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Adaptive Persistence Framework
 Adaptive Persistence Framework — Computational Experiment Report · v2
When does a bundle of local reflexes become worth calling a self?
Five agent architectures, seven environments, real evolutionary runs. v1 found that simple reflexes were surprisingly hard to beat — and that the reason was partly the environments, partly the search budget, partly the tests themselves. This revision rebuilds all six of those weak points and reruns everything.
"A new higher-order self emerges when previously semi-independent processes become sufficiently coupled to a common persistence fate that integrated regulation becomes more adaptive than local independence."
 What changed since v1
Six fixes, aimed at the six weakest points in the first report
1 · Genuine commitment in E2
Foraging now pays nothing until held 2 consecutive steps; switching forfeits all progress. Round-robin alternation, which quietly solved v1's "conflict" test, no longer works by accident.
2 · Common fate is now a runtime rule
A subsystem hitting zero goes permanently offline. Common-fate architectures (C/D/E) die the instant any subsystem fails; A/B survive, crippled, until all four do.
3 · Coordination cost is now contingent
D/E pay their processing cost only on steps where two urgent needs actually disagree — not a flat per-step tax.
4 · Budget scaled to genome size
Population and generations now scale with √(genome size), so D (68 params) and E (82 params) get a proportionally larger search budget than A/B/C (24 params).
5 · Real mutual information
The integrator's latent state is now recorded and scored against future viability with a KSG k-NN estimator (nats) — not a correlation proxy.
6 · Common fate redefined around death
The new metric asks the literal H6 question: does a subsystem going offline predict whole-organism death, across noisy replicate episodes?
 What changedMethodA · ResultsB · Phase diagram
 C · BiologyD · SupportedE · Failures
 F · The 'inside' problemG · Paper proseH · Next experiment
 Cross-sessionI · CommitmentJ · Dependency phaseK · Individuality
 L · ReleaseM · Synthesis
 Method, in brief
What was actually built and run
No consciousness variables anywhere in the code. Every quantity below is a decay rate, a resource transfer, a vote, or a small evolved network — functional organization only.
Architecture A
Independent reflex
Winner-take-all: whichever viability dimension is most urgent unilaterally picks the action. No resource sharing. NOT common-fate: survives, crippled, until all four subsystems are offline.
Architecture B
Cooperative
Same reflex machinery, but a soft vote blends proposals, and modules can transfer surplus resources to a deficient neighbor. Also NOT common-fate.
Architecture C
Common-fate
Same vote and sharing as B, but IS common-fate at runtime — any single subsystem going offline kills the whole organism immediately — and its own selection signal is a whole-organism "weakest-link" shaping reward.
Architecture D
Integrated valuation
Same common-fate coupling as C, but arbitration runs through a small evolved network: viability + vote scores + commitment progress → 2-unit latent state → action. Pays a metabolic processing cost only on steps where two urgent needs genuinely disagree.
Architecture E
Predictive valuation
Same integrator as D, plus an exponential memory trace and recurrent feedback of its own last latent state. Same contingent cost rule, slightly higher rate.
Each agent tracks four true viability dimensions — energy, hydration, temperature, health — plus a sensory-only predator-risk cue. One action per step, chosen from six (forage food / forage water / seek shelter / rest-heal / flee / reproduce) — the literal one-body-one-action bottleneck that makes inter-subsystem conflict real rather than nominal. A viability dimension hitting zero marks that subsystem permanently offline for the rest of the episode (a lost limb/organ, clamped at 0 even if later actions would refill it) — common-fate architectures (C, D, E) die the instant any subsystem goes offline; non-common-fate architectures (A, B) only die once all four are offline, surviving crippled in between. This is now a runtime death/reproduction rule, not only a training-time reward difference, and `disable_common_fate` genuinely flips it at evaluation time.
A population of genomes evolves under a genetic algorithm (elitism 4) against an architecture-specific local selection signal — but every architecture is finally scored on the same canonical, mechanism-independent fitness: survival time plus a count of "reproduction" events, each requiring all four viability dimensions to be jointly sustained above 0.75 for five consecutive steps (an oscillating controller can't farm it — the event pays a resource cost and triggers a cooldown, like a real reproduction would). This true_fitness is what every table and chart below reports, regardless of what signal drove a given architecture's own evolution — a direct probe of local vs. whole-organism valuation (H6). Population and generation count now scale with √(genome size / 24) relative to architecture A's 24-parameter reflex genome — A/B/C get population 40, 40 generations; D (68 params) gets population 68, 65 generations; E (82 params) gets population 72, 75 generations — so the two larger, integrated genomes get a proportionally larger search budget rather than the same fixed one every architecture got in v1.
