LLM Usage in Research Scenarios: Distribution, Task Fit, and Practical Deployment

1. Study Scope and Observation Method: Start With the Measurement Basis

A major large-model vendor recently published an analysis report on LLM usage in scientific research. The first thing to understand about this kind of report is not its conclusions, but its observation scope, because that determines how far the conclusions can be generalized.

The report analyzed 207,000 scientific research sessions over a roughly two-month window. Of these, 129,000 were conversational assistant and collaborative workspace sessions, while 78,000 were agentic coding sessions. The difference between these two session types is important. Conversational sessions are relatively low-coupling interactions: the user asks a question and the model answers. Agentic coding sessions are much higher-coupling, longer-chain tasks in which the model autonomously operates across code repositories, terminals, and file systems over multiple turns. These two modes have very different capability boundaries and risk models. Mixing them together without separation can obscure the real structure of usage.

The report’s privacy design is also worth noting. The system retains only classification labels such as discipline, research subfield, and task type. It does not extract or store research data, hypothesis content, experimental methods, preliminary findings, or the text of unpublished papers. All outputs are reported in aggregate. This is a typical “data minimization + label-based collection + aggregate publication” design, and it is often a prerequisite for research clients to accept third-party AI services. The following sections explain how this label taxonomy can be implemented in practice.

2. Discipline Distribution: Usage Intensity Must Be Normalized by Researcher Population

The raw distribution in the report is as follows: in conversational research sessions, computer science researchers accounted for 11.9%, engineering researchers for 8.4%, and medical researchers for 7.7%. Fields such as art, humanities, and some social sciences accounted for a lower share. Agentic coding sessions show an even stronger bias. Computer science researchers represent about 4.3% of global researchers, but they generated 32.7% of agentic coding research sessions. AI and machine learning researchers represent only 1.2%, yet they contributed 5% of conversational research sessions and 15.2% of agentic coding research sessions.

A common analytical trap here is to treat absolute call volume as penetration. A discipline may have a high number of calls simply because it has many researchers. The correct approach is to apply two normalizations:

• Divide by the number of researchers in a field to obtain per-researcher or per-session intensity.
• Divide by the total number of researchers in that field to obtain tool reach or adoption rate.

The report applies this correction, which makes its conclusions more credible: computer science, mathematics, and statistics still exceed their researcher population share after normalization. This indicates genuine high fit rather than a statistical artifact caused by a large population base.

The underlying drivers can be expressed as the product of two variables: first, the size of the researcher population; second, the degree of fit between research tasks and the capability form of large models. Task fit can be further broken down into three technical conditions:

1. Expressibility: Can the task be compressed into text or code as input to the model? Mathematical derivation and statistical modeling are naturally expressible. Wet-lab operations and sample acquisition are not.
2. Verifiability: Are there low-cost automated ways to validate the model’s output? Code has compilers and test cases, statistical conclusions have significance tests and reproduction scripts, but there is no objective validator for whether a hypothesis is valuable.
3. Context availability: Is the context required by the task already digitized, structured, and retrievable? Digitized data pipelines and code repositories satisfy this condition. Paper records and unstructured experimental observations do not.

The lower usage in healthcare and laboratory-heavy physical sciences is essentially because all three conditions are simultaneously unmet. Data collection involves physical constraints, experimental processes rely heavily on equipment and manual operations, and validation cycles are long and costly. As a result, the model struggles to form an effective feedback loop.

3. Task Distribution: Data Analysis and Writing Occupy the Main Value Area

The task-level data in the report make the conclusion very clear:

Task type	Share of conversational sessions	Share of agentic coding sessions
Data analysis	33.7%	48.1%
Writing and communication	32.0%	—
Data collection, hypothesis generation, etc.	Relatively limited	Relatively limited

A particularly informative comparison is that researchers spend an average of 27.6% of their working time on data analysis, while data analysis accounts for between one-third and nearly half of sessions. In other words, AI usage intensity in data analysis is higher than its share of work time, suggesting that this is a validated high-leverage point.

Why are these two task categories dominant? Returning to the three-condition framework:

• Data analysis: The inputs are already digitized tables and datasets, making the task expressible. Outputs can be checked through statistical tests, visualizations, and reproduction scripts, making the task verifiable. Required context can be obtained through patterns and metadata, making the task contextually available. All three conditions are satisfied.
• Writing and communication: Papers, reports, peer-review comments, grant applications, and slides are themselves text artifacts. The model’s native modality is text, so the marginal cost is lowest. Edits are also incremental: users can locally rewrite an unsatisfactory paragraph, which keeps the cost of tolerance very low.
• Hypothesis generation and data collection: The quality of a hypothesis depends on domain intuition and long-term literature accumulation, and it lacks an objective feedback signal. Data collection is constrained by equipment, samples, ethics approvals, and field conditions. These are not tasks that a model can directly reach.

A more rigorous statement is therefore this: the current research value of large models is concentrated in downstream processing and expression within the research pipeline, not in upstream problem definition and empirical acquisition. Expecting the model to replace research judgment is methodologically mismatched.

4. Why Agentic Coding Stands Out in Quantitative Disciplines

The difference in disciplinary skew between conversational and agentic coding sessions — 32.7% versus 11.9% — points to a mechanistic reason: code is a machine-executable intermediate representation. It turns “reasoning correctness” into “execution results.”

In traditional research, the intermediate products of data analysis are formulas and textual arguments, and correctness depends on peer review. Once the process is written as code, every modification can be immediately executed, asserted by test cases, and compared with reproduction scripts. This allows an agent to self-correct inside a closed loop, rather than producing a one-time output that humans must later rescue.

