arxiv: 2604.11978 · v1 · submitted 2026-04-13 · 💻 cs.AI

Recognition: unknown

The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break

Xinyu Jessica Wang , Haoyue Bai , Yiyou Sun , Haorui Wang , Shuibai Zhang , Wenjie Hu , Mya Schroder , Bilge Mutlu

show 2 more authors

Dawn Song Robert D Nowak

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:08 UTC · model grok-4.3

classification 💻 cs.AI

keywords LLM agentslong-horizon tasksfailure diagnosisagent benchmarkstrajectory analysisLLM-as-a-Judgeagentic systems

0 comments

The pith

HORIZON benchmark shows LLM agents degrade on longer task horizons and attributes failures via a validated judge.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces HORIZON as a cross-domain benchmark to construct tasks and systematically analyze why LLM-based agents break down on long-horizon tasks that demand extended interdependent actions. It evaluates multiple state-of-the-art agents across four domains by collecting over 3100 trajectories to map horizon-dependent performance drops. A trajectory-grounded LLM-as-a-Judge pipeline is developed for scalable failure attribution and is validated against human annotations with strong agreement. A sympathetic reader would care because current agents excel on short tasks but lack reliable methods for diagnosing and fixing issues in complex, extended sequences, slowing progress toward dependable agentic systems.

Core claim

Using HORIZON, state-of-the-art agents from multiple model families exhibit horizon-dependent degradation across four representative agentic domains when 3100+ trajectories are analyzed. The trajectory-grounded LLM-as-a-Judge pipeline enables scalable and reproducible failure attribution, achieving inter-annotator kappa of 0.61 and human-judge kappa of 0.84, which provides an initial methodological step toward diagnosing long-horizon agent failures and offers practical guidance for building more reliable agents.

What carries the argument

HORIZON, the cross-domain diagnostic benchmark for constructing long-horizon tasks and analyzing failure behaviors, together with the trajectory-grounded LLM-as-a-Judge pipeline that attributes causes at scale.

If this is right

Agents display consistent performance degradation as task horizons lengthen across model families.
The LLM judge pipeline supports reproducible failure attribution in different domains.
Cross-domain comparison of long-horizon behaviors becomes feasible for the first time.
Targeted improvements to agent designs can follow from identified failure patterns.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The method could be applied to additional domains to check whether degradation is universal rather than domain-specific.
Agent developers might integrate the judge pipeline into training loops to iteratively reduce specific failure modes.
Short-horizon successes in current systems may not transfer to long horizons without explicit diagnostic tools.

Load-bearing premise

The four chosen agentic domains represent the space of long-horizon tasks and the LLM-as-Judge attributions generalize without systematic bias beyond the tested models and trajectories.

What would settle it

Evaluating agents on a new fifth domain or additional model families where horizon-dependent degradation patterns vanish or human-judge agreement falls substantially below the reported kappa values.

Figures

Figures reproduced from arXiv: 2604.11978 by Bilge Mutlu, Dawn Song, Haorui Wang, Haoyue Bai, Mya Schroder, Robert D Nowak, Shuibai Zhang, Wenjie Hu, Xinyu Jessica Wang, Yiyou Sun.

**Figure 1.** Figure 1: Illustration of general agent execution and failure propagation. Given an instruction 1 , the agent iterates a standard loop: it observes the environment 2 to obtain observations 3 , plans and selects an action 4 drawing on memory-backed context 6 , executes that action to change the environment, and then updates its internal state 5 through memory 6 . Failures can originate at any stage and compound acros… view at source ↗

**Figure 2.** Figure 2: The HORIZON diagnostic pipeline for scalable long-horizon failure analysis. The pipeline consists of trace and context collection, taxonomy development with inter-annotator agreement, construction of the HORIZON diagnostic suite (unified horizon measurement and failure attribution), calibration of an LLM-based annotator, and large-scale failure annotation. This figure highlights our systematic approach to… view at source ↗

**Figure 3.** Figure 3: Current model performance as a function of horizon extension. Plots show success rate (accuracy) versus the compositional depth s, where s denotes the HORIZON-defined extension level corresponding to the number of high-level subtasks composed within a task. Accuracy is computed over task sets with the same s. Each point reports the mean over three independent runs with identical task sets; variability acro… view at source ↗

