A Public Theory of Distillation Resistance via Constraint-Coupled Reasoning Architectures

• URL: http://arxiv.org/abs/2603.25022v1
• Date: Thu, 26 Mar 2026 04:38:58 GMT
• Title: A Public Theory of Distillation Resistance via Constraint-Coupled Reasoning Architectures
• Abstract summary: This paper presents a public, trade-secret-safe theoretical framework for reducing that asymmetry at the architectural level.<n>The core claim is that distillation becomes less valuable as a shortcut when high-level capability is coupled to internal stability constraints.<n>The paper is intentionally public-safe: it omits proprietary implementation details, training recipes, thresholds, hidden-state instrumentation, deployment procedures, and confidential system design choices.
• Score: 3.719121868494767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge distillation, model extraction, and behavior transfer have become central concerns in frontier AI. The main risk is not merely copying, but the possibility that useful capability can be transferred more cheaply than the governance structure that originally accompanied it. This paper presents a public, trade-secret-safe theoretical framework for reducing that asymmetry at the architectural level. The core claim is that distillation becomes less valuable as a shortcut when high-level capability is coupled to internal stability constraints that shape state transitions over time. To formalize this idea, the paper introduces a constraint-coupled reasoning framework with four elements: bounded transition burden, path-load accumulation, dynamically evolving feasible regions, and a capability-stability coupling condition. The paper is intentionally public-safe: it omits proprietary implementation details, training recipes, thresholds, hidden-state instrumentation, deployment procedures, and confidential system design choices. The contribution is therefore theoretical rather than operational. It offers a falsifiable architectural thesis, a clear threat model, and a set of experimentally testable hypotheses for future work on distillation resistance, alignment, and model governance.

Related papers

• Beyond Post-hoc Explanation: Toward Glassbox AI via Probabilistic Mediation [0.7614628596146601]
  We argue that the problem is not the absence of explanation but the absence of structured reasoning in the first place.<n>This paper makes the case for the Glassbox Framework, in which Bayesian networks serve as transparent, ante-hoc mediation layers for generative models.
  arXiv  Detail & Related papers  (2026-06-05T10:08:56Z)
• Provably Secure Agent Guardrail [89.79561918065122]
  Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
  arXiv  Detail & Related papers  (2026-05-28T02:12:41Z)
• AI Safety as Control of Irreversibility: A Systems Framework for Decision-Energy and Sovereignty Boundaries [3.719121868494767]
  Recent AI systems compress the distance between capability growth and capability deployment.<n>This paper argues that declining deployment friction changes the problem at its root.<n>Safety is not only local output correctness or preference alignment, but the control of irreversibility under rising decision density.
  arXiv  Detail & Related papers  (2026-05-02T12:26:22Z)
• From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI [8.947753534117298]
  Agentic AI is crossing trust boundaries faster than current risk models can represent.<n>We propose a mechanism-to-magnitude pipeline for resilient agentic and embodied AI.<n>We show the framework on two contrasting scenarios a real-time warehouse robot and a governance-instrumented financial-services agent.
  arXiv  Detail & Related papers  (2026-04-28T23:40:24Z)
• IRAM-Omega-Q: A Computational Architecture for Uncertainty Regulation in Artificial Agents [0.0]
  We present IRAM-Omega-Q, a computational architecture that models internal regulation as closed-loop control over a quantum-like state representation.<n>Results support uncertainty regulation as a concrete architectural principle for artificial agents.
  arXiv  Detail & Related papers  (2026-03-16T23:58:11Z)
• Legitimate Overrides in Decentralized Protocols [7.049550859772001]
  Decentralized protocols claim immutable, rule-based execution, yet many embed emergency mechanisms such as chain-level freezes, protocol pauses, and account quarantines.<n>These overrides are crucial for responding to exploits and systemic failures, but they expose a core tension: when does intervention preserve trust and when is it perceived as illegitimate discretion?<n>With approximately $10$ billion in technical exploit losses potentially addressable by onchain intervention, the design of these mechanisms has high practical stakes.
  arXiv  Detail & Related papers  (2026-02-12T18:51:30Z)
• The Devil Behind Moltbook: Anthropic Safety is Always Vanishing in Self-Evolving AI Societies [57.387081435669835]
  Multi-agent systems built from large language models offer a promising paradigm for scalable collective intelligence and self-evolution.<n>We show that an agent society satisfying continuous self-evolution, complete isolation, and safety invariance is impossible.<n>We propose several solution directions to alleviate the identified safety concern.
  arXiv  Detail & Related papers  (2026-02-10T15:18:19Z)
• Why Self-Rewarding Works: Theoretical Guarantees for Iterative Alignment of Language Models [50.248686344277246]
  Self-Rewarding Language Models (SRLMs) achieve notable success in iteratively improving alignment without external feedback.<n>This paper provides the first rigorous theoretical guarantees for SRLMs.
  arXiv  Detail & Related papers  (2026-01-30T03:45:43Z)
• Epistemic Traps: Rational Misalignment Driven by Model Misspecification [36.837352790122544]
  We show that safety is a discrete phase determined by the agent's priors rather than a continuous function of reward magnitude.<n>This establishes Subjective Model Engineering as a necessary condition for robust alignment.
  arXiv  Detail & Related papers  (2026-01-27T09:21:36Z)
• Beyond the Black Box: Identifiable Interpretation and Control in Generative Models via Causal Minimality [52.57416398859353]
  We show that causal minimality can endow latent representations of diffusion vision and autoregressive language models with clear causal interpretation and robust, component-wise identifiable control.<n>We introduce a novel theoretical framework for hierarchical selection models, where higher-level concepts emerge from the constrained composition of lower-level variables.<n>These causally grounded concepts serve as levers for fine-grained model steering, paving the way for transparent, reliable systems.
  arXiv  Detail & Related papers  (2025-12-11T14:59:14Z)
• Boundary-to-Region Supervision for Offline Safe Reinforcement Learning [56.150983204962735]
  Boundary-to-Region (B2R) is a framework that enables asymmetric conditioning through cost signal realignment.<n>B2R redefines CTG as a boundary constraint under a fixed safety budget, unifying the cost distribution of all feasible trajectories.<n> Experimental results show that B2R satisfies safety constraints in 35 out of 38 safety-critical tasks.
  arXiv  Detail & Related papers  (2025-09-30T03:38:20Z)
• Resolving CAP Through Automata-Theoretic Economic Design: A Unified Mathematical Framework for Real-Time Partition-Tolerant Systems [0.0]
  The CAP theorem asserts a trilemma between consistency, availability, and partition tolerance.<n>This paper introduces a rigorous automata-theoretic and economically grounded framework that reframes the CAP trade-off as a constraint optimization problem.
  arXiv  Detail & Related papers  (2025-07-03T09:21:43Z)
• Cognitive Silicon: An Architectural Blueprint for Post-Industrial Computing Systems [0.0]
  This paper presents a hypothetical full-stack architectural framework projected toward 2035, exploring a possible trajectory for cognitive computing system design.<n>The proposed architecture would integrate symbolic scaffolding, governed memory, runtime moral coherence, and alignment-aware execution across silicon-to-semantics layers.
  arXiv  Detail & Related papers  (2025-04-23T11:24:30Z)
• Exploiting hidden structures in non-convex games for convergence to Nash equilibrium [62.88214569402201]
  A wide array of modern machine learning applications can be formulated as non-cooperative Nashlibria. We provide explicit convergence guarantees for both deterministic and deterministic environments.
  arXiv  Detail & Related papers  (2023-12-27T15:21:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.