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xDailyBench: Benchmarking LLMs on Professional Consultation for Real-Life Problems
Yongchang Peng, Qingshui Gu, Liya Zhu, Ge Zhang, Duo Wang, Haodong Wang, Jingzhe Ding, Tianhao Yu, Letian Gao, Yongjie Zhong, Chaoxin Li, Zixin Su, Jinchao Tao, Xingyu Ma, Xin'ao Guo, Feng Tian, Shiyuan Dong, Xiaoyan He, Sen Liu, Xin Chen, Jiajun Li, Zejia Zhang, Xi Lin, Wen Zhang, Yi Zhu, Duju Zeng, Xiang Gao, Yunyang Wang, Jiahao Wang, Yujia Qin, Jiaheng Liu, Shen Yan, Xiaolong Chang, Wenhao Huang
TL;DR
Existing benchmarks only partially capture everyday assistance, where users make open-ended, context-dependent requests that require both explicit instruction following and implicit-need inference. The paper introduces xDailyBench, an authentic-task benchmark with fine-grained binary rubrics, and finds that implicit requirement inference remains a persistent bottleneck for reliable real-world task completion.
Problem
Existing benchmarks leave realistic professional assistance underexplored because everyday requests require ambiguity handling, personalization, contextual reasoning, and actionable recommendations.
Method
xDailyBench contains 248 authentic open-ended tasks collected from users and evaluates them with rubric criteria covering explicit and implicit requirements and mapped capabilities.
Results
Strong leaderboard performance does not ensure reliable everyday task completion; the best-performing model achieves a 75.6% average score.
Takeaways & Limitations
xDailyBench provides fine-grained diagnostic signals and identifies implicit requirement inference as a persistent bottleneck in practical LLM assistance.
Takeaways & Limitations
The benchmark relies on contributors to provide authentic tasks, construct rubrics, conduct cross-review, and validate evaluations.
Abstract
from arXiv · showhide
Large language models (LLMs) are increasingly used for everyday assistance, yet existing benchmarks only partially reflect the requests users naturally make in practice. Real-world requests are often open-ended, casually specified, and context-dependent, requiring models not only to follow explicit instructions but also to infer unstated needs from user background and situational context. We introduce xDailyBench, a benchmark of 248 carefully curated tasks spanning 51 scenarios across personal life, white-collar work, learning and research, and cross-domain activities. The tasks are grounded in requests that users have actually completed or genuinely intended to accomplish with AI, and are evaluated with fine-grained binary rubrics covering both explicit and implicit requirements. We evaluate 11 frontier models under standardized agentic settings. The best models achieve a task-level score of 75.6\%, while all models perform substantially worse on implicit than explicit requirements, with gaps no less than 9 percentage points. These results reveal implicit requirement inference as a persistent bottleneck for reliably satisfying real-world everyday user needs.
1 Introduction
xDailyBench addresses the gap between conventional benchmarks and realistic professional assistance, where requests are open-ended, contextual, and require implicit-need inference. It introduces an authentic-task benchmark with rubric-based evaluation and shows that strong leaderboard performance does not ensure reliable everyday task completion.
- Existing evaluations often emphasize autonomous workflow execution, while professional assistance also requires interpreting underspecified requests, personalization, contextual integration, and actionable recommendations.
- 248 tasks across four domains and 51 scenarios were collected from more than 1,000 participants’ authentic or intended AI-assisted activities.The benchmark preserves underspecified objectives, implicit requirements, dispersed constraints, contextual information, and multiple acceptable solutions.
- 75.6% was the best model’s average score, showing that conventional leaderboard strength does not necessarily translate into reliable completion of realistic everyday tasks.The benchmark also attributes failures to specific evaluation criteria and capability dimensions.
- xDailyBench evaluates open-ended tasks with interpretable rubric criteria mapped to capabilities, supporting both end-to-end success assessment and fine-grained diagnosis without reference answers.
2 Related Work
Related benchmarks progress from examination-style tests toward agentic workflows and rubric-based evaluation, but they still leave realistic, personalized daily consultation insufficiently represented. Existing work also motivates structured criteria and human validation for automatic judging.
- Examination-style benchmarks measure domain knowledge and reasoning with controlled answers but isolate expertise from ambiguous, personalized situations.
- Agent benchmarks evaluate sequential decision making and tool use across general assistance, web interaction, planning, applications, and enterprise workflows.
- LLM-as-a-Judge enables automated evaluation, but evaluator bias and robustness concerns motivate structured criteria and validation against human judgments.
3 xDailyBench
xDailyBench is constructed from authentic, context-rich user tasks and evaluated with open-ended rubrics that cover explicit and implicit requirements. Its quality process combines contributor input, screening, cross-review, and validation of automatic judgments.
- Task Sourcing: The construction pipeline collects authentic open-ended tasks across diverse domains to preserve ambiguity, personalization, and contextual richness.
- Task Sourcing: Each submission may include workspace materials, user context, and a reference solution, reflecting requirements distributed across instructions, files, and background.
- Rubric Construction: Rubrics define success conditions rather than a single reference output, while alignment, completeness, and openness preserve multiple valid solutions.
