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Is Your Long-Horizon Benchmark Really Deep? Measuring and Synthesizing Depth in Terminal Tasks

Yu Jiang, Yue Huang, Hang Hua, Yuyang Wu, Zixuan Weng, Olexandr Isayev, Yue Zhao, Zinan Lin, Xiangliang Zhang

arXiv:2611.01927v1cs.AI

TL;DR

Long-horizon terminal tasks are needed to evaluate and train agents, yet common task-length and difficulty measures do not capture whether tasks require sustained interaction. The paper measures work and depth and generates tasks segment by segment in real software environments. Its 105 tasks exceed every audited dataset in work and depth, and none of six current models passes half of them.

  • Problem

    Common turn counts reflect model habits as well as task demands, while generation-time difficulty labels describe requested difficulty rather than how often models pass.

    Source: §1 · p. 2

  • Method

    The paper defines work and depth, estimates depth with turn-capped evaluation, audits six datasets, and generates tasks one segment at a time in a real environment.

    Source: §1 · p. 3, §2 · p. 4

  • Results

    The 105 generated tasks exceed every audited dataset in work and depth, and frontier models pass fewer than half of them.

    Source: §1 · p. 3, §5 · p. 12

  • Takeaways & Limitations

    The authors recommend reporting depth alongside work when building benchmarks and training data.

    Source: §5 · p. 12

Abstract

from arXiv · show

Long-horizon terminal tasks are used to evaluate and train agents. Human-curated datasets are scarce, so many synthesis methods have emerged. They usually measure the length of a task by the turns a model needs to pass it, and its difficulty by labels the LLM sets during generation. We find, however, that the turn count largely reflects the habits of the model, not only the demands of the task, and that the labels barely track how often models pass. We describe a terminal task by its work, the number of operations it requires, and its depth, the number of turns that must happen in sequence because the commands of each turn can only be chosen after reading the output of the previous one. Estimating depth as the smallest turn cap, stated to the agent, under which the task passes, we audit 734 tasks from six existing datasets. Synthesis has mostly added work, not depth: 94% of the solved synthetic tasks pass within three turns, yet depth, unlike the turn count, predicts difficulty beyond work. We propose DEPTH, in which a generation agent writes each task one segment at a time in a real software environment, carrying out each segment before writing it and starting the next from the state it leaves. Our 105 tasks exceed every audited dataset in depth and work, and none of six current models passes half of them. Fine-tuning Qwen3-8B on runs from these tasks improves it on two terminal benchmarks.

1 Introduction

Long-horizon terminal tasks are needed for evaluating and training agents, but existing measures of task length and generated difficulty do not reliably capture the sequential interaction tasks demand. The paper defines work and depth, audits existing datasets, and introduces segmented task generation to produce tasks with both high work and depth.

1 Introduction

  • Existing length measures reflect model habits, while generated difficulty labels record what was requested rather than how often models pass.Many synthetic tasks pass within a few turns, sometimes without the agent reading any output, reducing them to single-turn generation.Source: §1 · p. 2
  • Work counts operations needed when the environment state is known, while depth counts sequential turns whose commands depend on prior output.The paper estimates depth through turn-capped evaluation and audits six existing datasets, finding synthesis mainly increases work rather than depth.Source: Figure 1 · p. 2, §1 · p. 3
  • DEPTH generates tasks segment by segment in a real software environment, carrying each resulting state forward so later segments depend on earlier ones.The agent performs and tests each segment before writing the next.Source: §1 · p. 3
  • DEPTH tasks exceed all six audited datasets in work and depth, and frontier models pass fewer than half of them.The introduction also reports meaningful discrimination across models with different capabilities.Source: §1 · p. 3
  • The authors report that fine-tuning Qwen3-8B on trajectories from their deep terminal tasks, built from real software environments, improves model performance on two existing terminal benchmarks.Source: §1 · p. 3

2 Preliminaries

The preliminaries formalize terminal tasks, interaction turns, and work and depth. They distinguish ideal task properties from empirical estimates based on turn-capped evaluation and reference-solution lines.

