Paper reading
Agent Planning Benchmark: Diagnosing Planning and Failure in LLM Agents
The paper in 90 seconds
- Problem: End-task success combines plan quality, tool calls, environmental state, and recovery after failure. The final answer alone rarely reveals whether an agent misunderstood the goal, omitted a step, chose the wrong tool, or encountered an execution-side problem.
- Core insight: Separate planning tests by information conditions and failure pressures, then read correctness, plan grade, and error categories together. APB has five settings: Holistic Planning, Step-wise Planning, Tool-Extraneous, Tool-Broken, and Unsolvable.
- Strongest evidence: The authors report 4,209 cases across 22 domains and 12 multimodal models. They also compare direct execution, plan-first execution, and one round of APB-guided revision on 200 ToolSandbox tasks and 200 τ²-bench tasks; the refined condition has higher execution metrics than plan-first for all three representative models (Sections 4.2 and 5.1).
- Main boundary: The data include model-assisted augmentation and author-constructed cases, and LLM judges can still make mistakes. The two execution environments are controlled tests, not evidence of reliability in open-ended production work. The paper also reports that extended reflection can over-correct in some step-wise settings.
This reading follows the arXiv v3 preprint submitted on October 5, 2026; its peer-review status is not established. APB is not another single leaderboard asking which agent is strongest. It decomposes the planning work performed before execution: Can a model choose a complete tool chain in advance? Can it select the next action after seeing intermediate feedback? Can it adjust when tools are irrelevant or broken? Does it stop when the available evidence makes the task infeasible? The authors evaluate 12 models across five settings, then carry the diagnostic signal into controlled studies in two existing execution environments. This connects plan scoring to downstream behavior, but the evidence remains author-reported results on particular benchmarks and tasks, not a production reliability rate.
Prior approach limitation: why task success alone is not enough
End-to-end agent benchmarks matter because they answer whether a system eventually completed the task. But that number folds together several stages: how the model understood the request, whether it decomposed the work completely, whether its tools and arguments were appropriate, whether the returned data were correct, whether the environment was stable, and whether the system recovered after a failure. A temporary tool outage may lower success even when the chosen strategy was reasonable. Conversely, a tool can respond normally while an omitted prerequisite in the plan causes a later step to fail.
APB therefore asks a diagnostic question: before tools run, or after only part of a trajectory is available, is the proposed planning behavior sound? When the tools, their returns, or task feasibility change, does the model adapt appropriately? Outcome-oriented evaluations remain necessary; the gap is that they often do not locate which layer of planning caused a failure. The authors position APB as an upstream complement to full execution benchmarks, not a replacement (Sections 1, 3, and 6).
“Upstream” does not mean that a planning score can predict deployment outcomes by itself. APB constrains its inputs to natural-language or multimodal tasks, fixed tool specifications, and the trajectory content supplied to the model. It does not model stochastic state transitions, belief-state updates, or probabilities of action outcomes. The benchmark can ask whether a plan meets the task constraints; it cannot fully represent production tool randomness, permission policies, network outages, or long-term side effects (Limitations).
Core intuition: the same model faces different planning questions
APB treats planning as more than one abstract capability. It varies visible information, planning scope, and the source of pressure. The central contrast is between Holistic and Step-wise planning. In Holistic, a model receives a goal, system prompt, and tool set, then produces a complete plan and tool chain in one pass. In Step-wise, it receives a partial execution trajectory—including previous actions and tool returns—and predicts the next one to three actions (Section 3.1). The former tests global decomposition and long-range consistency; the latter tests whether the model uses the current state and feedback to make a local decision.
These are not merely two levels on one difficulty scale. A tool chain may look complete at a global level but contain an invalid argument at one step. A step-wise action may be locally plausible yet lead into a dead end because the model cannot see a more distant dependency. APB tests these different information conditions rather than assuming that success in the short term implies a reliable long-range plan.
