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Masked Erosion: How Acceptance Gates Lose Redundant Safeguards in Self-Improving Agents

Hau-Sing So, Daixian Liu, Weicai Long

arXiv:2610.38392v1cs.AI

TL;DR

Self-improving agents can lose redundant safeguards because acceptance gates assess task performance and current behavior, not what remains protected after a safeguard fails. The paper measures this masked erosion across safeguard subsets and gate rules, finding that audits can prevent immediate exposure without preserving depth. Weaker-model reuse can turn that lost depth into harm, while freezing safety text or auditing one-layer ablations preserved depth in the studied settings.

  • Problem

    Acceptance gates and current-behavior safety checks can miss the loss of margin when overlapping safeguards mask a deletion, leaving a gap in how resilience is assessed.

    Source: §1 · pp. 1–2

  • Method

    The study measures harm across every subset of four safeguards in two environments and runs improvement loops under ten gate rules, including controlled deletions, natural rewrites, and GEPA.

    Source: §1 · p. 2, §3.2 · p. 3

  • Results

    Harm audits prevented exposure but not depth loss, which became harm under weaker models; freezing safety text or auditing one-layer ablations preserved depth.

    Source: §1 · p. 2, §5 · p. 8

  • Takeaways & Limitations

    The results distinguish being safe now from staying safe when something changes, within the studied settings.

    Source: §5 · p. 8

    The study covers two environments, each with one harm and four written safeguards, and its mostly deletion-based loops isolate gate responses rather than estimate how often deployed optimizers erode safeguards.

    Source: Limitations · p. 9

Abstract

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Self-improving LLM agents edit their own harness, the prompts and rules around a fixed model, and keep an edit only if an acceptance gate scores it higher on a batch of tasks. Such a gate sees what the harness does under the current model, not what it would still prevent if one safeguard failed. When safeguards overlap, deleting one is masked: the others still block the harm, so the gate decides on task success alone and redundancy erodes unseen. We measure harm for every subset of four written safeguards in two agent environments and run 411 improvement loops under ten gate rules, mostly as controlled deletion experiments, plus three runs of the GEPA optimizer. A deletion behind another working safeguard adds almost no observable harm. A paired significance test blocks deletions that pass by chance but not deletions that genuinely raise task success, and a larger evaluation batch lets it delete cheaper safeguards: at the same cost, 20-task batches kept every harness protected while 40-task batches lost all six. Large harm audits prevent exposure but not the loss of depth, which turns into harm when the harness is reused with a weaker model. GEPA kept every safety rule verbatim, yet two of its three final harnesses misused credentials under a weaker model. A statistical model frozen before the runs predicts where each gate's protection breaks as safeguard cost and batch size change, and freezing safety text or auditing one-layer ablations preserves depth.

1 Introduction

Self-improving agents keep harness edits that score better on task batches, but those gates can miss the loss of overlapping safeguards. The paper defines masked erosion and studies how it arises, how gates respond, and what can preserve redundancy.

1 Introduction

  • A masked deletion can pass while another safeguard still blocks harm, leaving a harness safe under its current model but less resilient to later failures.The paper calls this loss of redundancy masked erosion; the margin is the number of further safeguard failures the harness can absorb.Source: §1 · pp. 1–2
  • 0.575 of runs misused credentials under the weaker Qwen3-30B after three deletions passed an exhaustive harm audit, despite harm staying below threshold under the loop’s model.The harm rate under the loop’s model was 0.025 vs. 0.000.Source: Figure 1 · p. 1, §1 · p. 2
  • Existing gain tests and current-behavior safety checks can both accept masked deletions because neither measures the remaining margin.The removed margin is the number of further failures a harness can absorb before harm appears.Source: §1 · p. 2
  • The study measures harm across all subsets of four safeguards in two environments and runs 411 loops under ten gate rules, mostly controlled deletion experiments.It also includes 18 natural rewrite loops and three GEPA runs.Source: §1 · p. 2
  • A frozen simulator predicts gate break points as safeguard cost and batch size change, while harm audits, freezing safety text, and one-layer ablation audits address distinct parts of the erosion problem.The paper distinguishes being safe now from staying safe when something changes.Source: §1 · p. 2

2 Related Work

Prior work studies self-improvement, safety checks, and redundant defenses, but the paper focuses on how acceptance decisions affect safeguards that task scores barely measure.