**Figure 4.** Figure 4: Distribution of failure modes across four task domains on 3100+ failure trajectories. Bars show the proportion of each failure type relative to the total failed traces within each domain. Failures are grouped into Process-level Risks (PFMEA) (72.5%), comprising failures that arise during sequential rollout—environment interaction, instruction following, planning errors, and history error accumulation—and D… view at source ↗

**Figure 5.** Figure 5: HORIZON overview with two orthogonal dimensions. Left (Horizontal / Horizon): four domain examples contrasting short- vs. long-horizon tasks under our task-structure definition, where intrinsic horizon H∗ increases with extension level s (and may also increase compositional depth C). From top-left to bottom-right: Web illustrates theoretical H∗ growth (e.g., “purchase a flight,” H∗ = 8, vs. “check the weat… view at source ↗

**Figure 6.** Figure 6: Failure mode distribution across the four task domains (SR = task success rate; n = number of failed traces). Embodied (SR 42.2%) and Database (SR 36.9%) are almost entirely dominated by Planning Error (94.9% and 79.3%, respectively), indicating that structured, well-defined action spaces surface planning as the primary bottleneck. Web (SR 24.1%) follows a similar pattern (Planning Error 74.9%) but additio… view at source ↗

**Figure 7.** Figure 7: Failure mode distribution for GPT-4o-mini vs. Claude 3.5 Sonnet aggregated across all domains. The two models exhibit qualitatively different failure regimes. GPT-4o-mini (1,145 failed traces, success rate 33.4%) is dominated by Planning Error (64.9%) and Memory Limitation (18.3%), reflecting difficulty maintaining coherent long-horizon plans and retaining intermediate state across extended rollouts. Clau… view at source ↗

**Figure 8.** Figure 8: Failure mode distribution broken down by domain and model. Database and Embodied only have GPT trajectories in the current dataset. Web: Both models are planning-error dominated (>72%), but GPT accumulates more Memory Limitation (10.6% vs. 2.0%) while Claude shows higher False Assumption (5.4% vs. 1.4%), pointing to distinct secondary failure mechanisms. OS: The model divergence is most pronounced here. GP… view at source ↗

read the original abstract

Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that require extended, interdependent action sequences. Despite rapid progress in agentic systems, these long-horizon failures remain poorly characterized, hindering principled diagnosis and comparison across domains. To address this gap, we introduce HORIZON, an initial cross-domain diagnostic benchmark for systematically constructing tasks and analyzing long-horizon failure behaviors in LLM-based agents. Using HORIZON, we evaluate state-of-the-art (SOTA) agents from multiple model families (GPT-5 variants and Claude models), collecting 3100+ trajectories across four representative agentic domains to study horizon-dependent degradation patterns. We further propose a trajectory-grounded LLM-as-a-Judge pipeline for scalable and reproducible failure attribution, and validate it with human annotation on trajectories, achieving strong agreement (inter-annotator \kappa=0.61; human-judge \kappa=0.84). Our findings offer an initial methodological step toward systematic, cross-domain analysis of long-horizon agent failures and offer practical guidance for building more reliable long-horizon agents. We release our project website at \href{https://xwang2775.github.io/horizon-leaderboard/}{HORIZON Leaderboard} and welcome contributions from the community.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

HORIZON supplies a usable benchmark and a well-validated LLM-as-Judge for long-horizon agent failures, but the four domains still need a clearer defense of representativeness.

read the letter

The main thing to know is that this paper delivers a concrete benchmark and a reproducible failure-attribution method that actually gets checked against humans. They build HORIZON tasks across four domains, run more than 3100 trajectories on current GPT-5 and Claude agents, and show horizon-dependent degradation. The LLM judge reaches human agreement of κ=0.84, which is better than most automated labeling pipelines in this area and makes the attributed failure modes more trustworthy than usual LLM self-reports. That part is useful and extends earlier short-horizon studies with scale and cross-domain data. The leaderboard release is also a practical plus for anyone who wants to compare agents on the same tasks. The softer spot is the representativeness claim. The paper labels the four domains representative but does not supply a taxonomy, coverage metrics, or comparison on properties like dependency depth or stochasticity. If the domains share similar structures, the degradation patterns and judge attributions could be narrower than presented. That is a fixable gap rather than a fatal one, but it limits how far the cross-domain conclusions can be pushed right now. This is the kind of work that people building and stress-testing LLM agents will want to read and build on. It is grounded enough in data and validation to merit a serious referee, even if the domain argument needs tightening in revision.

