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ActiveSaddler: Automated Curriculum Learning for Agent Harness Optimization

Sungho Park, Wonjoong Kim, Jue Zhang, Wook-Shin Han, Pengfei Gao, Chanyoung Park, Yongqiang Yao, Rao Fu, Elsie Nallipogu, Qingwei Lin, Victor Rühle

arXiv:2610.00906v1cs.AIcs.CLcs.LGcs.MAcs.SE

TL;DR

Harness optimization methods generally fix the training scenarios that generate update feedback, even though scenario usefulness changes as the harness evolves. ActiveSaddler adapts that curriculum through a non-stationary bandit over recurring failure patterns. It improves test Pass@1 over a fixed-order optimizer on GAIA2 and Terminal-Bench 2.0.

  • Problem

    Existing harness optimizers largely predetermine which training scenarios generate update feedback, although their usefulness can change as the harness evolves.

    Source: §1 · p. 2

  • Method

    ActiveSaddler uses a non-stationary bandit that builds failure-pattern arms from observed failures, reprioritizes them, and balances revisiting known weaknesses with exploring unseen scenarios.

    Source: §1 · p. 2

  • Results

    ActiveSaddler improves test Pass@1 over the same harness optimizer with a fixed scenario order by 4.4 percentage points on GAIA2 and 7.5 on Terminal-Bench 2.0.

    Source: §1 · p. 2, §6 · p. 10

  • Takeaways & Limitations

    The results establish curriculum selection as a complementary dimension of harness optimization alongside how execution feedback is used for updates.

    Source: §6 · p. 10

    The setup assumes fixed, mutually disjoint training, development, and test sets, and keeps the harness optimizer and candidate-evaluation procedure fixed while studying curriculum adaptation.

    Source: §3 · p. 4

Abstract

from arXiv · show

Automated harness optimization can substantially improve LLM agents by iteratively updating their prompts, tool interfaces, and control logic from execution feedback. However, existing methods primarily optimize how the harness is updated while largely fixing which training scenarios generate the feedback that drives those updates. As the harness evolves, the scenarios most useful for further optimization can change, suggesting that the training curriculum itself should adapt alongside the harness. We formulate this missing dimension of harness optimization as an automated curriculum learning problem and introduce ActiveSaddler. ActiveSaddler models the evolving curriculum as a non-stationary bandit with dynamically instantiated optimization targets. It abstracts recurring failures into reusable failure-pattern arms, estimates the potential learning progress from further targeting each pattern, and adaptively balances revisiting known weaknesses with exploring unseen scenarios for new ones. Optimization outcomes continually update both the set of discovered failure patterns and their priorities, allowing the curriculum to co-evolve with the harness. Experiments on GAIA2 and Terminal-Bench 2.0 show that ActiveSaddler consistently discovers stronger harnesses, improving test Pass@1 by 4.4 and 7.5 percentage points over the same harness optimizer using a scenario order fixed before optimization, respectively. Ablations further show that these gains depend on dynamically constructing optimization targets, estimating their evolving utility, and balancing continued optimization with new failure discovery. Together, these results establish automated curriculum learning as a new crucial optimization dimension for harness optimization.

1 INTRODUCTION

Fixed scenario schedules cannot adapt to changing harness weaknesses or to the shifting value of revisiting known failures versus exploring unseen scenarios. ActiveSaddler adapts scenario selection as the harness evolves and improves test Pass@1 over a fixed-order optimizer on both evaluated benchmarks.