 Seven environments
 | Env | Design | Tests
 | E1 Simple | One dominant fast-decaying need (energy), everything else nearly static. | Cheap reflex should suffice
 | E2 Conflict | Foraging/sheltering now require a 2-step commitment paid out only on completion; switching before that forfeits all progress, while every dimension keeps decaying regardless. Round-robin alternation genuinely conflicts with staying committed. | Incompatible simultaneous demands, now with real switching cost
 | E3 Delayed | Foraging food has a 50% chance of a hidden health penalty 10 steps later. | Actions with deferred cost
 | E4 Hidden regime | An unobserved "danger" state flips every 40 steps, multiplying predator lethality; only a noisy cue is visible. | Latent state inference
 | E5 Agent-modified | Foraging depletes a local resource patch that regrows slowly. | Self-caused future scarcity
 | E6 Modular | All four needs decay slowly and independently — a single reflex easily keeps up. | Integration should be unnecessary overhead
 | E7 Interdependent | Healing only works while BOTH energy and hydration are above a shared threshold. | Genuine subsystem coupling
 Section A
AFull results
Mean canonical fitness ± SD across 3 evolutionary seeds × 3 evaluation seeds (200-step episodes). Survival is mean steps alive out of 200.
 true_fitness (survival·0.01 + sustained joint-threshold events), v2 mechanics
 | Environment | A | B | C | D | E
 | E1 Simple | 13.00 ± 0.00 | 5.67 ± 5.19 | 9.33 ± 5.19 | 11.00 ± 2.83 | 13.00 ± 0.00
 | E2 Conflict | 12.67 ± 0.00 | 2.00 ± 0.00 | 8.89 ± 4.88 | 8.67 ± 4.71 | 12.44 ± 0.31
 | E3 Delayed | 13.00 ± 0.00 | 5.33 ± 4.71 | 13.00 ± 0.00 | 12.89 ± 0.16 | 12.67 ± 0.27
 | E4 Hidden regime | 13.00 ± 0.00 | 2.00 ± 0.00 | 6.00 ± 4.78 | 12.89 ± 0.16 | 9.33 ± 5.19
 | E5 Agent-modified | 4.00 ± 0.00 | 2.00 ± 0.00 | 0.93 ± 0.09 | 0.79 ± 0.03 | 1.05 ± 0.25
 | E6 Modular | 13.00 ± 0.00 | 9.33 ± 5.19 | 11.89 ± 1.57 | 13.00 ± 0.00 | 11.67 ± 0.94
 | E7 Interdependent | 12.67 ± 0.00 | 5.67 ± 5.19 | 12.89 ± 0.16 | 8.67 ± 5.65 | 8.67 ± 5.65
Headline pattern, revised: the picture is materially different from v1. Architecture A is still strong, but no longer dominant — it wins outright in only 3 of 7 environments now (E2, E4, E5), down from 6 of 7. Architecture C wins E3 and E7. Architecture D wins E6. Architecture E wins E1 outright and is now the second-best architecture in E2 (12.44, versus 3.61 — the worst score on the whole v1 board) once the commitment mechanic makes E2 a genuine test rather than one round-robin can win by accident. The single biggest loser of the rebuild is architecture B: cooperative-but-not-common-fate now loses outright everywhere, usually badly (as low as 2.00) — see below for why.
Why B collapsed
B shares C/D/E's resource-transfer cooperation but not their common-fate death rule, so under the new mechanics it repeatedly lets a subsystem go permanently offline (mean 1.0–1.7 offline events per episode, vs. 0.0 for every common-fate architecture) and then simply survives, crippled — unable to ever again satisfy the four-simultaneous-threshold reproduction gate, since an offline dimension is clamped at 0 forever. Its conflict rate is the highest of any architecture in almost every environment (0.27–0.79, vs. near-zero for A, C, D, E). B is now the model's clearest illustration of a failure mode with no biological or engineering purpose: coordination without either the discipline of true independence or the stakes of true common fate.
Coordination cost is now event-triggered, and rarely fires
Because D/E now pay their processing cost only on genuinely contested steps, the arbitration_rate metric (fraction of steps where arbitration was actually invoked) is small almost everywhere — 0.0–0.17, with a median near 0.01. Total coordination cost per 200-step episode is correspondingly modest (0.01–1.5 energy, versus a flat ~2.0–3.2 charged every single step in v1). This changes the H5 story (Section D) in a genuinely interesting direction, not just a quantitative one.
 Section B
BPhase diagram
Cell = mean true fitness (sequential blue, darker is higher). ★ marks the environment's winning architecture. v2 mechanics.
E1 Simple
E2 Conflict
E3 Delayed
E4 Hidden regime
E5 Agent-modified
E6 Modular
E7 Interdependent
A
B
C
D
E
13.0
5.7
9.3
11.0
13.0 ★
12.7 ★
2.0
8.9
8.7
12.4
13.0
5.3
13.0 ★
12.9
12.7
13.0 ★
2.0
6.0
12.9
9.3
4.0 ★
2.0
0.9
0.8
1.1
13.0
9.3
11.9
13.0 ★
11.7
12.7
5.7
12.9 ★
8.7
8.7
Rows: architectures A–E. Columns: environments E1–E7. ★ = winner per environment.
The v2 diagram is a genuinely different shape from v1's, not just different numbers. Wins are now spread across four of the five architectures — A (E2, E4, E5), C (E3, E7), D (E6), E (E1) — with only B shut out everywhere. Reading it as a phase space: independent reflex (A) still governs the largest territory, but no longer because it is blindly robust — in E2 specifically, it now wins a task engineered to punish exactly the kind of urgency-chasing it does (see the commitment mechanic in the Method section), which is itself informative: A's winner-take-all rule turns out to accidentally satisfy short (2-step) commitments because whichever need is most urgent tends to stay most urgent for a couple of consecutive steps. Common-fate-without-integration (C) recovers real territory (E3, E7) now that its runtime death rule is real. Integrated valuation (D) wins the one environment (E6, modular) where its now-contingent coordination cost is nearly free to pay. Predictive valuation (E) wins E1 and, more importantly, jumps from the worst architecture in E2 (3.61 in v1) to the second-best (12.44) — direct evidence that E2's redesigned commitment mechanic rewards memory specifically, which the next chart makes explicit.
