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REVIEW 3 major objections 6 minor 3 cited by

Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents

T0 review · 3 major / 6 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read The paper claims that proactive problem-solving in LLM agents can be decomposed into search, identification, and execution, and that even the best models achieve only a 40% end-to-end success rate on a new benchmark, PROBE.

desk verdict Useful benchmark with a plausible 40% ceiling, but the headline number is not yet disentangled from LLM-generation artifacts; worth reviewing, not yet citable as a settled result. read the letter →

arxiv 2510.19771 v4 pith:FADONVWV submitted 2025-10-22 cs.AI

Gil Pasternak , Dheeraj Rajagopal , Julia White , Dhruv Atreja , Matthew Thomas , George Hurn-Maloney , Ash Lewis This is my paper · ORCID
classification cs.AI
keywords proactiveagentsLLMbenchmarksagentevaluationbottleneckidentificationdocumentretrievaltaskexecutionsyntheticdatagenerationLLM-as-a-judge
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that 'proactivity' — an agent finding and fixing a problem the user never stated — can be measured, and introduces PROBE, a benchmark of 1,000 synthetic workplace datastores built from realistic personas. It decomposes proactivity into three testable stages: searching a user's documents for an unspecified issue, identifying the specific bottleneck, and choosing and parameterising one correct action among many plausible ones. The central empirical result is that even the strongest frontier LLMs (GPT-5 and Claude Opus-4.1) complete only about 40% of these end-to-end tasks, and dedicated agentic frameworks score below 11%. The paper's point is that current agents are reactive and cannot reliably anticipate hidden needs, and it offers a reusable measurement to track progress on that gap.

What carries the argument

PROBE (Proactive Resolution of Bottlenecks), a synthetic benchmark built by a multi-model generation pipeline. From real LinkedIn-derived personas, it constructs a world model, then generates a bottleneck, a set of true-positive documents whose combined evidence reveals it, a large set of plausible distractor documents, and an action space with exactly one correct action and its required parameters. The paper's innovation is that the three output components — retrieved documents, bottleneck description, and action plus parameters — are scored separately, so a model's failure at any stage can be localized, and the pipeline's multi-model data generation and adversarial filtering are meant to p

What would settle it

Run the evaluation again on a version of PROBE where the gold true-positive documents are supplied directly to the models, removing the search stage. If end-to-end success jumps far above 40%, the measured ceiling is a retrieval problem rather than a proactivity ceiling; if it stays near 40%, the limit is in bottleneck identification and task execution, confirming the paper's pipeline decomposition.

Watch

Extended reading notes

Core claim

The paper's central claim is that existing LLM agents fail at proactive problem solving when the problem is not handed to them. PROBE operationalizes proactivity as a three-stage pipeline — searching over a datastore, identifying the bottleneck, and executing the resolution — and generates 1,000 samples in which evidence for a hidden bottleneck is scattered across several documents amid about 75 distractors, with exactly one of about 25 actions being the right fix. Under this protocol, the best reported end-to-end performance is 40% (GPT-5 and Claude Opus-4.1), with best search F1 0.65, best bottleneck-identification score 0.43, and best task-execution score 0.40. The paper also reports that

Load-bearing premise

The benchmark's ground truth — the gold bottleneck labels, true-positive document sets, and the single correct action/parameter set — is generated and scored by LLMs (GPT-4.1 generation with GPT-5 adversarial filtering, plus LLM-as-a-judge), so if those labels carry systematic model-family bias, the 40% ceiling measures artifact compatibility rather than proactivity; the paper's human validation covers only 26 samples and 50 judge pairs.

