AgentIF-OneDay: Benchmarking Everyday AI Agents in Daily Tasks

AgentIF-OneDay: Benchmarking Everyday AI Agents in Daily Tasks

Table of Contents
- Introduction
- Why This Matters
- Open Workflow Execution
- What it is
- Real-world analogy and implications
- Latent Instruction Inference
- What it tests
- Why implicit rules matter
- Iterative Refinement
- Why ongoing collaboration is hard
- Practical takeaways
- Evaluation Framework and Findings
- How the benchmark is scored
- Who performed best and why it matters
- Limitations and future directions
- Key Takeaways
- Sources & Further Reading

Introduction
If you’ve ever wondered how close today’s AI assistants are to truly helpful, everyday partners, AgentIF-OneDay is a big step toward answering that question. This blog post digs into a new research effort that introduces a task-level, instruction-following benchmark designed to probe general AI agents operating in normal daily life—work, study, planning, and personal tasks. The core idea is to test not just how smart an agent is, but how reliably it can follow real instructions across multi-step workflows, infer unspoken rules from attachments, and iteratively refine outputs as a human collaborator nudges the work forward. For those curious about where the field is heading, this is essential reading. The study behind AgentIF-OneDay is described in detail in the new paper available here: AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios.

In short, the researchers argue that current AI evaluations tend to push for harder problems in isolated domains, while real-world use requires agents that can juggle long tasks, read and manipulate diverse file types, and stay aligned with a user’s evolving needs. To tackle that, they propose a framework—AgentIF-OneDay, or \method for short—that centers on three capabilities: Open Workflow Execution, Latent Instruction Inference, and Iterative Refinement. They don’t just propose a theoretical approach; they also build a robust pipeline for generating evaluation tasks, complete with attachments (PDFs, slides, spreadsheets, code, and more) and a rubric-based judging system. The goal is to measure how well a general AI agent can deliver end-to-end, grounded outputs while staying faithful to instructions, using tools as needed, and collaborating with humans over time.

Why This Matters
What makes this line of work particularly timely is not just the emergence of snazzy chatbots, but the growing need for AI to truly augment everyday activities—think drafting a research plan, planning travel with verified sources, editing a slide deck to match a corporate template, or balancing a budget while staying within constraints. This benchmark shifts the focus from “can AI solve a KPI in a contrived lab task?” to “can AI manage real-world workflows that involve long contexts, attached documents, multi-modal data, and human feedback?” That shift matters because real users encounter friction when an assistant forgets steps, misinterprets a constraint, or can’t integrate adjustments into an already-delivered draft.

There’s also a meaningful bridge to prior AI research. Earlier instruction-following benchmarks gave us a sense of surface abilities: following a sequence, obeying a rigid format, or performing well on isolated multi-step tasks. AgentIF-OneDay builds on that by emphasizing end-to-end task completion in authentic daily contexts, with long-context demands and multi-file reasoning. It also foregrounds long-horizon collaboration between human and machine—how an assistant updates a living document as new information arrives and constraints shift. In that sense, this work extends the conversation beyond single-turn instruction fidelity toward robust, time-aware, real-world utility.

For readers who want a direct read, the original paper provides the full framework and a detailed breakdown of datasets, scoring rubrics, and experiments: arXiv:2601.20613.

Open Workflow Execution, Latent Instruction Inference, and Iterative Refinement: The Core Concepts
The backbone of AgentIF-OneDay is its tripartite task taxonomy. Think of it as three different modes in which a general AI agent must operate to be genuinely useful across daily life:

1) Open Workflow Execution:
- What it is: When a user provides a clear, step-by-step operational procedure, the agent should execute the entire workflow precisely and exhaustively. This tests the agent’s ability to maintain long context, avoid “instruction forgetting,” and suppress hallucinations. It’s about faithful adherence to an explicit plan, not clever improvisation.
- Practical implication: If you hand an assistant a detailed process—say, verify a travel plan by cross-checking official sources, confirm dates, then generate travel options—the agent should reproduce and execute those steps without skipping or hallucinating gaps.

2) Latent Instruction Inference:
- What it is: Here the user doesn’t spell out all rules; instead, they attach documents or case materials. The agent must infer implicit, general constraints from those attachments and apply them to new tasks. This requires deep understanding of unstructured data and the ability to generalize beyond what’s explicitly written.
- Practical implication: You drop a pricing worksheet and a policy brief into the chat, asking for a decision that minimizes cost under a set of non-stated constraints. The agent should extract the relevant constraints from the documents and compute the solution, rather than simply regurgitating a generic method.

3) Iterative Refinement:
- What it is: This mimics real-life collaboration where outputs aren’t perfect on first pass. The agent must refine and rework content based on incremental feedback, while preserving the evolving state of the project.
- Practical implication: You hand the agent a draft plan or a layout and then say, “move this element here, adjust the typography, and fix the data in the chart.” The agent should update the output consistently, maintain version history-like continuity, and avoid resetting to a beginning state.