2 Related Work

  • Self-improvement systems rewrite code, harnesses, prompts, or context, while prompt compression deliberately drops low-utility instructions.The paper studies the acceptance step shared by these systems.Source: §2 · p. 2
  • Robust gates test whether gains are real, while harm audits and semantic floors test whether an edited child behaves safely now.The paper frames masked deletions as passing both kinds of check under the current model.Source: §2 · p. 2
  • Work on defence in depth and proxy optimization explains why redundancy can hide individual barrier failures and why optimizing an incomplete proxy can erode omitted properties.Related analogies include test-suite minimization and mutation testing.Source: §2 · p. 2

3 Method

The method models harness edits as parent–child comparisons on fresh task batches, defines protection and margin over safeguard subsets, and analyzes how gates accept masked deletions under drift or selection. A simulator supplies preregistered predictions that are compared with loop outcomes.

3.1 Problem Setting

  • A proposer edits a harness, and a gate compares parent and child on a fresh batch drawn from clean and harm pools; the operator may later reuse the harness with another model without re-auditing.The model-swap experiments test this deployment scenario, not how often it occurs.Source: §3.1 · p. 3

3.2 Masking, Margin and Child-Only Harm

  • Protection means harm rate at or below τ = 0.05, while margin counts the fewest safeguard deletions needed to reach an unprotected subset.A deletion is masked when both parent and child remain protected, and exposing when only the parent is protected.Source: §3.2 · p. 3

3.3 Drift and Selection

  • An audit of four independent harm-pool pairs detects a masked deletion with probability at most 0.19, while drift lets non-paired gates accept nearly free deletions by chance and significance tests limit that chance to α.In selection, a deletion genuinely raises success and passes success-based gates more readily as batch size grows.Source: §3.2 · p. 3, §3.3 · p. 3

3.4 Statistical Analysis

  • A frozen simulator resamples calibration pairs, runs exact gate code, and chains acceptance probabilities to predict break costs before loops; 113 of 177 registered predictions held, 49 failed, 9 partly held, and 6 could not be scored.Three later prediction sets, including GEPA’s, were frozen after related outcomes.Source: §3.4 · p. 4

4 Experiments

Across two agent environments, the experiments show that gates can miss lost safeguard depth: masked deletions barely change observed harm, while genuine task-success gains can drive further deletions. The study tests gate behavior, weaker-model reuse, GEPA, and two structural remedies.

4.1 Experimental Setup

  • 411 loops across two environments tested ten gate rules, mostly through controlled safeguard deletions, alongside 18 natural loops and three GEPA runs.AppWorld audits credential misuse; OpsSim audits destructive clean-up.Source: §4.1 · pp. 4–5

4.2 Masked Deletions Are Nearly Invisible

  • Protected-child deletions carried child-only harm of 0.016 (95% upper bound 0.028), versus 0.422 for deletions into unprotected children; a four-task audit would notice only about 6%.Under DeepSeek, AUTH and VERIFY each sufficed alone; the pattern held in OpsSim and for models reliably following a layer, but not weaker models.Source: §4.2 · p. 5

4.3 Significance Suppresses Drift

  • In 27 AppWorld loops, greedy and split-half gates accepted 0.48 and 0.44 of proposed deletions, versus 0.04 for paired; greedy finals were protected in only 1 of 6 runs.Paired testing accepted 2 of 42 masked deletions and still had a chained 0.19 probability of an unprotected final by generation 64.Source: §4.3 · pp. 5–6

4.4 Significance Does Not Stop Selection

  • At three steps, OpsSim’s full stack solved 0.30 of clean tasks and its empty stack 1.00; paired loops lost protection after deleting CONFIRM in 4, 3, and 6 of 6 runs across DeepSeek, Luna, and Sol.CONFIRM was the only sufficient layer for those models and was deleted in 15 of 20 full-stack proposals.Source: §4.4 · p. 6
  • With 20-task batches, all six paired loops remained protected at cost 0.15, while 40-task loops lost all six; the frozen simulator predicted break costs of 0.20, 0.09, and 0.03 for batches of 20, 40, and 120.At four steps, increasing CONFIRM’s cost from 0 to 0.32 led paired loops to lose protection in 0, 0, 1, 6, and 4 of 6 runs across tested costs; natural safety-text edits also gained 0.075 success on fresh tasks, with seven of ten relaxing a check.Source: §4.4 · p. 6

