Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge

Dhaval Patel; Chathurangi Shyalika; Suryanarayana Reddy Yarrabothula; Ling Yue; Shuxin Lin; Nianjun Zhou; James Rayfield

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Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge

Dhaval Patel, Chathurangi Shyalika, Suryanarayana Reddy Yarrabothula, Ling Yue, Shuxin Lin, Nianjun Zhou, James Rayfield

arXiv:2605.08518v1May 8, 2026cs.AI

TL;DR

Industrial agent competitions need retrospective analysis that clarifies what their leaderboards measure and how evaluation design shapes conclusions. This paper analyzes CODS 2025 using released competition artifacts and finds saturation, public–hidden mismatch, metric-scale sensitivity, and guardrail-centric strategies.

Problem
Evaluating industrial multi-agent systems remains difficult because deployment-relevant robustness, privacy-preserving execution, and orchestration are hard to benchmark and sensitive to metric design.
Method
The paper retrospectively analyzes CODS 2025 across isolated planning and execution tracks using competition artifacts, hidden evaluation records, and submission evidence.
Results
The challenge revealed leaderboard saturation, public–hidden mismatch, metric-scale sensitivity, and guardrail-centric top-submission strategies, with t-match contributing at most 0.05 composite points per track.
Takeaways & Limitations
Future competitions should isolate capability dimensions, report skill-aware subscores, make scoring components numerically commensurate, and release team metadata and versioned artifacts.
Takeaways & Limitations
Hidden-phase conclusions are bounded by released artifacts and rely on best-submission spreadsheets that required manual normalization.

Abstract

from arXiv · show

Competition retrospectives are useful when they explain what a leaderboard measured, how hidden evaluation changed conclusions, and which design patterns were rewarded. We revisit the CODS 2025 \assetopslive{} challenge, a privacy-aware Codabench competition on industrial multi-agent orchestration built on \assetops{}. We combine final rank sheets, a 300-submission server log, 149-team registrations, best-submission exports, the organizer winners report, the companion \assetopslive{} system paper, and verified planning-track source trees. Five results stand out. First, the public planning leaderboard saturates at 72.73\%, and richer prompts do not improve that peak. Second, hidden evaluation changes the story: public and private scores correlate moderately in planning ($r{=}0.69$) but negatively in execution ($r{=}{-}0.13$), with several 45.45\% public execution systems reaching 63.64\% on the hidden set. Third, the \tmatch{} term is numerically almost inert in the official composite -- combined on a 0--1 scale with 0--100 percentage scores, it contributes at most 0.05 points per track, and rescaling would swap the top two teams. Fourth, the competition is operationally account-based but substantively team-based: 149 registered teams reduce to 24 with non-zero public scores and 11 fully ranked, while 52.3\% of deduplicated registrations list multiple usernames. Fifth, successful execution methods mostly improve guardrails -- response selection, contamination cleanup, fallback, and context control -- rather than novel agent architectures. These findings identify which behaviors the evaluation rewarded, and motivate scale-aware composites, skill-level diagnostics, and versioned artifact release.

1 Introduction

Evaluating LLM-based industrial agents remains difficult because benchmark-style tests can misrepresent real-world capability. This paper therefore uses the CODS 2025 ASSETOPSBENCH competition to study what leaderboard-based evaluation measures in a privacy-constrained industrial setting.

Motivation: Benchmark-style evaluation can misrepresent real-world agent capability by favoring narrowly specified, easily optimized tasks.The challenge grows as LLM-based agents move from laboratory settings toward real-world deployment.
Motivation: Competition-based evaluation combines blind submissions, hidden test sets, and large-scale participation to expose failure modes static benchmarks miss.These include progressive reasoning failures and adaptive strategies that emerge through iterative evaluation.
Contribution: This retrospective analyzes what a leaderboard measures, extending prior competition studies that emphasize evaluation meaning over placement alone.The paper examines a large-scale agentic AI competition in an industrial setting.
Challenge Context: CODS 2025 ASSETOPSBENCH combines agentic evaluation, an industrial physical-asset domain, and privacy-constrained deployment in a competition-track benchmark.The challenge was hosted at CODS-COMAD and builds on prior ASSETOPSBENCH work.

