Pith. sign in

REVIEW 4 major objections 5 minor 18 cited by

xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A new benchmark scores AI agents on real recruitment and marketing work, aiming to predict their economic value.

desk verdict A genuinely useful profession-aligned benchmark pair, but the productivity-value claim in the abstract outruns the evidence. read the letter →

arxiv 2506.13651 v1 pith:IBPHXL2O submitted 2025-06-16 cs.LG

Kaiyuan Chen , Yixin Ren , Yang Liu , Xiaobo Hu , Haotong Tian , Tianbao Xie , Fangfu Liu , Haoye Zhang
show 25 more authors
Hongzhang Liu Yuan Gong Chen Sun Han Hou Hui Yang James Pan Jianan Lou Jiayi Mao Jizheng Liu Jinpeng Li Kangyi Liu Kenkun Liu Rui Wang Run Li Tong Niu Wenlong Zhang Wenqi Yan Xuanzheng Wang Yuchen Zhang Yi-Hsin Hung Yuan Jiang Zexuan Liu Zihan Yin Zijian Ma Zhiwen Mo
This is my paper · ORCID
classification cs.LG
keywords AIagentevaluationprofession-alignedbenchmarkrecruitmentinfluencermarketingLLM-as-a-judgeitemresponsetheorytechnology-marketfitproductivitymeasurement
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 introduces xbench, an evaluation suite designed to measure how much real-world productivity AI agents deliver in professional settings, rather than how well they perform isolated technical tasks. It argues that existing benchmarks fail to capture the economic value agents create, and that profession-aligned tasks defined by industry experts can close this gap. The first two implementations cover Recruitment, with 50 headhunting tasks, and Marketing, where agents match influencers to 50 advertiser briefs. The paper claims its metrics strongly correlate with productivity value, can predict technology-market fit, and can track capability growth over time. If correct, xbench could let businesses price agent services by benchmark score and help predict which agent products are commercially viable.

What carries the argument

The core machinery is the profession-aligned evaluation pipeline: tasks are collected live from expert business operations, categorized by feasibility and evaluability, and scored by an LLM judge that follows a chain-of-thought rubric (co-developed with domain experts) to produce a 1-5 score linearly mapped to 0-100. For Marketing, a rubric generator first summarizes the client-selected influencers into an 'ideal persona', then each agent-recommended influencer is scored against that persona. For tracking capabilities over time, the paper uses item response theory (IRT), modeling the probability of a correct response as $p(\theta) = 1/(1+e^{-a(\theta-b)})$, where $\theta$ is agent ability, $b$ is item difficulty, and $a$ is item discrimination; this allows estimation of capability from incomplete evaluation results across product versions.

What would settle it

Run a field study where a group of professional headhunters and marketing operations staff independently score the same agent outputs using the same rubrics, then compare their scores to the LLM judge's scores; if agreement is low (e.g., correlation near zero) or if the judge consistently favors a particular model family, the claim that scores track productivity value is weakened.

Watch

Extended reading notes

Core claim

The central claim is that profession-aligned evaluation, built from live business demands and scored with LLM-based judges using professional rubrics, produces benchmark scores that track the economic value agents deliver. For Recruitment, agents are tested on company mapping, people-to-info (completing a person's professional history), and info-to-people (finding people from constraints); for Marketing, agents recommend influencers from a curated pool of 836 candidates and are scored against the persona of influencers clients actually selected. The paper reports baseline results for leading contemporary agents, with the top-ranked agent scoring 78.5 on Recruitment and 50.8 on Marketing, and shows that scores vary by task theme in ways that highlight specific capability gaps. It also proposes item response theory to estimate underlying agent capability from an incomplete score matrix, so that capability growth can be tracked as both agents and evaluation sets evolve.

Load-bearing premise

The scores produced by the LLM judge are assumed to accurately reflect the quality and economic value of an agent's work in recruitment and marketing, an assumption the paper explicitly defers validating against human judgment.

Editorial extensions

If this is right

  • If benchmark scores track productivity value, businesses can use xbench scores to price agent services and decide which agent products to adopt in recruitment and marketing workflows.
  • The IRT-based tracking method would let developers and investors observe capability growth of agent products over time, even as the underlying evaluation tasks and environments change.
  • Cost-performance analysis of benchmark scores against human labor cost could indicate when a domain reaches technology-market fit, meaning agents can deliver value cheaper and faster than human experts.
  • The baseline results suggest that end-to-end trained agents with strong search capabilities currently lead in these professional domains, while models with weaker search or shorter responses lag.
  • Continuous updates to the evaluation set are designed to reduce test-case leakage and keep scores aligned with a dynamic internet environment.