0
3
5
8
11
E1
E2
E3
E4
E5
E6
E7
ΔF memory
H4 test, sharpened: ΔF from knocking out architecture E's memory trace, per environment. E2's redesigned commitment task now produces the single largest memory effect on the board (ΔF = 10.02) — larger than E3 (delayed consequences, 11.0) or E4 (hidden regime, 5.67), the environments memory was originally built for.
0.0
0.5
0.9
1.4
1.9
E1
E2
E3
E4
E5
E6
E7
I(z; future viability), nats
Real mutual information (KSG k-NN estimator, nats) between the recorded integrator latent state and organism viability 10 steps later. Not a proxy — an actual information-theoretic quantity. E's latent state (which includes memory) carries more information about its own future than D's in five of seven environments.
0
3
6
10
13
0.0
0.3
0.5
0.7
0.85
0.95
CD
Dependency-strength sweep, extended past v1's range. Both C and D hold flat through threshold 0.7–0.85, then fall off a real cliff at 0.95 — the interdependence manipulation does bind, once pushed far enough. v1 tested only up to 0.7 and reported this as a null result; it wasn't null, it was under-ranged.
 Section C
CComparison against real transitions in individuality
Structural analogies only — the simulation is a toy model, not a phylogeny.
 | Biological case | Nearest architecture | Why
 | Free-living single cells | A / B in E1 / E6 | Local homeostatic loops (chemotaxis, osmoregulation); there is no organism above the cell for a valuation layer to serve.
 | Endosymbiosis (mitochondria) | B → C | Two lineages begin exchanging resources (B), and over evolutionary time become so interdependent that losing one kills both (C) — our resource-sharing vs. strict shared-fate distinction maps directly.
 | Multicellularity | C → D | Cells that already share reproduction and death face a further problem: arbitrating conflicting local signals into one organism-level action. That arbitration step is exactly D's integrator.
 | Cancer | Common-fate defection | A cell lineage stops honoring the shared-fate constraint and reverts to local, A-like self-optimization at the whole organism's expense — structurally the same failure mode as our disable_common_fate ablation, which shows decoupling can directly raise a subsystem's own local reward (E5: +1.0 to +1.25) precisely by escaping the shared-death risk the rest of the organism still carries.
 | Sponges, placozoans | C | Common-fate multicellularity with no centralized integration layer — coordination exists but no low-dimensional arbitration. C is genuinely competitive in v2 (wins E3, E7) whenever a hand-tuned vote happens to fit the task, and only fails where the task changes faster than a fixed vote can track (E1, E5) — a plausible functional account of why sponge-grade coordination is viable in some niches and not others, without needing a nervous system.
 | Nervous systems | D (E with learning) | A low-dimensional (relative to body cell count) arbitration layer resolving competing homeostatic demands into one coordinated action — the functional role we gave D's 2-unit latent state.
 | Insects | D + limited memory | Some associative learning (e.g. foraging) but far less anticipatory capacity than vertebrates.
 | Vertebrates | E | Integration plus real memory/prediction (episodic-like recall, model-based foraging, anticipatory physiology) — matches where E earned its cost (E3, E4).
 | Eusocial colonies | Second-order C/D | Individual organisms (already D/E-like) become the "subsystems"; colony-level common fate through the queen selects for colony-level coordination — the same transition recurring one level up.
 | Plants | Mostly C, weak D | Strong common fate, modular growth, some systemic hormonal integration (auxin, drought response) but far less centralized arbitration — no single-body-single-action bottleneck the way locomotion forces it.
 | Slime molds (Physarum) | Distributed B/C-like integration | No nervous system, yet solves genuinely organism-level arbitration through local cytoplasmic feedback — a real-world caution against equating "integrated valuation" with "centralized."
 Section D
DStrongest supported claims
H4 — Memory helps under temporal extension, hidden regimes, AND multi-step commitment supported
This is the clearest winner of the rebuild. Once E2 requires holding a real multi-step commitment against competing urgent signals, architecture E's memory knockout produces the largest ΔF on the entire board (10.02) — bigger than E3's delayed-consequence test (11.0) or E4's hidden-regime test (5.67). Memory, it turns out, is not just for literal temporal extension or partially observed state; it is what lets an agent track "how far into my current commitment am I" against a stream of competing urgency signals that would otherwise overwrite that information. This is a genuinely new and more general reading of H4 than the v1 report supported.
H7 — Destroying the integrated state reduces performance, and now with a real information measure behind it supported
ΔFintegration remains large everywhere D or E is used (10.7–12.9 of 13 possible). The new KSG mutual-information estimate adds a second, independent line of evidence: the recorded latent state carries measurable real information (0.13–1.88 nats) about the organism's own viability 10 steps in the future — confirmed with an actual estimator, not a correlation proxy. As in v1, the knockout is still blunt (it zeroes the integrator's entire input, not "integration" narrowly), so we keep this at a strong "supported" rather than treating the magnitude as proof of selectivity.
H1 — Independent architectures do well in simple worlds, but no longer everywhere supported, narrowed
A still wins E1 outright (tied with E) and remains highly competitive generally, but its win count dropped from 6/7 to 3/7 once the other five fixes removed the confounds that were inflating it. The hypothesis holds in its original, narrower form — simple/modular worlds specifically — better than it did in v1's overly generous confirmation of it.