2 Competition Overview

The competition used a shared 141-scenario industrial benchmark and two tracks that separately varied planning prompts and workflow execution. Fixed-model evaluation combined public, hidden, and semantic t-match signals, with execution weighted more heavily in final ranking.

Benchmark: 141 industrial scenarios—99 single-agent and 42 multi-agent—formed the shared ASSETOPSBENCH evaluation set.Four domain-specific agents and their multimodal datasets were packaged in a Docker container.
Tracks: Track 1 varied prompt and planning code to improve agent DAGs, while Track 2 varied workflow execution and context handling.The editable surfaces separated participant-controlled planning from execution, although packaging, submission practices, and scorer details could still introduce residual variation.
Evaluation: All submissions used a fixed LLaMA-3-70B model and were evaluated on 11 public development scenarios plus 11 novel hidden scenarios from unseen asset classes.An optional local warm-up used 2–3 scenarios for pipeline validation.
Scoring: 60% execution and 40% planning determined the final ranking, alongside public, hidden, and semantic t-match components.The organizers selected each team’s best-scoring public submission per track for hold-out evaluation.
Artifacts and counting: 149 registered teams and 300 submission attempts were documented across registration, submission, scoring, team-selection, and solution-code artifacts.A submission attempt was one server-log row, while a Finished submission was one of the 234 attempts that completed platform evaluation.

3 Competition Results and Retrospective Analysis

The CODS 2025 results show coarse and scale-misaligned public evaluation, substantial participation and identity friction, and track-specific signals: planning saturated publicly, while execution rewarded guardrails more than novel architectures. Hidden evaluation, cohort differences, and artifact analysis therefore materially qualify leaderboard conclusions.

Participation and identity: 149 registered teams reduced to 24 non-zero public-score teams and 11 fully ranked teams, while 78/149 (52.3%) listed multiple usernames.Four of the 11 ranked teams used distinct accounts across tracks, illustrating the gap between team strategy and account-based platform records.
Operational friction: 17.7% of 300 submission attempts failed conformance checks, directly measuring platform-conformance cost across the two tracks.The remaining attempts comprised 78.0% finished, 3.0% cancelled, and 1.3% still in progress.
Planning results: 72.73% was the planning public-score ceiling despite 12× longer prompts and more than doubled code size, confirming saturation.Among three submissions tied publicly at 72.73%, hidden scores spanned an 18-point range.
Hidden evaluation: ρ = 0.69 in planning versus ρ = −0.13 in execution shows moderate public–hidden signal for planning but none for execution.Planning’s mean private score fell 11.30 points below its mean public score, while execution’s average drop was −1.40 points.
Score composition: 0.05 composite points per track is the t-match maximum, making its nominal 10% weight numerically inert and rescaling it would swap the top two teams.Public and private scores use [0, 100], whereas t-match uses [0, 1].
Execution methods: E1–E4 execution archetypes are structural variants of catching bad intermediate outputs and redirecting them, so improvements are better explained by guardrails than new architectures.E1 alone accounts for 28.9% of Track 2 submissions and uses soft-validation fallback after ValueError exceptions.

4 Discussion

The discussion finds that the competition satisfied six of ten trustworthy-agent-benchmark requirements by design, while two were only partially satisfied. It also cautions that hidden-phase analysis depended on manually normalized artifacts and that coarse leaderboards require semantic validation and evaluation transparency.

Alignment with the Agent-Eval Checklist: Six of ten checklist requirements were fully satisfied by design, including agent-evaluator isolation, answer secrecy, test-instance rotation, and tamper prevention.The infrastructure was mapped against all ten requirements.
Alignment with the Agent-Eval Checklist: Two requirements were partially satisfied because LLM judge input sanitization used structured dimensions but was not adversarially tested.
Limitations and Broader Impacts: Hidden-phase conclusions relied on best-submission spreadsheets that required manual normalization.
Limitations and Broader Impacts: Coarse leaderboards can produce misplaced confidence without semantic validation, making evaluation transparency more important than leaderboard-first interpretation.