Editorial extensions

If this is right

  • If correct, current state-of-the-art agents cannot be trusted to autonomously act on unstated user problems; human supervision remains necessary for such tasks.
  • The three-stage decomposition means progress can be tracked per stage; improving retrieval alone will not fix identification or execution, since the best scores on those stages are independently low (0.43 and 0.40).
  • Agentic frameworks such as ReACT, Reflexion, and ReWOO, at least with tool-based retrieval, underperform plain frontier LLMs in this setup, suggesting that retrieval-tool design is a current bottleneck for agent architectures.
  • Growing the context from 50 to 100 distractor items degrades performance, implying that long-context, high-noise settings are a specific weakness to target.
  • The low identification ceiling (0.43) despite imperfect-but-substantial retrieval points to a need for evidence-grounded reasoning that cites documents before committing to a diagnosis.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the 40% ceiling is taken at face value, a natural design implication not explored in the paper is that proactive agents should be built around an explicit retrieval-and-verification loop that cites evidence before acting, rather than single-pass reasoning over a long context.
  • The paper's own cross-family data-generation results (GPT-5 scores 0.951 retrieval F1 on GPT-family data vs 0.564 on Claude-generated data) suggest that any single-family benchmark, including this one, may overstate family-specific capabilities; a fully held-out, human-written test set would be the cleaner check.
  • Because the benchmark assumes a fixed world model and single-action resolution, the findings likely understate the difficulty of real proactive assistance; dynamic world models and multi-step resolutions would be a harder, more realistic extension.
  • The fact that human annotators also largely failed suggests the task tests a genuine difficulty rather than an LLM-specific artifact, but it also raises the question of what 'reasonable' proactive performance should be — a question the paper leaves open.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper introduces PROBE, a benchmark of 1,000 synthetic workplace-datastore instances designed to measure proactive problem solving in LLM agents. Proactivity is decomposed into three stages: searching for unspecified issues, identifying the specific bottleneck, and executing the appropriate resolution. The authors generate each instance from LinkedIn-derived personas using LLM-based world models, bottleneck injection, true-positive/distractor document construction, and action/parameter generation, with GPT-4.1 as the primary generator and GPT-5 adversarial filtering. They evaluate several frontier closed- and open-source LLMs plus three agentic frameworks (ReACT, Reflexion, ReWOO) under a common protocol, and report that the best end-to-end task-execution score is 0.40, achieved by GPT-5 and Claude Opus 4.1. The paper also presents error analyses, a context-size ablation, and a generator-diversity ablation. The central claim is that even state-of-the-art models struggle on this benchmark, exposing a capability gap in autonomous proactive behavior.

Significance. If the benchmark is valid, PROBE is a useful contribution: it operationalizes a relatively under-studied capability (proactivity) into three measurable sub-skills, uses realistic multi-document workplace scenarios, and releases code and data, with a thoughtful multi-model generation pipeline and adversarial filtering. The paper also provides one of the first direct comparisons of task-execution success across LLMs and agentic frameworks in this setting. However, the headline empirical claims currently outrun the evidence: the ground-truth labels and evaluation judges are LLM-generated, human validation is very small, and the reported 40% ceiling is not tied to a disclosed composition of generator families. These issues do not invalidate the benchmark as a proposal, but they do mean the paper's central quantitative conclusions are not yet established.

