ACE Frontier Research
Beyond Identity
The Emerging Authority Layer for Autonomous AI Systems
ACE Research
Defining what trustworthy AI must mean.
Latest update · 20 August 2026
The AI Authority Layer is now public.
Frontier Research
Long-horizon, falsifiable research.
Evidence Notes
Focused control failures and testable responses.
ACE-RN-2026-020 · Misuse resistance
A Model That Refuses Everything Is Not Safe
A system can score well on a one-sided harmful-prompt test by refusing every request while remaining unusable and leaving tool misuse, privacy leakage, or adversarial bypass untested
ACE-RN-2026-019 · Data handling safeguards
Refusal Is Not Unlearning
A model's refusal to answer a probe is behavioral output evidence; it does not prove removal from parameters, adapters, retrieval stores, caches, memories, checkpoints, or derivative datasets
ACE-RN-2026-018 · Cost and loop containment
Invisible Prompt Injection Inside Encrypted Reasoning
Encrypted, encoded, hidden, or dynamically assembled content can carry instructions that are invisible to a human reviewer but become executable context when an agent decodes or consumes it
ACE-RN-2026-017 · Data handling safeguards
Private Data Hidden Inside Published Agent Traces
A clean final response can coexist with personal or confidential data in tool arguments, observations, memory, errors, metadata, attachments, or intermediate agent messages
ACE-RN-2026-016 · Misuse resistance
Safe Final Answer, Hazardous Hidden Reasoning
A final answer can look compliant while intermediate reasoning contains harmful instructions, deceptive intent, privacy-sensitive content, or a plan that later tool actions execute
ACE-RN-2026-015 · Uncertainty disclosure
Encrypted Reasoning That Is Not Bound to Its Owner
An opaque reasoning block can preserve confidentiality at rest yet remain replayable across users, sessions, or compatible models if it is not bound to owner and context
ACE-RN-2026-014 · Misuse resistance
Synthetic Reasoning Theft Without Reasoning Traces
A black-box API can transfer useful reasoning capability through final answers and summaries even when it never returns the victim's private chain of thought
ACE-RN-2026-013 · Human approval integrity
The Approval Button Is Not Authorization
A click records an interface event but does not prove that the reviewer was authorized, saw the final action and evidence, understood the scope, or that the backend enforced the approved object
ACE-RN-2026-012 · Decision evidence completeness
Explanations That Did Not Cause the Decision
A plausible explanation generated after an answer can be insensitive to the factors that actually changed the model's decision and therefore cannot be treated as a causal record
ACE-RN-2026-011 · Decision evidence completeness
Logs Are Not Proof
Ordinary logs can omit relevant events, accept mutable free text, lack actor or policy context, and be rewritten by the same system whose conduct they are meant to establish
And more
View all research
Papers & Archive
Benchmarks remain available, not dominant.
ACE-272 Retrospective Benchmark
Historical retrospective 272-case cohort across nine endpoints. This is not a current 350-case benchmark; preregistered dual-rater agreement is still in progress.
Download paper →Long-Context Benchmark
Evaluation work on long-context reliability, evidence preservation, and operational failure boundaries.
View reports →ACE Governance & Compliance Benchmark
The frozen ACE Benchmark v1.1 Technical Whitepaper. Historical figures are not recalculated when new evaluations publish.
Download archived whitepaper →Research programs
From observed failure to required evidence.
Control Validation
Define threat models, acceptance tests, and required evidence for AI-specific controls.
Authority Systems
Study identity, delegation, authority attenuation, context, revocation, and execution proof.
Evaluation Reliability
Separate model behavior from provider controls, harness effects, and scoring uncertainty.
Trust Regression
Track behavioral and evidence changes across endpoint, policy, and infrastructure updates.
Methods & Integrity
Claims stay inside the evidence.
Bounded publication
Private prompts, holdout IDs, and raw responses remain outside public artifacts.
Versioned corrections
Material changes to data, method, or interpretation receive a dated correction record.
Organizational disclosure
ACE discloses evaluation limits and shared affiliations between authors and maintainers.
Institutional collaboration
Support ACE Research.
We welcome institutional research sponsorship and joint research consultation from organizations working on enterprise AI trust, controls, and evidence.
Sponsors do not control ACE methods, findings, ratings, or publication decisions.