Predictable Compression Failures: Order Sensitivity and Information Budgeting for Evidence-Grounded Binary Adjudication
arXiv:2509.11208v3 Announce Type: replace-cross Abstract: Transformers used for evidence-grounded binary adjudication (e.g., support/refute, yes/no, or verifier-backed pass/fail decisions) can be sensitive to the order in which exchangeable evidence is presented, producing dispersion across permutations and unreliable attempted answers under a verifier-relative Bernoulli predicate. We treat evidence order as a nuisance variable and formalize an expectation-realization gap: next-token training can minimize expected conditional description length over orderings while a fixed ordering remains pos
This research highlights a fundamental limitation in transformer-based AI systems, particularly as development moves towards more complex reasoning and agency, revealing a critical need for solutions to ensure reliable decision-making.
A strategic reader should care because unreliable AI decisions due to input order sensitivity pose significant risks to the deployment of autonomous agents, critical infrastructure, and advanced decision-making systems.
The focus for AI development shifts further towards robust verification and training methodologies that explicitly address and mitigate input order sensitivity, rather than solely optimizing for next-token prediction.
- · AI safety researchers
- · Companies developing robust AI validation tools
- · Sectors requiring high-assurance AI (e.g., defense, finance)
- · Developers solely focused on large language model scaling without foundational r
- · AI applications in critical domains relying on unverified models
AI systems used for critical 'yes/no' or 'pass/fail' decisions will exhibit unreliable outputs due to input order sensitivity.
Increased investment in research and development for AI architectures and training paradigms that are robust to input perturbations, leading to new verification standards.
The unreliability of current-generation AI-powered decision systems might temper their adoption in highly sensitive areas, creating a premium for certified, robust AI solutions.
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