major comments (3)
  1. [§3.4, §5.2, Table 3, Figure 4] The headline 'best end-to-end performance of 40%' is underdetermined by the reported data. Table 3 reports task-execution 0.40 for GPT-5 and Claude Opus 4.1 on the full 1,000 samples, but §5.2/Figure 4 shows GPT-5's end-to-end score is 51.9% on GPT-4.1-generated data, 30.2% on GPT-5-mini-generated data, and 26.4% on Claude Sonnet 4-generated data (all with 75 distractors). Section 2.4 says GPT-4.1 is the primary generator, while §5.2 says the full benchmark mixes three families, but no per-sample generator assignments or mixture proportions are reported. The aggregate 40% is therefore an average over an undisclosed weighting; the same model could score from about 26% to 52% depending on the mixture. Please report the generator distribution in the full benchmark, give per-generator results for all evaluated models, and either restate the ceiling as mix-specific or show that the headline n
  2. [§2.4, §3.1, §3.2, Appendix B.6] The benchmark's ground truth and scoring are LLM-generated, and the validation is too thin to certify label quality across 1,000 samples. The gold true-positive document sets, bottleneck descriptions, and the single correct action/parameter set are produced by GPT-4.1 with GPT-5 adversarial filtering; bottleneck-identification and parameter scores are assigned by LLM-as-a-judge. Human validation covers only 26 full samples (Table 2) and 50 judge pairs (Section 3.2), with no reported breakdown by generator family or error analysis of gold labels. Figure 4's large family-dependent swing (GPT-5 search F1 0.951 on GPT-5-mini data vs 0.564 on Claude data) shows that family-specific artifacts are real; mixing generators is a reasonable mitigation but is not demonstrated for the full benchmark. Please provide human-validated results on a held-out subset, per-family judge agreement, and a label-
  3. [§5.1, Figure 3] The context-size ablation is difficult to interpret and inconsistent with the main setup. The main benchmark is described as using 75 distractor documents per sample (§2.4, Table 1), but §5.1 and Figure 3 label 50 as the 'baseline' and plot k=50, 75, 100 while the caption says performance degrades 'from 50 (baseline) to 100'. Additionally, the y-axis is labeled 'Performance Score' without specifying which of the three metrics (search F1, bottleneck identification, task execution) is plotted. Please specify the metric, reconcile the baseline with the 75-distractor main configuration, and clarify whether the 100-sample ablation uses the full benchmark pipeline or a simplified variant.
minor comments (6)
  1. [Table 2] The 'Realistic Artifacts' column is not defined in the caption or text; clarify whether a green check means both realism questions received 'yes' and how the binary answer was elicited.
  2. [Table 3 note] The note says GPT-5-mini results are removed 'for brevity' despite GPT-5-mini being used as the base model for agentic frameworks. Report these results in an appendix or provide a public link to the full table.
  3. [§2.4] Adversarial filtering 'on a small sample set (of 5 datapoints)' is described, but it is unclear how five datapoints can validate that no artifact-based shortcuts remain in the final 1,000 samples. Clarify the procedure and sample size.
  4. [References] Lu et al. 2024a and 2024b are the same paper (Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance) with different identifiers; consolidate the citation or clearly distinguish the two uses.
  5. [Figure 4] The caption uses 'Retrieval F1' while the text refers to 'search performance' and 'end-to-end success rates'; use consistent metric names and state which metric is on the y-axis.
  6. [Section 4 / Table 5] The footnote marker after 'Root Cause Identification remains the primary challenge' appears to be mis-numbered (footnote 5 refers to Table 5 but is placed in body text before the table).

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; PROBE's scores are direct measurements with human-validated judge prompts, not predictions fitted to the benchmark.

full rationale

The paper makes no fitted-parameter claim: Table 3 reports measured search F1, bottleneck identification scores, and task-execution scores from executing models on a fixed dataset. There is no equation in which an output is defined as an input, and no parameter is fit to a subset and then 'predicted' on a closely related quantity. Ground truth (T, b, a*, P) is constructed by the generation pipeline (Section 2.4) and scores compare model outputs against that gold via structured matching for search and action labels, and via LLM-as-a-judge for bottleneck/parameter quality. The judge prompt (Appendix B.6) receives the actual gold bottleneck and expected parameters, so it is a measurement oracle, not a learned predictor of the benchmark. The paper validates this oracle with human annotators (Section 3.2: 80% human-LLM agreement, 84% inter-annotator agreement). Human annotation (Table 2) independently corroborates difficulty, so the 'even state-of-the-art models struggle' claim does not rest on a self-citation. There are no self-citations at all: references to Ayoobi et al. (personas), Jiang et al. (adversarial filtering), and Zheng et al. (LLM-as-judge) are external. The closest validity concern is Section 5.2/Figure 4, where GPT-5 scores 0.951 F1 on GPT-5-mini-generated data vs 0.564 on Claude Sonnet 4 data; the paper itself presents this as evidence FOR multi-model generation and does not hide it. The undisclosed mixture proportions in the 1,000-sample set make the aggregate 40% ceiling underdetermined, but underdetermination is a correctness/transparency risk, not a circular reduction of the result to its inputs. No step in the paper's derivation chain is equivalent by construction to its inputs.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The paper introduces no fitted physical parameters or new scientific entities. Its load-bearing assumptions are about the validity of LLM-generated ground truth, the single-action/single-bottleneck simplification, and the reliability of LLM-as-a-judge scoring. The one hand-chosen numerical configuration that directly affects results is the baseline distractor count.