- Rubric Construction: Each rubric is weighted, mapped to a capability dimension, and aggregated into a task-level score; the benchmark uses five capability dimensions and averages 13.3 binary rubric items per task.
- Quality Assurance: Quality assurance combines model-assisted pre-screening with contributor cross-review focused on authenticity, completeness, clarity, atomicity, and evaluability.
- Quality Assurance: Automatic evaluation is pilot-validated against independent human assessments because rubric quality alone does not establish reliable automatic judgment.
- Benchmark Statistics: The benchmark contains 248 tasks across four domains and 51 scenarios, with intentionally diverse scenario coverage and difficulty levels from easy to hard.
4 Experiments
xDailyBench evaluates 11 frontier models under standardized agentic conditions and finds meaningful differences in realistic user-request performance. The strongest models reach 75.6, while implicit requirements remain consistently harder than explicit ones and domain strengths vary.
- Evaluation Setup: 11 representative models are evaluated with identical instructions, tool access, execution budgets, and standardized agent harnesses.Each task allows a maximum of 120 agent steps, with no model-specific prompting or human intervention.
- Overall Results: 75.6 is the highest overall task-level score, achieved by Seed-2.1-Evolving and Kimi-K3.GPT-5.5 and GPT-5.6-sol follow with 72.6 and 72.4, respectively, while Gemini-3.1-Pro scores 51.3.
- Requirement Types: Implicit-requirement performance trails explicit-requirement performance by at least 9 points for every evaluated model.DeepSeek-V4-Pro has the largest reported gap, at 19.0 points.
- Domain Results: Domain averages are relatively close, but no model leads across all domains and individual models vary substantially by domain.Seed-2.1-Evolving leads White-collar Work at 78.8, Kimi-K3 leads Study & Research at 80.5, and GPT-5.6-sol leads Others at 82.4.
5 Analysis and Discussion
Capability analysis shows uneven strengths across models, with quantitative reasoning especially weak, while failure analysis identifies feasibility checking and evidence-collection convergence as recurring problems. These failures arise when models do not reconcile constraints or stop exploration to synthesize a deliverable.
- Capability Dimensions: Across models, Instruction Following averages 70.7 and Artifact Quality 70.2, while Quantitative Reasoning is lowest at 60.1.Information Understanding averages 68.2 and Analytical Reasoning 67.6.
- Capability Dimensions: Quantitative reasoning is the lowest-scoring dimension for nearly all models, including scores of 65.8 for GPT-5.5 and 62.6 for GPT-5.6-sol.The same models score 76.7 and 76.6 on instruction following, respectively.
- Capability Dimensions: Similar overall scores can conceal different capability profiles: Seed-2.1-Evolving and Kimi-K3 both score 75.6 overall but lead different dimensions.Seed-2.1-Evolving leads Instruction Following, Quantitative Reasoning, and Artifact Quality, while Kimi-K3 leads Information Understanding.
- Failure Mode Analysis: Models may produce detailed plans without checking whether scattered space, budget, deadline, work-limit, and risk constraints can hold simultaneously.The representative renovation case proposes a 14-week schedule that exceeds the three-month move-in deadline.
- Failure Mode Analysis: Evidence-collection non-convergence occurs when agents repeatedly search or revise intermediate materials instead of judging sufficiency, synthesizing evidence, and producing the final artifact.Open-ended tasks provide no natural retrieval endpoint, so repeated tool success does not itself signal progress.
6 Conclusion
xDailyBench is presented as a rubric-grounded benchmark for open-ended everyday tasks and as a testbed for evaluating reliable, verifiable agent outcomes. Its analysis exposes weaknesses in grounding, reasoning accuracy, feasibility checking, and deciding when to stop exploring and synthesize.
- Conclusion: xDailyBench evaluates whether language-model agents can satisfy open-ended requirements in everyday user scenarios.The benchmark is described as rubric-grounded and focused on realistic task completion.
- Conclusion: Experiments show persistent weaknesses in grounding, reasoning accuracy, feasibility checking, and deciding when exploration should stop and synthesis should begin.These weaknesses remain despite agents’ ability to interact with tools and produce complete artifacts.
- Conclusion: Open-ended tasks lack a natural retrieval endpoint, so relevant results do not establish that key sources are covered or evidence is sufficient.Tool-success reports therefore provide no stopping signal.
- Conclusion: In one non-convergent trajectory, the model exhausts the 120-step budget after repeatedly appending search keywords and produces no deliverable, receiving a score of 0.The first search already returned the required official statute page, but the model never opened it.
Contributions
The listed contribution and sponsorship roles identify the project leads, core contributors, additional contributors, and sponsor committee for the work.
- Project Roles: Yongchang Peng, Qingshui Gu, Liya Zhu, and Ge Zhang are listed as project leads.
- Project Roles: Duo Wang, Haodong Wang, Jingzhe Ding, Tianhao Yu, and the other named researchers are listed as core contributors.
- Project Roles: Xi Lin, Wen Zhang, Yi Zhu, Duju Zeng, Xiang Gao, Yunyang Wang, Jiahao Wang are listed as contributors.
