Nine Core Capabilities for a Research AI Assistant

Research coding assistants already help with literature notes, code experiments, and paper drafts. Producing a passage that looks acceptable is not the same as supporting research a reviewer can recheck. The central tension is speed versus rigor: generation is fast, while evidence, methods, and reproducibility are slow and strict. Treating the assistant as a general chat window leaves gaps in citations, data, and figures that cannot be traced. Designing it as a set of bounded capabilities, tied together by rules, retrieval, and human review, is what makes it stable enough for research.

What follows is not a comparison of ready-made prompts. It is nine capabilities worth building first in a research workflow. They can live in local scripts, editor plugins, or a cloud development environment. The evaluation bar should be the same: whether they reduce hallucination, leave evidence behind, and still mostly hold after the model is swapped.

1. Citation verification: block first, then explain

Fabricated references are among the most expensive errors in research writing. An assistant can assemble an entry with a complete-looking format, plausible authors, and a plausible year that does not exist in any serious bibliographic source. A reliable practice is not to search every item by hand after the fact. Citation verification should be a hard gate in the generation path.

A candidate citation should be cross-checked against several independent bibliographic sources—for example a broad scholarly index, an open citation corpus, a publisher registry, and a preprint server. Fields to compare include title, authors, year, venue, and persistent identifier. If nothing is found, the citation must be blocked from the manuscript, not accompanied by a note that says “please verify yourself.” If it is found, the system should return which sources matched, which did not, and possible formatting errors, so the author can tell “this work does not exist” from “the identifier was mistyped.”

A sketch of the flow can stay simple:

candidate citation -> normalize fields -> multi-source lookup -> keep and record evidence if consistent
                                                      -> mark for human confirmation if partial
                                                      -> refuse to write if all sources miss

This layer replaces model confidence with external evidence. It does not guarantee that the work is actually relevant to the claim. It only guarantees that the cited object exists and that its metadata is broadly consistent. Relevance remains the researcher’s judgment.

2. Evidence constraints: every factual claim must trace back

After existence is checked, the next constraint is why a sentence is allowed to stand. A common review failure is that the papers are real, but the assistant attaches paper A’s conclusion to paper B, or writes a result that held only under stated conditions as a general law.

Evidence constraints require factual statements in the body to bind to a locatable evidence unit: a page, section, figure or table number, dataset version, or experiment-log id. Sentences without a binding stay in the draft area and cannot enter submittable text. For a review, each thematic paragraph can be required to list supporting, opposing, and uncertain evidence, so the draft does not collect only material that matches a preset conclusion.

The implementation does not need elaborate reasoning. Structured cards are enough: claim, evidence excerpt, source id, conditions of applicability, counterexample, and a confidence note. The assistant fills cards and checks empty fields. The author decides whether to accept them. That reduces fabrication and keeps a literature review from becoming an unbounded advertisement.

3. Writing conventions: make rules explicit, but do not let them replace judgment

Many “academic writing assistants” are essentially exam notes: abstract structure, tense, figure captions, ethics statements, and reference format. Those rules help because they cut low-level formatting errors. They are also limited: they do not retune the argument to the specific research question, and they do not supply a real scientific idea when the author is stuck.

Turning conventions into a checkable list is reasonable. Does the abstract state the problem, method, results, and limits? Is the methods section enough to reproduce the work? Do the limitations match the strength of the results? Are conflicts of interest and data availability filled in as the target journal or institution requires? The list should be versioned and should name the genres it applies to, rather than pretending one rule set covers every discipline.

If there is only a static style sheet and no retrieval or verification, installing the assistant changes little. In that case it is better to feed the assistant the writing conventions the institution accepts, examples of the target genre, and explicit prohibitions—and to keep the right to rewrite by hand—than to rely on a black-box prompt. Conventions are a floor, not a ceiling.

4. Domain fit: name the task before choosing the toolchain

Research is not a single text task. Theoretical derivation, experimental science, engineering simulation, and humanistic interpretation ask different things of an assistant. Selection should start with a task map: are you handling literature, formulas, experimental data, signals, code, questionnaires, or archives?

Engineering and data-intensive work often also needs numerical computation, signal processing, statistical tests, and publication-grade plots. Without those, the assistant can only rewrite prose and cannot join the analysis that actually takes time. Adaptation is not a pile of discipline keywords. It is a defined input, output, and acceptance test for each task type. A signal-processing task should declare sampling rate, window function, filter parameters, and random seed. A statistical task should declare test assumptions, effect size, and multiple-comparison correction. If the parameters are unclear, a handsome figure still does not belong in the paper.

