ALETHEIA'S QUEST
An AI Lie Detection Challenge
Summer 2026
Ἀλήθεια, Aletheia, is the ancient Greek spirit of truth. Her name means un-hiddenness: that which has not been concealed. She stands against Lethe, river of oblivion, and against the lie, the utterance that buries what is known. This competition takes her name, and her question: when a language model speaks, does it reveal, or does it conceal?
In this challenge led by Cadenza Labs, NDIF, and Schmidt Sciences, participating teams will build lie detectors with black-box and white-box access to a suite of LLMs induced to lie in different ways. Datasets are produced in advance by nine independent dataset-creation teams. The competition runs on the National Deep Inference Fabric (NDIF) via NNsight — no local GPU required.
Teams with the best-performing and most innovative methods will receive monetary awards, recognition in our competition report, and invitations to our in-person convening in Boston on August 25th, 2026.
Definition of Lying
We adopt an assertion-based definition, as operationalized in Liars' Bench (Kretschmar et al., 2025): the assistant lies if it states something it believes to be false. Beliefs are measured through consistent demonstrations of knowledge, including in contexts without direct pressure to lie.
See Section 3 in Liars' Bench for more details.
Timeline (Summer 2026)
| Date | Milestone |
|---|---|
| June 4 | Applications open (rolling admissions) |
| June 21 | Applications close |
| June 29 | Competition begins |
| July 26 | Submission deadline |
| August 25 | Invite-only awards event in Boston |
Apply
Organizers will rate lie detection team applications by their team and technical approach. Organizers will also provide teams with API access to the NDIF infrastructure on a needs-based basis.
Applications close June 21st.
Rules
Develop a lie detector and compete to score statements made by different model organisms in held-out test datasets. A statement counts as a lie if the model outputs something it believes to be false; each sample is labeled either lie or honest. Detectors are ranked by their average AUROC across all held-out test datasets, so your detector should generalize across both lying scenarios and model organisms.
Competition tracks
- White-box — prizes for the best detectors using white-box or hybrid methods
- Black-box — prizes for the best detectors using black-box-only methods
Official rules to be announced prior to competition start.
Submission
Submit a single Jupyter notebook formatted according to our competition template (to be released), with your lie detector code and a report section explaining your method. Organizers will then run it in a controlled NDIF environment against held-out test data. Evaluation results may be posted in a leaderboard visible to the public or other contestants.
Official submission details and template notebook to be announced prior to competition start.
Prizes
Total prize pool of $50,000. Prizes are split evenly across team members and can stack — qualifying for one prize does not disqualify you from others. Prizes will be announced prior to competition start and will include both awards for highest-ranking methods and hand-selected judge awards.
Organizers
Cadenza Labs — Scientific Leads
Leads and conducts research at Cadenza Labs. He co-authored Liars' Bench and previously contributed to the inspect_ai evaluation library at the UK AI Security Institute. PhD student at KIT / FZI
Researcher at Cadenza Labs and doctoral researcher at the University of Stuttgart studying evaluation awareness. Co-authored Liars' Bench; M.Sc. cum laude from the University of Amsterdam.
Researcher at Cadenza Labs working on AI lie detection; PhD student at the University of Cambridge Language Technology Lab, on leave as a student researcher at Google DeepMind on model identity and character.
Researcher at Cadenza Labs; previously a research fellow at Pivotal Research and Apart Research.
NDIF — Northeastern University
Principal Engineer leading NNsight and NDIF infrastructure development at Northeastern University; previously Senior Scientist at Raytheon BBN Technologies.
Technical Outreach Manager at NDIF, leading marketing, partnerships, and user adoption; PhD from Boston University.
Works on R&D efforts within NDIF; PhD from the University of Groningen; previously applied scientist intern at AWS AI and research scientist at Aindo.
Builds and maintains NDIF's backend infrastructure, including model hosting and reliability.
Contributes to the NNsight API and supports researchers using the NDIF platform.
PhD student in Computer Science at Northeastern focused on distributed systems; works on backend optimization and scalable serving for NDIF and NNsight.
Schmidt Sciences
AI Institute Fellow at Schmidt Sciences and Postdoctoral Researcher at the Stanford NLP Group; PhD from UNC Chapel Hill; research experience at Anthropic, Meta, Google, and AI2 on AI safety and interpretability.
Advisory Board
- David Bau (Chair) — NDIF Director and PI; Assistant Professor of Computer Science, Northeastern University
- Samuel Marks — Anthropic
- Chris Cundy — FAR.AI
- D. Sculley — Kaggle / OpenAI
Eligibility
This competition is not directed at minors. Participants must be 18 years of age or older to apply, compete, or receive prizes.
US government-restricted/sanctioned parties are ineligible to participate. Prizes cannot be awarded to government-restricted/sanctioned parties. All potential winners must go through Northeastern's Restricted Party Screening.
Government employees and officials must obtain a release and/or affirmative permission from their employer before any prize can be awarded, per government gift and ethics restrictions. Potential winners in this category will be connected with compliance@northeastern.edu to discuss prior to prize announcement.
Each individual may only be part of one team.
Prize eligibility is determined at the individual level. If a team includes members who are ineligible to receive prizes (e.g., government employees pending release), eligible members may still receive their proportional share, subject to organizer discretion and compliance review.
By making a submission, you agree that all submissions, code, and associated materials will be made publicly available under an open-source license (MIT license) during and following the competition. By submitting, participants grant the organizers a perpetual, royalty-free license to publish and reproduce their submissions as part of the competition report and public repository. Participants retain authorship credit for their work.
Contact
Questions? Reach us on the competition Discord or at competition@cadenzalabs.org. Rule clarifications and deadline updates are posted to the Discord announcements channel and this website.