1 INTRODUCTION

  • Fixed schedules cannot respond to changing scenario value, risking early abandonment of unresolved failures or rollouts spent on scenarios with little remaining value.Under fixed-order optimization, many failures observed during training remain unresolved in the final harness.Source: §1 · p. 2
  • The value of revisiting known failures versus exploring unseen scenarios shifts during optimization, making adaptive allocation especially relevant under limited rollout budgets.Revisiting is more valuable when actionable unresolved failures remain; exploration gains value when few worthwhile observed targets remain.Source: §1 · p. 2
  • ActiveSaddler treats scenario selection as a non-stationary bandit, creating failure-pattern arms from observed failures and updating their priorities as optimization outcomes arrive.It balances revisiting existing arms with evaluating unseen scenarios to discover new failure patterns.Source: §1 · p. 2
  • ActiveSaddler improves test Pass@1 over the same harness optimizer with a fixed scenario order by 4.4 percentage points on GAIA2 and 7.5 on Terminal-Bench 2.0.It also achieves the strongest final harness performance across both benchmarks against existing harness optimizers and fixed curriculum baselines.Source: §1 · p. 2

2 RELATED WORK

Prior work studies harness updating, automated curricula, and adaptive task selection, but ActiveSaddler adapts the training scenarios that supply evidence for harness updates throughout offline optimization.

Automatic Harness Optimization

  • Automatic harness optimization spans search-based and learning-based methods, while ActiveSaddler leaves the harness-update mechanism unchanged and adapts which training scenarios provide its evidence.The paper focuses on offline, learning-based optimization under a standard train/dev/test protocol.Source: §2 · p. 3

Automated Curriculum Learning

  • Automated curricula prioritize training opportunities by evolving value, but harness weaknesses and their progress are implicit and must be inferred from failures and optimization history.A scenario may expose different failures as the harness changes, and one weakness may recur across scenarios.Source: §2 · p. 3

Adaptive Task Selection for Agent Optimization

  • Unlike methods that adapt evaluation-side task selection, ActiveSaddler adapts training scenarios throughout optimization to decide which known weaknesses to revisit and when to discover new ones.Task-CoEvolve selects validation tasks to distinguish candidate harnesses, while HarnessLens selects verification tasks relevant to proposed modifications.Source: §2 · p. 3

3 PRELIMINARIES

The preliminaries define harness performance from task-level execution outcomes, then frame training-scenario selection as a curriculum policy operating alongside a fixed harness optimizer.

Harness Optimization

  • A harness Hθ includes prompts, tool interfaces, and runtime control logic, and its execution on a task produces a trace and final answer under a stochastic distribution.Performance is evaluated with a task-level metric comparing the answer with the reference.Source: §3 · pp. 3–4
  • The setup assumes fixed, mutually disjoint training, development, and test sets, and selected training scenarios generate execution records for the current harness.The optimizer uses those records to diagnose deficiencies, construct and evaluate candidate updates, and retain the next harness.Source: §3 · p. 4

Automated Curriculum Learning for Harness Optimization

  • A curriculum policy selects training scenarios from information available before each iteration, while the optimizer and its candidate-evaluation procedure remain fixed.Scenario selection determines which evidence is collected; the optimizer determines how that evidence is used to update the harness.Source: §3 · p. 4
  • The final harness is selected using development-set performance, and test-set performance is reported only after optimization.Curriculum policies share a fixed rollout budget.Source: §3 · p. 4

4 PROPOSED METHOD: ACTIVESADDLER

ActiveSaddler treats budgeted scenario selection as a bandit whose optimization targets are instantiated from diagnosed harness weaknesses. It updates targets and priorities while alternating between revisiting known failures and exploring unseen scenarios.

Problem Formulation and Design Rationale

  • Failure-pattern arms group failures attributed to the same harness weakness, making a recurring deficiency—not an individual failed execution—the optimization target.The extractor derives candidate symptoms from failure evidence, then matches, composes, or instantiates arms; each arm retains supporting scenarios and evidence.Source: §4 · pp. 4–6
  • The curriculum balances optimizing known failure-pattern arms against exploring unseen scenarios that may reveal new weaknesses.The arm set is built during optimization because new patterns may emerge and only a subset of training scenarios is typically executed per iteration.Source: §4 · pp. 5–6

