Architecture Overview

Distil is a cost-optimized cache hierarchy with a statistical quality contract. Six components, one invariant: the agent makes the same decisions on compressed context as on the original — provably, not heuristically.

Cache-aware prefix stability

Module: compress/cache_aware.py · compress/stabilize.py

The dominant cost in a multi-turn agent loop is not context size — it is cache miss rate. A cache read costs ~0.1× fresh input price; a naive compressor that rewrites the context each turn destroys the prefix match and loses that discount on every turn. The result: naive recompression sends fewer tokens yet costs more than not compressing at all (measured: −11% vs. the baseline on an SRE trajectory).

Distil models this explicitly. Two lossless pre-processing steps keep the prefix byte-stable before any compression runs:

• Schema canonicalization — recursively sorts JSON object keys so semantically identical payloads hash identically and keep hitting the cache regardless of key-insertion order.
• Volatile-field extraction — timestamps, UUIDs, JWT tokens, and request IDs are lifted out of the stable prefix into the volatile tail. The stable portion stops churning; the tail is compressed separately.

The tier model

Every compression operation is assigned to a tier based on its loss profile. Higher tiers require stronger evidence before they are permitted.

The measure → discover → certify loop

Step 1 — Measure

The cache-aware cost engine (compress/cache_aware.py) simulates the full multi-turn loop with four variants: baseline (no cache, no compress), cache-only, naive compress + cache, and Distil. It computes per-turn cost breakdowns — cache reads, cache writes, fresh tokens — and reports the total dollar cost of each strategy. Run distil savings to see the numbers on any trajectory.

Step 2 — Discover

The causal ablation engine (replay/ablation.py) is not a ruler bolted to compression — it is a discovery engine. For each context block, it removes the block, replays the trajectory, and records whether any decision changed. The output is a per-block verdict: causally inert (safe to prune) or decision-driving (must be kept). This step produces the compression policy; it does not assume one.

$ distil prune
causal ablation over 'sre-disk-incident' — what is free to drop?

block                  occ   tokens  verdict
doc-0                    4      312  PRUNE (causally inert)
doc-1                    4      303  PRUNE (causally inert)
obs-0                    4      234  keep (changed a decision)
obs-1                    4      198  keep (changed a decision)
system                   4      218  keep (changed a decision)

tokens provably free to drop: 615 across 2 block(s).

Step 3 — Certify

The conformal DERC certificate (certify/stats.py via certify/gate.py and conformal.py) computes the decision-change rate across all turns and certifies it. The implementation uses Learn-Then-Test with Hoeffding–Bentkus p-values — distribution-free, valid at finite sample size, zero scipy/numpy. The guarantee: P(R(λ̂) ≤ α) ≥ 1 − δ.

A certified strategy has its policy registered in the compress registry. A failing strategy is rejected and produces a non-zero exit code from distil certify and distil bench.

$ distil certify --strategy distil
decision-equivalence match rate: 100.0%
TOST non-inferiority (margin=0.02, alpha=0.05): mean diff=+0.000, p=0.0000

VERDICT: PASS  (certified non-inferior)

$ distil certify --strategy aggressive
decision-equivalence match rate: 0.0%
TOST non-inferiority (margin=0.02, alpha=0.05): mean diff=−1.000, p=1.0000

VERDICT: FAIL  (NOT certified — would degrade quality)

The DERC certificate

Module: distil/conformal.py

DERC — the Decision-Equivalence Risk Certificate — is the conformal certificate that gates every lossy strategy. It answers one question: is this compression level's decision-change rate provably bounded at α with confidence 1 − δ?

Salience protection

Module: compress/salience.py · codec/keep_model.py

The salience layer is a model-free safety net that runs before the DERC gate, not instead of it. It protects content the gate might not have seen enough of to certify. The default is SalienceKeepModel — a deterministic rule-set that forces a keep score of 1.0 on decision markers (DECISION:), error keywords, structured data, and high digit-density lines. No model in the path, no training step required.

Because it only ever widens the set of blocks kept, salience protection is strictly conservative relative to the DERC certificate — it shifts the safe frontier further out, never closer in. The logistic keep-model (codec/learned.py) improves on the rules via a zero-dependency, pure-Python logistic classifier that ships in the wheel.

Byte-fidelity invariants

Module: distil/fidelity.py · Gate: distil verify

Two structural invariants are enforced across the entire corpus by the verify gate:

Reversibility

Every original block is recoverable — either because the compressed text is byte-identical to the original, or because the original lives in the local restore table (keyed by block ID for Tier-0, by content handle for Tier-1). Recovery is confirmed via SHA-256 equality: auditable and machine-checkable.

Append-only history

A block ID that appears in two consecutive turns must carry identical bytes. Mutating a previously-seen block ID is a violation. This invariant is what makes the prefix cache hit rate reliable — the cache cannot be invalidated by history rewriting.

Numeric precision

JSON canonicalization must not lose numeric precision. numeric_precision_preserved() confirms that the original and transformed strings parse to the same JSON value.

$ distil verify
byte-fidelity gate — reversibility + append-only across the corpus

  sre-disk-incident        reversible + append-only: ok
  coding-bugfix            reversible + append-only: ok
  support-refund           reversible + append-only: ok
  research-synthesis       reversible + append-only: ok
  data-analysis-sql        reversible + append-only: ok
  devops-rollback          reversible + append-only: ok
  finance-reconcile        reversible + append-only: ok

FIDELITY: PASS — Tier-0/1 byte-reversible and history append-only across the corpus.

Sub-millisecond inline proxy

Module: distil/proxy.py · distil/native.py

Distil compresses at ~0.026 ms/turn — roughly 1,000× faster than model-based compressors (LLMLingua-2: ~1,480 ms/turn; Headroom: ~26 ms/turn). The reason: there is no model in the path. Tier-0/1 transforms are pure string operations; the causal ablation cache is populated offline. The hot path runs on stdlib-only Python with an optional Rust extension (distil/native.py, rust/distil-core) for JSON minification and run-length encoding when the wheel is built.

This is what makes inline proxy deployment viable. LLMLingua-2 loads a transformer; Headroom loads ModernBERT. Both add hundreds of milliseconds per turn. Distil adds less than one.

Auth-mode gating

Module: distil/policy.py

Aggressive compression applied to a subscription or OAuth session can alter conversations in ways that violate provider terms — injected retrieval tools the user never authorized, rewritten history that changes the conversation record. Auth-mode gating is a safety boundary, not an optimization.

The policy is a tightening boundary — a project's config can never loosen it. A PolicyError is raised if a non-permitted strategy is requested under the active auth mode. This maps onto the proxy's --lossless-only flag for subscription-context deployments.

Holdout A/B validation

Module: distil/certify/holdout.py · Command: distil holdout

The holdout module is an additional validation layer on top of the DERC gate. It:

1. Deterministically partitions the corpus into a control group (default: 20%, held out and not counted toward the headline) and a treatment group, using a hash-based split for reproducibility.
2. Measures savings in both groups separately.
3. Bootstrap-resamples the treatment group to compute a 95% confidence interval on the savings estimate.

The reported confidence interval is honest: it reflects the uncertainty given the finite corpus size. If control and treatment savings diverge significantly, something is wrong with the partition or the measurement — not with Distil's estimate.

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