5. Figures and data handling: a figure must be redrawable from the data

Figures an assistant generates are the easiest to look professional and still be impossible to recheck. Acceptable data handling should separate raw data from derived data, make cleaning steps replayable, state axes and units, explain error sources, avoid relying on color alone to distinguish series, and trace every number in the figure to a specific run.

Do not let the model “draw the trend from memory.” Require it to call a controlled computation and plotting script. The script reads versioned data and writes the image plus a data table of the same name. If data is missing, fail explicitly instead of filling in a smooth curve. Exploratory analysis can be marked “unconfirmed” and must not be mixed with formal results.

6. Reproducible experiments: environment, parameters, and logs together

The minimum bar for research code is that someone else can obtain substantially the same result in the stated environment. The assistant should help pin dependency versions, record hardware and random seeds, write command-line parameters into an experiment manifest, and archive standard output, errors, and key metrics. One experiment corresponds to one immutable run id.

A cloud development or container platform is a natural place for this layer: an image describes the environment, object storage holds data and logs, a job queue schedules batch runs, and monitoring records failures and resource use. These should be ordinary platform capabilities, not reproducibility that depends on the memory of one chat. The conversation can be lost. The run record cannot.

The assistant may also draft an experiment note, but the note must be exported from the real configuration. It must not “recall” in natural language, after the fact, which parameters were used. Recall is a high-risk zone for hallucination.

7. Quality gates: grade by tiers, do not recommend by feel

Capabilities differ sharply in how robust they are. An internal rating can use four observations rather than popularity:

• After a model change or an environment change, do the critical checks still fire?
• On failure, does the system block, degrade, or pass silently?
• Does the output carry evidence and logs?
• Can the author spot an error without fully understanding the prompt?

A solid capability keeps its core logic in retrieval, rules, and scripts, not in how a particular model happens to perform. A capability that only uses a long prompt to sound “academic” will make manuscript quality swing widely and should be treated as weak assistance. A setup that cannot catch fake citations, fake data, or non-reproducible commands should not enter the formal writing path.

Quality control should also include adversarial checks: deliberately insert a nonexistent reference, alter a year, swap a number in a figure, or delete a random seed, and see whether the gate alarms. Passing only on smooth examples does not show that the capability works.

8. Division of labor: the assistant does not own scientific conclusions

No skill stack, however complete, replaces research judgment. The assistant is suited to retrieval and organization, format checks, script drafts, repetitive cleaning, and consistency comparison. It is not suited to setting the research question alone, interpreting anomalous results, choosing ethical boundaries, or deciding whether a piece of evidence is enough to support a conclusion.

A practical split is: the machine produces candidates and check reports; the human chooses and signs. Every automatic edit should be reversible, and important passages should keep a record of human confirmation. When the assistant and the author disagree, default to evidence that can be checked, not to the smoother paragraph. When an advisor or collaborator reviews the work, they should be able to see the citation-check report and the experiment run ids, not only a fully “polished” manuscript.

9. Ongoing evaluation: regression-test on your own problems

Once a capability enters daily research, it needs a regression set. Use a small batch from your own field: published, already-checked paper excerpts, known-bad citations, one small reproducible experiment, and one standard figure. Rerun them after a model or rule update. Watch three kinds of regression: missed fake citations, true works rejected by mistake, and drifting result numbers. Without a regression set, there is no way to know whether “optimizing the prompt” improved the system or planted a failure.

Keep the metrics plain: fake-citation block rate, false-rejection rate for real works, completeness of evidence fields, experiment rerun rate, and human rework time. Do not use a subjective sense that the text “looks more like a paper” as the only standard. The first quality of research text is that it can be checked. Readability comes second.

Order of implementation

Build in descending order of risk: citation blocking and evidence cards first, then a fixed writing checklist and experiment logs, then domain-specific computation and plotting, and language polishing last. The early steps prevent hard failures. The last step only improves expression. If resources are limited, a small flow with multi-source verification, stop-on-failure, and retained logs is worth more than a set of long prompts that cannot be audited.

An assistant can speed up the mechanical labor in research, but speed has to follow evidence. After these nine capabilities are implemented as checkable procedures, the author is still responsible for the question, the method, and the conclusions. The assistant’s job is to keep fabricated citations, lost parameters, and figures that cannot be redrawn from quietly entering the final manuscript.