Workflow Overview

  • Each iteration chooses exploration or a known arm, executes the selected batch under the current harness, optimizes it, and updates the arm set and curriculum state.Failure evidence can match existing arms or instantiate new ones, so the curriculum co-evolves with changing unresolved weaknesses.Source: §4 · p. 5, Figure 2 · p. 5

Arm Prioritization

  • For known arms, an LLM estimates learning progress from severity, fixability, repair breadth, and side-effect risk, recomputing scores as the harness evolves.The score estimates the benefit of the next optimization step because population-performance change is not directly observable during optimization.Source: §4 · p. 6
  • The Arm Prioritizer samples an arm using a stochastic distribution favoring higher estimated progress, then builds the batch from that arm’s supporting scenarios.The sampling rule preserves the possibility of revisiting lower-scored arms as their utility changes.Source: §4 · p. 6

Exploration Control

  • After all training scenarios have been explored and each arm revisited, previously successful scenarios that formed no arm can be explored again to rediscover regressions.A newly failing scenario is processed by the Failure-Pattern Extractor without re-evaluating all prior successes after every harness update.Source: §4 · pp. 6–7

5 EXPERIMENTS

Experiments on GAIA2 and Terminal-Bench 2.0 compare ActiveSaddler with existing optimizers and fixed curricula, then test how its curriculum components contribute to performance. ActiveSaddler leads on both benchmarks, while ablations show that failure-pattern arms, adaptive scoring, and adaptive exploration each matter.

5.1 EXPERIMENTAL SETUP

  • The evaluation uses GAIA2 and Terminal-Bench 2.0 with ReAct-based and Terminus 2 base harnesses, respectively, and reports mean Pass@1 over three test-time executions.Train, development, and test sets use disjoint task groups; fixed curricula rank categories or scenarios by base-harness accuracy.Source: §5.1 · p. 7

5.2 MAIN RESULTS

  • +4.4 pp on GAIA2 and +7.5 pp on TB2: ActiveSaddler improves test Pass@1 over the same AutoSaddler optimizer using a fixed randomly shuffled scenario order.It reaches 59.8% and 80.0% Pass@1, respectively, and achieves the strongest test performance on both benchmarks.Source: §5.2 · pp. 7–8
  • +3.0 pp: adding ActiveSaddler to GEPA raises average Pass@1 from 54.2% to 57.2%, while EMA-based scoring and count-based UCB-AIR trail ActiveSaddler by 3.1 pp and 4.2 pp.An independent GAIA2 run also outperforms all fixed-order baselines.Source: §5.2 · p. 8
  • 58.5% development accuracy costs $298 with ActiveSaddler on GAIA2, compared with $1,360 for AutoSaddler and $673 for category ordering to reach that accuracy.On TB2, ActiveSaddler reaches 78.9% at $128; AutoSaddler and scenario ordering require $220 and $363 to match it.Source: §5.2 · p. 8

5.3 ABLATION STUDIES AND ANALYSIS

  • Category- and scenario-level arms reduce test Pass@1 relative to failure-pattern arms by 3.0 pp and 3.6 pp on GAIA2, and 10.8 pp and 6.7 pp on TB2.Failure-pattern arms preserve unresolved weaknesses; category arms can mask them, while scenario arms fragment them across individual tasks.Source: §5.3 · p. 8
  • Removing adaptive arm scoring reduces test Pass@1 by 4.5 pp on GAIA2 and 7.5 pp on TB2; ActiveSaddler also has the highest observed-failure-to-success conversion rate.Figure 3 tracks shifting pull-probability mass across arms and compares conversion of observed failures into successes.Source: Figure 3 · p. 8, §5.3 · p. 9
  • Replacing adaptive exploration with a fixed schedule reduces test Pass@1 by 4.5 pp on GAIA2 and 8.3 pp on TB2.DRAW decisions become less frequent later in optimization, and unresolved arms are more numerous at PULL than DRAW decisions under ActiveSaddler.Source: §5.3 · p. 9, Figure 4 · p. 9

6 CONCLUSION

ActiveSaddler adapts which training scenarios generate feedback for harness updates by dynamically creating and prioritizing failure-pattern arms, while balancing repair with discovery. It improves test Pass@1 over fixed-order scenario selection on both benchmarks, supporting curriculum learning as a complementary harness-optimization dimension.