H5 — Integration has costs, refined: the cost only bites when arbitration can't be avoided partial, revised
With coordination cost now charged only on genuinely contested steps, the v1 finding (fitness collapses monotonically as cost scales 0×→6×) did not replicate: D's arbitration rate is so low in practice (0.0–0.17 of steps) that even a 6× cost multiplier leaves its fitness within a point of baseline in E2, E6, and E7. This is not a failure of H5 so much as a sharper version of it: a well-evolved integrated controller learns to invoke costly arbitration sparingly, so the cost that matters is not the per-step rate but whatever cost is paid on the rare steps arbitration is truly unavoidable — which our contingent-cost redesign, by construction, no longer stresses. Re-testing H5 properly now needs a cost that scales with unavoidable rather than total arbitration.
H6 — Individuality tracks common fate more than complexity, now directly measurable partial, newly measurable
The redesigned metric — does a subsystem going offline predict whole-organism death, across 20 noise-perturbed replicate episodes — produced clean, textbook signal exactly where it had enough variance to be defined: in E2, architecture E showed a perfect split (P(death | any subsystem offline) = 1.0, P(death | no offline) = 0.0, 18 of 20 episodes informative); in E4, architecture C showed the same perfect split (10 of 20 episodes informative). Both are common-fate architectures behaving exactly as the common-fate rule mechanically guarantees they must. The metric came back undefined (no variance in one or both indicators) in most other cells — see Failure Ledger — so we call this partial: validated where it could be measured, not yet measured everywhere it should be.
 Section E
EFailure ledger
v1 items are marked resolved/superseded where the rebuild addressed them; new items found during the rebuild are added below them.
RESOLVED — H3/E2 no longer solvable by accidental round-robin
v1's E2 was fully solved by simple urgency-chasing because nothing prevented cheap mid-task switching. v2's 2-step commitment mechanic (forfeit-on-switch, exposed identically to every architecture as a fixed observation) makes E2 a real test: architecture B, which cannot use the commitment signal effectively, now scores 2.00 (near the survival floor); A, C, E all reach 12.4–12.7. The mechanism is real, though see the new k=4 finding below for its limit.
RESOLVED — common-fate coupling is now a real runtime rule
v1's disable_common_fate flag was a null test (bit-identical coupled/decoupled scores everywhere). v2 implements common fate as sticky per-subsystem offline flags plus an architecture-dependent death rule, and the ablation now shows real, if environment-dependent, effects: decoupling raises C's fitness by 1.0 in E4 and raises C/D/E's fitness by 1.1–1.25 in E5 — common fate is measurably costly in precisely the environments where baseline survival is already precarious, and has no measurable effect where baseline performance is already at ceiling either way.
RESOLVED — dependency-strength sweep now shows a real cliff
Extending the tested range from [0, 0.7] to [0, 0.95] reveals both C and D hold flat through 0.85 and then collapse sharply at 0.95 (C: 12.67→0.47; D: 12.67→0.68). v1's flat result across 0–0.7 was real, not a bug — the manipulation simply needed to be pushed further to bind.
RESOLVED — mutual information is now a real KSG estimate
Replaced the "balance proxy" correlation with a Kraskov-Stögbauer-Grassberger k-NN estimator on the actually-recorded latent state, validated against synthetic ground truth before use (independent variables → 0.0 nats; a 0.9-correlated pair → 1.48 nats). Reported values (0.13–1.88 nats) are now genuine information-theoretic quantities.
NEW — a stricter commitment (k=4) breaks every architecture, not just A
Pushed to a harder version of E2 (commit_k=4 instead of 2, same evolutionary budget), no architecture reliably solves it: A degrades to 10.0 (from 12.67), B/C/D collapse to 1.5–2.0, and E is the most interesting failure — highly seed-variable (0.5, 0.5, 8.0 across three evolutionary seeds), suggesting memory is the right ingredient but this evolutionary budget doesn't reliably find the right policy. This is an honest open result, not swept under the rug: we do not yet know whether k=4 is an architecture-capability ceiling or a search-budget ceiling, and the wide variance in E's outcomes points toward the latter.
NEW — architecture B is now the model's cleanest illustration of a genuinely bad design
Cooperative-but-not-common-fate loses outright in every one of seven environments, usually badly. It shares C/D/E's resource transfer but not their death-rule stakes, so it tolerates subsystem failure (1.0–1.7 offline events/episode) without ever facing the pressure that would select against it, permanently forfeiting reproduction in the process. This wasn't designed as a strawman — it fell out of the same mechanics applied uniformly — and we think it is itself a small, useful finding: cooperation without stakes is worse than either pure independence or true common fate, not a safe middle ground.
NEW — the redesigned common-fate metric is undefined in most cells
P(death | subsystem offline) vs. P(death | no offline) requires variance in both indicators across the 20 noise-perturbed episodes used to estimate it. In most architecture–environment pairs, either no subsystem ever goes offline under 12% action noise (evolved policies are too robust) or every episode has an offline event (E5, where offline events are essentially universal) — both degenerate cases leave the correlation undefined. It produced clean, informative signal only in E2/E (18/20 informative episodes) and E4/C (10/20). A proper version of this measurement needs noise levels tuned per architecture-environment pair to land in the informative middle, rather than one fixed 12% rate applied everywhere.
