Research workflows that actually work

The situation

A senior researcher has just published a review paper synthesizing 53 sources across three competing theoretical frameworks. The paper is done, but the questions it raised are not. Several reviewers pointed to gaps he already suspected, and he wants to use the existing work as a launchpad for his next project. He needs a systematic way to find where his argument rests on the weakest evidence.

How Agent Bayes helps

He uploads all 53 cited papers and pastes the text of his published review into the conversation. Agent Bayes reads the text, builds mindmap nodes automatically, and attaches citation-backed evidence from the indexed corpus to each claim. He then asks the agent to identify which nodes rest on the thinnest evidence. The agent flags 11 nodes — three of them cluster around a methodological assumption that none of his 53 sources tested directly. He labels these as 'underexplored assumption' and asks for a prose synthesis of what the literature does and does not say about that assumption. The synthesis reveals two frameworks treat it as settled while empirical papers from a neighboring subfield suggest otherwise. By the end of the week, his published review has evolved into a structured research plan with new branches marking gaps and the specific questions his next proposal will address.

The situation

A third-year doctoral candidate in environmental sociology is writing a chapter on how rural communities in Southern Europe have responded to water scarcity policies over the past two decades. Her reading list has grown to over 200 papers and she is struggling to organize the material into a coherent argument. Policy papers and ethnographic case studies require different analytical lenses, and she cannot manage both corpora at once.

How Agent Bayes helps

She creates two Knowledge Bases — one for policy analysis papers and one for ethnographic case studies — and tags documents by country, methodology, and time period. She starts a mindmap with a rough chapter outline and asks Agent Bayes to populate each section scoped to the relevant knowledge base and tags. For ethnographic material, methodology tags ensure the agent draws only from fieldwork-based studies. Over three weeks she works through the chapter iteratively: directing the agent to expand thin sections, reorganizing nodes by hand to match her evolving argument, and pausing for a conference. When she returns, the conversation history and version stack let her resume exactly where she stopped. The result is a structured, fully cited chapter outline with 85 nodes across 12 subsections.

The situation

An epidemiologist is preparing a grant proposal that bridges public health, urban planning, and behavioral economics. The literature review must demonstrate command of all three fields and identify the gaps his proposed research will fill. He has six weeks and a reading list that keeps growing. The same terms mean different things across disciplines, and he cannot afford to conflate them in a funder-reviewed document.

How Agent Bayes helps

He builds three Knowledge Bases — one per discipline — and attaches all three to a single project. Document tags filter by evidence type: systematic review, cohort study, modeling paper, qualitative study, or policy evaluation. Custom instructions define the disciplinary boundaries and explain how each field uses overlapping terms differently. He starts the mindmap with his central research question and asks the agent to map the current state of evidence filtered by systematic reviews and cohort studies only. He then creates labels for identified gaps and opportunities, which the Labels view surfaces across all three fields in a single filtered list. When a colleague suggests adding environmental justice literature, he creates a fourth knowledge base, indexes 30 papers, and asks the agent to integrate the new evidence. The original structure stays intact while new branches grow alongside it.

The situation

A professor teaches an advanced seminar on the philosophy of mind. Each week she assigns two to three papers and leads a discussion mapping the argumentative structure of each text against the broader debate. Preparing these sessions means rereading, cross-referencing, and anticipating student questions about passages she may not have revisited in months. A French-language paper in the reading list adds another layer of complexity.

How Agent Bayes helps

She indexes the full semester's reading list — 40 papers spanning four decades of debate — into a single Knowledge Base. Before each session, she opens a fresh mindmap for the week's readings and asks the agent to extract each paper's central argument and position it relative to views already covered in prior weeks. She adds annotations to highlight passages she wants students to engage with, and uses the PDF reader's inline translation to prepare discussion questions for the French-language paper. During the seminar she projects the mindmap and navigates between nodes and cited passages in real time. When a student raises an unexpected connection to a paper from three weeks ago, she uses semantic search to find the relevant passage in seconds.

The situation

A research team lead is conducting a systematic review on the effectiveness of digital health interventions for chronic disease management. The team has completed title and abstract screening and arrived at 95 papers for full-text analysis. Reading, extracting data, coding themes, and synthesizing evidence across mobile apps, telehealth platforms, wearables, and web portals — across four chronic conditions — typically takes months.

How Agent Bayes helps

She indexes all 95 papers and tags each by intervention type, study design, and condition. Custom instructions define how the agent should distinguish between reported outcomes, author interpretations, and acknowledged limitations. She starts with a structured mindmap outline mirroring her review protocol's extraction categories: population characteristics, intervention design, outcome measures, and reported effects. The agent populates each category scoped by intervention type and condition, preserving original study language in citations. Where two studies report conflicting effect sizes, both appear as distinct nodes. She then asks for a prose synthesis per intervention type, alternating between studies reporting positive effects and those with null or negative results. Labels mark passages requiring methodological quality assessment. The final mindmap contains over 300 nodes and becomes the working document from which the team drafts the review manuscript.