A typical verifiable loop structure is as follows. This is illustrative code, not a specific product interface:

def research_assist_loop(goal, ctx, verifier, max_rounds=5):
    """Research assistance loop centered on executable and verifiable steps."""
    for round_idx in range(max_rounds):
        plan = model.propose(goal, ctx)          # 1. Propose an executable processing plan
        artifact = sandbox.execute(plan)         # 2. Run code/scripts in an isolated environment
        report = verifier(artifact, ctx)         # 3. Unit tests + statistical checks + reproduction verification
        if report.passed:
            return artifact, report              # 4. Return the result if verification passes
        ctx = ctx.with_feedback(report.failures) # 5. Feed failure information back into the context
    return None, escalate_to_human()             # 6. Escalate to a human after exceeding the round limit

Four engineering requirements cannot be omitted:

• Isolated execution environment: Prevent arbitrary code from affecting the host or data.
• Deterministic verifier: Use assertions, tests, and benchmark comparisons rather than relying on model self-evaluation.
• Context feedback: Feed failure information back into the context instead of stuffing in the entire history, to control token usage and noise.
• Escalation mechanism: Escalate to humans after the round limit is exceeded, avoiding long, ineffective loops that consume budget.

5. Deployment Architecture: From Usage Data to an Operable Research AI Platform

To reproduce similar capabilities inside an enterprise or research institution and continuously measure them, a layered design is recommended. Cloud components below are described using generic capability categories: object storage, message queues, container platforms, vector retrieval services, logging services, monitoring and alerting, and key management.

1) Data Ingestion and Governance Layer

Research data comes in complex forms: tables, images, sequences, instrument outputs, and literature. A practical path is to store raw files in object storage and manage their lifecycle. Long-running tasks such as ingestion, cleaning, and transformation should be decoupled through message queues and executed asynchronously. Metadata should be registered uniformly, forming a traceable relationship among “datasets — experiments — samples — papers.” The guiding principle is to keep raw data unchanged and layer processed outputs: the raw layer is read-only, the processed layer is recomputable, and the serving layer is optimized for retrieval.

2) Context Engineering Layer

This layer is critical to performance and is often where pilot projects fail. Three types of context sources should be built:

• Structured metadata, such as experimental parameters and sample information.
• Unstructured literature and notes, injected through retrieval augmentation.
• Code and process assets, such as data processing scripts and analysis templates.

Context assembly should be dynamically pruned according to relevance to the current task, rather than stuffing in all available context.

3) Privacy and Compliance Layer

This can follow the report’s approach while adding engineering details:

{
  "session_hash": "irreversible hash used only for deduplication and correlation",
  "session_type": "chat | agentic_coding",
  "discipline": "computer_science",
  "subfield": "machine_learning",
  "task_label": "data_analysis",
  "turn_count": 14,
  "tool_calls": 37,
  "feedback": "accepted_with_edit",
  "bucket": "aggregate_min_k_50"
}

There are three key points:

• Labels rather than content: Do not store research text, hypotheses, or preliminary findings in the database.
• Aggregation thresholds: Do not output statistics separately when the sample size is below a threshold under any statistical view, to prevent small-sample inference from identifying individuals. This follows a k-anonymity-like idea.
• Tiered deployment: For scenarios involving unpublished results or regulated data, use dedicated deployment and data-not-leaving-domain architectures, supported by key management and end-to-end audit logging.

In Baidu AI Cloud, such capabilities are typically supported by private network isolation, key management services, log auditing, and compliance qualifications. The exact implementation should be determined according to the institution’s data classification requirements.

4) Permission and Multi-Tenant Layer

Use tenant isolation by research group, department, or project. Both data and indexes should be isolated. The permission model should support dataset-level authorization, not only system-level authorization, because in research collaboration, sharing a single dataset is more common than sharing an entire environment.

5) Evaluation and Measurement Layer

Do not look only at call volume. At least four types of metrics should be established:

• Penetration: AI session volume divided by the number of active researchers, normalized by discipline, to identify real areas of fit.
• Task success rate: Verifier pass rate and human adoption rate, including accepted as-is, accepted with edits, and rejected.
• Reproducibility: Whether outputs include runnable scripts and fixed random seeds.
• Efficiency gain: End-to-end task time comparison, rather than single-turn response time.

6. Common Pitfalls

1. Making decisions based on absolute call volume. Rankings without normalization can misinterpret “many users” as “good fit.”
2. Treating agents as universal executors. Without deterministic verifiers, agent errors can be amplified across subsequent rounds instead of converging.
3. Forcing upstream tasks. The bottleneck in hypothesis generation and data collection lies in the physical world and domain intuition. Adding more model calls will not improve results.
4. Collecting content during pilots and adding compliance later. Data collection scope must be designed upfront. After-the-fact cleanup cannot truly erase data.
5. Ignoring hidden costs. Token consumption and failed retries for long-chain agentic tasks can be one to two orders of magnitude higher than a single conversational turn. Budget limits and round limits are necessary.

7. Conclusion

The most valuable part of the report is not “which discipline uses LLMs the most,” but its methodological point: to evaluate AI value in professional settings, tasks should first be broken down along three dimensions — expressibility, verifiability, and context availability — before discussing penetration and the engineering investment behind it.

In current research scenarios, data analysis and writing communication have become the main battlefield because they satisfy all three conditions. Agentic coding performs especially well in quantitative disciplines because code turns reasoning correctness into executable and verifiable facts.

For technical teams, three next steps are actionable:

• Build a label-based and aggregate usage measurement system.
• Add deterministic verifiers and isolated execution environments for core tasks.
• Put privacy and compliance constraints into the architecture, not just into process documentation.