Referee Report

1 major / 2 minor

Summary. The paper introduces HORIZON, an initial cross-domain diagnostic benchmark for long-horizon LLM agent failures. It evaluates SOTA agents (GPT-5 variants, Claude models) across four representative agentic domains, collecting 3100+ trajectories to characterize horizon-dependent degradation. It also proposes a trajectory-grounded LLM-as-a-Judge pipeline for failure attribution and validates the judge against human annotations (inter-annotator κ=0.61, human-judge κ=0.84). The work releases a leaderboard and offers practical guidance for reliable long-horizon agents.

Significance. If the central claims hold, the paper supplies a scalable, reproducible methodology and large-scale empirical resource (3100+ trajectories plus human-validated judge) for diagnosing where and why agentic systems degrade with horizon length. The explicit strengths are the empirical scale, the quantitative human agreement metrics, and the public leaderboard that invites community extension; these directly address the current lack of systematic, cross-domain failure analysis in the agent literature.

major comments (1)

[Benchmark Construction / Domain Selection] The central claim that HORIZON enables systematic cross-domain diagnosis rests on the four domains being representative of long-horizon tasks (interdependent action sequences with compounding errors). The manuscript labels the domains 'representative' but provides no taxonomy of task properties (dependency depth, state-space size, stochasticity, branching factor), no coverage argument, and no diversity metrics comparing the chosen domains to the broader space of agentic tasks. This absence makes it impossible to assess whether the observed degradation patterns and judge attributions generalize or are artifacts of domain similarity.

minor comments (2)

[Abstract / Introduction] The abstract and introduction use 'initial' and 'representative' without cross-referencing the specific justification (or lack thereof) later in the paper; adding a forward pointer would improve readability.
[LLM-as-a-Judge Validation] The human-judge agreement is reported as κ=0.84, but the exact annotation protocol, number of trajectories annotated, and breakdown by failure category are not summarized in a table; a small summary table would strengthen the validation claim.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of the paper's empirical scale and contributions. We address the major comment on domain selection and representativeness below.

read point-by-point responses

Referee: The central claim that HORIZON enables systematic cross-domain diagnosis rests on the four domains being representative of long-horizon tasks (interdependent action sequences with compounding errors). The manuscript labels the domains 'representative' but provides no taxonomy of task properties (dependency depth, state-space size, stochasticity, branching factor), no coverage argument, and no diversity metrics comparing the chosen domains to the broader space of agentic tasks. This absence makes it impossible to assess whether the observed degradation patterns and judge attributions generalize or are artifacts of domain similarity.

Authors: We agree that an explicit taxonomy of task properties, a coverage argument, and diversity metrics would strengthen the justification for claiming the domains are representative and would help readers evaluate generalizability. The current manuscript motivates the four domains (web navigation, code generation/execution, game playing, and simulated household tasks) by their prevalence in the agent literature and their coverage of distinct interaction modalities and error-compounding mechanisms, but does not include a formal taxonomy or quantitative comparison. In the revised manuscript we will add a new subsection in Section 3 (Benchmark Construction) that (1) defines a taxonomy along the suggested axes (dependency depth, state-space size, stochasticity, branching factor), (2) characterizes each domain with approximate values or ranges for these properties, and (3) provides a brief coverage argument referencing common categories in existing agent benchmarks. This addition will directly address the concern while leaving the core empirical results and judge validation unchanged. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark with external human validation

full rationale

The paper introduces the HORIZON benchmark, collects 3100+ trajectories across four domains, and proposes an LLM-as-Judge pipeline whose attributions are validated by human annotations (inter-annotator κ=0.61; human-judge κ=0.84). No equations, derivations, fitted parameters, or predictions appear; the central claims rest on direct empirical measurement and external human agreement rather than any reduction to self-defined inputs or self-citation chains. The representativeness of the four domains is presented as an assumption, not derived from prior results within the paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The central claim rests on the empirical construction of HORIZON and the human-validated judge pipeline; no free parameters, mathematical axioms, or postulated entities beyond the benchmark itself.

invented entities (1)

HORIZON benchmark no independent evidence
purpose: Systematic construction of long-horizon tasks for failure diagnosis
Newly introduced in this work; no external falsifiable evidence provided yet.