2 Preliminaries

  • A Harbor task includes an instruction, a Docker environment and initial state, a reference shell-script solution, and a verifier.The multi-segment form delivers segments one at a time in one container, and the agent sees neither the reference solution nor verifier.Source: §2 · p. 3
  • A turn is one reply containing a batch of commands, followed by terminal output; a run's turns are counted across all segments.Under turn-capped evaluation, the agent is told the cap and the harness stops the run when it is reached.Source: §2 · p. 4
  • Depth is the fewest turns sufficient across plausible initial states the agent cannot distinguish before acting.Dependencies add depth only when later commands cannot be written in the same reply without reading earlier output.Source: §2 · p. 4
  • Work counts command and file-content lines in a passing sequence from a known initial state.Exploration and error recovery count toward depth, not work.Source: §2 · p. 4
  • The empirical depth estimate D is the smallest evaluated turn cap under which a model in the chosen set passes; W counts nonblank, noncomment reference-solution lines.D depends on the models, harness, and single initial state, so it is an estimate rather than a bound on ideal depth.Source: §2 · p. 4

3 Method

The method estimates task depth through turn-capped evaluation and constructs multi-segment tasks by executing each segment in a real software environment before writing the next. It also uses verified task runs as training data.

3.1 Measuring Depth

  • Depth is estimated as the smallest stated turn cap under which a model passes, using the smallest estimate from models that provide one.Tasks passed only without a cap are recorded above the largest cap; tasks never passed have no estimate.Source: §3.1 · p. 5

3.2 Seed Environments

  • The authors selected 136 seed environments from public terminal-task packages after filtering and judging their software realism and capacity for multi-segment work.Generation and evaluation run offline, allowing reference solutions to be replayed later.Source: §3.2 · p. 6

3.3 Segmented Generation

  • Each segment is executed in the environment before the next is written from the resulting state, creating dependencies across task segments.A later segment must use a result that exists only after the previous segment’s key step, requiring the agent to read output and adapt.Source: §3.3 · p. 6

3.4 Verifier Design and Acceptance

  • Verifiers check outcomes through the running software, must fail before the segment and when its key step is removed, and name checked items in the instruction.Accepted tasks pass every segment when their reference solutions are replayed in a fresh offline container.Source: §3.4 · pp. 6–7

3.5 Training Data

  • Training samples retain consecutive passing segments from evaluated runs, split where context compression occurs, and are used to fine-tune Qwen3-8B.New samples after a split begin from terminus-2’s summary and questions and answers about the work so far.Source: §3.5 · p. 7

4 Experiments

The experiments audit 734 tasks across six datasets, test whether depth tracks difficulty, evaluate the authors’ tasks on six models, and assess fine-tuning on two benchmarks. They find that existing synthetic tasks are usually shallow, while the new tasks are deeper and challenging for current models.

4.2 Auditing Existing Datasets

  • 94% of solved synthetic tasks had D≤3, and nearly nine in ten TMax and SETA tasks had D=1.A task with D=1 can be passed by issuing every command in the first turn, without reading output.Source: §4.2 · p. 8
  • Synthetic tasks had median work W=68 lines versus 17 for human-curated tasks, but only 6% versus 10% had D≥5.Even RST, the deepest synthetic dataset, had 79% of solved tasks with D≤3.Source: §4.2 · p. 9

4.3 Depth Predicts Difficulty

  • With work controlled, depth correlated with lower pass rates on TB2 and TW (−0.31) and on the authors’ tasks (−0.29).Controlling for depth, turn count N was unrelated to public pass rates on TB2 and TW (+0.06, 95% CI −0.18 to +0.26); generated difficulty labels correlated only −0.07 to −0.11, while TB2 author ratings correlated with depth at +0.55 controlling for work.Source: §4.3 · p. 9

4.4 Our Tasks

  • 44 of the 54 solved author-created tasks had D≥40, and their median work was 315 lines versus the existing synthetic datasets’ 90th-percentile 226 lines.Depth did not decrease after segment merging for 68% of 25 sampled tasks; in 35 paired seed environments, the authors’ tasks had higher depth in every pair.Source: §4.4 · p. 10
  • Checks failed when a segment’s key step was removed in 99.0% of cases, and the next segment failed in 84.2%.Checks also failed before a segment was done in 99.8% of replayed segments.Source: §4.4 · p. 10

4.5 Evaluating Models

  • A per-segment pass-rate gap of at most 0.14 compounded over six segments into a nearly threefold gap in runs passing all six: 47% versus 16%.Per-segment pass rates ranged from 0.88 for Claude Sonnet 5.5 to 0.74 for GLM-5.3-Flash.Source: §4.5 · p. 10, Figure 6 · p. 11
  • 17%–47%: pass@1 ranges across the six models on the authors’ tasks, and 29 of 90 tasks were passed by none.The range is from GLM-5.3-Flash to Claude Sonnet 5.5.Source: §4.5 · p. 10, Table 2 · p. 11

4.6 Fine-Tuning

  • 2.6% to 4.9% on TB2.1 and 3.4% to 9.1% on TBLite: fine-tuning Qwen3-8B raised pass@1 on both benchmarks.The fine-tuned model also exceeded the base model’s thinking mode on both.Source: §4.6 · p. 11

5 Conclusion

The paper defines and measures terminal-task depth, finding that existing synthesis mostly adds work rather than sequential interaction. It introduces segmented task generation and reports deeper, larger tasks, model challenges, and fine-tuning gains.