5 Conclusion … B.3 Registration Interface

The conclusion frames ASSETOPSBENCH as an evaluation instrument whose hidden execution phase and scoring artifacts expose weaknesses in leaderboard interpretation. The appendices document the competition and provide a nine-dimensional retrospective template for future agentic benchmark competitions.

5 Conclusion: ASSETOPSBENCH reveals leaderboard saturation, public–hidden mismatch, metric-scale sensitivity, and guardrail-centric strategies through hidden execution, track isolation, scoring traces, and submission artifacts.The conclusion presents these observations as evidence that agent competitions should be analyzed as evaluation instruments, not only ranking mechanisms.
5 Conclusion: Four proposed prescriptions are to isolate capability dimensions across tracks and surface skill-aware diagnostics in future competitions.The supplied conclusion passage introduces four prescriptions but is truncated after the first two.
A Technical appendices and supplementary material: 9 analytical dimensions extend the competition’s base layer of design, participants, and evaluation protocol, with each addressing a distinct question beyond aggregate leaderboard results.The appendix organizes the retrospective across competition design, participation, submission behaviour, ranking robustness, computational footprint, strategy attribution, evaluation-agent robustness, clustering methodology, and failure-mode taxonomy.
B Competition Related Materials: The competition-related materials include the official challenge website, challenge advertisement page, and registration interface.Appendix B is identified as containing competition-related materials.
A Technical appendices and supplementary material: The retrospective’s goal is to document ASSETOPSBENCH rigorously so its design choices, outcomes, and failure modes can be verified, compared, and built upon.The authors present the nine-dimensional retrospective as a reusable analytical template for future agentic benchmark competitions.
B.1 Official Challenge Website: The official website provides task descriptions, objectives, and participation guidelines.It serves as an overview of the challenge.
B.2 Challenge Advertisement Page: The CODS 2025 challenge page announces and disseminates the benchmark by outlining its motivation and scope.The passage identifies the page as an advertisement and dissemination platform.
B.3 Registration Interface: Participants register through an online form that collects team and contact information.The registration interface is represented in Figure 11.

C Dimension 1: Relevant Competitions · D Dimension 2: Participation and Setup · D.1 Participating Teams

ASSETOPSBENCH extends competition benchmarks toward agentic, multi-step evaluation in industrial and privacy-constrained settings. Its participation design withheld evaluation utterances while enabling diagnostic feedback, and analysis focused on the top 11 of 149 registered teams.

C Dimension 1: Relevant Competitions: ASSETOPSBENCH follows a recent shift from static prediction tasks to agentic, multi-step evaluation and from consumer or scientific domains to regulated or privacy-constrained ones.
C Dimension 1: Relevant Competitions: 2025 competitions increasingly evaluated systems rather than predictions, including multi-modal retrieval agents and healthcare agents interacting with patient simulators.CRAG-MM scored roughly 5,000 submissions from ∼1,000 participants, while CUREBENCH assessed 390 participants over 2,773 submissions.
C Dimension 1: Relevant Competitions: ASSETOPSBENCH addresses a gap by combining agentic tasks, industrial physical assets, and privacy-constrained evaluation infrastructure.
C Dimension 1: Relevant Competitions: The competition used hidden-phase evaluation and privacy-preserving infrastructure, including Codabench containers and freeze/edit blocks isolating participant code from evaluation data.
C Dimension 1: Relevant Competitions: Only three of ten surveyed benchmarks were agentic, only ASSETOPSBENCH targeted industrial physical assets, and only Codabench-hosted entries supported container-based isolation.
D Dimension 2: Participation and Setup: Evaluation integrity was preserved by withholding scenario utterances entirely, while rich per-category diagnostic feedback supported iteration without revealing evaluation questions.
D.1 Participating Teams: 149 registered teams participated, and the analysis focused on the top 11 by final combined score, identified alphabetically as Teams A through K by ranking position.