pith-pipeline@v0.9.0 · 5573 in / 1101 out tokens · 67045 ms · 2026-05-10T16:08:28.910595+00:00 · methodology

discussion (0)

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

VibeServe: Can AI Agents Build Bespoke LLM Serving Systems?
cs.AI 2026-05 unverdicted novelty 8.0

VibeServe demonstrates that AI agents can synthesize bespoke LLM serving systems end-to-end, remaining competitive with vLLM in standard settings while outperforming it in six non-standard scenarios involving unusual ...
Why Retrying Fails: Context Contamination in LLM Agent Pipelines
cs.AI 2026-05 conditional novelty 7.0

A Context-Contaminated Restart Model derives exact success probabilities and an optimal pipeline depth T* = sqrt(B * log(1/(1-ε1)) / log(1/(1-ε0))) for fixed budget B, validated on SWE-bench where it fits data far bet...
LearnMate^2: Design and Evaluation of an LLM-powered Personalized and Adaptive Support System for Online Learning
cs.HC 2026-05 unverdicted novelty 6.0

LearnMate^2, an LLM-driven personalized learning support system, improves learning outcomes and user experience over existing online platforms combined with generic LLM assistance in small-scale user studies.

Reference graph

Works this paper leans on

10 extracted references · cited by 3 Pith papers

[1]

almost correct

Unable to Detect Environment Change (limitations within the agent’s interaction mechanisms) This failure occurs when anagent cannot reliably perceive, verify, or internalize a change (or non-change) in the environment, so its internal belief state diverges from the actual state.It often manifests as false transitions (believing a navigation or command suc...
[2]

The failure is localized at the sub-plan level, but it breaks the global task by introducing deadlocks, irreversible actions, or cascading inefficiencies

Sub-plan.Sub-plan planning errors occur when anagent mis-decomposes a high- level goal into incorrect, incomplete, poorly ordered, or inefficient sub-steps, even though the overall objective is understood.This includes choosing the wrong API or parameter format for a specific step, omitting prerequisite actions, violating constraints within a sub-plan, or...
[3]

As a result, the action either fails outright or silently alters the environment in unintended ways

Action.Action-level planning errors occur when anagent selects or executes an incorrect concrete action, despite having an appropriate high-level plan or sub-task in place.These failures include execution crashes (e.g., syntax or type errors), semantically invalid actions that do not achieve the intended effect in the environment, or mismatches between pl...

2024
[4]

We adopt WebArena Zhou et al

Web Navigation. We adopt WebArena Zhou et al. (2024a) as the base environment. From the benchmark task pool, we select tasks that achieve 100% success under a baseline run with GPT-5-mini, and apply our horizon extension procedures to construct task families with increasing intrinsic horizon
[5]

We build on OS tasks from AgentBench Liu et al

Operating Systems (OS). We build on OS tasks from AgentBench Liu et al. (2023c), again selecting tasks with perfect baseline performance under GPT-5-mini before applying depth-based horizon extension
[6]

We build on MAC-SQL Wang et al

Databases (DB). We build on MAC-SQL Wang et al. (2025a), a multi-agent Text-to- SQL framework, to support controlled query decomposition and task composition. The agent follows a modular pipeline consisting of three components: aSelector, a Decomposer, and aRefiner. Given a user query, the Selector first identifies relevant tables and schema elements. The...
[7]

We developed a bimanual robot arm simulation environment in IsaacSim 5.0 Kachaev et al

Embodied. We developed a bimanual robot arm simulation environment in IsaacSim 5.0 Kachaev et al. (2025). This environment consisted of two Franka Emika Panda Robot Arms, each equipped with a Tesollo DG-3F-B three-finger gripper. To evaluate variable-horizon tasks in an embodied setting, we developed a bimanual robot arm simulation environment using Isaac...

2025
[8]

What do customers say about brush from sephora
[9]

Tell me the total cost of my latest cancelled order
[10]

Canceled

Tell me the total cost of my latest pending order I'm currently on the One Stop Market homepage, but I need to go to Sephora for the first part of the objective. Let me navigate to the homepage to find Sephora. In summary, the next action I will perform is goto [http://homepage.com] Action: goto [localhost:4399] Step 2 Summary:Seeing a Chrome error page, ...

2068