Deep research (DR) agents address open-ended information needs through multi-step reasoning, long-horizon web search, and evidence synthesis. However, existing benchmarks primarily focus on academic research or synthetic tasks, leaving DR agents' capabilities in real-world life and professional productivity settings insufficiently evaluated. To bridge this gap, we introduce LifeSearch, a challenging benchmark comprising 203 Chinese and English tasks derived from real-user query seeds across 7 concrete life and productivity domains. With more than 4,500 hours of human annotation, we construct over 4,000 expert-written, fine-grained rubrics, covering both report-content criteria for coverage and accuracy and concrete criteria specifying values, units, dates, and URLs. We conduct extensive experiments on 12 advanced DR agents under a unified evaluation harness, systematically analyzing their performance patterns and failure modes. We find that even the best-performing frontier agent, GPT-5.6 Sol, achieves a score of only 63.4%, leaving substantial room for improvement on realistic life and professional DR tasks. We select the grading configuration by comparing candidate LLM judges against human rubric labels, and a human ranking study shows strong alignment between aggregate benchmark scores and human judgments (Spearman's ρ=0.89, Kendall's τ=0.81). LifeSearch provides a rigorous evaluation framework and foundation for advancing DR agents toward real-world life and productivity.
Figures & tables
(a) Overall performance
(b) Score vs. output tokens
Benchmark
Life & productivity
Real-user query
Expert-written
Chinese & English
output format
BrowseComp ( Wei and others, 2025 )
✗
✗
✗
✗
Short answers
Mind2Web 2 ( Gou et al., 2025 )
✗
✗
✗
✗
Lists/tables
DeepResearch Bench ( Du et al., 2026 )
✗
✗
✗
✓
Reports
WideSearch ( Wong et al., 2026 )
✓
✓
✗
✓
Tables
ResearchRubrics ( Sharma et al., 2026 )
✗
✗
✓
✗
Report
LiveResearchBench ( Wang et al., 2026a )
✗
✗
✗
✗
Reports
Table 1: Comparison of LifeSearch with representative Deep Research benchmarks.
Figure 2: Overview of LifeSearch . (a) Collect and filter query seeds written by hired crowd workers. (b) Refine queries into research tasks with expert guidance. (c) Construct fine-grained rubrics. (d) Evaluate reports with an LLM-as-a-Judge and aggregate rubric judgments into scores.
Figure 3: Dataset composition of LifeSearch . (a) Task counts across seven domains. (b) Annotation attributes across 4,014 rubrics and summary statistics of rubrics per task. Constructing the benchmark required more than 4,500 hours of human annotation.
Dimension / field
Definition
A. Assessment dimensions
Instruction following
Fulfillment of explicit user requests.
Information coverage
Coverage of the requested entities, attributes, and scope.
Factual accuracy
Correctness of concrete facts and numerical values.
Evidence and citation quality
Required source identity, attribution, and temporal context.
Reasoning and analysis
Logical connections between evidence and conclusions.
Table 2: Rubric assessment dimensions and annotation fields.
Judge model
Reasoning
Temp.
Mean accuracy (%)
Self-consistency (%)
Majority vote (%)
Gemini 3.1 Pro
Low
0
84.25
98.8
84.33
Gemini 3.1 Pro
High
Default
84.00
94.5
84.33
Gemini 3.1 Pro
High
0
83.50
98.0
83.33
Gemini 3.5 Flash
High
Default
82.42
94.8
82.34
DeepSeek V4.1 Flash
High
0
81.43
94.8
81.34
DeepSeek V4.1 Flash
High
Default
81.43
91.3
81.84
Table 3: Comparison of 11 judge configurations on 402 human-labeled rubrics for judge selection, sorted by mean accuracy over three runs. Default denotes the model’s default setting. Bold numbers mark the best result in each metric.
Domain scores (%) ↑
Overall
Resource usage
Model
Comp.
Acad.
Fin.
Pol.
Ind.
Tech.
Everyd.
Score (%) ↑
Iter.
Output tokens (k)
GPT-5.6 Sol
62.9
67.8
60.7
65.6
59.5
65.6
58.3
63.44±0.27
86.2
51.5
Claude Opus 5
52.4
60.7
55.7
58.2
52.6
59.7
53.7
56.10±0.28
39.0
48.9
Hy4 Preview
46.7
51.4
48.1
52.9
51.0
51.9
47.1
49.73±0.86
40.0
33.6
GLM-5.3
47.8
49.0
47.5
52.4
48.5
50.9
51.9
49.19±1.20
22.3
49.3
Qwen3.8-Max
48.2
47.3
46.9
50.4
47.1
45.4
45.1
47.52±1.64
25.0
33.1
Table 4: Performance and resource usage on LifeSearch . Scores are reported as mean ± sample standard deviation across three independent agent runs. Resource statistics are per-task means; output tokens include generation throughout execution, not only the final report. Domain abbreviations: Comp., company and competitor research; Acad., academic and professional literature; Fin., financial market analysis; Pol., policy and government data; Ind., industry news and trend aggregation; Tech., technical tools and developer resources; Everyd., everyday facts.
Appendix figures & tables2 assets
Supplementary material from the paper’s appendix.
Appendix
Aspect
Definition or requirement
Illustrative rubric or value
A. Assessment dimensions
Instruction following
Fulfillment of explicit user requests.
The response should recommend a choice between Plan A and Plan B.
Information coverage
Coverage of the requested entities, attributes, and scope.
The response should report the monthly subscription fees of both Plan A and Plan B.
Factual accuracy
Correctness of concrete facts and numerical targets.
The response should state that Plan A costs USD 20 per month as of 30 June 2026.
Evidence and citation quality
Required source identity, attribution, and temporal context.
The response should identify Plan A’s official pricing page as the source of its monthly fee.
Reasoning and analysis
An explicit connection between evidence and conclusions.
The response should explain how Plan A’s lower fee supports the recommendation under a USD 25 monthly budget.
Appendix
Table 6: Rubric assessment dimensions, record fields, and authoring requirements. Examples are illustrative and refer to fictional subscription plans A and B.
Model
Mean answer length (characters)
GPT-5.6 Sol
23,679
Claude Opus 5
12,264
Hy4 Preview
9,959
GLM-5.3
10,381
Qwen3.8-Max
9,166
DeepSeek-V4 Pro
9,189
Appendix
Table 7: Mean final-answer length per task. Length is measured in characters, not tokens.