D.2 Evaluation Scenarios … E.3 Team Activity Patterns

The benchmark combined hidden paraphrase evaluation with aggregate and diagnostic feedback, then revealed distinct participation, specialization, convergence, and late-activity patterns across planning and execution tracks.

D.2 Evaluation Scenarios: 11 scenario pairs tested generalization from Phase 1 development utterances to semantically related, undisclosed Phase 2 evaluation utterances.The mean Sentence-BERT similarity was 0.830 (σ = 0.138), with TSFM pairs at 0.555 and 0.617 as the most demanding shifts.
D.3 Submission Feedback Design: Participants received an aggregate Task Completion score for leaderboard ranking plus structured diagnostics identifying skill-level outcomes and failure modes.Feedback was designed to support iterative improvement without compromising evaluation integrity.
D.3.2 Qualitative Diagnostic Feedback: 90.9% was the illustrative submission’s strongest pass rate for Clarity and Justification, while Data Retrieval Accuracy was weakest at 63.6%.Agent Sequence Correct reached 81.8%, and the diagnostics prioritized data-retrieval grounding as the highest-leverage improvement target.
E Dimension 3: Submission Behaviour: 300 submission attempts were recorded across 25 unique team identities, with 234 Finished submissions used in the analysis and scores normalized to [0, 100].The aggregate score measures the proportion of scenarios successfully completed.
E.1 Score Distributions and Track Specialization: 27 points was the maximum planning-track interquartile range for high-submission teams, versus below 10 points for most execution-track submissions.Best planning and execution scores had a weak, statistically nonsignificant correlation of Spearman ρ = 0.41 (p = 0.21).
E.3 Team Activity Patterns: 31 finished execution submissions made Team A the activity leader, while Team H submitted 31 planning and 5 execution runs, indicating contrasting track engagement.Team H’s specialization produced a higher planning component score but a weaker combined score than more balanced teams.
E.2 Score Progression over the Competition Window: Within five points of their final best score, most planning teams converged during the first two weeks, after which improvements were marginal.The pattern was consistent with prompt-engineering variations on a fixed scaffold.
E.3 Team Activity Patterns: 11 teams displayed two engagement patterns: sustained high activity or concentrated late-stage activity, with Team A peaking at 11 submissions in one week.A competition-wide submission surge occurred during the final two weeks, especially for Teams A and C.

E.4 Learning Dynamics and Submission Reliability · F Dimension 4: Ranking Robustness · F.1 Final Competition Rankings

Most teams reached their best scores within five submissions, while execution submissions were less reliable than planning submissions. Final rankings rewarded balanced cross-track performance, with near-ties exposing sensitivity to the scoring rule.

E.4 Learning Dynamics and Submission Reliability: Most teams achieved their best score within the first five finished submissions, followed by plateauing and diminishing returns.Team A continued improving beyond submission 20, whereas Team H plateaued early despite 31 submissions.
E.4 Learning Dynamics and Submission Reliability: 31 planning-track submissions by Team H illustrated repeated marginal refinement without addressing remaining failures.This pattern was identified as a diminishing-returns failure mode.
E.4 Learning Dynamics and Submission Reliability: Most planning teams exceeded 80% submission success, while several execution teams experienced failure rates exceeding 30%.Success rate was defined as the proportion of non-cancelled attempts reaching Finished status.
E.4 Learning Dynamics and Submission Reliability: Planning and execution performance correlated weakly and non-significantly, with Spearman ρ = 0.41 and p = 0.21.The summary treats the tracks as testing qualitatively distinct agent capabilities.
F.1 Final Competition Rankings: The final score F combined planning component score Cplan and execution component score Cexec, with both components incorporating semantic t-match under fixed weights.Table 13 contains 11 valid ranking rows after blank lines were removed.
F.1 Final Competition Rankings: BlueCube matched WaterLevel’s planning score at 60.049 but ranked two positions lower because its execution score was 46.406 versus 54.593.The aggregation penalised track specialization and rewarded balanced cross-track performance.
F Dimension 4: Ranking Robustness: LostSouls and BlueCube were separated by only 0.001 points, with final scores of 51.864 and 51.863 respectively.The passage states that a minor scoring-function perturbation would reverse their ordering.