Reading between the lines

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

  • An implicit testable extension is to validate the claimed correlation by running a field study where agents with high xbench scores are used in real client engagements and measuring actual client re-selection rates or hiring outcomes; the paper defers this validation.
  • The same profession-aligned construction could be applied to other labor-intensive domains (e.g., legal research, financial analysis) where expert tasks are information-heavy and scorability is feasible, potentially broadening the suite into a general productivity index.
  • The IRT approach could be extended to predict future capability growth from early score trajectories, turning the benchmark from a measurement tool into a forecasting tool for agent development, though the paper does not yet demonstrate such forecasting.
  • Because the marketing benchmark scores agents on matching to an ideal persona derived from client selections, it implicitly assumes client selections are stable and rational; shifts in client preferences or influencer availability would require re-normalization of the persona.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper introduces xbench, a profession-aligned evaluation suite for AI agents in two commercially significant domains: recruitment and marketing. For recruitment, 50 tasks were collected from headhunting business scenarios and divided into Company Mapping, People-to-Info, and Info-to-People categories. For marketing, 50 advertiser requirements are paired with a pool of 836 influencers, and agents recommend influencers for each campaign. All open-ended responses are scored by Gemini-2.5-Flash as an LLM judge, with recruitment tasks scored against human-annotated verifier answers and marketing tasks scored against an LLM-generated 'ideal influencer persona' derived from client-selected influencers. The paper reports leaderboards for nine agents (Tables 8 and 9), proposes an IRT-based xbench-index for tracking capability growth over time, and sketches technology-market-fit (TMF) curves. The intended contribution is a dynamic, profession-aligned benchmark whose metrics 'strongly correlate with productivity value' and can track real-world agent progress.

Significance. If the validity concerns are resolved, xbench would be a useful complement to capability-centric benchmarks: the tasks are grounded in real business workflows, co-constructed with domain professionals, run in live web environments, and the authors commit to continuous updates, which addresses the saturation and contamination problems of static benchmarks. The paper is also commendably transparent in acknowledging the potential bias of using Gemini-2.5-Flash as its judge. However, the central claim that the metrics 'strongly correlate with productivity value' is not demonstrated in the current manuscript. All reported scores come from a single LLM judge with no human validation, no inter-rater agreement, no repeated runs, and no linkage to any external productivity outcome such as client re-selection rates, hiring outcomes, or time saved. The marketing evaluation is additionally self-referential: the ideal influencer persona is generated by an LLM and then scored by the same model family. Consequently, the benchmark is currently best interpreted as an internally consistent LLM-opinion leaderboard, not as a validated measure of economic value.

