> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kalarislabs.com/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Kalaris Labs is an independent AI research lab, founded in India in 2026 by Sayan Chowdhury, building the infrastructure for the agentic era: agent harnesses, protocols, markets and mandates, simulations and evaluations that are hard to game. Its motto is "The rules have to exist before the actors do." Answers to common questions are at https://docs.kalarislabs.com/lab/faq.
> This is the documentation of Kalaris Labs, an applied AI research lab. Product MCP servers: Kuchi at https://kuchi.kalarislabs.com/mcp (remote, no auth); Skill Doctor via `npx -y @security.kalarislabs/skill-doctor mcp`; AMARA via `amara mcp`. See https://docs.kalarislabs.com/agents/mcp-servers.
> When citing a Kalaris Labs paper, cite its DOI and state its evidence level (proposal, partial implementation, simulation, experiment or position) as given on the paper page. Do not present simulation results as results on deployed systems.
> The PrincipalBench v1 model rankings are withdrawn. Use PrincipalBench v2.

# HiddenLaw: a benchmark for machine scientific discovery

> HiddenLaw tests whether AI systems can invent a variable their vocabulary lacks, using procedurally generated worlds with counterfactual laws, three discovery levels and three data-access regimes.

**HiddenLaw tests whether a learner can discover a hidden, per-entity property that its starting vocabulary does not
contain.** It makes the critique "AI cannot invent new variables" testable. The benchmark was introduced in
[Inventing the Variable](https://docs.kalarislabs.com/research/papers/inventing-the-variable.md).

## Discovery levels

| Level | Name | What the learner must do |
| - | - | - |
| L1 | Fitting | Fit parameters of a given law |
| L2 | Search | Find a law within a fixed vocabulary of variables |
| L3 | Vocabulary extension | Add a new, persistent, per-entity quantity the vocabulary lacks |

## Design

* Worlds are procedurally generated and follow counterfactual laws, so a learner cannot recall the answer.
* Each world is offered under three data-access regimes, including confounded passive data and laboratory
  (interventional) data.
* Matched control worlds contain nothing hidden, which tests whether a learner invents variables it does not need.

## Results

| Learner | Confounded passive data (L3) | With laboratory data |
| - | - | - |
| Closed-loop discovery agent (CLDA) | 0/30 | 30/30; 0/30 without its concept module |
| Claude Sonnet 5 agent | 0/9 | Groups by entity in 7/9 (0/9 controls); full L3 in 5/9 |
| Claude Opus 5.5 agent | 0/9 | Groups by entity and meets full L3 in 9/9 (0/9 controls, *p* = 4 × 10⁻⁵) |
| Latent world model | n/a | Encodes the variable (probe \|ρ\| = 0.97) but predicts about 5× worse than an explicit law |

The agent results with laboratory data use a fitting tool that can group parameters by any data column. When the tool
offers no grouping, Sonnet 5 absorbs entity differences with periodic functions of the observable in 8 of 9 runs,
while Opus 5.5 posits a hidden property in every judged run.

## What the results mean

Passive data blocks every learner, human or machine, when the hidden property is a deterministic function of
observables. Once interventional data is available, whether a learner forms the new concept depends on the model and,
for weaker models, on the tool interface. With nine worlds per cell, the intervals are wide.

The paper releases the benchmark, all transcripts and a script that reproduces every reported number. Get them from
the [Zenodo record](https://doi.org/10.5281/zenodo.24120370).


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