What the five settings diagnose
| Setting | Model input and required output | Diagnostic question |
|---|---|---|
| Holistic (1,109 cases) | Goal and tool set; output a complete plan and tool chain | Can the model decompose a long-horizon goal while preserving step order, dependencies, and constraints? |
| Step-wise (900 cases) | Partial trajectory and tool feedback; predict the next action or next two to three actions | Can the model update its local decision from the current state rather than conclude too early? |
| Tool-Extraneous (1,500 cases) | Add 2, 4, 6, 8, or 10 semantically similar but irrelevant tools to the original tool set | Does the model select distractor tools when the catalog becomes noisy? |
| Tool-Broken (300 cases) | A critical tool returns an error, and a replacement tool is available | Can the model diagnose the failure and switch tools instead of retrying the broken one or abandoning the task? |
| Unsolvable (400 cases) | Information or tools are missing, visual evidence is inaccessible, or constraints conflict | Does the model recognize infeasibility and refuse instead of inventing missing facts? |
The paper gives the size of each setting and says that Tool-Extraneous cases are expanded from selected base cases, while Unsolvable cases are also derived from existing holistic tasks. These counts do not represent five entirely disjoint, independently sampled task groups (Section 3.1; Appendix B).
Worked example: follow one input through the evaluation
Imagine a user asks an agent to compare several documents, extract their constraints, and create a summary with tools. A Holistic model sees the goal and tool descriptions at once. It must propose the sequence of steps, tools, and arguments; the evaluator then judges whether the whole plan is feasible and covers the request. A Step-wise model instead sees earlier searches and tool returns—for example, one document lacks a date—and predicts only the next action. This tests whether it incorporates new evidence rather than preserving its initial assumption.
Now add similarly named but irrelevant tools to the catalog and test tool selection under distraction. Replace a critical tool’s return with an error and provide a substitute to test recovery. Finally, remove necessary data or create conflicting constraints to test whether the model stops. For each case, the model generates an answer and the evaluation process assesses correctness, severity, and error type. This example is an instructional adaptation of the paper’s protocol, not one particular author-reported case or an additional result.
Method flow: three complementary views of performance
APB does not rely on a single score. The first layer is Plan Correctness: is the plan logically sound, executable, and complete with respect to the task? The second is Plan Grade, a discrete score from 0 through 1 in increments of 0.2. It measures the severity of deficiencies and considers goal attainment, completeness, constraint satisfaction, and logical consistency; it is not textual distance from one canonical plan. The third layer is the E1–E6 error taxonomy, which allows multiple problems to be marked in the same response (Section 3.3; Appendix C.2).
| Error | How to read it | Important boundary |
|---|---|---|
| E1 Goal Understanding | Misreads what the user wants to accomplish | Not a measure of surface textual similarity |
| E2 Premature Conclusion / Task Incompleteness | Stops before completion in Step-wise, or omits required subtasks in Holistic | The label has protocol-specific subtypes |
| E3 Constraint Violation | Breaks an explicit system-prompt instruction or task constraint | Judge against conditions actually supplied in the task |
| E4 Logic Error | Uses invalid dependencies, ordering, premises, or causal reasoning | A different path is not an error when it remains feasible and satisfies the constraints |
| E5 Tool Use Error | Misunderstands a tool’s function or operating semantics | Includes using an irrelevant injected tool |
| E6 Hallucination | Fabricates facts, tools, or intermediate results that were not obtained | Not every estimate qualifies; the task and evidence matter |
Together, the views answer different questions: Correctness asks whether the plan passes overall, Grade asks how serious the deficiencies are, and the taxonomy asks what kind of mechanism failed. The paper also uses task-specific behavior categories for tool recovery—Replace, Alter, Retry, Refuse, and Other—and refusal rates for unsolvable tasks. These categories should not be collapsed into one generic error count. In Tool-Broken, Replace is the intended recovery path; for an Unsolvable task, refusal is the appropriate outcome (Sections 3.1 and 4.2.3–4.2.4).