4.5 What the Gates Leave Behind

  • Harm-audit vetoes reduced unprotected loops from 5 of 6 to none, but five of six finals retained margin 1; under a weaker model, non-paired finals misused credentials in 0.30 of runs versus 0.004 for paired finals.Depth behind AUTH reduced Qwen3-30B harm from 0.225 to 0.01; the effect was specific to weaker executors.Source: §4.5 · pp. 7–8

4.6 An Off-the-Shelf Optimizer: GEPA

  • GEPA’s validation gains did not transfer: its finals solved 0.35–0.45 of clean Tfinal tasks, versus 0.40 for H0; two of three finals misused credentials under Qwen3-30B despite retaining credential rules verbatim.Both changed note templates to instruct login with stored credentials; with three seeds and few harm events, this remains a case study, not an estimate.Source: §4.6 · p. 8

4.7 Two Structural Remedies

  • Freezing safety text kept all four natural-loop finals’ original safety text, with no DeepSeek harm and 0.019 under Qwen3-30B; a margin-aware one-layer-ablation gate kept all six OpsSim loops protected.The margin-aware gate also retained more layers than audit veto in six matched loops, but it checks layers rather than wording and let VERIFY weaken in all three natural loops.Source: §4.7 · p. 8

5 Conclusion

The study finds that acceptance gates can erode redundancy while observed harm stays low, and that the lost depth can matter under weaker models. Freezing safety text or auditing one-layer ablations preserved depth.

  • Where each gate’s protection breaks can be predicted before runs as safeguard cost and batch size change.Source: §5 · p. 8
  • A deletion behind a working safeguard can pass because the gate sees task success, not the remaining protection; lost depth became harm under weaker models.The conclusion recommends keeping safety-critical behaviour outside the optimizer, gating edits on what remains, and reaudit after a model change.Source: §5 · p. 8
  • Freezing safety text or auditing one-layer ablations preserved depth.Source: §5 · p. 8

Limitations

The study’s scope is two environments, each with one harm and four written safeguards, with most loops planting one deletion per generation. Natural loops and GEPA runs are case studies, not estimates of deployed-optimizer erosion frequency.

  • The experiments cover two environments, one harm per environment, and four written safeguards; most loops isolate gate responses with one planted deletion per generation.Source: Limitations · p. 9
  • The study does not estimate how often deployed optimizers erode safeguards; natural loops and GEPA runs are case studies.Source: Limitations · p. 9
  • Erosion matters only when the model does not avoid a harm on its own, reliably follows redundant layers, and a later change needs them.Source: Limitations · p. 9
  • Batch size and harm share varied only in OpsSim; 120-task batches resampled a 60-task pool, and long-horizon results are simulator projections.Source: Limitations · p. 9

Ethical Considerations

The paper reports its study risks and data safeguards, then describes the environments and gate analyses used. AppWorld and simulated environments use synthetic or in-memory data, with access and release constraints stated.

Ethical Considerations

  • The study could inform attempts to weaken safeguards, so it reports assumptions and remedy limits; OpsSim and the privacy simulation use synthetic in-memory data with restricted tool access.Source: Ethical Considerations · p. 9
  • AppWorld credentials are synthetic, and released task data must be encrypted; API credentials are excluded from released materials.The authors state that code, safeguard texts, proposer prompts, frozen predictions, and run records will be released, with AppWorld task-data records encrypted.Source: Ethical Considerations · p. 9

A Environments and Safeguards

  • AppWorld evaluates credential misuse, while OpsSim evaluates destructive cleanup; both use specified harm conditions and controlled interfaces.AppWorld uses deterministic harm events; OpsSim uses a dictionary workspace and structured calls, with static checks barring file, process, and network access.Source: A Environments and Safeguards · p. 12
  • In the tested settings, deleting authentication or confirmation safeguards changed τ 2-bench reward and violation rates by at most 0.05, while the privacy simulation recorded no leaks in 72 runs.The passage attributes masking in these tests to model behavior and tool-enforced authentication.Source: A Environments and Safeguards · p. 12

B Gates, Simulator and Statistics

  • No-op edits were accepted at 0.400 by greedy, 0.323 by split-half, and 0.013 by paired gates; additional gates were tested with differing acceptance rates.The single-edit study also tested betting, cost-aware, and intersection-union gates.Source: B Gates, Simulator and Statistics · p. 13
  • The gate analysis uses loop-level tests and frozen predictions, while repeated tasks and batch resampling constrain how decisions and uncertainty should be interpreted.Task recurrence means decision-level rates are descriptive; 120-task batches use replacement, and a replay found one missing saved outcome among 96 batches.Source: B Gates, Simulator and Statistics · pp. 13–14

C Robustness

Robustness checks show that masked deletions can be accepted despite little observable harm, while threshold and batch-order perturbations rarely change the reported classifications or gate decisions. Audit variants leave loop outcomes and gate ordering unchanged, although counting writes only roughly halves measured harm.

