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, et al.
cs.AI
Quick verdict
248-task benchmark of real-user AI requests: implicit needs trail explicit by ≥9pts across 11 models; solid rubric design, but no variance or bias checks.
Summary
xDailyBench is a benchmark of 248 open-ended tasks spanning 51 everyday scenarios across personal life, white-collar work, and study/research, built from requests that 1,000+ survey participants actually completed or genuinely intended to complete with AI. Tasks preserve underspecified, context-dependent instructions plus optional workspace files and user background, and are scored by fine-grained binary rubrics (mean 13.3 criteria per task) tagged to five capability dimensions and split into explicit (70.5%), general-implicit (18.5%), and user-specific-implicit (11.0%) requirements. Eleven frontier models run in a standardized agentic harness (Nanobot; OpenCode for three models) under 120-step budgets, with GLM-5.1 as sole judge, validated against human judgments at ≥90% per-criterion agreement on a sampled subset. Seed-2.1-Evolving and Kimi-K3 top the leaderboard at 75.6% task-level score; Gemini-3.1-Pro trails at 51.3%. Every model scores at least 9 points lower on implicit than explicit requirements (DeepSeek-V4-Pro: 19.0), and quantitative reasoning is the weakest capability (60.1 average). The authors also identify missing feasibility checks and non-convergent evidence collection as recurring failure modes. Main limitations: the sole judge model is itself an evaluated system, the leaderboard reports no variance estimates or significance tests, and models ran on different harnesses.
Review complete
Assessment at a glance
5
risk flags identified
● high 2
● medium 2
● low 1
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- xDailyBench contributes 248 authentic user-sourced open-ended tasks across 51 scenarios with binary rubrics covering both explicit and implicit requirements.
- Even the best evaluated models reach only 75.6% task-level score, indicating a substantial gap between leaderboard performance and reliable everyday-task completion.
- All 11 evaluated models show at least a 9-percentage-point deficit on implicit versus explicit requirements, identifying implicit-requirement inference as a persistent bottleneck.
- Quantitative reasoning is the weakest of the five capability dimensions (60.1 average), even for frontier models.
- Model failures concentrate in two modes: detailed planning without feasibility checks and non-convergent evidence collection.
Strengths
Authentic task provenance with preserved underspecification
§3.1.1 sources tasks from 1,000+ survey participants' real completed or intended AI requests, retaining original informal instructions, optional workspace files, and user context rather than normalizing into benchmark-style self-contained problems.
Criterion-level diagnostic instrumentation
Binary rubrics (mean 13.3 per task, Table 2) tagged to five capability dimensions let Table 4 expose what aggregate scores hide: Seed-2.1-Evolving and Kimi-K3 tie at 75.6 overall yet lead different dimensions (Seed: Instruction Following 78.4, Quantitative 74.7, Artifact Quality 81.5; Kimi-K3: Information Understanding 77.1), and quantitative reasoning is weakest on average (60.1).
Human-validation loop for the automatic judge
The explicit/implicit requirement axis yields the paper's clearest, actionable finding
Trace-level failure forensics
Weaknesses
Judge–evaluatee overlap unexamined
No uncertainty quantification for rankings
Harness confound across models
Contributor design and weighting opacity
Scenario-level sparsity
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Scope & Claims
xDailyBench (arXiv 2609.07784; ByteDance Seed, M-A-P, TokenWave.AI) proposes a rubric-grounded benchmark for LLMs acting as everyday professional assistants. Central claims: (1) 248 tasks sourced from 1,000+ survey participants' real AI usage span 51 scenarios in four domains (Personal Life 35.9%, White-Collar Work 33.1%, Study & Research 19.4%, Others 11.7%, Figure 3); (2) fine-grained binary rubrics (mean 13.3 per task, Table 2) tagged to five capability dimensions separate explicit (70.5%), general-implicit (18.5%), and user-specific-implicit (11.0%) requirements; (3) across 11 frontier models under a standardized 120-step agentic harness, the best task-level score is 75.6%, and every model shows a ≥9-point explicit-vs-implicit gap (largest 19.0 for DeepSeek-V4-Pro); (4) quantitative reasoning is the weakest capability (60.1 average, Table 4).
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is xDailyBench?
A benchmark of 248 open-ended tasks drawn from real users' AI requests across 51 everyday scenarios (personal life, white-collar work, study/research, cross-domain), scored with task-specific binary rubrics that cover both explicitly stated and implicitly inferable requirements.
Which models scored best on xDailyBench?
Among the 11 frontier models evaluated, ByteDance's Seed-2.1-Evolving and Moonshot's Kimi-K3 tie for first at 75.6% task-level score, ahead of GPT-5.5 (72.6%) and GPT-5.6-sol (72.4%); Gemini-3.1-Pro is lowest at 51.3%.
What is the paper's main finding?
Every evaluated model scores at least 9 percentage points lower on implicit (unstated, context-inferable) requirements than on explicit ones — up to 19.0 points for DeepSeek-V4-Pro — making implicit-requirement inference the clearest bottleneck; quantitative reasoning is the weakest capability dimension overall (60.1% average).
How are the open-ended tasks evaluated without reference answers?
Each task carries about 13 binary rubric criteria; GLM-5.1 acts as LLM/Agent-as-a-Judge assigning pass/fail per criterion under a standardized agent harness with a 120-step budget. On a sampled subset, automatic judgments were required to reach at least 90% per-criterion agreement with human contributors before the benchmark was finalized.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures 1–10 are available only as extracted text labels and numbers; visual layout could not be inspected, though every number cited in this review appears in the extracted text or tables.
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