5 Conclusion

  • Depth measures sequential turns via turn-capped evaluation, while work measures required operations; the audit finds synthesis has mostly added work, not depth.The paper also reports that deeper tasks are harder once work is controlled.Source: §5 · p. 11
  • The 105 generated tasks exceed every audited dataset in work and depth; frontier models pass fewer than half, and fine-tuning Qwen3-8B on their runs improves performance on two benchmarks.The conclusion recommends reporting depth alongside work.Source: §5 · p. 12

A Related Work

  • Prior long-horizon benchmarks measure length by human time, model turns, or milestone chaining; this paper instead records the smallest passing turn cap.This estimates turns that must happen in sequence rather than turns a model happens to take.Source: A Related Work · p. 15
  • Prior task pipelines obtain dependencies by construction or LLM checks; DEPTH has a generation agent execute each segment before writing the next.The pipeline also replays the reference solution in a fresh container and checks that removing a segment's key step causes failure.Source: A Related Work · p. 15

B Measurement Details

The measurement protocol estimates depth by testing turn caps and compares two models, while standardized harness settings govern task execution. For generated multi-segment tasks, later instructions arrive only after earlier segments are completed.

Measurement procedure

  • Depth estimation first checks two uncapped runs, then tests lower caps in increasing order; the first passing cap supplies the estimate.If no uncapped run passes, the task receives no estimate.Source: B Measurement Details · p. 15
  • GLM-5.3-Flash runs only below DeepSeek-V4.1-Flash's estimate, so it can lower depth estimates but is not tested on tasks the first model does not solve.The second model is run with up to two attempts per cap, without uncapped runs.Source: B Measurement Details · pp. 15–16
  • Under a turn cap, the harness tells agents the limit and encourages batching commands; multi-segment tasks apply the cap across all segments together.The harness stops a run after the cap, and unfinished parts fail.Source: B Measurement Details · p. 16

Harness settings

  • Existing and generated tasks use terminus-2, which returns at most 10,000 bytes of output per turn and allows up to 150 turns without a cap.Existing tasks receive a 30-minute limit; generated tasks receive three hours, with shorter limits for some multi-segment tasks.Source: B Measurement Details · p. 16

Segment delivery

  • Multi-segment tasks deliver one part at a time in the same session, and later instructions build on prior environment changes without exposing future instructions or checks.A run stops at the first failed segment; the final-segment-only completion confirmation avoids adding turns to earlier segments.Source: B Measurement Details · p. 16

Model selection

  • Calibration tasks show that DeepSeek-V4.1-Flash and GLM-5.3-Flash pass parallel and static-chain tasks under cap 3, but no tested model passes the six-step control.The control encrypts each step's instruction and data using the preceding step's answer, requiring a turn of its own.Source: B Measurement Details · pp. 16–17

C Dataset Selection and Sampling

The audit samples six runnable terminal-task datasets under shared eligibility and verification rules. Sampling favors tasks with prior model passes and light dependencies, so the authors compare samples with original label distributions and reweight depth shares.

C Dataset Selection and Sampling

  • The audit covers four synthetic datasets and two human-curated datasets; all sampled tasks must have runnable packages, pass the reference-solution check, and fail when left untouched.TB2 and TW contribute all eligible tasks; FACET and RST are extended to about 50 solved tasks because fewer tasks are solved.Source: C Dataset Selection and Sampling · pp. 17–18
  • Sampling excludes tasks requiring GPUs, privileged containers, systemd, external services, large downloads, or large and lengthy builds, and tasks are used as released.Tasks are selected within reported label groups, with a fixed seed and interleaving to balance prefixes.Source: C Dataset Selection and Sampling · p. 18
  • Reweighting to original label proportions lowers the solved-task share with D≥5 from 2.2% to 1.5% for SETA and from 20.7% to 14.9% for RST.The hardest TMax level is underrepresented because few tasks were ever passed; its seven solved sampled tasks all have D≤3.Source: C Dataset Selection and Sampling · p. 18, Table 5 · p. 19

D Additional Audit Results

Additional analyses find that depth-related difficulty results persist under alternate model estimates, while broader work measures and audit details clarify task comparisons. Some shallow tasks replace environmental observation with additional reasoning.