- Project Roles: Yujia Qin and Jiaheng Liu are listed on the sponsor committee.
A Typical Cases of xDailyBench from Different Domains
Representative cases across xDailyBench’s domains expose recurring failures in constraint satisfaction, evidence grounding, scenario adaptation, and risk compliance. The cases show that models can produce structured outputs while still missing critical task-specific requirements.
- Cross-domain cases: The appendix presents representative tasks across personal planning, education and real estate, museum guidance, and overseas-admission rubric auditing.These cases collectively illustrate major model failure modes, including neglected requirements, unmet constraints, superficial reasoning, factual fabrication, and non-compliant risk warnings.
- Personal life: Healthcare planning outputs overlooked hard constraints, relied on unverified heuristics, and selected incorrect pharmacies and dispensing details.The case also lacked contingency information such as official service hotlines.
- Personal life: The education and real-estate case produced incorrect tuition and expense calculations, proposed prohibited leverage, and supplied actionable ways to evade residency inspections.Its quantitative analysis also exhibited pseudo-precision.
- Learning and research: The museum-guide case required source-only synthesis, conceptual differentiation, and adaptation for junior-high students and elderly visitors.The broader case analysis identifies superficial format compliance, weak evidentiary grounding, and insufficient scenario customization as persistent challenges.
- White-collar work: In admission-rubric auditing, the model preserved some required formatting but altered trigger words and retained wording that weakened independent critical thinking.The task required line-by-line review of Rules R01–R06 and actionable detection parameters for cross-cultural narrative risks.
B.1 Annotation Workflow of xDailyBench
xDailyBench builds tasks and rubrics through a multi-stage workflow that preserves authentic, context-dependent user requests while making success conditions independently judgeable and automatically evaluable.
- Recruitment: Contributors are recruited for frequent everyday AI use and receive standardized guidance before submitting benchmark tasks.Eligibility emphasizes practical consultation experience rather than domain expertise alone.
- Stage 1: Authentic Task Contribution: Stage 1 preserves authentic requests by retaining relevant user background, workspace materials, contextual dependence, and natural underspecification.Requirements may remain distributed across the query, materials, and background when that reflects the original interaction.
- Stage 2: Rubric Construction: Stage 2 converts explicit, unstated, and user-specific success conditions into weighted, independently judgeable binary rubric criteria.Criteria are designed to cover essential requirements without prescribing a unique solution and are organized around alignment, completeness, and openness.
- Stage 3: Cross-Review and Revision: Stage 3 uses cross-review and revision to check task sufficiency, criterion atomicity, wording clarity, binary evaluability, coverage, and unsupported criteria.Reviewers revise task–rubric pairs when they identify missing requirements, overlap, or ambiguity.
- Validation and evaluation: The workflow validates automatic judgments against human judgments and evaluates performance across five dimensions: information understanding, instruction following, analytical reasoning, quantitative reasoning, and artifact quality.Each rubric criterion must reach at least 90% agreement between human and automatic evaluation in sampled trials.
D Model Performance on General and User-specific Implicit Requirements on xDailyBench
Table 5 reports detailed model performance on different types of implicit requirements in xDailyBench, separating general implicit from user-specific implicit rubric items.
- Implicit requirements: Table 5 reports model performance across different types of implicit requirements in xDailyBench.
- Implicit requirements: The table distinguishes general implicit requirements from user-specific implicit requirements.This distinction is part of the reported fine-grained performance analysis.
E Comparison between GPT-5.5 and GPT-5.6-sol
GPT-5.5 and GPT-5.6-sol have nearly identical overall task-level scores but differ substantially in requirement handling and task-domain strengths. Fine-grained evaluation shows GPT-5.6-sol is stronger on implicit and personal-life tasks, while GPT-5.5 is stronger on explicit and structured white-collar tasks.
- Overall comparison: GPT-5.5 and GPT-5.6-sol score 72.6 and 72.4 overall, respectively, so their aggregate difference is only 0.2 points.The small aggregate gap does not indicate that either model is uniformly stronger.
- Requirement types: GPT-5.5 scores 77.2 on explicit requirements versus 75.0 for GPT-5.6-sol, while GPT-5.6-sol scores 66.0 on implicit requirements versus 62.3.The explicit–implicit gap is 14.9 points for GPT-5.5 and 9.0 points for GPT-5.6-sol.
- Task domains: GPT-5.6-sol improves on Personal Life tasks from 69.7 to 74.5, while GPT-5.5 leads on White-collar Work with 72.5 versus 66.5.The models perform similarly on Study & Research and Others.
- Capability profiles: GPT-5.6-sol is stronger on inferring implicit needs and handling contextualized personal tasks, whereas GPT-5.5 is more reliable on explicit requirements and structured professional tasks.The comparison characterizes the change as a shift in capability emphasis rather than uniform improvement or degradation.
- Evaluation implications: Fine-grained evaluation reveals behavioral differences that a single task-level score would obscure.The preferable model may depend on whether users need implicit-need inference or structured, explicitly constrained task completion.