6 CONCLUSION

  • ActiveSaddler models curriculum construction as a non-stationary bandit, dynamically instantiating failure-pattern arms and prioritizing them by evolving learning potential.It balances revisiting known weaknesses with exploring unseen scenarios.Source: §6 · pp. 9–10
  • +4.4 and +7.5 percentage points: ActiveSaddler improves test Pass@1 on GAIA2 and Terminal-Bench 2.0, respectively, over the same optimizer with a fixed scenario order.The conclusion also reports that it outperforms existing harness optimizers and fixed curricula.Source: §6 · p. 10
  • The results present automated curriculum learning as a complementary harness-optimization dimension: performance depends on which scenarios generate feedback as well as how that feedback updates the harness.Source: §6 · p. 10

A ADDITIONAL DETAILS ON EXPERIMENT SETUP

The appendix details benchmark splits, rollout settings, the pattern registry, and the ActiveSaddler algorithm. The algorithm alternates between exploring unseen scenarios and optimizing failure-pattern arms, then updates arms and curriculum state from execution outcomes.

A ADDITIONAL DETAILS ON EXPERIMENT SETUP

  • The Pattern Registry stores symptom-level failure patterns, tagged harness-trace-scenario tuples, root-cause evidence, activity observations, and learning-progress scores; the CLI provides scoped read views and controlled registry updates.Each session receives a capability manifest that restricts which commands it can use.Source: A ADDITIONAL DETAILS ON EXPERIMENT SETUP · p. 14, Table 2 · p. 14
  • GAIA2 splits by Universe to test generalization to task groups unseen during optimization, whereas Terminal-Bench 2.0 uses a uniform random partition because it has no natural grouping axis.Terminal-Bench 2.0 contains 89 tasks across heterogeneous domains.Source: A ADDITIONAL DETAILS ON EXPERIMENT SETUP · p. 15, Table 3 · p. 15
  • The shared rollout budget is 10 times the combined training and development set size—1,400 rollouts on GAIA2 and 490 on Terminal-Bench 2.0—with at most three scenarios per iteration.The arm-selection temperature is 0.15.Source: A ADDITIONAL DETAILS ON EXPERIMENT SETUP · p. 15

B ALGORITHM OF ACTIVESADDLER

  • At each iteration, the general algorithm chooses unseen scenarios or a failure-pattern arm, runs the fixed harness optimizer, extracts and normalizes failure patterns, and updates the curriculum state.The final harness is selected by development-set performance.Source: B ALGORITHM OF ACTIVESADDLER · pp. 15–16
  • Previously successful scenarios that did not instantiate an arm become eligible for renewed exploration only after every training scenario has been explored and every arm visited.All task-level executions, including optimizer candidate evaluations, count toward the fixed rollout budget.Source: B ALGORITHM OF ACTIVESADDLER · p. 15
  • The AutoSaddler instantiation keeps the three curriculum components unchanged but uses diagnosis-and-patching, reflection, and evolution operations to update its harness.Its extractor receives failures diagnosed before patching and failures that remain or emerge after post-patch evaluation.Source: B ALGORITHM OF ACTIVESADDLER · pp. 16, 18

C ROBUSTNESS TO OPTIMIZATION STOCHASTICITY

Two independent GAIA2 optimization runs test whether ActiveSaddler's gains persist across optimization trajectories. Both runs outperform fixed-order baselines across three held-out Universes.

