Nature Loops Labs
Preliminary research note
Work in progress · July 2026
NLL Physics Research 1
Research-grade environments for theoretical and mathematical physics.
NLL-bench/2607.001
natureloops.ai
Why this work exists
Physics research is a chain of decisions.
Physics research combines formulation, derivation, tool use, verification and recovery from failed routes. Many benchmarks compress that work into a short answer.
NLL Physics Research 1 examines the longer chain. Each problem is reconstructed from a recent research paper and rewritten so it can stand on its own. The paper, worked solution and proof-bearing files stay outside the agent’s environment.
The agent must build the argument from the problem statement, use mathematical or computational tools where needed, and reach a result that can be checked.
A useful benchmark records where the reasoning failed as well as whether the final result was correct.
Capabilities under test
The environment tests the work research requires.
The aim is to make difficult reasoning observable and trainable.
Make the problem precise
Translate prose, definitions and conventions into a complete mathematical representation.
Carry a long argument
Connect intermediate claims without a source paper or worked route to imitate.
Compute when reasoning requires it
Write code, manipulate symbolic expressions and run numerical checks inside a controlled workspace.
Try to break the answer
Check dimensions, limits, symmetries, special cases and independent physical constraints.
Recognise a failed route and change course
Preserve useful partial work, identify the point of failure and attempt a different derivation.
From paper to environment
Every task starts with a result that can be checked.
Select
Choose a research paper with a deep, exact result and enough structure to support a self-contained problem.
Reconstruct
Work backwards from that result, defining every convention needed for a unique answer without leaking the route.
Verify
Write the golden answer and complete solution, then check the mathematics symbolically, numerically or against formal constraints.
Grade
Build a strict check for the final result and a weighted rubric for meaningful intermediate progress.
Isolate
Give the agent only the task and its tools. Keep the source, solution and private grader outside the runtime.
Audit
Review the task, answer and grader independently before freezing the evaluation set.
From benchmark to training programme
Cloud compute lets us test whether these environments improve a model.
Baseline
Freeze the evaluation protocol
Run selected models under matched conditions and record trajectories, costs, failures and verifier behaviour.
Training
Build a difficulty curriculum
Train on progressively harder environments while preserving unseen task families for evaluation.
Measurement
Test transfer and reliability
Compare frozen checkpoints, probe reward hacking and measure whether gains survive new problems and physical regimes.
The longer-term direction is a ladder of environments that moves from exact local calculations toward longer mathematical-physics arguments, including capabilities relevant to gauge theory and Yang–Mills.
The six-month pilot does not claim to address the mass-gap problem. It tests whether controlled training improves the component skills that such research demands.