free parameters (1)
  • Baseline distractor count (k=75) = 75 (baseline); Table 1 reports mean 79.3 documents per sample
    Section 5.1 states 75 distractors per sample was selected as the maximum feasible within the computational budget. It is a hand-chosen dataset configuration that directly affects measured performance (Figure 3), not a parameter fitted to a target result.
assumptions (5)
  • domain assumption A single bottleneck is associated with each sample, and the true-positive set T is unique to that sample (Section 2.1).
    Simplifies benchmark construction but removes multi-bottleneck and overlapping-datastore situations; acknowledged as a limitation in Section 8.
  • domain assumption Exactly one action in A resolves the bottleneck; all other actions are plausible but suboptimal (Section 2.3).
    Central to the task-execution scoring; real-world multi-step resolutions are excluded and acknowledged in Section 8.
  • domain assumption LLM-as-a-judge scores for bottleneck identification and parameter quality are a valid proxy for human judgment.
    Validated on only 50 GPT-4.1 prediction-output pairs with 80% human-LLM agreement (Section 3.2), not across the full benchmark. The evaluation prompts in Appendix B.6 define the rubric.
  • domain assumption Synthetic world models built from LinkedIn personas with GPT-4.1 generate realistic workplace datastores and correct ground-truth bottleneck labels.
    Annotators judged 26 samples as realistic (Table 2), but this is a small fraction of 1,000; gold labels are not independently verified per sample.
  • domain assumption Mixing data from three LLM families (GPT-5-mini, GPT-4.1, Claude Sonnet 4) removes model-family artifacts enough for fair cross-model evaluation.
    Figure 4 shows large family-specific differences (GPT-5 search F1 0.951 on GPT-5-mini data vs 0.564 on Claude data), so the mitigation is only partially demonstrated; the full benchmark's family mix is not quantified.

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Cite this review

Pith. "Pith review of Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents." pith.science (2026). https://pith.science/paper/FADONVWV

@misc{pith2026251019771,
  author       = {Pith},
  title        = {Pith review of: Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FADONVWV}},
  note         = {Machine review of arXiv:2510.19771}
}
read the original abstract

LLM-based agents are increasingly moving towards proactivity: rather than awaiting instruction, they exercise agency to anticipate user needs and solve them autonomously. However, evaluating proactivity is challenging; current benchmarks are constrained to localized context, limiting their ability to test reasoning across sources and longer time horizons. To address this gap, we present PROBE (Proactive Resolution Of BottlEnecks). PROBE decomposes proactivity as a pipeline of three core capabilities: (1) searching for unspecified issues, (2) identifying specific bottlenecks, and (3) executing appropriate resolutions. We apply PROBE to evaluate leading LLMs and popular agentic frameworks, showing that even state-of-the-art models struggle to solve this benchmark. Computing our consistent measurements across frontier LLMs and agents, we find that the best end-to-end performance of 40% is achieved by both GPT-5 and Claude Opus-4.1. Additionally, we demonstrate the relative capabilities of each model and analyze mutual failure modes. Our results highlight the current limitations of autonomous action in agentic systems, and expose promising future research directions.

Figures

Figures reproduced from arXiv: 2510.19771 by the authors.

Figure 1
Figure 1. An end to end depiction of the PROBE task setup. The model (or agent) needs to use the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. End-to-end proactive benchmark generation pipeline. A synthetic world model ( [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Performance degradation with increasing context window size. All models show declining [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: GPT-5 performance on data generated by different models. GPT-5 exhibits much higher [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

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

  1. ProEvent: An Event-centric Benchmark for Proactive Agents

    cs.AI 2026-07 conditional novelty 6.0 of 10

    ProEvent is a benchmark showing LLM agents keep a user's event timetable from chats poorly, with the best fully-correct score at 27.2%.

  2. Just A Rather Very Intelligent Spoken Agent

    cs.AI 2026-07 conditional novelty 6.0 of 10

    JarvisBench, a two-track benchmark with a reference spoken mediator, shows mediation can raise WildClaw task scores by up to 11.78 points and improve trace-grounded user answers.