C ROBUSTNESS TO OPTIMIZATION STOCHASTICITY

  • 59.8% and 61.6% Pass@1: ActiveSaddler exceeds fixed-order baselines by 3.9–4.4 pp and 5.0–5.7 pp in Runs 1 and 2, respectively, across three held-out Universes.Each harness is evaluated on 300 scenarios with three repeated test-time executions.Source: C ROBUSTNESS TO OPTIMIZATION… · p. 18, Table 4 · p. 18

D ALTERNATIVE ARM PRIORITIZATION AND EXPLORATION STRATEGIES

The tested LLM-based curriculum modules outperform simple alternatives for prioritizing known failure patterns and deciding when to explore. ActiveSaddler’s curriculum also improves GEPA across all three held-out universes.

D ALTERNATIVE ARM PRIORITIZATION AND EXPLORATION STRATEGIES

  • The alternatives replace either the Arm Prioritizer or Exploration Controller while keeping the Failure-Pattern Extractor, arm-selection distribution, and harness optimizer unchanged.EMA scoring tracks residual failure rates; UCB-AIR schedules exploration using execution and instantiated-arm counts rather than current optimization state.Source: D ALTERNATIVE ARM PRIORITIZATION AND… · pp. 18–19
  • 59.8% average Pass@1 for ActiveSaddler exceeds EMA scoring (56.7%), UCB-AIR exploration (55.6%), and removing the Arm Prioritizer (55.3%).EMA scoring is 1.4 points above removing the Arm Prioritizer, while UCB-AIR is 0.3 points above the fixed exploration schedule; both alternatives remain below ActiveSaddler.Source: Table 5 · p. 18, D ALTERNATIVE ARM PRIORITIZATION AND… · p. 19

E APPLYING ACTIVESADDLER TO GEPA

  • 3.0 percentage points: GEPA with ActiveSaddler reaches 57.2% average Pass@1 versus 54.2% for GEPA across three held-out universes.It also improves GEPA on each universe: 56.4% to 58.6%, 50.0% to 54.2%, and 57.2% to 59.7%.Source: Table 6 · p. 19, E APPLYING ACTIVESADDLER TO GEPA · p. 20

F END-TO-END OPTIMIZATION COST CHARACTERIZATION

The cost analysis combines optimizer-side computation with task-agent execution to compare end-to-end cost and development accuracy. Despite higher optimizer-side cost per patch, ActiveSaddler attains a better cost–accuracy trade-off on both benchmarks.

F END-TO-END OPTIMIZATION COST CHARACTERIZATION

  • ActiveSaddler’s optimizer-side cost per generated patch is $11.44 on GAIA2 and $11.37 on Terminal-Bench 2.0, above the listed alternatives on each benchmark.These costs include the curriculum layer’s failure-pattern extraction, arm scoring, and unseen-scenario exploration.Source: F END-TO-END OPTIMIZATION COST… · p. 20
  • ActiveSaddler reaches 58.5% development accuracy at $298 on GAIA2 and 84.2% at $181 on Terminal-Bench 2.0, with a better end-to-end cost–accuracy trade-off on both benchmarks.AutoSaddler reaches 58.5% on GAIA2 only at $1,360 and 78.9% on Terminal-Bench 2.0 at $220; Figure 5 tracks best-so-far accuracy against cumulative cost and usage.Source: Figure 5 · p. 21, F END-TO-END OPTIMIZATION COST… · p. 21
  • Average task-agent rollout cost is broadly comparable across methods on each benchmark, so total task-agent cost differences primarily reflect rollout allocation over the optimization trajectory.The end-to-end accounting combines optimizer-side and task-agent usage, with accuracy-ordered variants’ five full training-set evaluations included in cumulative cost.Source: F END-TO-END OPTIMIZATION COST… · p. 21

G ADDITIONAL ANALYSIS FOR RQ1

Failure-pattern arms preserve specific weaknesses as targets for retargeting and repair refinement, while category and scenario arms have contrasting granularity limitations. Their observed failure-hit rates and discovered patterns provide further evidence about this representation.