Much of the scoring relies on an LLM-as-a-Judge. The authors describe comparing outputs with reference trajectories while accepting semantically valid alternatives, and analyze judge consistency, solution diversity, and self-evaluation bias in Appendix D. On the same 720 human-annotated instances, Gemini 3 Pro reaches 92.22% correctness agreement and an F1 of 0.935; agreement falls to 80.83% when the judge is blind to reference answers (Table 7). This shows that reference trajectories matter to the judge’s assessment and that its performance depends on the available reference evidence. The 720 instances are part of judge evaluation; they are not 720 complete, independently human-evaluated benchmark samples, nor do they establish human adjudication of every task type.
Reading the experiments: capability differences matter more than a total rank
The authors evaluate 12 multimodal models: six open-weight models and six proprietary models. The main results are author-run benchmark aggregates. No independent third-party rerun or statistical uncertainty estimates establishing generalization across deployments were verified. When reading Table 1 and Figure 3, first ask which setting and error category each result represents, before focusing on model names or rankings (Sections 4.1–4.2.1).
Complete long-range plans versus local decisions with feedback
The paper reports lower correctness for every model in Holistic than in Step-wise, with open-weight models particularly fragile in long-horizon holistic planning. In the error breakdown, E4 Logic Error is a shared bottleneck. Holistic planning also shows more incompleteness, constraint violations, and hallucination; Step-wise errors cluster around local tool use and logic. The authors additionally compare the settings on the same 300 underlying cases and report paired results and significance tests in Appendix G.1. This supports the claim that the two planning conditions differ in difficulty and error patterns. It does not prove that a model is always incapable of long-range planning.
Irrelevant tools can derail a long tool chain
Tool-Extraneous has a larger effect on Holistic than on Step-wise planning. The authors report holistic correctness drops of 22.0 and 12.3 percentage points for GPT-5 and Claude Sonnet 4.5, respectively; Step-wise drops range from 0.9 to 6.7 points (Table 2). Figure 4 shows a positive relationship between the number of distractor tools and erroneous distractor use. This is consistent with a plausible mechanism: in a one-shot tool chain, an early selection error can cascade into later steps; when deciding only one or a few actions at a time, the model has opportunities to reorient from feedback.
This difference does not mean “step-wise planning is generally better.” Step-wise receives execution feedback, so its information differs from Holistic; it still has tool-selection and logic errors. The result indicates relative resilience under this test design, not that an actual agent will keep its success rate when its tool catalog grows.
Recovery and refusal are different capabilities
Tool-Broken asks whether a model can switch to a replacement or another valid tool after seeing an error return. Unsolvable asks whether it refuses when missing evidence, unavailable tools, or conflicting constraints make the task infeasible. The authors find that models handle explicit constraint conflicts better than missing information. Some open-weight models still attempt tasks when necessary tools have been removed. Proprietary models also show weaknesses on particular tool-removal or missing-visual-input cases (Sections 4.2.3–4.2.4).
These capabilities point in different directions: recovery means “the task can still be completed by another route”; refusal means “the current conditions do not support a completion claim.” Combining them into one broad robustness score would hide a model that is good at switching tools but poor at acknowledging missing evidence. Engineering reports should retain that distinction.
Does plan revision improve execution? A controlled bridge study
Version 3 adds execution validation separate from the 4,209-case main benchmark: 200 ToolSandbox tasks and 200 τ²-bench tasks. The authors test three modes in each environment. Direct executes without first planning. Plan-first produces a holistic plan and then executes it. Refine starts from the same initial plan, applies one round of APB-guided critique, revises the plan, and executes it. The three representative models are GPT-4o, Qwen3-VL-235B-A22B, and Gemini 2.5 Flash. Gemini 3 Pro supplies plan grades, E1–E6 indicators, and a diagnostic rationale under an anti-leak instruction; the tested model then revises its own plan. The executor, sampling configuration, and environment are held constant between Plan-first and Refine (Section 5.1; Appendix C.10).
ToolSandbox uses trajectory similarity, while τ²-bench uses task reward; higher is better for both. Compared with Plan-first, Refine improves ToolSandbox trajectory similarity by 3.4–5.5 percentage points and τ²-bench reward by 0.03–0.07 across the three models. For example, GPT-4o moves from 79.2 to 83.3 and from 0.56 to 0.63, respectively (Table 3). The authors also report increases in APB correctness and grade after revision. This means the diagnostic signal is not only associated with plan scores: in this controlled comparison, the refined condition also has better downstream execution metrics.
