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AI-assisted coding systems now influence repository edits, dependency selection, and deployment decisions, which creates coupled attack surfaces spanning prompt-channel abuse, supply-chain compromise, and unsafe escalation policies. We present a compositional security framework that unifies trust-boundary enforcement, action-intent validation, fail-closed provenance gating, and uncertainty-conditioned review routing for mixed-trust coding workflows. The manuscript integrates three formally stated components with executable symbolic checks and evaluates them on a reproducible multi-stage benchmark covering chat-agent, IDE-copilot, and CI-bot settings. Across the current benchmark implementation, the full compositional policy reduces attack success relative to alignment-only and advisory-only comparators while preserving usable task utility, and it yields non-dominated frontier behavior in most modality slices. We also identify bounded failure modes, including parser-evasion sensitivity and high-productivity-penalty regimes, to define where conclusions are stable and where additional validation is needed.

Goal-conditioned reinforcement learning often faces a practical tension: intrinsic novelty bonuses accelerate discovery in sparse and deceptive environments, but poorly controlled intrinsic coupling can distort the asymptotic objective. This paper introduces Curiosity-Conditioned Goal-Optimal Reinforcement Learning (CCGO-RL), a dual-value framework that treats curiosity as a controlled exploration mechanism inside a goal-conditioned control loop rather than as a permanent co-objective. The method combines an extrinsic value stream, an intrinsic value stream, confidence-bounded intrinsic annealing, and uncertainty-gated contrastive coupling. We formalize the setting with explicit decision variables (policy and scheduler), a measurable feasible scheduler set, and an optimality criterion based on extrinsic return. We prove a perturbation bound between mixed and extrinsic objectives and an extrinsic suboptimality envelope for mixed-objective optimization, and we derive a gate-sensitivity and conditional variance-increment bound for uncertainty-gated contrastive coupling. Empirically, synthetic benchmark suites covering sparse navigation, deceptive mazes, and goal-conditioned control show improved return-speed trade-offs and lower critic instability against strong baselines, with quantitative checks tied to theorem assumptions. The empirical gains are strongest for exploration efficiency and variance control, while one symbolic limit-corollary check remains mismatched, which narrows the interpretation of the asymptotic claim to the audited admissible schedule regime.

Modular language-agent systems increasingly combine large language models, tool calls, and symbolic operators, but objective design and evaluation practice remain misaligned: trajectory-quality surrogates, benchmark-native outcomes, and deployment constraints are often optimized in isolation. We present a hybrid framework for SymbolicAI workflows that jointly optimizes constrained routing, bridge-calibrated metric alignment, and uncertainty-qualified acceptance under CPU-only budgets. The method defines a constrained router objective that couples trajectory quality, native task loss, route cost, and uncertainty terms; a bridge model that maps trajectory-level signals to heterogeneous benchmark-native outcomes; and a one-sided confidence predicate that controls deployment acceptance under practical gain thresholds. Across a benchmark suite spanning interactive tasks and code-oriented slices, the proposed router improves mean joint objective relative to strong planning and tool-use baselines (0.739 versus 0.701 for fixed-route SymbolicAI and 0.688 for OR-Toolformer-style routing), and the full bridge model improves both gain and calibration relative to distance-only controls (mean \Gamma=0.060; AUROC =0.742; ECE =0.118). Drift stress tests show higher robust success and lower invalid-call rates for symbolic fallback routing than static alternatives. Symbolic audits support most formal obligations while exposing two unresolved obligations, so theorem-strength claims are stated conditionally rather than globally. The resulting manuscript provides a claim-evidence-uncertainty closure that is explicit about what is proven, what is empirically supported, and where boundary failures begin.

Continual learning systems are increasingly limited by memory behavior rather than arithmetic throughput: the same memory substrate must support stable recall and adaptive updates while respecting strict latency and energy constraints. We study this bottleneck through an entropy-parameterized access model that treats deterministic retrieval as a limit case of stochastic replay, then couple that model to an entropy-conditioned projection rule that recovers classical A-GEM behavior when slack is zero. The manuscript provides a formal derivation of the limit identity and shifted projection closed form, followed by protocol-locked validation evidence under a CPU-only experimental contract. Across surrogate continual-learning streams, entropy-aware replay improves frontier-area metrics against static replay with a reported 95\% confidence interval of approximately [0.0533,0.0598], while entropy-conditioned projection shows a positive aggregate crossover interval (95\% confidence interval approximately [0.028,0.194]) relative to fixed A-GEM in validated operating regions. Symbolic theorem audits pass all recorded checks in the current run, and protocol compliance remains high under matched-memory controls. The resulting contribution is a hybrid formal-and-empirical framework that clarifies where entropy-aware control is supported, where it is conditional, and how to translate those boundaries into implementable continual-learning system design.