G ADDITIONAL ANALYSIS FOR RQ1

  • Retargeting a failure-pattern arm refined an initially unsuccessful repair: extending its hook to multiline replies made the scenario pass.The first hook handled only single-line drafts and therefore did not trigger for the model’s multiline reply.Source: G ADDITIONAL ANALYSIS FOR RQ1 · pp. 21–22, Figure 6 · p. 22
  • Category arms can let successes on unrelated scenarios obscure unresolved weaknesses, while scenario arms fragment optimization across many arms without encoding the underlying weakness and repair.The category-arm variant never sampled tnxtee; the scenario-arm variant had 75 arms, more than twice the maximum of 35 failure-pattern arms.Source: G ADDITIONAL ANALYSIS FOR RQ1 · pp. 22–23
  • Failure-pattern arms achieve failure hit rates of 52.9% on GAIA2 and 60.4% on TB2, exceeding category and scenario arms on both benchmarks.A failure hit occurs when an iteration’s sampled mini-batch contains at least one unresolved failure; budgets are 1,400 rollouts on GAIA2 and 490 on TB2.Source: G ADDITIONAL ANALYSIS FOR RQ1 · p. 23, Figure 8 · p. 23

H DISCOVERED FAILURE PATTERNS

  • Failure-pattern arms consolidate recurring weaknesses across scenarios while allowing one scenario to expose multiple patterns; the induced patterns were not predefined by a taxonomy.The reported many-to-many examples include GAIA2 P8 and Terminal-Bench 2.0 P13, each spanning four scenarios or tasks.Source: Table 9 · p. 24, H DISCOVERED FAILURE PATTERNS · p. 25, Table 10 · p. 25
  • A repaired weakness’s arm drops in priority after an accepted patch and successful rechecks, whereas P8 retains priority through transient successes and gains evidence when it recurs in new scenarios.For P8, a later accepted patch raises best development-set accuracy from 61.5% to 63.1%.Source: Figure 9 · p. 26, Figure 10 · p. 26

I ADDITIONAL ANALYSIS FOR RQ2

Arm priorities change as supporting-scenario evidence accumulates and the pool of discovered weaknesses expands. The resulting stochastic selection favors high-priority arms while retaining opportunities to revisit others as their relative value changes.

I ADDITIONAL ANALYSIS FOR RQ2

  • The arm pool grows from 6 to 35 as patterns are discovered, requiring priorities to be reassessed over an expanding target set.Pull probabilities are recomputed at arm-selection iterations; the analysis covers 34 such iterations between Iterations 1 and 51.Source: I ADDITIONAL ANALYSIS FOR RQ2 · p. 27, Figure 11 · p. 27
  • At Iteration 51, 24 of 35 arms whose supporting scenarios had all succeeded on re-evaluation receive 26.2% of pull-probability mass, while the other 11 receive 73.8%.Those shares are 0.38× and 2.35× their respective uniform-allocation baselines.Source: I ADDITIONAL ANALYSIS FOR RQ2 · p. 27
  • Additional scenarios exhibiting an existing failure pattern are followed by increased pull probability: 4.8 percentage points for P8 and 9.4 points for P10.The added scenarios expand the arms’ supporting sets and provide evidence that the weaknesses remain relevant.Source: I ADDITIONAL ANALYSIS FOR RQ2 · p. 27
  • Across 34 pulls, the sampler selects 19 of 35 patterns; 62% of pulls come from the top three arms, but selections extend to rank 20.The selected arm has a median priority rank of third, indicating that selection favors high-priority arms without being greedy.Source: I ADDITIONAL ANALYSIS FOR RQ2 · p. 28, Figure 12 · p. 28
  • The changing arm pool and evidence produce a focused but non-greedy curriculum, with substantial probability assigned to current priorities while other arms remain selectable.The set of arms receiving substantial probability and those actually selected changes over time.Source: I ADDITIONAL ANALYSIS FOR RQ2 · p. 28

J ADDITIONAL ANALYSIS FOR RQ3

ActiveSaddler’s PULL/DRAW decisions track the number of unresolved weaknesses, while its prompts operationalize failure extraction, arm scoring, and exploration decisions.

