk periods, allow more aggressive compression.
            self.policy.variance_threshold *= 1.2

    def _is_threshold_crossing(self, window, threshold):
        return any(e.value >= threshold for e in window)

    def _confidence_from_variance(self, variance: float) -> float:
        # Toy mapping: lower variance → higher confidence, clipped to [0, 1].
        return max(0.0, min(1.0, 1.0 / (1.0 + variance)))

This is not a normative API, but it shows how goal- and risk-aware behavior can show up even in a very simple SCE.

3.2 SIM / SIS — semantic memories

SIM and SIS are the semantic memory layers:

SIM — Semantic Intelligence Memory (operational store for SI-Core / SI-NOS).
SIS — Semantic Intelligence Store (longer-term, analytical / archival).

You can think of them as views over your existing databases and data lakes, but with structural guarantees:

each record is a semantic unit with type, scope, provenance,
references to raw data are explicit and reversible (when policy allows),
ethics and retention policies are attached structurally.

SCE writes into SIM/SIS; SI-Core reads from SIM/SIS when it needs structured observations.

3.3 SCP — Semantic Communication Protocol

SCP is the on-the-wire protocol for moving semantic units between components or systems.

At minimum, a semantic message needs:

type — what category of semantic unit this is (e.g. flood_risk_state),
payload — the actual structured data,
scope — spatial / temporal / entity scope,
confidence — how certain we are,
backing_refs — optional links to raw data or SIM/SIS entries,
goals — which goals this unit is relevant to (for routing and prioritization).

Very roughly:

{
  "type": "city.flood_risk_state/v1",
  "scope": {"sector": 12, "horizon_min": 60},
  "payload": {"risk_score": 0.73, "expected_damage_eur": 1.9e6},
  "confidence": 0.87,
  "backing_refs": ["sim://city/sensor_grid/sector-12@t=..."],
  "goals": ["city.flood_risk_minimization", "city.hospital_access"]
}

3.4 SPU / SIPU / SI-GSPU — hardware help (optional)

SPU / SIPU — Structured Processing Units / Structured Intelligence Processing Units.
SI-GSPU — General Structured Processing Unit for semantic compute pipelines.

These are hardware or low-level runtime components that can accelerate:

streaming transforms (windowed aggregation, filtering),
semantic feature extraction (e.g. from video/audio),
graph-style reasoning over semantic units.

The semantics themselves are defined at the SCE / SIM / SCP level. Hardware simply executes those patterns faster.

4. Contracts and invariants (non-normative sketch)

Normative contracts live in the SIM/SIS / SCP / evaluation specs. This section gives a non-normative sketch of the kinds of constraints you typically want.

4.1 Basic semantic compression contract

For a given stream and goal set (G):

You pick a compression policy (P) with:
- target semantic ratio $R_s^{\text{target}}$ ,
- utility loss budget $\varepsilon^{\text{max}}$ .

The SCE + SIM/SIS + SCP stack should satisfy:

R_s ≥ R_s_target
ε ≤ ε_max

Where $\varepsilon$ is evaluated with respect to:

decision policies that use this stream,
relevant goals in $G$ ,
a specified horizon (e.g. next 6h for flood control).

In practice, $\varepsilon$ is estimated via:

historical backtesting,
sandbox simulations,
expert judgement for early phases.

4.2 GCS-aware constraint

If you have a Goal Contribution Score (GCS) model for a goal $g$ :

Let $GCS_g^{\text{full}}(a)$ be the expected GCS of an action $a$ with full data.
Let $GCS_g^{\text{sem}}(a)$ be the expected GCS when using semantic data only.

A GCS-aware compression policy wants:

$|GCS_g^{\text{full}}(a) - GCS_g^{\text{sem}}(a)| \leq \delta_g$

for all actions in a given class (or at least, for the high-risk ones), where $\delta_g$ is a small, goal-specific tolerance.

In practice, you rarely know these exactly, but you can treat them as validation targets:

Step 1: Historical validation For past decisions where outcomes are known:
- recompute $GCS_g^{\text{full}}(a)$ using archived raw data,
- recompute $GCS_g^{\text{sem}}(a)$ using only the semantic views available at the time,
- record $|GCS_g^{\text{full}}(a) - GCS_g^{\text{sem}}(a)|$ across many cases.
Step 2: Choose $\delta_g$ based on criticality Example bands (non-normative):
- safety goals: $\delta_g \leq 0.05$ ,
- efficiency / cost goals: $\delta_g \leq 0.15$ ,
- low-stakes UX goals: $\delta_g \leq 0.30$ .
Step 3: Continuous monitoring In production, track the distribution of $|GCS_g^{\text{full}} - GCS_g^{\text{sem}}|$ on sampled decisions where you can still compute both (e.g. via sandbox or replay). Raise alerts or trigger a policy review when:
- the mean or high-percentiles drift above $\delta_g$ , or
- drift is concentrated in particular regions or populations.