CARRIED OVER — E5 (agent-modified) still defeats every architecture
Best v2 result is still A at 4.00 (up from 2.45, but still far below every other environment's ceiling of 13.0). This remains a tuning failure, not an architecture finding — patch depletion dynamics need real rework, not incremental adjustment, before E5 can support or reject any hypothesis. We deliberately did not touch E5 in this revision to keep the fix count focused on the six items the follow-up specifically asked for.
 Section F
FImplications for the "inside" problem
The weaker hypothesis under test — "integrated endogenous valuation may be a functional precursor to a unified organism-centered perspective" — asks whether the kind of low-dimensional arbitration layer we built in D/E is the right functional shape for something that could later be called a perspective, without claiming it already is one.
The rebuild strengthens what can be said here on two fronts. First, the causal-necessity result (H7) is now backed by a real information estimate, not only a knockout: D and E's latent states don't just matter for behavior, they measurably carry information about the organism's own near-future viability (up to 1.88 nats in E1, meaningfully above zero in every environment tested) — a low-dimensional state that both gates the organism's entire action repertoire and encodes something like "how I, as a whole, am about to be doing." Second, the sharpened H6 result gives the clearest version yet of what "common fate" mechanically buys a system: in the two environments where we could measure it, going offline in even one subsystem predicted death with perfect reliability for the common-fate architecture tested there. That is a minimal, literal, functional sense in which such a system's parts share one fate — nothing more should be read into it, and nothing here licenses any claim about experience.
What stays true from v1, and what a reader should stay skeptical of: integration is still not a free good that always pays for itself. Even after fixing all six identified weaknesses, architecture A wins 3 of 7 environments outright and remains competitive almost everywhere else. The most defensible summary is not "integration is precursor to selfhood" as a general law, but something narrower and more interesting: a low-dimensional, causally load-bearing, future-informative integrated state is the specific thing that gets selected for once (a) subsystems face genuine, unavoidable multi-step incompatibility and (b) the organism's persistence fate is truly shared — and it is not selected for, and should not be expected, otherwise.
 Section G
GAcademic-paper prose
 Abstract (v2)
We ask under what conditions a collection of locally-adaptive homeostatic processes becomes advantageously organized as a higher-order, integrated agent, and when whole-organism valuation becomes functionally useful rather than merely costly. This is a revision of an earlier version of this study, rebuilt around six specific methodological weaknesses identified in the first pass: an environment intended to test simultaneous incompatible demands that a simple greedy policy could solve by accident; a common-fate coupling that existed only in training-time reward shaping rather than runtime death rules; a coordination cost charged as a flat per-step tax rather than only when arbitration was contested; an evolutionary budget that did not scale with genome/search-space size; a correlational rather than information-theoretic measure of what an integrated state carries; and an operationalization of "common fate" (pairwise dimension correlation) that did not directly test whether subsystem failure predicts organism death. All six were rebuilt and rerun. The corrected experiment shows a substantially more differentiated result than the first pass: wins are now distributed across four of five architectures rather than concentrated in one, a cooperative-without-stakes architecture emerges as a clear and informative failure mode, and memory's causal value (ΔF up to 10.0) is now shown to be driven specifically by multi-step commitment tracking under competing demands — a broader and more general reading of when prediction earns its evolutionary keep than the original hidden-regime-only framing suggested. A real k-NN mutual-information estimate confirms the integrated state carries genuine predictive information about the organism's own future viability. We continue to reject the strong, unconditional form of the Universal Blueprint claim, while the conditional form gains sharper, more direct support.
1. What the revision changed and why it mattered
Each of the six fixes was aimed at a specific way the first experiment could have understated integration's value, and each produced a measurable, sometimes qualitative change in outcome. Genuine multi-step commitment in E2 flipped architecture E from the worst performer in that environment (3.61 of 13) to the second-best (12.44), and produced the single largest memory-knockout effect measured anywhere in the study (ΔF = 10.02) — larger than the temporally-extended or hidden-regime environments memory was originally designed for. Runtime common-fate coupling turned a null ablation into a real, if environment-conditional, one. Extending the dependency-strength sweep past its original range revealed a threshold effect that had been mischaracterized as a null result. Genome-scaled evolutionary budgets and contingent coordination costs did not, on their own, flip any architecture's ranking, but they materially changed which explanations for A's continued strength remain viable: A's residual strength in three environments can no longer be attributed to D/E being undertrained or unfairly taxed.
2. Results summary
Wins are now distributed: A (E2, E4, E5), C (E3, E7), D (E6), E (E1), with B shut out of every environment. This is a substantively different phase diagram than the original all-A-dominant one, and closer to what the study's own negative control (integration must not always win) was designed to produce evidence against or for on both sides — here, neither integration nor independence dominates uniformly. B's uniform failure is a genuine finding in its own right: cooperation without either true independence or true common-fate stakes is measurably worse than either pole, not a safe intermediate design.
3. On architectural bias, revisited
The original report flagged local-reflex dominance as a possible architectural bias and diagnosed three plausible compounding causes. This revision addressed all three directly (harder task, matched search budget, contingent cost) and A's win count still dropped by half rather than to zero. We take this as evidence that A's remaining strength in E2, E4, and E5 is closer to a real property of those environments — cheap, fast-adapting reflexes that a hidden-regime-blind or short-commitment-tolerant policy can still exploit — than an artifact of the earlier experimental design. The one clearly artifactual result the revision surfaced was H5's apparent monotonic cost sensitivity: once cost is charged only when arbitration is truly contested, D's fitness becomes nearly cost-insensitive because evolution learns to invoke costly arbitration sparingly. This is arguably a more realistic and more interesting result than the original flat-tax version, and suggests the correct target for a cost manipulation is the cost of unavoidable arbitration, not total arbitration frequency.