Throughout, the methodology uses a File-centered Automated Agentic Pipeline to generate tasks from seed human-authored tasks, then expands them with attachments and rubrics. This ensures the evaluation is grounded in realistic data and diverse contexts. The judging framework is rubric-based and multimodal, relying on tools (and sometimes vision-language models) to verify file contents and factual accuracy. The authors even note that when Gemini-3-Pro is used as the judge, the agreement with human scoring hits around 80.6%—a surprisingly solid alignment for automated evaluation in this space.

Main Content Sections

Open Workflow Execution
What’s being tested
- The agent must execute an entire, explicit workflow from start to finish when given a detailed protocol. It’s not about creative reinterpretation; it’s about faithful, verifiable step-by-step execution across long-context information.
- The tasks are designed to enforce a “verify-then-plan” mindset. For example, a NeurIPS 2025 travel-planning scenario requires the agent to check a convention center’s official site, cross-check third-party sources, confirm deadlines, and then generate travel options that balance economy and speed. The emphasis is on constraining output to a strict procedural chain that’s grounded in verified data.

Why this is a meaningful test
- Real-world assistants must operate under a fixed procedure while integrating fresh data. If the instruction set is long and detailed, many models struggle with memory and consistency. This benchmark highlights those weaknesses in a way that’s directly translatable to daily productivity tools: you need an agent that can “remember” the sequence, apply every prior step, and not drift.

Practical implications
- For developers: Building robust long-context handling, reliable state maintenance, and strict adherence to procedural steps is as important as raw reasoning ability. It’s a cue to invest in memory management and reliable tool usage so the agent doesn’t forget critical steps mid-work.
- For users: You can expect a more dependable assistant for structured tasks—like end-to-end document workflows or multi-step planning—provided the task aligns with the explicit procedure the agent is given.

Latent Instruction Inference
What’s being tested
- This dimension focuses on the agent’s ability to mine implicit rules from attached materials and apply them to new tasks. The classic “read the docs and infer the pricing logic” problem is a good mental model: you hand the agent a plan and a document, and it must extract non-obvious constraints and apply them correctly.

Why implicit rules matter
- In real life, we rarely spell out every constraint. We rely on prior knowledge embedded in contracts, templates, company policies, and prior decisions. A truly capable agent should see those latent rules and act accordingly rather than making a best-guess that ignores essential constraints.
- The iPhone plan example in the paper illustrates this: the agent must deduce trade-in values, base device costs, and plan prices from attached documents to compute the cheapest viable option.

Practical implications
- For product developers: This is where robust document understanding, accurate extraction of numeric rules, and reliable application to new tasks become crucial. It’s not enough to parse text; you must interpret structured data in tables, recognize pricing formulas, and apply them consistently to a new scenario.
- For users: Expect tools that can adapt to your own documents and policies—without you having to craft every constraint in your prompt.

Iterative Refinement
What’s being tested
- The benchmark simulates a multi-turn collaboration where the user can correct or refine outputs. The agent’s job is to adjust content and calculations while preserving its existing state and ensuring all updates stay consistent over time.

Why this matters
- Real-world work is rarely a single-shot interaction. Your assistant often needs to revise a document after feedback, re-run a calculation after a correction, or re-layout a slide deck after new data arrives. The ability to maintain coherence across edits and multiple feedback loops is essential for genuine productivity.

Practical implications
- For teams: A good agent should not only produce a solid first draft but should also support ongoing tweaks without “breaking” the previous state. This means robust versioning, stable state management, and predictable updates in response to incremental inputs.

Evaluation Framework and Findings
How the benchmark is scored
- The scoring approach is instance-level and rubric-based. Each rubric item is a binary decision: satisfied or not. There’s a clear distinction between bonuses (key capabilities) and penalties (critical mistakes). This helps separate capability from error rates and provides a disciplined, objective way to compare models.
- Outputs include multimodal elements (text plus attachments). Verifiers analyze the files and the textual results, not just the words on a page. When facts must be verified, the evaluation uses web search grounding to ensure current accuracy. The scoring framework combines per-rubric judgments with a normalization step to a final agent score.
- The evaluation also uses an LLM-as-judge setup. In their experiments, Gemini-3-Pro provided the highest alignment with human judges (about 80.1%), while other models like GPT-5.1 lagged behind in accuracy (around 63.8%). This gap highlights ongoing challenges in consistent instruction following and hallucination-free scoring when automated judges are used.