J ADDITIONAL ANALYSIS FOR RQ3

  • PULL decisions occur with 2.0× as many unresolved arms as DRAW on GAIA2 (1.62 vs. 0.81) and 6.9× as many on Terminal-Bench 2.0 (0.76 vs. 0.11).The fixed schedule’s corresponding ratios are 0.8× on GAIA2 (0.21 vs. 0.25) and 1.3× on Terminal-Bench 2.0 (1.14 vs. 0.86), indicating little dependence on unresolved-arm counts.Source: J ADDITIONAL ANALYSIS FOR RQ3 · p. 29

K INSTRUCTIONS USED IN ACTIVESADDLER

  • Failure extraction abstracts each failed execution independently, then links symptoms to existing patterns, composes patterns, or instantiates new failure-pattern arms.The instructions separate extraction from normalization, consulting existing patterns only during normalization to avoid anchoring bias.Source: K INSTRUCTIONS USED IN ACTIVESADDLER · pp. 29–30, Figure 14 · p. 31
  • Arm scoring estimates each pattern’s learning progress from severity, fixability, breadth, and side-effect risk, then uses scores to prioritize known targets.The scoring prompt defines the score as the mean of severity, fixability, breadth, and one minus side-effect risk.Source: K INSTRUCTIONS USED IN ACTIVESADDLER · pp. 29, 32, 33, Figure 15 · p. 34
  • The exploration controller chooses between repairing a known failure-pattern arm (PULL) and probing unseen scenarios for new failure types (DRAW).It weighs whether a worthwhile unresolved known target remains against the value of further exploration.Source: K INSTRUCTIONS USED IN ACTIVESADDLER · pp. 29, 34, Figure 16 · p. 36
  • The PULL/DRAW decision is holistic: it weighs available evidence rather than relying on a fixed formula, threshold, or single statistic.The prompt defines P_t as discovered failure patterns and U_t as scenarios still unseen at iteration t.Source: K INSTRUCTIONS USED IN ACTIVESADDLER · pp. 34–35

L LIMITATIONS AND RESPONSIBLE DEPLOYMENT

The paper’s benchmark study does not establish production readiness: it omits dedicated safety, security, and privacy evaluations and calls for independent governance and deployment controls.

L LIMITATIONS AND RESPONSIBLE DEPLOYMENT

  • The benchmark results characterize controlled research settings, not production safety, security, privacy, or governance readiness.ActiveSaddler prioritizes failures and optimizer objectives but does not determine whether resulting behavior meets independent requirements.Source: L LIMITATIONS AND RESPONSIBLE DEPLOYMENT · p. 37
  • The study does not evaluate misuse or unsafe deployment, and task-level objectives and failure signals are not a complete specification of acceptable behavior.The paper identifies independent safety criteria, adversarial testing, and safety regression checks as needs for production deployment.Source: L LIMITATIONS AND RESPONSIBLE DEPLOYMENT · p. 37
  • Production security risks—including unsafe command execution, privilege misuse, and unauthorized access—are outside the study’s evaluation.The paper calls for explicit security boundaries and review of updates affecting permissions, capabilities, tools, or other security-critical behavior.Source: L LIMITATIONS AND RESPONSIBLE DEPLOYMENT · p. 37
  • Public and synthetic benchmarks without real user data do not exercise privacy risks from sensitive production traces.Production privacy controls are outside the study’s scope.Source: L LIMITATIONS AND RESPONSIBLE DEPLOYMENT · p. 37
  • Automatic acceptance of an update is not production approval; deployment still requires independent, context-sensitive governance and risk review.The paper says oversight should depend on exposed capabilities, resource sensitivity, and potential impact.Source: L LIMITATIONS AND RESPONSIBLE DEPLOYMENT · p. 37

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