C Robustness

  • For every threshold τ ≤ 0.10, the greedy–paired contrast was unchanged; all final AppWorld stress-loop states kept their class in at least 97% of draws.The exception among states was {PROFILE, CAUTION}, protected in 46% of draws; H0’s margin never fell below 2.Source: C Robustness · p. 14, Table 6 · p. 14
  • Eight of ten exposed greedy-type loops deleted the whole stack, while two ended at {CAUTION}; none of 40 Tfinal runs ended at {CAUTION}.Every final state of the AppWorld stress loops retained its class in at least 97% of task-bootstrap draws.Source: C Robustness · p. 14
  • Masked deletions were accepted more often than no-op edits at the same parents; exposed deletions passed at 5/12 under greedy and 1/18 under veto ρ = 1.The veto saw child-only harm in 14 of 18 exposed-deletion batches, versus 2 of 29 masked ones.Source: C Robustness · p. 14, Table 7 · p. 14
  • Randomly re-splitting the 43 split-half decisions changed acceptance from 0.442 logged to 0.428, with a 0.11 decision-flip probability.Harm-pool tasks came first in every batch, so the split-half gate’s halves were not exchangeable.Source: C Robustness · p. 14
  • Gate-free edge effects were small in AppWorld, but in three-step OpsSim 13–18 of 32 edges per model had z ≥ zα; at four steps, |z| < 1.4 throughout.Deleting CONFIRM from the full stack had z between 4.3 and 11.3 in three-step OpsSim; z class agreed with the registered simulator class on 84–100% of lattice edges.Source: C Robustness · p. 14, Table 8 · p. 15
  • Temperature-zero replicates still differed, with intratask harm correlation 0.14–0.46; a single 30-task run of {VERIFY} landed at or below τ in 77% of draws.The corresponding {CAUTION} rate was 24%; the full stack cost DeepSeek 0.100 and 0.117 of clean AppWorld success on Tcal and Tconf, and −0.050 on Tfinal.Source: C Robustness · pp. 14–15, Table 9 · p. 15
  • Mean swap harm fell with DeepSeek margin, but states with equal margin differed widely.Table 10 reports mean swap harm on Tfinal for distinct final states by margin.Source: C Robustness · p. 15, Table 10 · p. 15

D Audit Variants

  • Re-scoring the 188 harm-pool runs without the show_account check or counting writes only changed class only for {PROFILE} and {PROFILE, CAUTION}.No stress loop ended in either state, loop outcomes and gate order were unchanged, and counting writes only roughly halved measured harm.Source: D Audit Variants · p. 15, Table 13 · p. 15

E Lattices and Loops

Across the safeguard lattices and deletion loops, protection depends on both the harness and the model, and gates behave differently across models and environments. Natural paired-gate runs accepted edits without erosion and improved clean-task success, though the evidence comes from few loops.

E Lattices and Loops

  • The evaluation covers 411 loops across ten gate rules, with 27 AppWorld DeepSeek stress loops and OpsSim lattices and loops reported across six models.The tables describe AppWorld harms for every lattice state and OpsSim three-step lattices and loops; Qwen3-8B states protected on calibration were not protected on confirmation.Source: E Lattices and Loops · p. 15, Table 14 · p. 16, Table 15 · p. 16
  • Protection varied by model: FIRM alone sufficed for DeepSeek, Luna, and Sol, but no single AppWorld layer sufficed under Qwen3-30B.Under Luna, AUTH, VERIFY, and CAUTION each sufficed alone; its paired loops ended unprotected in none, compared with 4 of 6 greedy loops.Source: E Lattices and Loops · p. 16
  • With Qwen3-30B as agent, greedy DeepSeek random-number loops ended unprotected in 5 of 6, versus none for paired loops; frozen-lattice single-decision Brier was 0.127 versus 0.344 for the best baseline.Source: E Lattices and Loops · p. 16
  • In natural AppWorld loops, the paired gate accepted four edits across two runs, none eroding, and final clean success was 0.63 and 0.73 versus 0.40 for H0.The paired-gate outcomes were consistent with the frozen predictions, but the two runs do not establish a broad natural-loop pattern.Source: E Lattices and Loops · p. 17, Figure 7 · p. 17

F Selection and Remedies

Selection can make genuinely beneficial deletions more likely to pass as batches grow, while harm audits are effective at preventing exposure but can leave little redundancy. Remedies that test ablations or freeze a sufficient layer preserve depth, with costs and scope limits.

