The Cognitive Architecture of an AI Scientist: Inside the Scientific Harness

The discussion about AI scientists often begins with the model. Measuring model intelligence is an important benchmark for progress, although it cannot measure the effectiveness of an AI scientist on its own. In the previous essay, I described why scientific computation and scientific interpretation pose different problems. An AI scientist has to perform both across one investigation. This essay examines the systems that make that possible.

An AI scientist is a complete scientific system made of the model, its tools, and its harness. The model supplies intelligence. The harness plans the investigation, selects methods, acquires context, preserves state, evaluates quality, involves people, and produces a reviewable work product. Harness is useful language because it names the tools, data, memory, controls and interfaces that coordinate the model.

• A planner can create a sound plan while memory drops a premise it depends on.
• Retrieval can find the right evidence while evaluation weights it incorrectly.
• A runtime can complete every step and still stop with a weak scientific position.

Model intelligence becomes effective scientific work when these subsystems remain calibrated. They must exchange context, confidence and control signals coherently from the original objective to the final work product.

The capability map can be grouped into four systems. To make those systems concrete, I will use one example: moving from a public psoriasis RNA-seq dataset to five reviewable target hypotheses.

A full run can take several hours, and broader investigations can take days. The runtime therefore needs a durable execution environment that can allocate resources, create and manage the session, run code and tools, preserve files and intermediate results, and recover from failures without losing the scientific objective.

The planner first decides what kind of analysis is possible from the available inputs. It may use the processed expression matrix or reprocess raw reads, then verify the cohort labels, study design, sample quality and possible confounders. From there it can plan differential-expression and co-expression analyses, pathway interpretation, candidate extraction, evidence retrieval, target ranking, critique and memo assembly. The plan stays dynamic because the data may expose a quality issue, a tissue-composition effect, or a method that does not fit the objective.

The computation may produce thousands of differentially expressed genes and several co-expression modules. Those outputs are signals, not yet target hypotheses. Scientific cognition begins by acquiring the context needed to interpret them: the dataset metadata and associated paper, psoriasis disease biology, relevant pathways and cell types, human genetic evidence, known targets, safety information, competitive context, internal evidence and connected tools. Retrieval follows the emerging questions in the analysis instead of operating as one generic search step.

In GSE54456, dermally expressed genes were downregulated, but the authors concluded this was probably caused by expansion of the epidermal compartment. An AI scientist must therefore avoid interpreting every downregulated gene as a causal disease mechanism.

Scientific skill primitives provide stable methods for recurring parts of this investigation. One skill can inspect an RNA-seq dataset and select an appropriate differential-expression approach. Others can assess human genetic support, mechanistic evidence, or the dimensions used in target prioritization. When the skill library lacks a suitable method, the AI scientist can retrieve current methodological evidence, compare accepted approaches, match them to the data and study design, and construct a traceable plan. Repeated and validated approaches can later become stable skills, creating a disciplined path from dynamic reasoning to reusable scientific operating knowledge.

A one-million-token context window is roughly 1,500 pages under an explicit text-density assumption. At about 15 pages per full-text scientific publication, that represents only around 100 papers. A typical Causaly Agentic Research run can work across evidence retrieved from 1,200 to 2,000 scientific papers in approximately ten minutes, before accounting for the data, code, pathway tables, candidate lists, tool outputs, hypotheses and revisions. The psoriasis investigation can therefore exceed the model’s active context long before a run lasts for hours or days.

As the run grows, some material can be compressed safely, including repetitive search snippets, verbose logs and unsuccessful tool output. Other information constrains the scientific path: premises, method choices, the execution path, branches already explored, reasons a candidate was rejected, and references supporting each claim. If compression removes one of those constraints, the investigation can diverge. Scientific context compression reduces volume while preserving the decisions and evidence that determine what the AI scientist may do next.

The published analysis identified 3,577 differentially expressed genes. Early computational and biological filtering might reduce that set to roughly one hundred candidates, while the team ultimately needs five target hypotheses. The rubric defines how those candidates are compared, which dimensions matter, how deeply each candidate satisfies them, and what evidence would change the ranking.

The rubric is multidimensional and depends on context. For psoriasis, it may consider human genetics, evidence that a candidate sits inside an immune or epidermal disease mechanism, relevant cell and tissue expression, directionality, safety, tractability, competitive context and organizational fit. The weights still change with the objective. A team developing an antibody may emphasize extracellular accessibility and tissue specificity. A team seeking a genetically anchored mechanism may place more weight on human genetics and causal coherence.

The rubric evaluates two fundamentally different transitions. Data-to-signal evaluation asks whether the study design, sample quality, analytical method, statistical assumptions and resulting module signals are robust. Signal-to-insight evaluation asks whether those observations support a mechanism, whether tissue architecture or another explanation remains credible, and whether the evidence justifies advancing a candidate as a target hypothesis. As the AI scientist runs the evaluation it determines next steps: rerun an analysis, retrieve another evidence type, compare a competing explanation, reduce confidence, ask a scientist, or finalize.

Consider the checkpoint before the AI scientist finalizes five psoriasis target hypotheses. A scientist may decide that human genetic evidence should carry more weight, that a signal associated mainly with changing tissue composition should be downgraded, or that a candidate compatible with the company’s antibody capabilities should rank above one requiring an unfamiliar modality. They may exclude a target that failed in a previous internal program, or ask the system to investigate a safety concern well known inside the team but absent from public data.

Those decisions combine scientific judgment with tacit organizational knowledge, strategic objectives, platform capabilities, prior successes and failures, and practical constraints. Human intervention lets the scientist change the rubric or challenge the interpretation at the point where their knowledge has the greatest leverage. The system then carries those decisions forward into the target ranking and the rationale behind it.

The final work product is a target-prioritization memo. It should follow a stable structure covering the dataset and its experimental context, methods and parameters, and reflect the standards the company already uses for target review. The reader must be able to inspect how every part of that memo was produced. Sentence-level provenance should connect each material statement to the dataset, a computational result, or the associated publication. A reviewer can then challenge the evidence and trace a conclusion to its source to understand why the system placed one target above another.

A decision-ready scientific output exposes both the position and the conditions that could change it.

Runtime and control sustain the analysis. Context acquisition, reasoning and skills turn transcriptomic signals into a scientific position. Memory preserves the premises and branches that shaped it. Evaluation and human judgment determine whether five targets can be recommended. The final memo makes the position reviewable through stable structure and provenance. The effectiveness of the AI scientist comes from both the capabilities of these systems and the way they are calibrated.

Causaly already provides much of the scientific context and reasoning foundation through Bio Graph, Pipeline Graph, scientific information retrieval, Agentic Research, causal inference, internal-data connectivity and MCP interfaces. The architecture described here connects those capabilities to long-running computation, scientific state, evaluation, human judgment and reviewable work products. The architecture is broad, so implementation has to begin somewhere and improve iteratively.