  3. Knowing Isn't Understanding: Re-grounding Generative Proactivity with Epistemic and Behavioral Insight

    cs.CY 2026-02 conditional novelty 6.0 of 10

    Proactive AI should be grounded in the user's epistemic state, not just in action prediction, so that commitment scales with warranted understanding rather than confidence.

Reference graph

Works this paper leans on

131 extracted references · 2 linked inside Pith · cited by 3 Pith papers

  1. [1]

    Generate exactly {{ num_actions }} proactive actions total

  2. [2]

    Binfeng Xu, Zhiyuan PENG, Bowen Lei, Subhabrata Mukherjee, and Dongkuan Xu

    URLhttps://arxiv.org/abs/2507.09313. Binfeng Xu, Zhiyuan PENG, Bowen Lei, Subhabrata Mukherjee, and Dongkuan Xu. DE- COUPLING REASONING FROM OBSERV ATIONS FOR EFFICIENT AUGMENTED LAN- GUAGE MODELS, 2024. URLhttps://openreview.net/forum?id=CpgoO6j6W1. Bufang Yang, Lilin Xu, Liekang Zeng, Kaiwei Liu, Siyang Jiang, Wenrui Lu, Hongkai Chen, Xiaofan Jiang, Guo...

    arXiv 2024

  3. [3]

    The remaining actions should be realistic and detailed workplace actions that DON’T solve any of the bottlenecks

  4. [4]

    **EXACTLY ONE ACTION** should solve each bottleneck - no more, no less

  5. [5]

    actions" array. Each action should follow this structure: {

    **NO NAMED ENTITIES**: Actions must NOT contain specific person names, company names, or proper nouns from the bottlenecks ## ACTION CATEGORIES: - send_email: Send new emails to individuals or groups - reply_email: Reply to existing email threads - schedule_meeting: Create new meetings or events - reschedule_meeting: Move or modify existing meetings - can...

  6. [6]

    Make it clear which action solves which bottleneck through the action’s description and parameters

  7. [7]

    Never mentions other bottlenecks The document should read like an authentic workplace artifact that someone would search for when investigating the bottleneck. 25 Listing 11: generate_document_evidence.j2 B.4.5 GENERATEDYNAMICSOURCES Usage Context This prompt identifies additional data sources or systems where evidence might be found, expanding beyond the...

  8. [8]

    Each bottleneck has EXACTLY ONE action that can solve it, all other actions should certainly not solve the bottleneck

Show all 131 references
  1. [9]

    The action description of the correct action should address the bottleneck, but without mentioning the bottleneck, keywords, or its details in any way

  2. [10]

    Other actions are detailed and realistic but explicitly DON’T solve any of the listed bottlenecks

  3. [11]

    Total number of actions equals {{ num_actions }}

  4. [12]

    It’s one of the first prompts exe- cuted to establish the professional environment

    **CRITICAL**: The actions should not include any mention of the people or situations involved in the bottleneck Listing 1: generate_actions_for_bottleneck.j2 18 B.1.2 GENERATEORGANIZATIONSTRUCTURE Usage Context This prompt generates the organizational context around a persona,...

  5. [13]

    Company name and type

  6. [14]

    Department structure

  7. [15]

    Reporting relationships

  8. [16]

    company_name

    Key processes and workflows The organization should be realistic for someone in their role and location. Provide your response as a JSON object with this structure: { "company_name": "Name of the company", "company_type": "Type of company (startup, enterprise, etc.)", "departm...

  9. [17]

    Work style and preferences

  10. [18]

    Current goals and priorities

  11. [19]

    Time constraints and challenges

  12. [20]

    Communication preferences

  13. [21]

    easy" %} Keep the context simple with straightforward preferences and minimal constraints. {% elif difficulty ==

    Tools and systems they use {% if difficulty == "easy" %} Keep the context simple with straightforward preferences and minimal constraints. {% elif difficulty == "medium" %} Include moderate complexity with some competing priorities and constraints. {% elif difficulty == "hard"...