F.2 Top-Rank Stability Under Metric Reparameterization … G.6.2 Per-Question Difficulty: Development Phase

The analysis finds that leaderboard conclusions are sensitive to metric reparameterization and hidden evaluation, while computational demand varies sharply by phase, domain, scenario, and question. Held-out performance remains comparable to development performance, but development difficulty is concentrated in work-order questions with near-zero completion and high hallucination.

F.2 Top-Rank Stability Under Metric Reparameterization: Mean Kendall τ = 0.61 (SD = 0.19), with the official top-ranked team stable only near the released scoring configuration.The sweep varies execution weight α ∈[0, 1] and t-match scaling s ∈[1, 100] across all 11 teams.
F.3 Score Saturation of the Public Leaderboard: 72.73 is the maximum public planning score shared by four teams, so execution scores determine their combined-ranking order.With 11 scenarios contributing approximately 9.09 points each, public scores lie on a fixed discrete grid; more scenarios or partial credit would improve resolution.
F.4 Public vs. Hidden Score Alignment: ρplan = 0.62 (p = 0.04) versus ρexec = −0.13 (p = 0.71), showing moderate planning alignment but essentially uninformative public execution scores.Maximum absolute rank shifts are 6 positions in planning and 8 positions in execution, supporting independent hidden evaluation from the competition’s start.
G Dimension 5: Computational Footprint / G.1 Overall Cost Distributions: 2,196 successful execution traces across 22 scenarios show a heavily right-skewed computational-cost distribution, with the top 10% consuming 42.5% of sent tokens.Token consumption has median 54K versus mean 110K and CV = 1.47; API-call depth and duration are similarly right-skewed.
G.2 Phase Comparison: p = 0.004, with Phase 1 averaging 11.5 API calls versus 10.0 in Phase 2, while token consumption and duration are indistinguishable across phases.The phase asymmetry is consistent with more exploratory multi-step behavior during development.
G.3 Agent Domain Cost Profile: 244K tokens and 13.5 API calls make Work Order the most expensive domain, while TSFM requires 35K tokens and 6.1 calls.E2E uses 63K tokens but takes 203 seconds, exceeding WO’s 145 seconds and exposing orchestration latency.
G.4 Single-Agent vs. Multi-Agent Overhead: 121K vs. 63K tokens, t = 7.18, p < 0.001, shows that single-agent executions consume nearly twice the tokens of multi-agent executions.The inversion reflects the single-agent pool’s dominance by token-intensive WO Decision Support scenarios, while multi-agent executions have higher wall-clock duration.
G.5 Scenario-Level Difficulty and Cross-Run Variance / G.6 Question wise Evaluation Results / G.6.1 Aggregate Performance by Phase / G.6.2 Per-Question Difficulty: Development Phase: 373K tokens for Q424 versus 20K for Q201 yields an 18-fold difficulty range, while cross-phase scenario ordering remains consistent at ρ = 0.89 (p < 0.001).Held-out evaluation performance is comparable to or slightly higher than development performance across higher-is-better metrics, with hallucination decreasing from 0.437 to 0.330; Q424, Q405, and Q400 have near-zero completion and hallucination above 0.73.

G.6.3 Per-Question Difficulty: Evaluation Phase · G.6.4 Discussion · H Dimension 6: Strategy Attribution

Evaluation difficulty is concentrated in a small set of hallucination-prone work-order questions, while held-out performance suggests limited development overfitting. Strategy attribution uses public-to-private score gaps as an associative signal, supported by verified code and organizer summaries.

