DEST Runtime v0.6-alpha — Adaptive Multi-Round Evidence Acquisition Report

Date: 2026-08-13
Development benchmark: 40 episodes × 25 debts = 1,000 debts
Frozen holdout: 60 episodes × 25 debts = 1,500 debts
Relation calibration set: 12,000 independently generated groups
Regression suite: 25 tests after final packaging checks

1. Research question

v0.5 chooses a complete evidence portfolio once:

initial beliefs
→ one-shot portfolio
→ inspect selected set

v0.6 asks whether sequential evidence acquisition helps:

inspect A
→ observe result
→ update beliefs about related B/C/D
→ recompute remaining portfolio
→ inspect next item

The key restriction is that hidden validity outcomes never enter the scheduler.

2. Runtime changes

New replayed state:

evidence_beliefs
evidence_relations

New event types:

BELIEF_INIT
RELATION_INIT
EVIDENCE_OBSERVED
BELIEF_UPDATE

The first implementation replayed the entire ledger after every inspection and was caught by the test suite as an O(T²)-style performance misuse. v0.6 now uses an incremental in-memory projection cache while keeping the event log authoritative and validating full replay at the end of each episode.

3. First development result: adaptivity initially lost

On the original 40-episode development set:

Policy	Total realized value	Avg residual exposure
v0.5 static EIV	83.018	4.0580
v0.6 heuristic adaptive	79.713	4.1420
v0.6 calibrated adaptive	82.299	4.0777

The raw adaptive update therefore made the portfolio worse.

This falsified the naive rule:

\boxed{\text{correlated debts} + \text{adaptivity} \Rightarrow \text{better scheduling}}

The problem was that relation strength was being used as an uncalibrated fixed log-odds shift.

4. Separate relation calibration

A new calibration set uses seed 6062026 and 12,000 independently generated evidence groups.

It estimates, by relation-strength bin:

P(target\ invalid\mid source\ invalid)

and:

P(target\ invalid\mid source\ valid).

The calibrated runtime converts these conditional rates into a likelihood-ratio adjustment and tempers it by observed evidence reliability.

This calibration data is separate from both the development and frozen holdout episode seeds.

5. Frozen holdout result

The Strong-Coupling Gate (average relation strength >= 0.75) was locked before generating the new holdout. The holdout uses seed 20260813606.

Policy	Total realized value	Avg residual exposure	Invalid-harm recall	Avg rounds
v0.5 static EIV	135.777	3.8995	34.44%	1.00
v0.6 heuristic adaptive	137.140	3.8764	34.37%	7.78
v0.6 calibrated adaptive	139.564	3.8374	35.53%	7.87
v0.6 coupling-gated	136.224	3.8919	34.41%	1.78

Relative to static v0.5, calibrated adaptive raises total realized value by:

[ 2.79%. ]

Average residual exposure changes by:

[ -0.0621. ]

The fixed coupling gate only produced a small improvement over static and substantially underperformed full calibrated adaptivity on this holdout. Therefore the gate is not promoted to the canonical scheduler.

6. Paired bootstrap on the 60 holdout episodes

30,000 paired bootstrap resamples:

Realized inspection value

\Delta V=0.0631\quad\text{per episode}

95% interval:

[ [-0.0339,\ 0.1609] ]

Residual exposure

Lower is better:

\Delta E=-0.0621

95% interval:

[ [-0.1613,\ 0.0374] ]

Invalid-harm recall

\Delta R=0.0109

95% interval:

[ [-0.0151,\ 0.0382] ]

All three intervals still cross zero.

Therefore the correct conclusion is:

\boxed{\text{calibrated adaptivity shows a positive holdout signal, but no statistically decisive advantage yet.}}

Episode-level realized-value comparison:

adaptive wins : 24
exact ties    : 24
adaptive loses: 12

7. What did not survive

Claim A — “Any adaptive update is better than static scheduling”

Rejected by the development benchmark.

Claim B — “Strong relation coupling alone tells us when to use adaptivity”

Not supported strongly by the frozen holdout. The 0.75 gate barely improved over static and missed gains captured by full calibrated adaptivity.

Claim C — “Adaptive-submodular theory automatically applies to DEST debts”

Not established. The cited theory provides guarantees only when its structural assumptions actually hold. DEST currently has no proof that verification-debt utility is adaptive monotone/submodular, especially with branch, certificate, and quarantine interactions.

8. External formal alignment

Golovin & Krause (2011) introduce adaptive submodularity for sequential decisions under partial observability and obtain greedy guarantees when the property holds.
Parthasarathy (2020) studies adaptive submodular maximization with stochastic item costs and knapsack-style constraints.
Ma & Tzamos (2023) study buying information for stochastic optimization, including an adaptive setting where information can be purchased after actions.

These are structural backends/analogues, not proofs about DEST.

9. Current engineering interpretation

The evidence so far supports a narrower architecture:

relation exists
→ estimate/calibrate evidence transfer
→ update only after resolved observation
→ replay every belief change
→ compare against a strong static portfolio

Adaptivity should be earned by calibrated evidence structure, not activated because two debts share a label or embedding cluster.

10. Next falsifiable frontier

v0.6 still assumes a fixed relation model and fixed inspection costs. The next stronger runtime should introduce:

dependency cascades
+ certificate revocation graph effects
+ changing inspection cost
+ deadlines
+ multi-round budget carryover

and then test whether the marginal value of an inspection exhibits any empirical diminishing-return / adaptive-submodular-like structure.

That test should precede any attempt to claim a theoretical greedy guarantee.