  14. [22]

    The person’s full name

  15. [23]

    Type of relationship (colleague, client, manager, stakeholder, collaborator)

  16. [24]

    How they interact with {{ persona.name }}

  17. [25]

    relationships

    Current status of the relationship Provide your response as a JSON object with this structure: { "relationships": [ { "name": "Full name", "role": "Their job title", "type": "colleague|client|manager|stakeholder|collaborator", "interaction": "Description of how they work toget...

  18. [26]

    References REAL NAMES from the relationships

  19. [27]

    Mentions SPECIFIC documents, meetings, or deadlines

  20. [28]

    Has a clear timeline or urgency

  21. [29]

    Can be discovered through search/investigation

  22. [30]

    easy" %} Make it straightforward with clear cause and solution. {% elif difficulty ==

    Is solvable through proactive action {% if difficulty == "easy" %} Make it straightforward with clear cause and solution. {% elif difficulty == "medium" %} Include some complexity and multiple stakeholders. {% elif difficulty == "hard" %} Make it complex with competing priorit...

  23. [31]

    Each references REAL NAMES from the relationships

  24. [32]

    Mentions SPECIFIC artifacts (documents, meetings, systems)

  25. [33]

    Has clear urgency or timeline

  26. [34]

    Can be discovered through search

  27. [35]

    easy" %} Make them straightforward with clear causes. {% elif difficulty ==

    Is solvable through action 21 ENSURE VARIETY: - Different types of problems (delays, missing info, conflicts, etc.) - Different people involved - Different urgency levels - Different solutions needed {% if difficulty == "easy" %} Make them straightforward with clear causes. {%...

  28. [36]

    Each document should contain a different aspect/angle of the bottleneck

  29. [37]

    Together they should tell the complete story

  30. [38]

    Avoid ANY mention of other bottlenecks

  31. [39]

    evidence_distribution

    Make evidence discoverable but not too obvious Provide your response as a JSON object: { "evidence_distribution": [ { "document_type": "email|calendar|document", "evidence_role": "What aspect this covers", "key_information": "Specific info to include", 23 "sender_or_creator": ...

  32. [40]

    Contains clear evidence of the bottleneck

  33. [41]

    Fits the evidence role specified

  34. [42]

    Includes realistic email metadata

  35. [43]

    Uses actual names from relationships

  36. [44]

    Avoids any mention of other bottlenecks

  37. [45]

    Sounds natural and professional The email should be substantial (200-400 words) and include: - Proper email headers (From, To, CC, Subject, Date) - Natural greeting and sign-off - Specific details that reveal bottleneck information - Realistic workplace communication style For...

  38. [46]

    Reveals important timing/scheduling aspects of the bottleneck

  39. [47]

    Includes realistic attendees from relationships

  40. [48]

    Has detailed agenda or description

  41. [49]

    Shows urgency or conflicts if relevant

  42. [50]

    Completely avoids other bottlenecks Include: - Title: Specific and professional - Date/Time: Realistic and relevant to bottleneck - Duration: Appropriate for the meeting type - Location: Physical or virtual - Attendees: Mix of required and optional - Agenda/Description: Detail...

  43. [51]

    Has proper header (title, date, author, recipients)

  44. [52]

    Contains multiple sections with clear headings

  45. [53]

    Embeds bottleneck evidence naturally throughout

  46. [54]

    References real people and specific details

  47. [55]

    Maintains professional tone and formatting

  48. [56]

    Includes actionable information

  49. [58]

    Name the specific system/platform

  50. [59]

    What evidence would be found there

  51. [60]

    How to search/access it

  52. [61]

    Provide specific names and identifiers, not generic categories

    Why it’s relevant to this bottleneck Examples: JIRA tickets, Confluence pages, Slack channels, CRM records, Github PRs, etc. Provide specific names and identifiers, not generic categories. Listing 12: generate_dynamic_sources.j2 B.4.6 REVIEWEVIDENCE Usage Context This prompt i...

  53. [62]

    Does the evidence clearly support discovering this bottleneck?

  54. [63]

    Is it discoverable through realistic search queries?

  55. [64]

    Does it avoid ALL mentions of other bottlenecks?

  56. [65]

    Is it natural and realistic for the workplace context?