D Additional Audit Results

  • In synthetic datasets, mean pass rates drop mainly at the hardest label levels, which contain few tasks.Figure 8 compares pass rates across released difficulty labels and reports their rank correlations with those labels.Source: D Additional Audit Results · p. 19, Figure 8 · p. 19
  • At cap D, passing runs issue about 1.06 times as many command lines as uncapped runs; at D=1, output contains 1.98 times as many tokens.The authors interpret this as work staying the same while the model substitutes reasoning about the environment for looking at it.Source: D Additional Audit Results · pp. 19–20
  • Across all sampled tasks, uncapped runs take 6 to 13 turns on existing datasets versus 45 turns on generated tasks.The means span 1.0 to 3.3 minutes and 7 to 24 thousand tokens for existing datasets, versus 8.9 minutes and 57 thousand tokens for generated tasks; generated-task values are lower than for solved tasks because runs stop at the first failed segment.Source: D Additional Audit Results · p. 20, Table 6 · p. 20
  • Our tasks have the largest median work under lines, BPE tokens, and characters; compared with FACET, they are higher by 41%, 50%, and 9%, respectively.Across 739 tasks, lines correlate 0.98 with tokens and 0.97 with characters.Source: D Additional Audit Results · p. 20, Figure 9 · p. 20

E.1 The Model Set

  • GLM-5.3-Flash lowers D on 40 of 160 existing tasks it tested and brings 11 of 40 tasks estimated at D≥5 by DeepSeek below 5; the share falls in five datasets and is unchanged in SETA.On the 54 solved generated tasks, it lowers D on four, each by one cap and none below 20.Source: §E.1 · p. 21, Figure 10 · p. 21
  • With DeepSeek-V4.1-Flash alone, 92% of solved synthetic tasks have D≤3, compared with 94% using both models; deeper tasks remain associated with lower pass rates after controlling for work.For TB2 and TW, the partial correlation is −0.29 with one model and −0.31 with both; on TB2 alone it is −0.54 and −0.49, respectively.Source: Figure 8 · p. 19, §E.1 · p. 21

E.2 Failed Runs

The analysis tests whether failed runs can inflate measured depth and its association with task difficulty. Restricting the analysis to tasks with two successful uncapped runs leaves the depth–difficulty relationship and depth distributions intact.

E.2 Failed Runs

  • D may be raised by a cap when both runs fail, potentially creating a correlation with difficulty unrelated to depth.This can occur when DeepSeek-V4.1-Flash rarely passes a task.Source: §E.2 · p. 21
  • Figure 11 compares rank partial correlations using D from both models or DeepSeek-V4.1-Flash alone, with task counts shown.For ours and pooled rows, pass rate averages GPT-6 Luna, MiMo-V2.6-Flash, and GPT-6.1 Sol, excluding GLM-5.3-Flash.Source: Figure 11 · p. 22
  • On tasks with R=1, depth’s rank partial correlation with pass rate controlling for work is −0.43 for human-curated sets, −0.39 for ours, and −0.40 for both pooled.All three are larger in magnitude than for all solved tasks; Figure 12 also shows the corresponding distributions.Source: §E.2 · p. 22, Figure 12 · p. 22
  • On tasks with R=1, 95% of synthetic tasks have D≤3, while all ours have D≥5 and 70% have D≥40.The depth distributions do not change under this restriction.Source: §E.2 · p. 22, Figure 12 · p. 22

F Checks

The checks evaluate task outcomes through software state and observable evidence, rather than trusting generated artifacts or stale values. The section also describes replay tests that assess whether checks detect changes to key steps and events.

F Checks

  • Each segment passes only when every check succeeds, and a script that stops before its final check fails.Check functions print PASS or FAIL lines; the generation agent chooses what to ask the system and what answer to require.Source: F Checks · p. 23, Table 7 · p. 23
  • Readers support report fields and table columns, while comparisons normalize formatting and provide typed checks for numbers, versions, booleans, sets, and sequences.Commands have a 120-second limit and a check script must finish within 10 minutes.Source: F Checks · p. 23
  • Check values are computed from the live system, and report files are read through declared field and column names using the exact format stated in the instruction.This prevents checks from relying on values recorded during generation.Source: F Checks · p. 23
  • Checks rely on software traces for requested events and on a service’s own client for service state; files written by the solver are not event evidence.Ambiguous requirements accept both defensible readings, while measured values are excluded from names stated in the instruction.Source: F Checks · p. 24
  • The replay study changes one marked key step or requested event in each of 105 tasks and records whether the affected segment’s checks fail.Table 8 reports counts over segments, except removed events counted individually; crashes do not count as failures, and 2–15 segments per change are omitted when replays do not produce a result.Source: F Checks · p. 24, Table 8 · p. 24

G Our Dataset

The dataset contains 105 tasks built from seed environments, with long reference solutions and substantial measured depth. A separate merging experiment finds that putting segment requirements into one instruction usually does not reduce measured depth.