A simple flood-control example:

goal: city.flood_risk_minimization, with $\delta_g = 0.08$ (safety-critical),
decision: adjust a floodgate profile,
estimates: $GCS_g^{\text{full}}(a) = +0.75$ , $GCS_g^{\text{sem}}(a) = +0.71$ ,
difference: $|0.75 - 0.71| = 0.04 \leq 0.08$ → acceptable under current policy.

This is a structural way to say:

“Even after compression, we still make almost the same decisions for this goal.”

4.3 SI-Core invariants

Semantic compression is not allowed to bypass SI-Core.

Changes to compression policies for safety-critical streams must be:
- treated as actions with [ID] and [ETH] traces,
- optionally gated by [EVAL] for high-risk domains.
Observation contracts still hold:
- if Observation-Status != PARSED, no jumps;
- semantic units must be parsable and typed.

SCE, SIM/SIS, and SCP live inside an L2/L3 core, not outside it.

Measuring the semantic compression ratio $R_{s}$ is straightforward; measuring $\varepsilon$ is harder and usually approximate.

Semantic compression ratio $R_{s}$
For each stream type and time window:
- track bytes_raw that would have been sent without semantic compression,
- track bytes_semantic actually sent under SCE/SCP,
- compute:
```
R_s = bytes_raw / bytes_semantic
```

Aggregate over time (p50 / p95 / p99) and per compression policy to see how aggressive you really are.

Utility loss $\varepsilon$ You almost never know $U_{\text{full}}$ and $U_{\text{sem}}$ exactly, but you can estimate $\varepsilon$ via:
- A/B or shadow testing (when safe): run a full-data path and a semantic-only path in parallel, compare downstream goal metrics.
- Sandbox replay: for stored scenarios, re-run decisions with full vs semantic views and compare the resulting GCS or goal metrics.
- Expert review: sample decisions where semantic views were used and ask domain experts whether they would have acted differently with full data.

A non-normative reporting sketch:

{
  "stream": "canal_sensors",
  "window": "24h",
  "R_s_p95": 18.3,
  "epsilon_est": 0.03,
  "epsilon_method": "sandbox_replay",
  "confidence": 0.85
}

In SIC, these measurements do not need to be perfect. They are engineering dials:

“Are we compressing too aggressively for this goal and risk profile, or can we safely push (R_s) higher?”

5. A simple city pipeline

To make this concrete, imagine a flood-aware city orchestration system.

Raw world:

water level sensors per canal segment,
rainfall radar imagery,
hospital capacity feeds,
traffic sensors.

Naive design:

stream everything in raw form to a central cluster;
let models deal with it.

SIC-style semantic compression stack:

Local SCEs near the edge
- compute per-segment aggregates (mean, variance, trends),
- detect threshold crossings and anomalies,
- produce semantic units like canal_segment_state.
Write to local SIM
- store semantic state vectors per segment / time window,
- attach provenance and back-pointers to raw streams.
Use SCP to ship only semantic deltas upstream
- only send changes in risk state,
- drop low-variance noise.
Central SI-NOS reads from SIM/SIS
- observation layer [OBS] consumes semantic units, not raw ticks,
- evaluators [EVAL] and goal-native algorithms use these to compute GCS and decisions.
On-demand drill-down
- when an evaluator or human insists, use backing_refs to fetch raw data from SIS for forensic analysis.

The result:

massive reduction in bandwidth and storage,
but the system still behaves as if it “knows what it needs to know” about the flood state.

6. Safety-critical vs low-stakes channels

Not all streams are equal.

6.1 Safety-critical channels

Examples:

flood barriers,
hospital capacity,
power grid stability.

For these channels, you typically:

accept lower $R_{s}$ (more bits),
demand tighter $\varepsilon$ and $\delta_g$ bounds,
involve [EVAL] in policy changes,
log compression decisions with rich [ETH] traces.

You might:

send both semantic summaries and occasional raw snapshots,
require periodic full-fidelity audits to check that semantic views are not systematically biased.

6.2 Low-stakes channels

Examples:

convenience features,
low-risk UX metrics,
background telemetry.