  57. [66]

    Are all names, dates, and details consistent? Provide:

  58. [67]

    Quality score (1-10)

  59. [68]

    Strengths of the evidence

  60. [69]

    Any issues or contamination found

  61. [70]

    Suggested improvements

  62. [71]

    quality_score

    Search queries that would find this evidence Format as JSON: { "quality_score": 8, "strengths": ["Clear timeline", "Specific names"], "issues": ["Might be too obvious"], "improvements": ["Add more context about..."], "search_queries": ["Michael Park ENG-4521", "Q3 roadmap revi...

  63. [72]

    Emails must be completely unrelated to any bottleneck

  64. [73]

    Should be realistic workplace communications

  65. [74]

    Vary the types: updates, requests, FYIs, discussions

  66. [75]

    Use different senders and recipients

  67. [76]

    Include realistic dates and subjects

  68. [77]

    Format each email with proper headers (From, To, Subject, Date) followed by the body

    Length: 150-300 words each Generate diverse emails about: - Routine status updates - General team communications - Company announcements - Non-critical planning - Social/cultural events - Training or development - General process discussions Ensure NONE of the emails could be ...

  69. [78]

    Regular recurring meetings (1-on-1s, team standups)

  70. [79]

    Training or development sessions

  71. [80]

    Planning sessions (unrelated to bottlenecks)

  72. [81]

    Reviews or retrospectives Each event needs: - Title: Professional and specific - Date/Time: Spread across different days/times - Duration: Realistic for the meeting type - Attendees: Appropriate mix of people - Location/Link: Physical or virtual - Description: Detailed agenda ...

  73. [82]

    Process documentation

  74. [83]

    Team updates or newsletters

  75. [84]

    Project proposals (unrelated to bottlenecks)

  76. [85]

    Policy documents CRITICAL - AVOID ALL BOTTLENECKS: {% for bottleneck in bottlenecks %} DO NOT REFERENCE: {{ bottleneck.description }} {% endfor %} Each document should: - Be 400-800 words - Have professional formatting and structure - Reference real people from relationships -...

  77. [86]

    Is completely unrelated to any bottleneck

  78. [87]

    Fits naturally in the persona’s work life

  79. [88]

    Could plausibly be important

  80. [89]

    Uses real names and realistic details

  81. [90]

    Matches typical {{ kind }} format and style Focus on routine work activities that would generate {{ kind }}s but don’t relate to the specific problems being evaluated. Listing 17: generate_natural_distractor.j2 B.5.5 ENHANCEDISTRACTOR Usage Context This prompt enhances basic d...

  82. [91]

    Add more specific details (names, dates, numbers)

  83. [92]

    Include realistic workplace jargon

  84. [93]

    Add urgency or importance markers

  85. [94]

    Reference real systems or processes

  86. [95]

    Make it harder to distinguish from true positives MAINTAIN: - Core message/purpose - Avoidance of all bottlenecks - Professional tone - Realistic length The enhanced version should be a high-quality distractor that requires careful analysis to determine it’ s not relevant to t...

  87. [96]

    Discuss alternative scenarios or outcomes

  88. [97]

    Be clearly hypothetical/counterfactual

  89. [98]

    Not provide evidence for actual bottlenecks

  90. [99]

    If we had chosen vendor B instead of vendor A

    Maintain professional tone Example: "If we had chosen vendor B instead of vendor A..." Listing 19: counterfactual_min_email.j2 B.5.7 ENTITYSWAPTEMPLATE Usage Context This prompt creates distractors by swapping entities (people, projects, systems) to create plau- sible but inco...

  91. [100]

    Clearly references the distractor timeframe

  92. [101]

    Discusses similar topics but in a different temporal context

  93. [102]

    Could be confused with current issues if not reading carefully

  94. [103]

    Create a document that follows the same template as important documents but with unrelated content

    Maintains realism for that time period Listing 21: time_scope.j2 30 B.5.9 TEMPLATECLONEDOCUMENT Usage Context This prompt creates documents that follow the same template or format as important docu- ments but contain different, unrelated content. Create a document that follows...