The evidence remains bounded. It covers two environments, 200 tasks per environment, and three models; revision depends on a Gemini 3 Pro judge providing structured critique. The result supports “under this one-round protocol, these prompts, and these execution configurations, Refine scores higher.” It does not support “APB improves any production task,” nor does it remove the effects of model versions, task construction, judge quality, and tool environments. The paper has not been independently rerun.
The cost of more reflection: extra deliberation is not always better
The authors compare Self-Refinement and Critic-Guided Refinement, with and without explicit error metrics (Table 4). For Holistic planning, revision guided by E1–E6 metrics often improves correctness. Step-wise results are less consistent. Some models decline after extra reflection on a short-horizon decision, particularly with unconstrained self-refinement. This over-correction suggests that checking a whole-plan deficiency and optimizing the next immediate action may not benefit from the same depth of reflection. For example, InternVL3.5-30B-A3B falls from 60.00% to 43.33% step-wise correctness (−16.67 points) after metric-guided self-refinement in Table 4; metric-guided critic refinement instead raises it to 68.00% (+8.00 points). The same model’s holistic correctness rises from 9.33% to 42.00% under critic refinement. This is not simply “reflection helps” or “reflection hurts”: revision strategy interacts with planning setting.
In Step-wise settings, correctness falls as the prediction horizon grows from one to two and three steps; the error profile also shifts with planning scope (Figure 5; Appendix G.1). This fits the intuition that dependencies accumulate across local decisions, but it remains a result under the authors’ specified prediction protocol. Table 5’s efficiency-aware planning setting separately evaluates cost-annotated tools, alternative valid paths, and trap tools. It is evidence about cost-aware tool selection, not a cost-benefit analysis of the revision experiment (Section 5.4; Appendix C.9).
Evidence map: how far the conclusions go
| Author claim | Evidence in the paper | What it supports and does not support |
|---|---|---|
| Planning needs multi-setting diagnosis beyond end-task success | 4,209 cases, 22 domains, five settings, 12 models, and E1–E6 analyses (Sections 3–4; Table 1) | Demonstrates benchmark coverage and differing error profiles; does not show that the categories cover every real planning problem |
| Tool noise, failure, and infeasibility expose different weaknesses | Tool-count comparisons, tool-failure behavior classes, and refusal analyses across four unsolvable conditions (Tables 1–2; Section 4.2) | Supports model differences observed on these tasks and prompts; cannot establish the same failure rates in deployment |
| APB-guided revision may improve execution | Two 200-task environments and three execution modes (Table 3; Section 5.1) | Refine scores higher than Plan-first in two controlled environments; does not establish a universal causal guarantee across tasks |
| More reflection is not a solution for every planning step | Self/Critic and with/without-metric comparisons, plus horizon results (Table 4; Figure 5) | Shows over-correction for some tested models and settings; does not imply that every step-wise task needs less deliberation |
For data construction, the authors aggregate OpenCUA, GTA, GAIA, ToolBench, FrameThinker, and interaction data from a public online agent platform. Claude Sonnet 4.5 expands some queries, tool sets, and trajectories. Rule-based checks, two-stage LLM validation, and author review form the quality pipeline (Section 3.2; Appendix B). The authors say that augmentation is not wholesale generation from scratch and that tested models do not see the generator identity, synthesized trajectories, or reference plans. They also acknowledge that pretraining contamination in proprietary models cannot be directly audited. The mixture of sources broadens the task set while making representativeness of constructed cases an external-validity question.
The comparison with 720 human annotations helps assess judge scores, but automated judging remains affected by model bias, decision boundaries, and reference representations. Some data are synthetic or constructed, and the authors acknowledge that the benchmark cannot cover open-ended work and may under-represent specialized fields (Limitations; Appendix D). The strongest contribution is a framework that separates planning modes and failure types, paired with an initial downstream validation. The main extrapolation risk is connecting a fixed task set, fixed tool descriptions, and judge scores to real workflows that change over time.