G Our Dataset

  • Of 54 solved tasks, 23 have D=40 and 17 have D=80; four tasks are not passed within 80 turns.The 105 tasks comprise 98 six-segment tasks, four five-segment tasks, and three three-segment tasks.Source: G Our Dataset · p. 24, Table 9 · p. 25
  • Across all 35 seed-environment pairs, the new task’s reference solution is at least twice as long as the original, with median lengths of 324 versus 10 lines.Figure 13 compares depth and reference-solution length for the paired tasks.Source: G Our Dataset · p. 24, Figure 13 · p. 25
  • The example task has D=40, N=37, and W=162, with later segments depending on discoveries and changes from earlier ones.For example, segment 3 uses the plugin layout found in segment 2, and segment 6 relocates the package built earlier.Source: G Our Dataset · p. 24, Table 10 · p. 26
  • Task statistics show median reference-solution lengths of 330 lines per task and 52 per segment, with 1,406 instruction words per task and 234 per segment.The instructions state a median of 83 names per task and 14 per segment; later-segment instructions are longer than first-segment instructions (244 versus 196 words).Source: G Our Dataset · pp. 24–25, Table 9 · p. 25

H Merging Segments into One Instruction

  • For 17 of 25 tasks, merging all segment requirements into one instruction does not decrease D; eight retain the original cap and nine are not solved within it.Among the other eight, seven drop by one cap level and one by two; the experiment preserves work and environment while combining instructions, scripts, and checks.Source: H Merging Segments into One Instruction · pp. 26–27

I Evaluation Details

The evaluation reports model-specific refusals and run-ending behaviors, alongside examples of early submission, broken sessions, and timeouts. Run limits and provider settings varied by model, and invalid or refused runs were excluded from the affected model’s denominator.

I Evaluation Details

  • Models ran with a 150-turn cap and task time limits, using fixed serving providers for several models and provider-default reasoning effort except for GPT-6 Luna.GPT-6 Luna used medium reasoning effort; evaluation runs for DeepSeek-V4.1-Flash were separate from depth-measurement runs.Source: I Evaluation Details · p. 27
  • GPT-6.1 Sol refused seven tasks and Claude Sonnet 5.5 refused 12 and returned empty responses on three more; the seven GPT refusals overlap with Claude’s 15 invalid tasks.Refused tasks mostly concern system security, although every task runs offline; tasks without valid results are omitted from that model’s denominator.Source: I Evaluation Details · p. 27
  • GPT-6 Luna and GLM-5.3-Flash often submit within three turns without verifying work (18% and 20%), while GLM-5.3-Flash breaks the terminal session in 13% of runs.MiMo-V2.6-Flash reaches a segment time limit or turn limit in 33% of runs; Figure 14 classifies uncapped outcomes by how runs stop.Source: I Evaluation Details · p. 27, Figure 14 · p. 27
  • Examples trace early submission to a command awaiting confirmation and broken sessions to persistent set -e behavior in the interactive shell.In the Pacemaker task, the first command swallowed later commands; in the Laravel task, a later nonzero status exited the shell and ended the session.Source: I Evaluation Details · p. 28
  • A sample-job run timed out after MiMo-V2.6-Flash investigated a second cause without first saving a passing output.The task’s problems were a wrong entry point causing a null-pointer exception and a log level that hid the needed output line.Source: I Evaluation Details · p. 28

J Training Data Details

Training data is adapted from long-context runs to reproduce terminus-2’s context-compression boundary. Samples model the post-compression inputs and the three compression calls, while unparseable replies are excluded from training loss.

J Training Data Details

  • Runs are cut wherever terminus-2 would compress context, because the collected long-context runs contain no compression.The sample after each cut begins with the messages terminus-2 would give the model.Source: J Training Data Details · p. 28
  • Post-cut samples contain an LLM-generated summary, questions, answers, and an instruction covering all segments delivered so far.The three compression calls also become samples with prose targets.Source: J Training Data Details · p. 28
  • Harness replies that cannot be parsed are retained with the error message but excluded from the loss.Source: J Training Data Details · p. 28

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