  95. [104]

    Follows the exact same structure

  96. [105]

    Contains completely different, unrelated content

  97. [106]

    Maintains professional appearance

  98. [107]

    Generate {{ kind }} content discussing counterfactual scenarios for {{ world_model.persona_full_name }}

    Could be mistaken for an important document if not read carefully Listing 22: template_clone_doc.j2 B.5.10 GENERICCOUNTERFACTUAL Usage Context This prompt generates generic counterfactual content that discusses alternatives, hypotheticals, or what-if scenarios unrelated to act...

  99. [108]

    Is clearly marked as hypothetical

  100. [109]

    Doesn’t relate to actual bottlenecks

  101. [110]

    Provides interesting but irrelevant information

  102. [111]

    {{ identified_bottleneck }}

    Maintains professional tone Listing 23: generic_counterfactual.j2 B.6 LLM-AS-JUDGEEVALUATIONPROMPTS Usage Context This prompt is used to for evaluating bottleneck identification as described in section 3 You are evaluating whether an AI agent correctly identified a bottleneck ...

  103. [112]

    **WHO_BLOCKED**: The exact person/entity being blocked (specific names, not roles)

  104. [113]

    **WHO_BLOCKER**: The exact person/entity causing the blockage (specific names, not roles)

  105. [114]

    **WHAT_TASK**: The specific deliverable/work item that’s blocked (exact names, not generic descriptions)

  106. [115]

    **WHY_ROOT_CAUSE**: The true underlying reason for the blockage (not symptoms) ### NON-ESSENTIAL DETAILS (Can have minor errors/omissions) These provide context but aren’t critical for immediate resolution:

  107. [116]

    **WHEN_DEADLINE**: Specific dates and timelines

  108. [117]

    **WHERE_SYSTEM**: Exact system/tool names and locations

  109. [118]

    **HOW_MECHANISM**: Detailed process or workflow information

  110. [119]

    Timothy" not

    **IMPACT_SCOPE**: Downstream effects and dependencies ## Scoring Rules **CORRECT (1.0)**: All essential details are accurate AND all non-essential details are accurate **PARTIALLY_CORRECT (0.5)**: All essential details are accurate BUT has errors/omissions in non- essential de...

  111. [120]

    **Purpose**: What this parameter accomplishes

  112. [121]

    **Criticality**: Is it essential for resolving the bottleneck?

  113. [122]

    **Flexibility**: Can alternatives achieve the same goal? ### Step 2: Map Parameters Semantically Compare agent’s parameters to expected parameters:

  114. [123]

    **Direct matches**: Same parameter name and equivalent value

  115. [124]

    **Semantic matches**: Different representation, same effect

  116. [125]

    **Missing parameters**: Expected but not provided

  117. [126]

    **Extra parameters**: Provided but not expected

  118. [127]

    Would the agent’s parameters successfully resolve the bottleneck?

    **Wrong parameters**: Provided but incorrect for the goal ### Step 3: Evaluate Effectiveness Ask: "Would the agent’s parameters successfully resolve the bottleneck?" ## Scoring Rubric 33 ### CORRECT (Score: 1.0) All of the following must be true: - âIJ¸ S All critical paramete...

    2024

  119. [128]

    **Over-penalizing format differences**: JSON structure vs semantic meaning

  120. [129]

    **Ignoring context**: Parameters should fit the specific bottleneck

  121. [130]

    ASAP" vs

    **Requiring exact matches**: "ASAP" vs "urgent" may be equivalent

  122. [131]

    judgment

    **Missing parameter relationships**: Some parameters depend on others ## Output Instructions Analyze systematically, then provide your judgment in this JSON format: ‘‘‘json { "judgment": "<CORRECT|PARTIALLY_CORRECT|INCORRECT>", "reasoning": "<2-3 sentences explaining how the p...

  123. [2025]

    Yueqian Wang, Xiaojun Meng, Yifan Wang, Huishuai Zhang, and Dongyan Zhao

    URLhttps://arxiv.org/abs/2507.20534. Yueqian Wang, Xiaojun Meng, Yifan Wang, Huishuai Zhang, and Dongyan Zhao. Proactivevideoqa: A comprehensive benchmark evaluating proactive interactions in video large language models,

    arXiv

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.