Hippo Tightrope: An Adversarial Reasoning, Evidence Recovery, and Human-Gated Review Framework (Revision 1)
ABSTRACT 1 - METHOD Hippo Tightrope is a bounded adversarial reasoning, evidence-control, recovery, and adjudication framework for pressure-testing claims, artifacts, systems, experiments, architectures, analyses, and decisions without confusing skepticism with truth. Revision 1 formalizes target identity and ballot integrity, truth-necessity classification, knowledge-state separation, validated material attacks, survivor recovery, collision and failure routing, bounded recursion, multi-model review roles, hourly maintenance, post-compression recovery, and a terminal human gate. The framework preserves useful observations, methods, controls, negative findings, and unresolved structure when a headline interpretation fails, while preventing those survivors from inheriting unsupported authority. Hippo Tightrope is offered as a falsifiable methods architecture, not as a truth engine, guarantee of correctness or completeness, empirical superiority claim, or substitute for experiments, peer review, professional advice, or human authority. ABSTRACT 2 - PUBLIC USE Download HIPPO_TIGHTROPE_PUBLIC_LAUNCHER_REV1.txt. Submit Prompt 1 to initialize Hippo Tightrope, then enter the actual task as Prompt 2 in ordinary language. The framework automatically resolves the target, objective, evidence boundary, deliverable, constraints, run mode, relevant versions, baselines, tolerances, and truth-necessity state. Each run separates evidence-backed conclusions, derived hypotheses, speculative connections, and playful labels; validates attacks before allowing material damage; preserves surviving value with only earned standing; reports critical-low percentage states without averaging them away; and leaves final authority with the human operator.
Authors
- Andrew Paul Roebuck
Institutions
- Sarguja University (IN)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-10
- DOI
- https://doi.org/10.5281/zenodo.22698877
- Primary Topic
- Adversarial Robustness in Machine Learning
- Type
- preprint