G.6.3 Per-Question Difficulty: Evaluation Phase: Q201 scores exactly 0.000, with zero on all higher-is-better metrics and zero hallucination, indicating abstention rather than confabulation.The remaining seven questions exceed 0.40, with Q7 reaching 0.811.
G.6.4 Discussion: 0.107: held-out evaluation has an absolute hallucination-rate drop of 0.107 and marginally higher scores on every higher-is-better metric than development.The discussion interprets this pattern as evidence against overfitting to development questions.
G.6.4 Discussion: r ≈−0.93: composite score and hallucination rate are strongly negatively correlated across all 22 questions.Every question below zero has hallucination of at least 0.5, while every question above 0.5 has hallucination at or below 0.20.
G.6.4 Discussion: All three hardest questions in each phase are work-order tasks, involving multi-year event aggregation or bundling in development and evaluation.The pattern identifies long-horizon reasoning over historical events as the main capability gap.
H Dimension 6: Strategy Attribution: Verified code and organizer summaries provide the evidence base for attributing implemented strategies to score differences between submissions.Verified code means the source was examined directly; organizer summaries cover the remainder.
H Dimension 6: Strategy Attribution: δ = scorepublic −scoreprivate: small gaps indicate generalization, whereas large positive gaps indicate possible overfitting to the public evaluation signal.These strategy attributions are associative rather than causal because targeted ablations would require rerunning submissions.

H.1 Method Taxonomy · H.2 Planning Prompt Scaffold · H.3 Strategy-to-Score Attribution

The taxonomy links method families to public–private score gaps, while verified planning submissions shared a strong structural scaffold and differed mainly in prompt elaborations. Hidden-score analysis shows that worked examples generalized better than reviewer loops or anti-hallucination rules, while fallback designs could reverse public rankings.

H.1 Method Taxonomy: Table 19 classifies verified submission families by public score, private score, δ gap, evidence level, and strategy motif.The gap is defined as δ = scorepublic − scoreprivate; positive δ indicates public-signal overfitting, while negative δ indicates stronger private generalization.
H.1 Method Taxonomy: Track 1 entries are predominantly verified code, whereas Track 2 entries are predominantly organizer summaries.Table 19 reports 4/6 Track 1 named entries as verified code and 4/5 Track 2 named entries as organizer summary, with one incomplete archive.
H.2 Planning Prompt Scaffold: The shared planning scaffold restricts agents and tools, requires fewer than five steps, and makes Task, Agent, Dependency, ExpectedOutput, and dependencies explicit.The excerpt is representative of the public starter template rather than a verbatim reproduction of one submission.
H.2 Planning Prompt Scaffold: Higher- and lower-scoring planning strategies diverged mainly through five elaborations: agent taxonomies, worked examples, input typing, dependency sanitization, and reviewer rewrite loops.These differences lay outside the common outer scaffold.
H.2 Planning Prompt Scaffold: 72.73 public planning scores were achieved by many teams through prompt-engineering variations alone, without architectural changes.This suggests the public scaffold was strong enough to support near-ceiling performance despite qualitatively different elaborations.
H.3 Strategy-to-Score Attribution: δ = +9.09 was the smallest gap among maximum-public-score planning submissions, achieved by jainrishi601’s diverse worked examples.The passage interprets worked examples as demonstrating expected output structure and improving genuine task understanding.
H.3 Strategy-to-Score Attribution: Reviewer–rewrite loops and anti-hallucination instructions produced larger gaps: abc111 had δ = +18.18, while vamsikv28 had δ = +25.98.The passage attributes this pattern plausibly to suppressing error types represented in the public scenarios.
H.3 Strategy-to-Score Attribution: samah’s fallback design inverted the public ranking, rising from 45.45 public to 63.64 private and yielding δ = −18.18.This was the only negative gap in the taxonomy; under private evaluation, samah would outrank several systems scoring 18 points higher publicly.

I Dimension 7: Evaluation Agent Robustness

Across 56 execution trajectories and 32 logged features, success was negatively correlated with complexity signals, while successful runs were shorter, more focused, and more controlled. The analysis found no evidence that verbosity, execution length, or tool-use complexity systematically biased evaluation outcomes.

