OmniScientist — workbench

Not a mock-up. Every card is lifted from one recorded run — the request that was sent, the images it opened, the papers it read, the check that sent it back, the code it ran and the manuscript it produced.

Watch it work

Three complete runs, replayed from their recorded traces: the raw evidence it opened, the papers it read, every line of code it ran, and every time a check sent it back. Pause anywhere, drag the scrubber, take the paper and the log with you.

Open all three demos
Technical report · 2026

OmniScientist: An Omni-Modal Omni-Discipline AI Scientist

National University of Singapore University of Oxford
Contact libobo@nus.edu.sg · haofei7419@gmail.com (* corresponding author)
10evidence modalities
20+disciplines
34end-to-end cases

News

  • Interactive replays of three complete runs are live: step through the evidence, the code and the checks, and take the paper and the log with you.
  • Technical report completed, covering the full OmniScientist evaluation.
  • Source code and the one-command research workflow are available on GitHub.
  • Five representative papers produced end to end by OmniScientist are listed with their scores.
  • Project page updated with results and multimodal evidence.

Raw evidence in, a finished study out

Observations of every kind go in; the engine reads them, tests what it reads, and writes the result up. Nothing on the way through is staged: the code runs against the real data, and a claim the numbers do not carry gets sent back rather than polished.

From raw observations through the engine to scientific discoveries

Which relations survive the interface

A caption or a feature vector arrives with the structure already removed. Three cases start at the raw record instead, and the cues read off it are what the hypothesis is built on. The band at the bottom is the interface most systems expose in its place.

Three cases from raw record to structural cues, hypothesis and verified finding, above the blind precomputed interface

One command, the whole research lifecycle

Evidence enters in its native form, and a deterministic pipeline routes the run through ideation, experiment and writeup, reporting only what the execution record supports. Perception stays available to every stage, and a new discipline takes a specification file, not a new engine.

The OmniScientist framework: raw evidence entering three agents for ideation, experiment and writeup, over a lifecycle-wide perception band

Only what the record carries becomes a claim

The rigour check asks whether the code really ran, whether every attempted test is accounted for, and whether the headline rests on an analysis that supports it. The claim check then matches every number and every sentence against the analysis that produced it.

Rigour check and claim check auditing a reported result against the execution record

One engine, ten kinds of evidence

The same pipeline reads photographs, spectrograms, waveforms, volumes, point clouds, video, trajectories, tables and sequences, across more than twenty disciplines, without first flattening every research object into a scalar benchmark.

Reviewed by a cross-family judge panel

Scores run 0 to 10 across seven review dimensions. Composite is their mean. Blue marks the best in a column, green the second.

BackboneNoveltySound.ClaritySignif.Reprod.MM ground.FactualComposite
GLM-5.26.27.16.86.45.96.67.56.6
Sonnet 56.37.07.06.36.15.17.86.5
Kimi K2.76.27.26.76.25.55.88.06.5
GPT-5.65.26.36.35.05.24.27.75.7
Qwen3.5-122B4.75.56.24.84.84.86.55.3
Qwen3.5-27B4.95.55.94.94.64.96.35.3
Gemma-4-31B4.75.15.64.54.44.76.55.1
Gemma-4-26B4.44.45.04.03.73.85.14.3
Qwen3.5-9B4.04.14.83.73.73.94.84.1

Papers it wrote, start to finish

PaperDisciplineEvidenceScore
Cramér-Rao scaling of exponent precision in monomial Feynman lawsPhysicsFormula7.1
Sequential versus bursty leaf initiation in 3-D plant scansPlant science3-D scan7.2
A continuum-removed index for residue and tillage orderingRemote sensingHyperspectral6.3
Transient-impulsivity features across machine typesMachineryAudio7.1
Coherent polarized signals in noise-labelled STEAD tracesSeismologySignal6.9
@techreport{omniscientist2026,
  title  = {OmniScientist: An Omni-Modal Omni-Discipline AI Scientist},
  author = {Li, Bobo and Fei, Hao and Ju, Tianjie and Lee, Mong-Li and Hsu, Wynne},
  year   = {2026},
  note   = {Technical Report}
}

Every number on this page comes from the run that produced it. Observations are public research datasets; sources and licences are listed in the release documentation.

OmniScientistAn Omni-Modal Omni-Discipline AI Scientist