For these, you can:

aggressively increase $R_{s}$ ,
tolerate larger $\varepsilon$ ,
rely on cheaper GCS approximations.

The important principle:

Risk profile, not technology, should drive how aggressive semantic compression is.

7. Patterns for semantic compression policies

Here are a few recurring patterns.

7.1 Hierarchical summaries

Keep fine-grained semantics for local controllers.
Keep coarser summaries at higher levels.

Example:

local SCE: per-second water levels → per-5s semantic state;
regional SCE: per-5s state → per-1min risk summaries;
central SI-NOS: per-1min risk summaries only.

7.2 Multi-resolution semantics

Provide multiple semantic views at different resolutions:

flood_risk_state/v1 — high-res, local;
flood_risk_overview/v1 — medium-res, regional;
flood_risk_global/v1 — coarse, city-wide.

Consumers choose the view matching their time/compute budget and risk level.

7.3 Fallback to raw

For some goals, you want a rule like:

if semantic confidence < threshold → request raw.

This can be implemented as:

SCE annotates low-confidence units,
SI-NOS [OBS] and [EVAL] treat these as “under-observed,”
a jump-sandbox or forensics process can fetch raw from SIS on demand.

8. Implementation path on existing stacks

You do not need a full SIC hardware stack to start using semantic compression.

A practical migration path:

Define semantic types for your domain
- e.g. user_session_state, payment_risk_snapshot, grid_segment_state.
Build a minimal SCE
- start as a library or service that turns raw logs/events into semantic units,
- attach provenance and goal tags.
Introduce a semantic store (SIM-ish)
- could be a dedicated DB / index for semantic units,
- expose a clean read API for your decision systems.
Gradually switch consumers from raw to semantic
- first for low-risk analytics,
- then for parts of operational decision-making.
Add SCP framing on your internal buses
- wrap semantic units in SCP-like envelopes,
- start measuring $R_{s}$ and crude $\varepsilon$ proxies.
Integrate with SI-Core / SI-NOS when ready
- treat semantic compression policy changes as first-class actions,
- wire in [OBS]/[EVAL]/[ETH]/[MEM]/[ID] everywhere you touch safety-critical data.

The core idea is incremental:

Start by naming and storing semantics, then gradually let them replace raw streams in the places where it is safe and beneficial.

9. Where semantic compression lives in SI-NOS

In an SI-NOS-based system, semantic compression touches several modules:

Observation framework ([OBS])
- consumes semantic units instead of raw bytes as the primary observation medium.
Evaluators ([EVAL])
- decide when semantic views are sufficient,
- route rare cases to sandbox with raw data.
Ethics interface ([ETH])
- ensures compression does not systematically degrade fairness or safety for particular groups or regions.
Memory ([MEM])
- keeps an audit trail of what semantic views were used, and when raw was consulted.
Goal-native algorithms
- use semantic views to estimate GCS,
- can request higher fidelity when uncertainty or risk spikes.

In other words, semantic compression is deeply integrated with SI-Core; it is not a bolt-on preprocessor.

10. Summary

Semantic compression in SIC is not about fancy codecs. It is about:

deciding what meaning to keep,
making that decision goal-aware,
ensuring that compressed views are structurally traceable and reversible,
tying compression policies back into SI-Core’s invariants.

The SCE / SIM/SIS / SCP stack gives you the mechanics. Goal-native algorithms and SI-Core give you the governance.

Together, they let you move from:

“we log and stream everything, hope for the best”

…to…

“we intentionally choose which parts of the world to preserve in semantic form, and we can prove that these choices still let us meet our goals.”

That is what semantic compression means in the context of Structured Intelligence Computing.

Appendix A: Sketching a multi-stream SCE (non-normative)

Example streams:

canal_sensors: water levels per segment
weather_radar: rainfall intensity grids
traffic_flow: vehicle density near critical routes

The multi-stream SCE would:

Align streams on a common time grid (e.g. 1-minute windows).
Compute per-stream features:
- canal: mean / trend / variance
- weather: rainfall intensity over catchment areas
- traffic: congestion scores near evacuation routes
Derive cross-stream semantics:
- "high rainfall + rising water + growing congestion"
- "rainfall decreasing but water still rising" (lagged response)
Emit joint semantic units:
- type: "compound_flood_risk_state"
- payload: { risk_score, contributing_streams, explanations }
- confidence: combined from individual streams
Attach backing_refs into SIM/SIS for each contributing stream.

This appendix is intentionally illustrative rather than normative: it shows how an SCE can treat correlations across streams as first-class semantic objects, without prescribing a particular fusion algorithm.

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