major comments (4)
  1. [Abstract; §4.1] The abstract claims that xbench 'creates metrics that strongly correlate with productivity value,' but the manuscript provides no evidence for this correlation. Section 4.1 states that 'alignment with human judgment' is deferred to future work, and all scores in Tables 8 and 9 are produced by a single LLM judge (Gemini-2.5-Flash) without human validation, inter-annotator agreement, or an external productivity criterion. As written, the reported scores are self-consistent LLM opinions, not validated measures of productivity. This is load-bearing because the paper's headline contribution is the productivity-value claim, not merely the task collection. I request either (a) a human validation study on a sample of tasks demonstrating that judge scores agree with expert ratings and correlate with at least one external productivity proxy, or (b) a revised abstract, title, and discussion that remove or substantially weaken the correlation claim.
  2. [§3.3, Figure 5] The marketing evaluation is self-referential: the 'ideal influencer persona' is generated by an LLM from client-selected influencers, and the same model family (Gemini-2.5-Flash) is then used to score candidate influencers against that persona. If the LLM-generated persona is inaccurate, incomplete, or style-biased, the scores will reward conformity to the LLM's stereotype rather than to the client's actual preferences. The paper does not validate the LLM-generated rubric against the client's own criteria, nor does it measure agreement between the LLM persona and human experts. Because the marketing metric is described as an 'estimated re-selection rate,' this loop is load-bearing. A concrete remedy is to have human experts independently produce personas for a subset of campaigns from the same client selections and report agreement of the LLM judge with human judgments on the final influencer lists, together with any actual client re-selection data that can be disclosed.
  3. [§4.2, Tables 8 and 9] All reported leaderboard scores are point estimates from a single evaluation run, with no error bars, no repeated runs, and no significance tests. Several adjacent ranks are separated by very small margins (e.g., marketing ranks 2–4: 47.6, 46.5, 45.9; recruitment ranks 3–4: 61.4 for both), so the ordering may not be robust. Since the paper's stated purpose is to establish baselines for Recruitment and Marketing and to track progress over time, the absence of uncertainty quantification makes the ranking claims unsupported. Please report per-task score distributions, bootstrap confidence intervals, or repeated runs with different random seeds or judge calls.
  4. [§5.1, Figure 8, Figure 9] The IRT-based xbench-index and the technology-market-fit analysis are presented as deliverables in the abstract, but the manuscript contains no demonstration on xbench data. The OpenCompass validation in Figure 8 concerns standard LLM benchmark scores, not agent task scores in dynamic environments, and the text does not explain how IRT parameters (θ, a, b) are estimated from an incomplete score matrix, how identifiability is handled, or how changes in tasks and environments over time are modeled. Figure 9 is a schematic with no fitted curves or data. These proposals are reasonable future directions, but the abstract and summary should not present 'prediction of TMF' and 'tracking of product capabilities over time' as achieved results unless a concrete demonstration is added.
minor comments (5)
  1. [§4 heading] The section heading 'EVALUTIONS' contains a typo; it should read 'EVALUATIONS'.
  2. [Figure 6 caption] Figure 6 is in the Marketing section and describes marketing task distribution, but its caption reads 'Task distribution across recruitment tasks’ categories and human time cost.' This appears to be a copy-paste error from Figure 4 and should be corrected.
  3. [§4.1 vs. Tables 8–9] The list of evaluated agents in §4.1 does not include Grok3-Search, yet Grok3-Search appears in both Tables 8 and 9. Please clarify the evaluation setup and version used for Grok3-Search.
  4. [§5.1, Eq. (1)] The text states that 'Items with a higher discrimination index a typically exhibit a gentler slope in relation to ability θ,' but in the logistic IRT model a higher a produces a steeper slope. Please correct this description.
  5. [Table 10] Table 10 appears to be a placeholder with colored cells but no actual data or legend explaining the colors. Either provide the actual available results or remove the table until data exist.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: xbench's scoring pipelines use fixed external ground truth and separate agent outputs; the unvalidated productivity-correlation claim is an empirical/correctness risk, not a circular step.

full rationale

The paper's central evaluation pipelines are not circular by construction. Recruitment scoring compares agent responses to headhunter-annotated verifier answers (Appendix A.1, Tables 12-14), and the judge only performs coverage and hallucination checking against that fixed ground truth. Marketing scoring constructs an 'ideal influencer persona' from client-selected influencers (Section 3.3, Tables 16-17) and then scores agent-recommended influencers against that persona. Although this makes the score a similarity-to-history proxy rather than an observed re-selection rate, the agent outputs are new data, not the same records used to build the persona; no equation forces the reported score to equal the claimed 'productivity value.' The paper explicitly defers the required validation: 'We plan to subsequently update our analysis with more details on metric stability, alignment with human judgment, and consistency across different judge models' (Section 4.1). The acknowledged use of Gemini-2.5-Flash as judge for Gemini models (Section 4.2) is a same-family bias risk, not a self-referential derivation. The IRT capability-tracking validation on OpenCompass (Section 5.1, Figure 8) is independent external evidence. The abstract's assertion that the metrics 'strongly correlate with productivity value' is therefore an unvalidated empirical claim, a correctness and validity limitation rather than a step that reduces to its own inputs. No circularity step can be exhibited with the paper's own equations, so the appropriate score is 0.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The benchmark construction relies on domain experts and real business data, but the scoring pipeline depends on unvalidated LLM judge assumptions and on the representativeness of client or expert ground truth. The paper provides no independent evidence that the scores map to economic productivity.

