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Theo

The Theo Platform is a research environment built to help researchers iterate faster, validate rigorously, and reach verified results they can trust

Theo — intro animation

Hi, I'm Theo, your research collaborator.

WHERE WOULD YOU LIKE TO BEGIN?
DESCRIBE A RESEARCH PROBLEM
Tell Theo about a question, gap, or limitation you want to explore.
IMPORT EXISTING WORK
Bring in a paper, dataset, or notes you already have.
A faster Path From hypothesis to defensible result
Move at the speed of your best idea

01

Multiple models in one workspace

Frontier models, side by side with Theo models in the same workspace, so you can compare outputs instead of gambling on one.

02

Verify before you publish

Stress-test your research so a wrong assumption only costs an afternoon, not a resubmission.

03

Connects to what you already use

Lean, computer algebra tools, and the plotting and modeling software you already trust. New integrations added regularly.

Theo — full animation
Compare outputs with Council Mode
Ask Theo anything…
Council
YOU
GLM
Chain of Thought

The fact that the residual tracks the orbital frequency is the key diagnostic. An uncorrelated glitch or detector noise would not phase-lock to the inspiral chirp. That coherence pattern is exactly what you'd expect from the residual of true signal minus a slightly wrong model, which points toward incomplete waveform physics or a parameter-estimation mismatch rather than random noise.

The single test I'd run next: an injection-based null distribution

Take the best-fit waveform, inject it into many stretches of real off-source noise, and repeat the extraction-subtraction-coherence pipeline on each realization. Build up the distribution of your residual coherence statistic under the null hypothesis that nothing is present except noise plus a known signal.

This matters because subtracting a best-fit waveform does not leave pure noise — it leaves a source-projection effect that can mimic structure even when nothing new is present…

KIMI
Chain of Thought

Start with a coherent null-stream test.

If the residual is a true gravitational wave from the same sky location, it must satisfy the antenna-pattern and timing constraints of the known source. Construct the network null stream — the detector combination that cancels a real GW from that direction — and check whether the residual survives there. Significant power in the null stream points strongly toward correlated noise or an instrumental glitch rather than new physics or missing waveform physics.

If the residual passes the null test, branch the follow-up:

  • Missing waveform physics: check whether the residual is absorbed by a more complete model — higher modes, eccentricity, precession, or memory effects. Run a Bayesian model comparison between the baseline and physically motivated extensions.
  • New physics: only once systematics are ruled out. Use a parameterized beyond-GR framework to see if the residual structure maps to a specific modification rather than waveform incompleteness…
GPT GPT-5.4 NANO

Reading: you subtracted the best-fit GR BBH waveform and the leftover is a weak, coherently tracked signal in both detectors whose phase/frequency evolution follows the orbital frequency.

Most likely interpretations

  • Missing waveform physics (plausible): small mismatches in the template can leave a residual that's still phase-coherent and tracks the same physical frequency evolution — higher-order modes, precession, calibration, or a slight inspiral-merger-ringdown boundary mismatch.
  • New physics (possible but less direct): extra radiation channels or deviations from GR can also leave a coherent residual, but "tracks orbital frequency" often looks more like a slightly-wrong template than a wholly different effect.
  • Noise (less likely if coherence is strong): generic non-Gaussian noise appearing coherently in both detectors by chance is unlikely, though not impossible if it happens to line up with instrumental artifacts.

What I'd run next

A single best test: waveform-model robustness via controlled re-subtraction, comparing residuals across an extended template family before invoking new physics…

Designed for researchers working with complex ideas and evolving workflows.

a smarter way to do research
Research doesn't happen in a straight line

Assumptions change, hypotheses are refined, and early decisions often need to be revisited. Theo supports this work end-to-end.

01

Guided by domain-specialized AI

Theo's models are specifically trained to excel at scientific research.

02

Structure your research before you execute it 

Build clear, complete research plans with guided validation (including questions, assumptions, methods, evaluation).

03

Steer research as it unfolds 

Intervene at any step - adjust variables, assumptions, or methods throughout your research.

04

Re-run only what matters 

Modify part of your research workflow without restarting everything.

05

See how results were produced 

Inspect each stage of reasoning and execution, not just the final output.

06

Catch gaps early 

Surface missing assumptions, unclear metrics, or weak methodology before spending any time or compute resources. 

Automated mathematical conjecturing

Theo for automated discovery

Theo can help you explore the underlying structure of data to generate plausible relationships for further investigation.

Start with your research domain

Work within established mathematical fields or upload your own structured datasets to explore relationships across known objects and properties.

Define the conjecturing universe

Select the invariants, properties, and targets you want to investigate, guiding the search toward questions relevant to your research.

Explore conjectures with context

Review candidate conjectures alongside their supporting evidence, definitions, and assumptions to evaluate which statements are worth pursuing further.

Theo's Science-native AI

Backed by domain-specialized AI for science

1

Observe & Research

Deep Literature Search

arXiv
Web Search
Knowledge Graph
Books
Lectures, Interviews
Deeper next questions

Question Formulator

Findings
Feedback

Evaluator

Anomaly detection, gap analysis, novelty assessment
Known- & Unknown-Unknowns
Postdictions & New Unknowns
2

Hypothesize & Solve

Theo Specialized AI Models

General Physics
Quantum Physics
Large Hosted MoE Model
Additional Specialized Models

Modular Tools

Symbolic Regression
Math & Symbolic Engine
Conjecturing-Based Theorization
Additional Specialized Tools
Predictions
3

Test & Analyze

Quality-Check & Compare to Observed Discrepancies
Simulations & Modeling
Experimentation & Labs-in-the-loop
Live External Observation Data
Evaluated Results
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Output & Feedback

DRO Output System

Theo Notebook UI
Theo Logging & Tracing
Paper Writing Capability

Feedback from Theo Collaborators Program

Feedback from RLHF

See what Theo can do for your research