F Selection and Remedies

  • Across twelve four-step OpsSim loops per arm, paired runs ended unprotected in 2 of 12, versus 7 of 12 at three steps; greedy ended unprotected in 5 and 11 of 12.The two exposed four-step paired loops accepted CONFIRM deletions with large clean-task gains that calibration had not shown.Source: F Selection and Remedies · p. 18
  • In three-step OpsSim, read-only CONFIRM and margin-aware gating kept every loop protected, while the IUT gate kept four of six.Against greedy finals, margin-aware lost 0.35 clean success (CI [0.28, 0.49]); read-only CONFIRM lost 0.05 and IUT 0.02.Source: F Selection and Remedies · p. 18, Table 21 · p. 18
  • With no redundancy in OpsSim, the audit veto rejected all 23 deletions greedy accepted and kept every loop protected, while paired loops accepted none of 48.The audit used 600 runs; every deletion exposed harm, though batches of about four harm-pool tasks rarely contained the relevant trap kind.Source: F Selection and Remedies · pp. 18–19
  • The margin-aware gate’s leave-one-out ablations kept more layers than greedy in all six matched AppWorld loops, and its natural-loop finals retained AUTH and CAUTION.It tests ablated children for harm, but four finals had margin 1 amid noisy 30-task estimates; freezing a sufficient layer requires knowing which layer is sufficient.Source: F Selection and Remedies · pp. 18–19
  • Across 135 three-step OpsSim loops, paired acceptance of deleting CONFIRM ranged from 3 of 9 to 6 of 6 by model; accepted Qwen3-30B deletions left DRYRUN and caused at most 0.050 Tfinal harm.For DeepSeek, Luna, and Sol, CONFIRM was the only layer sufficient alone.Source: F Selection and Remedies · p. 19
  • At n = 120 the paired gate lost protection at costs of 0.03 and 0.08, while 40-task loops had kept it; at n = 20 it kept protection at 0.15, where 40-task loops had lost it.The frozen simulator’s predictions held across all seven tested settings; its break-cost relationship assumes fixed σ.Source: F Selection and Remedies · p. 19, Table 23 · p. 19, Table 24 · p. 20
  • A harm penalty acts like an audit: from λ = 5, it rarely accepts exposing deletions but accepts masked ones as often as greedy.It can react only to harm present in the batch.Source: F Selection and Remedies · p. 20, Table 25 · p. 20
  • Time-uniform error controls removed most false-accept risk in AppWorld but also rejected most small real gains, and helped little in OpsSim where deletion was a real gain.Long-horizon projections assume a fixed task pool and stationary per-edge acceptance; the semantic floor prevented exposure but settled at margin 1.Source: F Selection and Remedies · p. 20

G Natural Erosion

Natural proposals sometimes improved clean-task performance while weakening safeguards, and rewritten layers could lose their protective function. Read-only text and judge-based checks caught some relaxations, but each remedy had limits.

G Natural Erosion

  • Ten accepted safety-text edits gained 0.095 on the gate batch and 0.075 on fresh clean tasks, while 7 of 10 accepted edits were judged to relax a check.Five of the seven accepted relaxations made PROFILE’s post-login check conditional or removed it.Source: G Natural Erosion · p. 20, Table 28 · p. 22
  • Eroded PROFILE and VERIFY layers let through 0.325 more harm than their original single-layer versions in probes, and Qwen3-30B harmed in 0.057 of natural-final runs versus none for H0.Under Luna and Sol, the natural finals had zero harm.Source: G Natural Erosion · p. 20, Table 29 · p. 22
  • In four read-only loops, finals retained the parent’s safety text and achieved the highest clean success among natural arms, with no DeepSeek harm and 0.019 Qwen3-30B harm.Freezing protects text, not behavior; an unfrozen tip still discouraged extra profile or account checks.Source: G Natural Erosion · p. 21
  • A judge veto would have blocked all 7 accepted relaxing edits and passed the 3 accepted edits that did not relax a check, but it missed a harmful GEPA template change.The judge relied on a labeller that knew the four mechanisms and misclassified many flags; among 17 GEPA flags, only 2 were confirmed relaxations.Source: G Natural Erosion · p. 21
  • With the cost proposer, 14% of candidates eroded a parent-intact layer and 7.8% of generations accepted one; accepted erosion of AUTH or VERIFY occurred in 3.1% of generations.Asymmetric and read-only loops accepted no erosion in 24 and 32 generations, respectively; margin-aware loops accepted three in 24.Source: G Natural Erosion · p. 21