free parameters (1)
  • IRT latent ability theta and item parameters (a, b) = not reported; estimated from OpenCompass
    Section 5.1 fits these to OpenCompass leaderboard data to demonstrate capability tracking; central xbench IRT index is not yet implemented.
assumptions (4)
  • domain assumption All tasks in the recruitment benchmark can be solved using publicly available information.
    Section 3.2 Task Distribution states: 'we confirmed that all tasks can be solved using publicly available information'. If false, agent scores would be unfairly penalized for lacking private knowledge.
  • domain assumption LLM judge scores approximate expert human judgment.
    Section 4.1: 'All LLM Judge scoring is performed using the Gemini-2.5-Flash model. We plan to subsequently update our analysis with more details on metric stability, alignment with human judgment...' The entire leaderboard depends on this unvalidated assumption.
  • ad hoc to paper The ideal influencer persona derived from client-selected influencers captures campaign requirements.
    Section 3.3 constructs the persona from the overlap and differences between initial recommendations and client choices, assuming these choices are consistent and fully representative of the campaign brief.
  • domain assumption Profession-aligned task scores correlate with real-world productivity value.
    Abstract claims metrics 'strongly correlate with productivity value', but no correlation or field validation is provided in the paper.

how reviews work

0 comments
Cite this review

Pith. "Pith review of xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations." pith.science (2026). https://pith.science/paper/IBPHXL2O

@misc{pith2026250613651,
  author       = {Pith},
  title        = {Pith review of: xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IBPHXL2O}},
  note         = {Machine review of arXiv:2506.13651}
}
read the original abstract

We introduce xbench, a dynamic, profession-aligned evaluation suite designed to bridge the gap between AI agent capabilities and real-world productivity. While existing benchmarks often focus on isolated technical skills, they may not accurately reflect the economic value agents deliver in professional settings. To address this, xbench targets commercially significant domains with evaluation tasks defined by industry professionals. Our framework creates metrics that strongly correlate with productivity value, enables prediction of Technology-Market Fit (TMF), and facilitates tracking of product capabilities over time. As our initial implementations, we present two benchmarks: Recruitment and Marketing. For Recruitment, we collect 50 tasks from real-world headhunting business scenarios to evaluate agents' abilities in company mapping, information retrieval, and talent sourcing. For Marketing, we assess agents' ability to match influencers with advertiser needs, evaluating their performance across 50 advertiser requirements using a curated pool of 836 candidate influencers. We present initial evaluation results for leading contemporary agents, establishing a baseline for these professional domains. Our continuously updated evalsets and evaluations are available at https://xbench.org.

Figures

Figures reproduced from arXiv: 2506.13651 by the authors.

Figure 1
Figure 1. Profession-aligned evaluation define domain agents, predict Tech-Market Fit (TMF) and [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Difference between AI-capability-centric and profession-aligned benchmarks [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Evaluation pipelines for recruitment tasks. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Task distribution across recruitment tasks’ themes and human time cost (in minutes). [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: The evaluation pipeline for the Influencer Search task in marketing benchmark. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Task distribution across recruitment tasks’ categories and human time cost (in minutes). [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Score chart of xbench by themes. given time, comparing the performance improvements of these products at different time points is challenging. Secondly, the external environments with which agents interact are also dynamic. Even for the identical task, if its solution …
Figure 8
Figure 8. Figure 8: OpenCompass origin evaluation and IRT capability estimation. [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: Tree stages of Tech-Market-Fit. For AI applications that have achieved TMF, human resources should be increasingly allocated to the frontiers of the domain and to tasks that remain challenging for effective evaluation. Consequently, the market will re-price the value o…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 18 Pith papers

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

  1. Student-Centered Distillation Narrows the Agentic Gap Between Small and Large LLMs

    cs.CL 2025-09 conditional novelty 7.0 of 10

    A 7B LLM agent trained with student-led distillation and one-step teacher corrections nearly matches a 72B teacher on reasoning and tool-use benchmarks.

  2. Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent

    cs.CL 2026-06 unverdicted novelty 6.5 of 10

    Agents-A1, a 35B MoE agent, matches or exceeds selected 1T models on long-horizon agent benchmarks by scaling trajectory length and multi-domain distillation rather than parameters.