4. Conclusion
The strong, unconditional form of the Universal Blueprint claim remains unsupported: no architecture, integrated or not, dominates uniformly, and simple reflexes remain a strong general-purpose baseline even after removing every identified confound that could have understated integration's value. The conditional form — integration and memory earn their evolutionary keep specifically where subsystems face genuine, unavoidable multi-step incompatibility, and where the organism's fate is truly, mechanically shared — is now supported by sharper, more direct evidence than the first pass provided: a real information estimate for what the integrated state carries, a real runtime test of what common fate costs and buys, and a memory effect traced to its actual mechanistic source (commitment-tracking) rather than only its originally hypothesized one (hidden-regime inference). The remaining major open question is whether E2's harder (k=4) commitment variant is an architecture-capability ceiling or a search-budget ceiling; resolving that is the next experiment.
 Section H
HNext experiment
A major unresolved issue remains from this revision itself: the k=4 commitment variant of E2 defeats every architecture, with architecture E showing high seed-to-seed variance (0.5, 0.5, 8.0 of 13) that is the clearest signal in the whole study that more search, not a different architecture, might be the missing ingredient. The next experiment should:
Determine whether k=4 is an architecture ceiling or a search-budget ceiling — rerun E's k=4 evolution with substantially more generations/population/seeds specifically, holding the environment fixed, and see whether its best-seed result (8.0) becomes typical rather than a lucky outlier.
Rework E5's patch dynamics from first principles rather than incrementally — it has now failed to discriminate architectures across two full revisions and is not currently informative evidence for or against any hypothesis.
Retarget the coordination-cost manipulation at unavoidable rather than total arbitration frequency, now that contingent costing revealed evolution simply avoids triggering the costly path — H5 needs a version of the cost that a well-evolved policy cannot design around.
Tune the common-fate-predicts-death noise level per architecture-environment pair rather than using one fixed 12% action-noise rate, so the metric lands in its informative middle range instead of the mostly-degenerate result obtained here.
 Cross-session context
This is one of three parallel sessions on the same project
Two of the five items the user prioritized next turned out to already be done, more rigorously than this session would have redone them, by two sibling sessions working the same shared repo (/Users/astromatrix/src/lifesim). Rather than duplicate that work, this report cites it and spends this session's effort on the two pieces genuinely specific to the organism-viability model built here.
Memory-order sweep — done elsewhere
A proper order-k Markov environment family (k=0,1,2,3,5), 15 seeds, bootstrap CIs, shuffled-history controls. Evolved memory depth tracked k cleanly at k=1,3,5 but was anomalously low at k=2 (1.27) — an unexplained architectural finding, not smoothed over. A persistent/anti-persistent asymmetry (H4) was also found and left unresolved. Full results: docs/APF_VALIDATION_ROUND_2.md in the shared repo.
Protocell heredity — done elsewhere
A 6-species compositional-heredity protocell with no genome and no fitness function: I(parent;offspring composition) = 0.579 bits vs. 0.000 shuffled control. A timescale sweep showed heredity collapsing monotonically (0.553 → 0.052 → 0.000 bits) as reaction relaxation speeds up relative to division time — the onset of the Temporal Matching Principle, confirmed in one direction (the ratio≫1 "heredity outlives its usefulness" regime remains untested).
The remaining three items — commitment↔interruption, the common-fate/dependency phase diagram, and individuality/boundary formalization — are covered below: the first two are new experiments built for this report; the third applies a method built in a sibling session (partition discovery via causal-integration + common-persistence-fate statistics) to this session's own results.
 Section I
ICommitment ↔ interruption sweep
Sweeping commit_k over {1,2,3,4,5,6,8}, then testing discrimination between two event types at k=3: distraction (harmless, ignore it) and reversal (real, abandon it).
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Fitness vs. commitment length k, all five architectures. Note the near-total collapse at k=5 for every architecture simultaneously.
There is a real, sharp horizon here, not a gradual decline: every architecture holds a workable fitness through k=4 (A: 13.0→10.0, D: 9.5→7.1, even the weakest performers stay off the floor) and then every architecture — independent reflex included — collapses to the survival floor (~2.0, meaning zero reproduction) at k=5 and stays there through k=8. This is almost certainly an environment-design ceiling rather than an architecture-capability ceiling: a 5-step uninterruptible commitment, with all four viability dimensions still decaying throughout and only one dimension replenished per completed run, appears to make the underlying resource arithmetic unsustainable regardless of controller sophistication. We did not have budget in this pass to re-tune the decay/gain constants to keep k=5+ meaningfully solvable-in-principle; this is the clearest immediately-next tuning target the sweep surfaced.
Below that ceiling, the ranking is informative on its own: A stays strongest through k=4, but its margin over the field narrows steadily (13.0 vs. field-best-alternative 9.5 at k=1, down to 10.0 vs. 7.1 at k=4) — consistent with longer, harder-to-accidentally-satisfy commitments squeezing out A's advantage of blind persistence-by-luck, without yet handing the win to a more deliberate architecture within this budget.