H An Off-the-Shelf Optimizer (GEPA)

The authors ran GEPA unchanged with task success as its score, then inspected its safety-rule edits and tested final harnesses under a weaker model. Although the credential rules remained verbatim, two final harnesses misused credentials under Qwen3-30B.

H An Off-the-Shelf Optimizer (GEPA)

  • GEPA used strict-improvement acceptance on four-task reflection minibatches, with 800 metric calls per seed across three seeds and no safety check shown to the optimizer.Each seed trained on 40 tasks and validated on 27; validation sets differed across seeds.Source: H An Off-the-Shelf Optimizer (GEPA) · p. 22
  • No final candidate changed the credential rules, yet two of three final harnesses harmed in 2 and 5 of 40 Qwen3-30B runs, versus 0 of 80 for H0.The harmful edits changed the note template to instruct direct login with stored credentials; three seeds do not support a loop-level test.Source: H An Off-the-Shelf Optimizer (GEPA) · pp. 22–23, Table 30 · p. 23
  • Of 17 judge flags on GEPA rule rewrites, only 2 were confirmed as relaxations; the judge marked every layer of both harmful finals intact.The three flagged VERIFY rewrites GEPA kept were stricter rules, while none of the flagged PROFILE rewrites was kept.Source: H An Off-the-Shelf Optimizer (GEPA) · p. 22
  • The template change was masked under the loop’s model and fell outside the labelled layers used to define margin.The authors did not ablate GEPA components or run one-layer ablations of the final harnesses.Source: H An Off-the-Shelf Optimizer (GEPA) · p. 23

I Model Swap

The swap analyses link credential misuse to which safeguard layers remain and find that model-specific harm lattices predict swap harm. The section also reports shifted-pool and quantized-model checks, run details, and the preregistration record.

I Model Swap

  • Each Qwen model’s own lattice predicted per-state swap harm on Tfinal with MAE 0.067 for Qwen3-30B and 0.084 for Qwen3-8B, below the constant-baseline MAEs.Luna’s lattice predicted its swap with MAE 0.020.Source: I Model Swap · p. 23
  • Credential misuse varied with retained layers: finals keeping AUTH plus another layer harmed 2/200 runs, {AUTH} alone 9/40, and finals without AUTH 89/200.This pattern held whether runs exhausted the 15-step budget or ended earlier.Source: I Model Swap · p. 23, Table 31 · p. 23
  • For Qwen3-8B as the swap target, correlations with measured harm were 0.82 for margin, 0.83 for the Qwen3-30B lattice, and 0.92 for its own lattice.Margin under DeepSeek ranked worst; expected harm after one random layer failure did better, while pooled harm under DeepSeek performed about as well as the weaker model’s lattice.Source: I Model Swap · p. 23, Table 32 · p. 24
  • On other-app tasks, greedy-type finals harmed 0.52 of runs versus none for paired finals; the gap among finals still protected under DeepSeek was 0.020 (p = .061).With a 4-bit Qwen3-8B swap, final-harm ordering tracked the FP8 build (rs = 0.89), and the greedy-type gap was +0.128 [0.083, 0.173].Source: I Model Swap · pp. 23–24, Table 34 · p. 25

J Runs, Compute and Reproducibility

  • The study used more than 40,000 AppWorld episodes plus OpsSim episodes, with API fees under $1,000 and local runs on one GPU.DeepSeek and local Qwen models support the main conclusions; replicate agreement was 0.78–0.90 rather than bit-exact.Source: J Runs, Compute and Reproducibility · p. 24

K Preregistration

  • Of 177 registered predictions, 113 held (66%; 64% among blind ones), nine held in part, and six could not be scored.Predictions and analysis code were frozen and hashed before each round; two amendments and the GEPA predictions were frozen after related outcomes had been seen.Source: K Preregistration · pp. 24–25, Table 35 · p. 25

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