  3. CommitKV: Lifecycle-Aware KV Cache Compression via Commit Transitions for Multi-Turn Agents

    cs.LG 2026-08 conditional novelty 6.0 of 10

    AI agents that use tools remember each past step as cached tokens; CommitKV deletes a chunk only when its influence drops from high before a tool call to low after the observation returns.

  4. ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment

    cs.AI 2026-08 conditional novelty 6.0 of 10

    Answer-backtracked clue recovery plus clue-anchored step scoring converts sparse pass/fail outcomes into dense per-step rewards that improve SFT and GRPO for search agents.

  5. SearchAuditor: Auditing and Attributing Failures in Long-Horizon Search Agents

    cs.AI 2026-08 conditional novelty 6.0 of 10

    SearchAuditBench and SearchAuditor let LLM auditors localize, attribute, and repair failures in long-horizon search agents, reaching a 32.3% end-to-end pass rate with GPT-5.5 versus 26.6% for the strongest baseline.

  6. Fetch-then-Explore: Decoupling Selection from Extraction over a Persistent Workspace for Search Agents

    cs.AI 2026-08 conditional novelty 6.0 of 10

    Fetch-then-Explore, which stores fetched pages in a per-question workspace and extracts evidence on demand with grep/read, beats visit-and-read and browsing baselines on BrowseComp across three LLM backbones.

  7. SearchMaster: Grounded and Regulated Self-Play for Search Agents

    cs.AI 2026-08 conditional novelty 6.0 of 10

    SearchMaster trains a 9B LLM search agent through self-play with evidence-chain task generation, search-depth rewards, and over-opening penalties, lifting average accuracy on six deep-search benchmarks from 38.19% to 51.52%.

  8. Delegation Intelligence in Deep Search: A Controllable Framework for Disentangled Capability Diagnosis

    cs.AI 2026-07 conditional novelty 6.0 of 10

    End-to-end deep-search accuracy masks distinct failures in search decision-making and evidence synthesis; a controllable reverse-engineered benchmark exposes those failures.

  9. Mach-Mind-4-Flash Technical Report

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Post-training alone—parallel domain RL experts, Multi-Teacher On-Policy Distillation, and Hybrid Median-length Policy Optimization—lifts a 3B-activated MoE to roughly 100B-class agent and reasoning scores.

  10. APPO: Agentic Procedural Policy Optimization

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    APPO refines branching and credit assignment in agentic RL via a Branching Score and procedure-level scaling, improving baselines by nearly 4 points on 13 benchmarks.

  11. PBSD: Privileged Bayesian Self-Distillation for Long-Horizon Credit Assignment

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    PBSD reweights RL trajectory advantages with Bayesian evidence scores from privileged answer-conditioned likelihoods, improving credit assignment in long-horizon search agents.

  12. ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Emergent Adaptation

    cs.AI 2026-02 conditional novelty 6.0 of 10

    An LLM agent that can call a reconfiguration tool to update its sub-goals, toolbox, strategy, and context outperforms static-config agents across FRAMES, xbench, GAIA, and SWE-bench Lite.

  13. SafeWork-R1: Coevolving Safety and Intelligence under the AI-45$^{\circ}$ Law

    cs.AI 2025-07 conditional novelty 6.0 of 10

    SafeWork-R1 shows that a staged RL pipeline with safety, value, and knowledge verifiers can improve both safety and general reasoning scores over a base multimodal model.

  14. Tree-of-Experience: Hierarchical Experience Management for Self-Evolving Agents

    cs.CL 2026-08 conditional novelty 5.0 of 10

    ToE organizes agent experience as a hierarchical tree of reasoning perspectives with outcome-calibrated reliability, and reports gains over experience-free baselines on Game of 24 and FinEvolveBench.

  15. Contrastive Reinforced Policy Optimization via Privileged Self-Distillation

    cs.LG 2026-07 conditional novelty 5.0 of 10

    CRPO turns on-policy self-distillation into group-wise contrastive learning gated by student–teacher entropy gaps, improving multi-turn agentic LLM post-training over GRPO, ARPO, and OPSD.

  16. TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents

    cs.LG 2026-07 conditional novelty 5.0 of 10

    Turn-level rewards from TD changes in a frozen reference model's gold-answer log-probability improve long-horizon search-agent RL on closed- and open-web benchmarks.