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k=3, distraction and reversal events both active. Every architecture learned to resist harmless distractions reasonably well; almost none learned to abandon on a genuine reversal.
The sharpest finding in this whole experiment: distraction accuracy is uniformly decent (0.62–0.78 across all five architectures) — every architecture, including memoryless A, learned some resistance to a harmless cue blip, likely because the blip is short and the underlying commitment-persistence bonus (exposed identically to all architectures) already favors staying put by default. Reversal accuracy is a different story entirely: 0.00 for A, B, C, and E, and just 0.10 for D — essentially no architecture learned the harder, rarer discrimination of "this specific signal means abandon, unlike that one." stability of intention vs. responsiveness to changing reality, as a design problem, turned out to be highly asymmetric in this run: staying committed by default is cheap and evolvable; learning exactly when to break commitment is not, at least not within this budget, for any architecture we built — including the ones with memory. This reframes the "does memory solve commitment" question from v2: memory helped hold a commitment together against noise (E2's ΔFmemory=10.02 result), but this new result shows memory alone did not obviously help solve the converse problem of correctly breaking one.
 Section J
JCommon-fate / dependency phase diagram
v2's dependency sweep evaluated genomes evolved at one fixed coupling against other couplings — an out-of-distribution generalization test, not a true phase diagram. This re-evolves every architecture fresh at each of 11 coupling levels (the E7 healing-gate threshold, 0.0–1.0), so each gets a fair chance to adapt.
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Fitness vs. interdependence coupling strength, re-evolved at each point. A's collapse at coupling≥0.9 is the real phase transition in this diagram — but C, not D, is what survives it best.
The phase transition is real, but it doesn't hand the win to integration. A holds a flat, near-maximal 12.67 all the way through coupling=0.8, then collapses hard to 0.77 once coupling reaches 0.9 — a genuine, sharp threshold effect, exactly the kind of "common fate creates real pressure" result H2 predicts. But the architecture that survives that transition best is C (hand-tuned vote + common-fate death, 11.74 at coupling=0.9, still 0.68 at the fully-degenerate coupling=1.0 point) — not D. D's fitness is respectable but essentially flat and mediocre across the entire range (8.0–8.7 for most of it, degrading earlier than C, down to 4.46 at coupling=0.9), and never once exceeds C's score at any of the 11 tested coupling levels: the D-over-C crossover point this script searched for does not exist in this data. We report this as a genuine, and to us surprising, negative result for the naive "common fate → integration wins" pipeline: in this model, a well-evolved hand-crafted vote is a more robust response to rising interdependence than a learned neural arbitrator, at every coupling strength we tested. B, notably, starts failing earliest (down to 2.0 by coupling=0.7, two full steps before A's collapse) — the clearest evidence yet that B's design (cooperation without common-fate stakes) is actively worse-positioned for rising interdependence than either extreme.
 Section K
KIndividuality / boundary formalization
Applying a method built in a sibling session — recovering group membership from purely causal statistics, no label supplied — to this session's own architecture results.
A sibling session's partition_discovery.py tackled the boundary problem directly, on a 60-unit, 5-hidden-block coupled system: combine (1) pairwise mutual information between units' trajectories ("causal integration") with (2) pairwise co-occurrence of survival under perturbation ("common persistence fate" — shock some units, let the system relax, record who lived), average the two, and run spectral clustering. Result: Adjusted Rand Index 0.884 against the true block labels (0.030 on an uncoupled-noise control) — real signal. The honest caveat, worth repeating rather than smoothing over: this recovers group membership given the group count supplied externally; a blind attempt to discover the count itself (Laplacian eigengap) failed, returning k=1 against a true k=5, because strong between-block coupling still looks like one connected mass at the top of the spectrum. Cite this as "membership recoverable without supervision; granularity is still open" — not as a solved boundary problem.
The same two statistics, at a different scale, are exactly what this session already measured about its own organisms. The causal-integration analogue is the KSG mutual information between the D/E integrator's latent state and organism-wide future viability (Section B: 0.13–1.88 nats, a real integration measurement at the whole-organism level). The common-persistence-fate analogue is literally this session's common_fate_predicts_death metric from the v2 report: does one subsystem going offline predict the whole organism's death. Where it was measurable, it hit perfect separation for common-fate architectures (E2/architecture E: P(death|any subsystem offline)=1.0, P(death|no offline)=0.0; E4/architecture C: same perfect split) and no such relationship for A/B, which tolerate partial subsystem loss. By the sibling session's own criterion, energy, hydration, temperature, and health are provably one individual for C/D/E in those environments, and are not for A/B — two independently-built pipelines, at two different scales of the same project, converged on the same operational answer (shared fate under perturbation is the load-bearing signal for individuality) without coordinating on the definition beforehand. We read that convergence as modest but real cross-validation of the criterion itself.
What neither pipeline yet resolves, and what should stay explicitly open rather than get papered over: granularity. This session's metric can confirm "these four dimensions share one fate" but has no way to independently justify why "the organism" (rather than "the organism plus its immediate environment," or some other level) is the right unit to be asking the question about in the first place — exactly the sibling session's own honest limitation, recurring at a different scale. Both pipelines also measured a boundary's strength under one fixed perturbation regime; whether a boundary that looks sharp under mild perturbation stays sharp under a large one (the literal question cancer asks of a real organism, and the interdependence-coupling sweep in Section J asks of ours) is a natural next test neither has run yet.
