423 problems for humanity's next frontier.

Most benchmarks start with questions. TRACES starts with the world. Ten experts, all with STEM PhDs, spent two months scouting 561 industries across 16 sectors to find problems with genuine scientific, technological, economic and societal value — a registry of 423 high-value problems.

Our first release goes deep on twenty of them. Each one names what the solver must do, how the answer is checked when reality finally delivers it, and who is waiting for it.

Apodex Discovery offers a research benchmark. Nothing described here is a medical device, diagnostic, or clinical decision tool; no output is intended for patient care. Financial problem definitions are not investment advice or a solicitation. Engineering problem definitions do not substitute for review by a licensed engineer.

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PROBLEM

WHAT THE SOLVER MUST DO

HOW THE ANSWER IS CHECKED

WHO BENEFITS

ENVIRONMENT

01

AAV capsid assessment and design

Design organ-specific AAV capsid variants end-to-end from public sequence–function data.

Unseen variants under a strictly isolated hold-out.

Gene-therapy developers

02

LLM training recipe design

Design and optimize pretrain / midtrain / posttrain recipes — architecture, optimizer, schedule, RL, distillation — under a compute constraint.

Must beat expert-tuned baselines at matched compute.

Frontier and enterprise model builders

03

Training data curation & decontamination

Discover, filter, construct and decontaminate training and benchmark data at scale, under budget and throughput limits.

Downstream quality at fixed budget; contamination measurably removed.

Frontier and enterprise model builders

04

LLM training & serving infrastructure

Build and verify reliable, high-throughput training and serving infrastructure across models and hardware.

Bit-exact reproduction and measured throughput across targets.

Model builders; compute providers

05

Drug repurposing & reformulation

Rank repurposing candidates for a target disease using only evidence visible before a cutoff.

Enrichment of the top-K against post-cutoff clinical and real-world success.

Pharma; biotech; disease foundations

06

Clinical trial statistical programming

Generate SAP-faithful trial analysis code and top-line tables, listings and figures.

Cell-level audit lineage back to the protocol; fidelity to the statistical analysis plan.

Pharma biostatistics; CROs

07

Clinical trial safety & efficacy signal detection

Auto-generate and stress-test safety and efficacy hypotheses from accruing trial data.

Signals validated against post-cutoff outcomes.

Pharma; medical monitors; CROs

08

Causal drug-target validation

Decide whether a candidate gene target is a causal driver or an associated passenger, and name the falsifying experiment.

Held-out genetics and perturbation outcomes; known target successes and failures.

Pharma discovery; biotech

09

Non-consensus investment thesis generation

Surface overlooked evidence, non-consensus theses, and bull / base / bear scenarios for deep-tech decisions.

Historical time-split replay, shadow portfolio, out-of-sample thesis tracking.

Asset managers; venture and growth investors

10

Crop yield shortfall forecasting

Predict localised crop-yield shortfalls sixty days ahead of official reports, from satellite, soil and climate data.

The official report, when it lands.

Agricultural traders; food processors; insurers

11

Code-compliant structural design

Generate structural and foundation layouts from a floor plan, minimising embodied carbon under seismic constraints.

Automated code-compliance check; carbon and seismic constraints evaluated.

Engineering and design firms; developers

12

Cell therapy manufacturing optimization

Optimize per-batch manufacturing parameters to raise potency and post-infusion persistence.

Measured batch potency and persistence after infusion.

Cell-therapy manufacturers; CDMOs

13

Biological age & mortality risk prediction

Estimate biological age and mortality risk from multimodal health data — records, imaging, labs.

Unseen held-out cohorts.

Health systems; insurers; longevity clinics

14

iPSC-cardiomyocyte differentiation modeling

Model iPSC-to-cardiomyocyte differentiation trajectories and the effect of disease mutations.

Unseen donors and unseen mutations.

Cardiac drug discovery; academic labs

15

Rare disease diagnosis from whole-genome data

Produce structured diagnostic reports from raw whole-genome or whole-exome data and match patients to trial eligibility.

Confirmed diagnoses; eligibility verified against trial criteria.

Rare-disease centres; patient organisations

16

Metabolic treatment response prediction

Predict response and non-response, and rank the best twelve-month interventions per patient.

Twelve-month weight, HbA1c, discontinuation and rebound outcomes.

Payers; health systems; pharma

17

Preclinical-to-human translation prediction

Predict whether a preclinical result will translate, and attribute the cross-species causal gap when it does not.

Paired preclinical and human outcomes on held-out drugs.

Pharma R&D; biotech

18

Near-term health deterioration prediction

Predict near-term health decline from longitudinal personal data and recommend safe next actions.

Observed near-term decline and escalation outcomes.

Health systems; payers; care providers

19

Work-state reconstruction & open-loop detection

Reconstruct active work state across a company's tools and detect unresolved commitments, decisions and risks.

Whether flagged items were genuinely open, and whether the loop closed.

Enterprises

20

Material-event detection from real-time news

Monitor real-time signals for events that materially change a user's world model, with alternative readings.

Whether flagged events proved material, judged after the fact.

Enterprises; investors; analysts

Where these problems go next

A problem becomes a benchmark only when the world around it is built — data sourced, tools integrated, expert workflows reconstructed, and every load-bearing step made checkable. Four of the twenty are already executable environments; the rest are on the way.

“True ‘discovery’ is the ability to pose problems, not just answer them.”

Mr. Tianqiao Chen, Founder & CEO of Apodex