Research automation
Agents and pipelines that run parts of the research workflow, from data pull to replication.
Automate SEC filings research with a coding agent, from EDGAR to a finished panel
One typed goal makes Claude Code pull SEC filings, build a firm-quarter panel, run the checks and commit the result. Auto mode and /goal keep it working until the checks pass.
Agent-first coding: use the frontier tools and pay for them
Claude Code or OpenAI Codex, desktop app or command line. Generic and budget setups hide what the new tools can do.
Make your data pull something an agent can run
Automation starts with the stage that has the clearest pass or fail. Turn the data pull into one command with checks and a manifest.
Using an AI agent on licensed data without risking your campus's access
Most data licenses forbid putting the content into AI systems. An agent can still write and repair the whole pipeline if the licensed rows never enter its context.
API, MCP, CLI or computer use: how to connect an AI agent to your research tools
Use the lowest-level, most structured interface the job allows. That means SQL or an API for data, the command line for software, and the screen only when nothing else reaches it.
Automated research pipelines run in four fields, and you can point one at finance
Machine learning, mathematics, biomedicine and finance each have a system that runs most of the research loop. Two of them you can install today.
Claude Code explained, and why it beats an AI plugin in VS Code
Claude Code is a program that takes a task, runs the commands and checks the result. In six months it passed GitHub Copilot among professional developers.
Give your agent a memory: the instruction file and your first skill
Two plain text files turn an agent from a stranger into a research assistant who knows the project. The same content works in Claude Code and OpenAI Codex.
Token management for researchers, and why to buy usage before you hire help
Cached input costs a tenth of the normal price and batch jobs cost half. Spend on usage for the people you have before you spend on support staff.
A new frontier model ships every 10 days, and no university is built to keep up
Anthropic, OpenAI and Google released at least 28 language models between 1 January and 9 October 2026. Higher education staff name the pace of change as their top AI challenge.
Academic research is far behind the AI frontier, from finance to mathematics
A paper about a model is out of date before it clears review. In mathematics, the newest results come from a model no academic can run.
AI has arrived for finance research, and data vendors cannot police it
Agents now operate a computer better than the human baseline on the standard test. Licenses forbid most AI use of data, and almost none of it can be detected.
AI is obliterating the pipeline from finance programs to industry jobs
Data vendors and AI companies now sell to banks and asset managers directly. The junior tasks that absorbed finance graduates are the first ones their products automate.
Researchers in China, Japan and Korea run whole papers through coding agents and post how
Their own forums and blogs describe pipelines of 30 agents, a seven-week paper with 108 experiment runs, and an eight-hour draft. Every account keeps the checking for a person.
If the platform forbids agents, build the dataset yourself from the source
Filings, financial statements and earnings calls were public before any vendor packaged them. An agent can go back to the source, and the tools to do it are free.
Researchers are using AI against the rules, because being left behind is worse
At a major 2026 conference, more than one in five reviewers who were told not to use an LLM used one anyway. The fear driving that is rational.
OpenAI releases machine-produced math results with Lean proofs and compute figures
The results come from an internal model. OpenAI says the average result used compute equal to roughly three hours of ChatGPT Pro thinking.
A benchmark of nine factor-mining methods finds no approach consistently wins
FactorBench compares roughly five thousand machine-mined factors across five equity markets, from genetic programming to LLM agents.
Self-evolving research agents show no consistent gain from accumulated skills in a factor test
Across 18 long-horizon alpha-research runs and 48 continuation branches, evolved capabilities did not reliably beat the starting set.
LLM literature reviews hold up for broad claims and break down for paper-level ones
A test on economics papers that use rainfall as an instrument finds accuracy falls as the reading task needs more context.
Agents can propose investment factors, but a frozen referee has to judge them
A working paper splits factor research in two and finds that a referee the agent cannot touch admits 5 to 11 times fewer false factors.
Scientists report saving nearly 7 hours a week with AI, and the bottleneck moves to verification
A study of 15 million Gemini interactions, over 2,600 specialized models and a survey of over 600 scientists maps how AI is used in research.
Daiwa Securities moves its Sakana AI consulting platform from trials into production development
Sakana AI, the Tokyo lab behind The AI Scientist, is building a total-asset consulting system for Daiwa under a partnership signed in October 2025.
Stock Claude Code and Codex wrote 117 papers unaided, and none cleared a top venue's bar
With only a light scaffold, Claude Code matched the average human ICLR 2025 submission on manuscript review. Scores fell once reviewers opened the code behind each paper.
A paper-writing pipeline for Claude Code went from 6,400 GitHub stars to 51,000 in five months
academic-research-skills runs 32 agents across ten stages with two integrity gates. Its builder says the checks caught 15 fabricated citations in one real paper.
Mirae Asset moved research assistant work to AI and made the assistants junior analysts
The Korean broker renamed its research unit the AI Research Center and expects an analyst to cover 20 to 30 stocks, up from 10 to 15, BizWatch reports.
In 912 AI-written economics papers, the ideas trail human work far more than the execution
Against 41 papers from the AER and AEJ: Economic Policy, a study puts 71% of the quality gap on the research idea and finds no significant gap on robustness.