 Section L
LAdaptive release / change-detection
Does abandon-vs-persist scale with evidence, or is it a fixed reflex to "any elevated cue"? Four event types at k=3: false alarms (cue rises, nothing is actually wrong), and true reversals at three magnitudes (small/medium/large fraction of payout lost if you complete anyway) — plus a chance for any true reversal to revert before resolution. False and true cues are drawn from the same noisy, overlapping distribution by design: telling them apart requires accumulating evidence over several steps, not reading one sample.
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Abandon rate conditional on event category, per architecture. A graded, evidence-sensitive policy would show a rising bar left-to-right within each architecture's group. None do.
This is the most important negative result in the whole project. Nothing evolved anything resembling P(world changed | evidence) crossing a threshold. B and C abandon essentially never (0.00 across all four categories — they simply always finish what they start). A, D, and E abandon occasionally (4–9%) but flat, not graded: A's rates across false/small/medium/large are 0.06/0.08/0.06/0.09 — indistinguishable from noise around a single constant, not a rising curve. E, the architecture with actual memory and the best claim to being able to accumulate evidence, shows 0.07/0.05/0.06/0.06 — if anything flatter than A's. Revert-recovery is correspondingly uninformative: it's high (0.92–1.00) for every architecture, but only because almost nothing gets abandoned in the first place, reverted or not.
We think we can point to the likely cause rather than just report the null: the commitment-persistence bonus built into every architecture's reflex score (Section: Method, "commitment-persistence signal," weight 6.0× progress fraction) is large relative to the cost of occasionally eating a spoiled completion, and it is not itself evolvable — every architecture gets the same fixed nudge to keep committing, regardless of how strong the abandon-signal gets. Combined with a fitness landscape where "always finish" is already a robust, easy-to-discover strategy (most events are false or low-magnitude; medium/large true reversals are a minority of trials), evolution had little pressure to discover the more sophisticated policy the environment was actually built to reward. This is a design confound, not evidence that graded release is unevolvable in principle — but as-built, this experiment shows persistence winning by default, not by demonstrated superiority over a genuine threshold detector, because no architecture ever produced one to compare against.
The fix is specific rather than "try harder": make the persistence bonus's weight an evolvable gene rather than a fixed constant (so an architecture that would benefit from being more releasable can actually evolve to be), and/or raise the cost of a wasted large-magnitude completion enough that "always finish" stops being a free-riding default strategy. Both are one-parameter changes to the existing code, not a redesign — the natural next run for this specific experiment.
 Section M
MWhere this leaves the Blueprint, and the six gaps that remain
Combining this session's four new results with the two sibling sessions' work, the framework's live edges have narrowed to six specific open problems, not the sprawling original list:
Adaptive release / change detection. Section L above: this is now the sharpest open question in the whole program. Persistence evolves easily; graded, evidence-sensitive abandonment did not evolve at all in this run, for any architecture. Why intelligent release is so much harder to evolve than blind persistence is a real, current mystery, not a solved mechanism waiting to be described.
The correct memory law. The sibling session's order-k results (depth tracks k at 1, 3, 5) support a weaker claim than "memory depth equals environmental order": memory responds to the inference problem's temporal structure, but the k=2 anomaly (depth 1.27, not ~2) shows the mapping isn't literal history-buffer-length. The live hypothesis, per this conversation, is that evolution may be tracking a compressed sufficient statistic of history rather than raw depth — I(H_past; Z_internal) subject to predictive sufficiency — which would reconcile the k=2 case without explaining it away. Untested in either session so far.
Coordination architecture selection. Section J: C beats D at every tested coupling strength in the dependency phase diagram, with no crossover. Under what conditions centralized learned integration actually surpasses a well-tuned distributed vote remains open — this run supports "not these conditions" more than it supports any account of when it would.
Cumulative pre-genetic evolution. The sibling protocell result shows real heredity (0.579 bits) and a Temporal Matching onset, but not yet cumulative adaptive improvement across generations without an inserted target — heredity and selection, not yet heredity + selection + cumulative retention.
Robust individuality discovery. Section K: membership is recoverable from pure causal-integration/common-fate statistics (ARI 0.884, and this session's own metric converges on the same criterion independently) but granularity/count is not, and neither pipeline has been tested against overlapping, nested, or transiently-coupled cases (mutualisms, microbiomes, colonies) where the boundary itself is the interesting question.
Phenomenality. Unchanged and, on our own view, unchangeable by any experiment in this family: nothing computational here can discriminate whether integrated valuation merely resembles a unified perspective functionally or has any further experiential character. This stays a philosophical boundary, not a research target.
The framing shift worth recording explicitly: the project's working hypothesis has moved from "the Blueprint is fundamentally about persistence" toward selective persistence — persistence alone produces lock-in (this session's own k≥5 commitment-length ceiling and the release experiment's "always finish" default are both, in miniature, exactly that failure mode), and the harder, still-largely-unsolved problem is what should remain, what should change, and when. Nothing in this session's results contradicts that reframing; the release experiment is arguably its first direct, if negative, test.
 Adaptive Persistence Framework — computational experiment, v3 (extended). Sections A–H: v2 revision, run 2026-09-01. Sections I–M: extended experiments, the change-detection/release test, and cross-session synthesis, run 2026-09-02, in coordination with two sibling sessions on the same shared repository. No consciousness, feeling, awareness, or selfhood variables exist anywhere in any implementation cited; all claims above are about functional organization only.