Limitations, reproducibility, and accessible artifacts
The paper is an arXiv v3 preprint submitted on October 5, 2026; peer-review status is not established. The authors explicitly state that APB focuses on planning and does not model stochastic transitions or belief-state updates. It should complement, not replace, end-to-end deployment tests. Parts of data construction and judging rely on proprietary foundation models, and external readers cannot confirm whether provider-side model versions are identical to those used in the authors’ runs (Limitations).
As of October 11, 2026, the authors’ evaluation repository and Hugging Face dataset pages are publicly inspectable. The dataset page currently reports a schema CastError while generating data in its viewer. This blocks that browsing route; it does not establish that the underlying downloadable files are absent. Explicit licenses for the code and dataset have not been verified, so readers should confirm the licenses and preparation path before downloading, adapting, or redistributing the artifacts. The repository documents data preparation and benchmark commands, but a full experiment requires Python dependencies, model API access, and multiple model calls. A large self-hosted judge also has substantial compute requirements.
This reading did not independently rerun the benchmark; all reported numbers are from the authors. A small reproduction could, after license checks, prepare one split, evaluate a small sample with one model, and compare plan-first execution with one round of diagnostic revision. Such a smoke test would only show that the command, schema, and local path work; it would not reproduce the full paper comparison or remove viewer errors and provider-model drift.
Bloss0m engineering judgment: connect planning scores back to execution evidence
The following is Bloss0m engineering judgment, not an author-proposed or author-validated deployment process. If an internal agent evaluation adopts APB’s design, treat planning and execution as adjacent but independent gates. Record whether the plan covers the goal, respects constraints, and selects the intended tools; then record what the same plan actually does in the environment. A high plan score and low execution result may justify investigation of permissions, tool stability, state synchronization, or the executor. The planning score should not stand in for execution success.
Tool-failure tests should record the error return, available replacements, retries, and whether side effects already occurred. Unsolvable tests should state exactly which information or tool is missing and distinguish “temporarily unable to decide” from “logically contradictory.” This makes it possible to see whether a model knows when to switch routes, ask for clarification, or stop, rather than looking only at its refusal rate. These logging recommendations extend APB’s categories and still require validation against the actual tool permissions, business risks, and task distribution.
APB is better suited to a diagnostic suite across versions than to a single leaderboard gate. An adoption could fix representative cases and model versions, set separate expectations for long-horizon planning, step-wise planning, distractor tools, tool replacement, and missing information, and retain repeated-run variation, judge audits, and full end-to-end evaluations. Products with stochastic environments, dynamic permissions, multi-user side effects, or completion criteria that cannot be specified in advance need additional environment interaction and human review. APB cannot answer those questions by itself.
Three things to remember
- Method: APB separately tests holistic plans, feedback-conditioned local planning, irrelevant tools, broken tools, and unsolvable tasks; correctness, severity, and E1–E6 categories describe different aspects of failure.
- Evidence: The 12-model comparison reports greater difficulty in holistic planning and greater impact from distractor tools on full tool chains. In two controlled 200-task studies, one APB-guided revision improves specified execution metrics for three tested models.
- Boundary: Constructed tasks, LLM judging, limited environments, and the lack of an independent rerun constrain generalization. Planning diagnosis can help locate a problem, but cannot replace real execution tests or prove production reliability.
Further reading
- Tool Call Succeeds, Workflow Fails: the gap between a successful tool response and a completed workflow.
- Claim-Safe Closed-Loop AI Evaluation: evaluation data, feedback loops, and traceable claims for agent systems.
- ScienceIDE: Turning World’s Scientific Codebase into Agent Learnable Environments: another design for evaluating long-horizon agents through executable environments and domain checks.
Primary sources
- Sun et al., Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents, arXiv v3, October 5, 2026. All experimental results in this reading are author-reported and were not independently rerun.
- APB evaluation repository and APB dataset, page access checked October 11, 2026; code and data licenses have not been verified.