Antineutrino monitoring is a promising route for early safeguards signals, but fusion-adjacent deployment requires robustness to prior disagreement, detector nuisance variability, and transfer stress. We present a hybrid formal-empirical framework that combines contradiction-aware sequential calibration, an information-decomposed minimum detectable diversion criterion, and finite robust detector-material co-design. The method is grounded in source-lineaged reactor-spectrum and safeguards detection formalisms and extends them with manuscript-defined robust operators. We execute a CPU-only benchmark over 17,280 runs spanning prior families, drift rates, standoff distances, and detector resolutions with matched false-alarm operating points across comparators. Formal consistency checks pass for all theorem-linked symbolic obligations (4/4). Empirically, the robust method achieves strong delay robustness (delay win rate 0.9965 versus single-prior likelihood ratio; median delay ratio 0.6943 versus fixed-threshold test-statistic baseline), supporting the detectability-improvement claim under the tested open-parameterized regime. However, calibration and transfer closure remain conditional: calibration violation rate is 0.9524, leave-one-prior-out FAR inflation p95 is 1.2871, and hard-versus-easy transfer degradation ratio is 1.5402. These outcomes establish a defensible contribution boundary: robust detectability gains are supported, while policy-grade calibration and transfer claims require targeted recalibration and transfer-stability refinement. The manuscript reports both supported and mixed claims through explicit claim-evidence linkage, enabling reproducible iteration rather than optimistic overclaiming.

Quantum reservoir computing for image classification is currently constrained by a reproducibility problem: many reported improvements can be explained by uneven preprocessing, readout mismatch, or benchmark saturation rather than by reservoir physics. We study this issue in a parity-locked protocol where quantum and classical reservoirs share fold-local PCA, feature budget, readout family, tuning budget, and split seeds, and where a transverse-Ising reservoir is evaluated with matched entangling and non-entangling branches. We formalize three audit quantities: a robust non-easy-tier gap functional \Delta_\mathrmrob, a matched-control entanglement effect \tau(\eta,S) over noise-shot strata, and an attribution ratio \rho(\eta) that separates observable-policy gains from dynamics gains with an explicit undefined-denominator guard. Symbolic checks verify the algebraic identities used by the audit quantities, including gap-error equivalence and ratio-domain conditions. Simulation evidence on tiered image regimes shows positive robust gaps on mid and hard tiers under protocol parity, regime-dependent entanglement effects with unresolved cells at high noise and low shots, and mixed operator-vs-dynamics dominance because ratio estimates become undefined near small dynamics denominators. The resulting conclusion is conditional rather than universal: parity-controlled quantum advantage is supported in identified robustness regimes, but attribution and frontier claims require explicit caveats where uncertainty or denominator instability dominates.

Quantum reservoir computing (QRC) is often evaluated with heterogeneous comparator strength, making it difficult to determine whether reported gains are genuinely quantum-mechanistic or induced by feature-map geometry and protocol asymmetry. This paper studies image classification with PCA-compressed inputs, angle encoding, a transverse-Ising reservoir, and partial Pauli-observable readout under strict comparator parity. We combine formal analysis and executed simulation evidence to answer three coupled questions: where advantage regions exist across rank, whether entanglement-observable interactions are consistently positive, and when non-entangling regimes are effectively emulable by classical kernels. The formal component proves a unique ridge readout optimum under parity constraints, identifies the interaction term isolated by a difference-in-differences design, and derives a non-entangling predictor-gap bound linked to kernel-emulation error. The executed simulation component reports contiguous positive confidence intervals against strong classical comparators over multiple rank regimes, mixed interaction effects for entanglement strength, and bound-ratio behavior below unity in tested non-entangling regimes. Together, these findings support a conservative claim boundary: QRC gains are regime-conditioned and auditable under parity controls, broad monotonic entanglement-utility claims are not supported by current evidence, and kernel-null conclusions are restricted to fixed-regularization assumptions under the present proxy-dataset execution setting.

Quantum reservoir computing has recently reported encouraging image-classification performance, yet many claims remain sensitive to fairness controls, measurement-policy confounds, and benchmark selection. We study a parity-constrained evaluation program for PCA-encoded image inputs and transverse-Ising-style reservoir dynamics, and we combine formal analysis with staged simulation evidence under fixed CPU-only constraints. First, we formulate advantage assessment as a bi-level optimization problem with matched preprocessing, readout family, observable budget, and search budget across quantum and classical branches, and we prove that parity-controlled deltas cannot exceed naive deltas computed with asymmetric policy optimization. Second, we formalize a non-monotone entanglement-utility criterion, showing that boundary derivative sign changes and interior concavity imply a unique interior optimum in coupling strength. Third, we derive an operator-attribution framework based on balanced crossed random effects and prove range and unbiasedness properties for operator-share estimators. Using a staged validation run over MNIST, Fashion-MNIST, EMNIST Balanced, Kuzushiji-MNIST, and a grayscale PCA variant of CIFAR-10, we observe conditional practical advantage on one hard dataset under a pre-registered acceptance tuple, consistent interior-optimum signatures in the tested regime, and strong operator-share estimates with parity-audit closure. These results support a guarded conclusion: formal guarantees are strong, empirical gains are conditional, and attribution quality improves materially when parity is enforced at the row level.