What the experiments found
- Task distribution across the evaluation set is heavily tilted toward Open Workflow Execution (about 53.8%), followed by Latent Instruction Inference (25.0%) and Iterative Refinement (21.2%). The tasks are mostly Work-oriented, with meaningful shares in Study and Life domains. Attachments vary widely, including PDFs, PNGs, HTML, Python code, Excel and CSV files, and even SRT subtitles. Some problems involve up to ten files, testing multi-file reasoning and multimodal tool usage.
- Four leading agents were tested: ChatGPT-Agent, Genspark, Manus 1.5, and Minimax-Agent Pro. Manus came out on top with an overall score of around 0.6450, followed by Genspark (0.6350) and ChatGPT-Agent (0.6260). Minimax-Agent lagged (0.5620). The results suggest that high overall performance often requires a balanced mix of open-workflow execution, inference capabilities, and efficient iterative editing—no single strength guarantees the best overall score.
- In terms of capability dimensions, Genspark led in implicit instruction inference and attachment handling, Manus excelled in open workflow execution and robustness to attachments, and Minimax-Agent showed the strongest logic and functionality through deeper reasoning, albeit with slower overall performance. Latency is a practical concern: the best-performing systems tended to be faster (Genspark and Manus hover around 4–8 minutes per task on average in the study), while Minimax-Agent’s latency was exceptionally high (over 20 minutes on average). This latency likely correlates with its heavier reasoning steps.

Real-world alignment and future directions
- The authors find a notable convergence between API-driven and reinforcement-learning-based agents in baseline capabilities, suggesting that foundational capabilities are becoming standardized among modern foundation models. Yet, gaps remain, especially in implicit constraint inference and long-horizon consistency. The study also reveals that evaluation accuracy varies with the judge model, reinforcing the need for reliable, multi-faceted verification approaches and robust benchmarking pipelines.
- The researchers also emphasize the synthetic data generation pipeline as a practical way to scale up evaluation without overburdening annotators. The pipeline extracts workflows from seed tasks, searches for attachments, and generates new tasks that preserve core logic while diversifying domain contexts. This approach helps address the high annotation cost and scalability challenges inherent to real-world task collection.

Limitations and future directions
- The authors acknowledge several bottlenecks, including the high time and human effort needed for each task, the challenge of verifying diverse contexts across daily life scenarios, and the need to prevent “gaming” the benchmark. They propose extending the approach to longer time horizons (beyond one day) and creating broader “OneWeek” benchmarks that still reflect real-world tasks across work, study, and daily life.
- There’s also an ongoing need to improve implicit instruction inference, ensure stable multi-turn collaboration, and further reduce evaluation discrepancies between automated judges and human raters. The paper hints at the potential of more advanced multimodal judges and richer verification pipelines to address these issues.

Key Takeaways
- AgentIF-OneDay introduces a practical, real-world benchmark for general AI agents, focusing on end-to-end task completion across daily life scenarios. It emphasizes three capabilities: Open Workflow Execution, Latent Instruction Inference, and Iterative Refinement.
- The benchmark’s design—multimodal tasks with diverse attachments, robust rubrics, and software tool usage—provides a more realistic test bed for assessing how well agents can operate in everyday settings, not just in isolated lab tasks.
- Experimental results show that even top agents like Manus, Genspark, and ChatGPT-Agent struggle with long-horizon consistency and implicit constraint inference. There is still meaningful room for progress in long-context management, multi-file reasoning, and robust human-agent collaboration.
- The synthetic data generation pipeline is a promising path to scaling evaluation while maintaining quality, though it comes with trade-offs around annotation effort and domain realism.
- For practitioners and researchers, this benchmark highlights the importance of building agents that can stay aligned with user instructions over time, integrate diverse data sources, and maintain coherent state across revisions—capabilities that are essential for true, reliable daily assistance.

Sources & Further Reading
- Original Research Paper: AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios
- Authors: Authors:
Kaiyuan Chen,

  Qimin Wu, 
  Taiyu Hou, 
  Tianhao Tang, 
  Xueyu Hu, 
  Yuchen Hou, 
  Bikun Li, 
  Chengming Qian, 
  Guoyin Wang, 
  Haolin Chen, 
  Haotong Tian, 
  Haoye Zhang, 
  Haoyu Bian, 
  Hongbing Pan, 
  Hongkang Zhang, 
  Hongyi Zhou, 
  Jiaqi Cai, 
  Jiewu Rao, 
  Jiyuan Ren, 
  Keduan Huang, 
  Lucia Zhu Huang, 
  Mingyu Yuan, 
  Naixu Guo, 
  Qicheng Tang, 
  Qinyan Zhang
  , et al. (20 additional authors not shown)

Notes and cautions
- The blog post above translates a dense technical article into an accessible narrative while preserving key data points, metrics, and takeaways. If you’d like deeper dives into any subsection (for example, more on the exact rubric scoring or a side-by-side comparison of the four agents’ capabilities), I can add targeted explainer subsections or pull out additional figures from the original paper.