Correlation analysis: r ≈−0.43 for tool entropy, r ≈−0.39 for token usage, and r ≈−0.36 for both steps and API calls were negatively correlated with task success.The analysis used 56 execution trajectories and 32 behavioral, cost, and error features.
Execution patterns: Successful executions were shorter, used fewer tokens, and exhibited lower tool entropy than failed executions.Failed executions were longer, more exploratory, and less consistent.
Robustness conclusion: The evaluation agent did not reward verbosity, execution length, or tool-usage complexity; success instead tracked concise and controlled execution patterns.The study compared feature distributions across successful and failed executions and correlated trajectory features with task success.
Robustness conclusion: No evidence showed that superficial trajectory characteristics systematically influenced evaluation outcomes, supporting robustness across implementation styles.The analysis was designed to test bias toward specific execution patterns such as verbosity, execution length, or tool usage complexity.

J Dimension 8: Clustering Methodology (Supporting Appendix for Dimension 6)

The clustering methodology converts each submission into a standardized skill description, then releases the embeddings, clustering analyses, projections, medoid manifests, term tables, and composite implementations used to characterize clusters.

Skill-description construction: Submission-level skill descriptions summarize each source across four planning blocks or two execution blocks, using a fixed LLM.Planning blocks cover module variables, agent descriptions, post-processing, and prompt template; execution blocks cover the task-revision helper and dynamic-workflow run-loop.
Preprocessing: Shared section headers, code fences, and HTML comments were stripped from the generated Markdown before embedding.
Released artifacts: Released clustering artifacts include per-submission embeddings, K-means sweeps, HDBSCAN labels, UMAP projections, medoid manifests, class-TF–IDF top-term tables, and canonical composite implementations.The composite implementations are derived from each cluster’s medoids.

K Dimension 9: Failure Mode Distribution and Taxonomy Analysis of Submissions … K.6 Key Findings

Failure analysis reveals a diverse but semantically concentrated label space dominated by process-level breakdowns in task completion, context handling, redundancy, and error recovery. The findings motivate stronger termination, context tracking, memory consistency, and robust recovery mechanisms.

K Dimension 9: Failure Mode Distribution and Taxonomy Analysis of Submissions: 36,884 annotated failure instances comprise 999 unique titles and 3,183 unique descriptions, indicating a highly diverse but semantically overlapping label space.Average title length is 26.06 characters, while descriptions average 136.37 characters.
K.1 Frequency Distribution of Failure Modes: A small number of dominant failure modes account for much of the dataset, chiefly involving task completion, error handling, and context understanding rather than domain-specific errors.The distribution is highly skewed, with failures concentrated in general execution processes.
K.2 Common Patterns in Failure Descriptions: 7,181 occurrences of “final answer” and 3,868 of “indicating potential” expose recurring patterns of incomplete execution and weak convergence.Other frequent phrases include “trace shows” (3,637), “failed handle” (2,882), and “agent failed” (2,614).
K.2 Common Patterns in Failure Descriptions: Failures are primarily process-level rather than output-level, reflecting incomplete execution, lack of convergence, and insufficient robustness.This pattern indicates that systems often fail during execution rather than only in their final responses.
K.3 Failure Mode Clustering: Clustering consolidates semantically similar labels into a highly skewed distribution, with most clusters of size 1–3 and a few large clusters capturing dominant patterns.Large clusters include lack of final answer and inadequate error handling.
K.3 Failure Mode Clustering: The clustering results show high surface-label diversity but semantic concentration, while remaining sensitive to the distance threshold, embedding model, and title-only inputs.Description-level embeddings or supervised clustering could incorporate contextual information omitted from titles.
K.4 Failure Mode Taxonomy: Failure modes are organized into parent categories and subcategories, with inability to complete tasks overwhelmingly dominating isolated technical issues.The taxonomy therefore emphasizes broad execution bottlenecks rather than narrowly domain-specific errors.
K.5 Representative Clusters: Representative clusters frequently co-occur, indicating compound failure modes rather than isolated issues.The largest clusters highlight dominant failure behaviors, including recurring execution breakdowns.