  17. LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent

    cs.AI 2026-04 unverdicted novelty 5.0 of 10

    LiteResearcher uses a lite virtual world to make agentic RL training scalable and stable, enabling a 4B model to achieve 71.3% on GAIA and 78.0% on Xbench, outperforming larger open-source and commercial systems.

  18. AWorld: Orchestrating the Training Recipe for Agentic AI

    cs.AI 2025-08 conditional novelty 5.0 of 10

    AWorld, a distributed rollout framework, cuts agent experience-collection time 14.6x and trains a Qwen3-32B agent scoring 32.23% on GAIA, above GPT-4o and near DeepSeek-V3.

Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages · cited by 18 Pith papers

  1. [1]

    Unless otherwise specified, only consider candidates within China

    work page

  2. [2]

    If the number of returned results exceeds the actual demand, we will truncate to the specified number of objects at the beginning for evaluation

    Do not over-search. If the number of returned results exceeds the actual demand, we will truncate to the specified number of objects at the beginning for evaluation. At the end of your analysis, you need to return in the following format: ## Search Results Search Object 1: xx, xx Search Object 2: xx, xx, xx Prompt for People-to-Info: You are a talent info...

    work page

  3. [3]

    Unless otherwise specified, only consider the background information of Chinese individuals

    work page

  4. [4]

    The target individual is unique; the reference information is to help you pinpoint the target indi- vidual

    work page

  5. [5]

    Prompt for Info-to-People: You are a talent information search specialist

    We will prepare verification questions regarding the target individual, and the LLM will use the information you provide to attempt to answer them. Prompt for Info-to-People: You are a talent information search specialist. Based on the following reference information about talent, please identify the target person (or people, as needed): Question:{questio...

    work page

  6. [6]

    Table 11: Prompt for recruitment agents’ response collection

    Unless otherwise specified, only consider individuals from{country}, or{type of person}. Table 11: Prompt for recruitment agents’ response collection. A.2 COMPLETEEXAMPLES OF THEEVALUATIONTASKS 20 Prompt for Recruitment Response Evaluation Evaluation Prompt for Jd Analysis: Please act as a recruitment evaluation expert. Strictly follow the judgment criter...

    work page

  7. [7]

    First, extract{search object}from the results, and summarize the results for each type of {search object}

    work page

  8. [8]

    Determine the count for each type of{search object}in the extracted answers

    work page

Show all 22 references
  1. [9]

    For each type of{search object}, truncate the list of results if it is longer than the quantity in the standard answer

  2. [10]

    If covered, mark as True; otherwise, mark as False

    For each piece of information in the standard answer, check if it is covered by the information provided by the AI. If covered, mark as True; otherwise, mark as False. Calculate the coverage of the standard answer

  3. [11]

    Score: X

    For content that is too long or mismatched, check if it is fabricated and analyze its hallucinatory nature. Your final score should comprehensively consider coverage, hallucination, and information quality, etc. Then you need to consider scoring the results. Your scoring shoul...

  4. [12]

    Information related to dates, times, etc., are not conditions; please ignore them

  5. [13]

    Conditions are selected based on blogger characteristics, not sales strategies or product informa- tion

  6. [14]

    Please ignore any blank fields that are not filled in

  7. [15]

    Each condition must be given a specific weight score

  8. [16]

    Necessary Conditions

    Please return the response in JSON format, as shown below: { "Necessary Conditions": [ { "Condition": "Condition Description", "Weight": Score, "Reason": "Why this is a necessary condition" }, ... ], "Flexible Conditions": [ { "Condition": "Condition Description", "Weight": Sc...

    2023

  9. [17]

    Responsible for the backend optimization of the short-form video recommendation system, which increased the click-through rate by 18%

  10. [18]

    Developed real-time interactive features for live streaming rooms, supporting tens of millions of concurrent users

  11. [19]

    Answer 2:

    Constructed a user profiling and analytics platform. Answer 2:

  12. [20]

    Led the architectural redesign of the music playback service, reducing latency by 40%

  13. [21]

    Developed a personalized playlist recommendation algorithm

  14. [22]

    Answer 3: Technical Expertise: Java/Golang development, distributed systems, microservices architecture

    Optimized the high-concurrency processing capabilities of the comment system. Answer 3: Technical Expertise: Java/Golang development, distributed systems, microservices architecture. Industry Experience: Financial payments, short-form video, music entertainment, and social med...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.