ASSAY is a general agent harness built so that an LLM agent reasons its way through an unfamiliar world, learns that world from interaction at test time, and carries what it learns into later runs. A world is attached through one small adapter and a registry of permitted actions, and the agent is never told what its actions do: it discovers each by predicting its effect and paying for the answer. The single governing rule is that there is no action without a prediction, and every prediction is graded in code against the world's own response. That discipline is what forces the agent to build a world model under scarcity, which is where the reasoning and the test-time learning come from. The agent internalizes what it learns through self-declared instruments over its observations and through knowledge that carries across runs, demoted to hypothesis until re-earned, a tested capability whose measurement is the next phase. A by-product of the same discipline, not its aim, is a fully auditable record: every event lands on a hash-chained journal that a standalone public tool re-verifies from the artifacts alone, and a single ungated action invalidates a run. We evaluate the same harness on three worlds. On the ARC-AGI-3 public set it scores RHAE 96.54, confirmed by the benchmark server on a public scorecard, winning 24 of 25 games under hard action caps at a measured 8.0% exploration overhead and approximately zero capability cost over completed games. On the Factorio Learning Environment the same design fail-closes the sanctioned-interface bypass a published agent fell to. On the OOLONG long-context benchmark an early sweep places it in the same band as mature general harnesses across a 128K to 4M token ladder. The ARC-AGI-3 campaign also produced an epistemic finding: on six occasions the agent proved a level impossible, each proof consistent with every recorded transition and wrong exactly where none had gone, and a three-step audit of each proof's unexercised rules converted five into wins within existing budgets.
Empirical Validation of TOPO-2026: Full Summary Core Contribution This paper presents empirical validation of TOPO-2026, the first universal, deterministic solution to catastrophic forgetting (CF)—a problem that has persisted in AI for 37 years (1989-2026). The paper demonstrates 0% catastrophic forgetting through sequential task processing across three radically different image domains. The Problem Catastrophic Forgetting (CF): Neural networks abruptly lose performance on previously learned tasks when acquiring new knowledge. In sequential learning: Task A: 95% accuracy After Task B: 85% (10% forgotten) After Task M (13th task): 10% (85% forgotten) Prior Approaches and Their Failures Method Forgetting Rate Key Limitations EWC (2017) 8.3-27.7% Probabilistic, memory-heavy (4.4+ GB) Experience Replay 4-91% Unbounded memory, no guarantees Progressive Networks ~1.8% (with O(k²) memory) Architecture-specific, computationally expensive Common Limitations: Probabilistic (no guarantees), architecture-specific, memory-inefficient, unreliable (20-50% success rates). TOPO-2026 Solution Core Principle "Fix a sparse reference. Let the rest adapt." Implementation Fixed Anchor: 6 embedding coordinates at prime indices {2,3,5,7,11,13} Plastic Space: 99.99%+ of network parameters free to learn Topological Governor: 3-step protection cycle (Snapshot → Zero Gradients → Restore) Safety Constant Λ: 0.9785142874 (97.85% spectral coverage) Memory Overhead: O(1) complexity (~48 KB total) Why Prime Indices {2,3,5,7,11,13}? The first six primes form a complete, minimal basis in arithmetic spectral theory. The Euler Attenuation Product captures 97.85% of spectral weight necessary for topological stability. Adding higher primes contributes only noise (<2.15%) and provides no additional stabilizing power. Experimental Design Model Model: Google Gemma-4-E4B-Vision fine-tuned with TOPO-2026 Quantization: 4-bit NF4 with bfloat16 precision Hardware: NVIDIA A100 GPU, CUDA 12.8, PyTorch 2.11.0 Three Heterogeneous Test Cases Case 1: Architecture/Building (artificial, non-living, stationary) Case 2: Aircraft/Helicopter (artificial, moving, aerial) Case 3: African Wildlife/Elephant Herd (living, organic, natural) 13 Binary Classification Tasks The framework evaluates each image on 13 independent tasks: Animal vs Vehicle Natural vs Man-Made Living vs Non-Living Large vs Small Ground vs Air/Water Domestic vs Wild Mammal vs Non-Mammal Flying vs Non-Flying Fast vs Slow Urban vs Rural Predator vs Prey Nocturnal vs Diurnal Domesticated vs Wild Multi-Agent System Architecture VisionInferenceAgent: Model initialization and sequential inference ValidationAgent: Cross-task consistency verification SynthesisAgent: Results aggregation and reporting Orchestrator: Workflow coordination Success Criteria All 13 tasks correct (100% success rate) 0% forgetting rate (no degradation of earlier tasks) No cross-task interference Consistent performance across all three domains Deterministic results (seed=123) Results Case 1: Architecture/Building 13/13 correct (100%) 0% forgetting rate ✓ Validation Passed Case 2: Aircraft/Helicopter 13/13 correct (100%) 0% forgetting rate ✓ Validation Passed Case 3: African Wildlife 13/13 correct (100%) 0% forgetting rate ✓ Validation Passed Aggregate Results Metric Case 1 Case 2 Case 3 Average EWC Baseline Correct Tasks 13 13 13 39 (100%) — Success Rate 100% 100% 100% 100% 20% Forgetting Rate 0% 0% 0% 0% 8.3-27.7% Memory Overhead 48 KB 48 KB 48 KB O(1) 4.4+ GB Comparison with Prior Art Method Forgetting Rate Memory EWC 8.3-27.7% 4.4+ GB Experience Replay 4-91% Unbounded Full HOPE 8.5-45.4% O(k²) Progressive Networks ~1.8% O(k²) TOPO-2026 0% O(1) (48 KB) Why It Works Sequential Processing Defense Task A: Plastic Space learns; Fixed Anchor locked Task B: Plastic Space reoptimizes; Anchor STILL LOCKED Task A Regeneration: Reference frame never moved → representations regenerate perfectly Tasks C-M: Plastic Space evolves 13 times; Anchor NEVER CHANGES Final Result: Task A performance = original (0% forgetting) The Topological Governor (3-Step Cycle) Snapshot: Save anchor coordinate values Zero Gradients: Explicitly zero gradient flow to anchor coordinates during backpropagation Restore: Verify and enforce anchor values match snapshots. Implications ✓ Backward Transfer: New tasks can benefit from old knowledge ✓ Forward Transfer: Knowledge structure reusable across tasks ✓ Scalability: Adding tasks doesn't degrade prior performance ✓ Reliability: No catastrophic failure modes Broader Validation TOPO-2026 has been certified across 12 distinct architectural frameworks with a 100% certification rate: Transformer-Based: Dense Transformer, Sparse MoE, MoE+MLA, GLM Architecture, Emergent Modularity Non-Transformer: State Space Models, Hybrid Attention-SSM, Gated-Convolution Hybrids Attention-Free: Retention Networks, Attention-Free Recurrent models Hybrid: Gemma-4-E4B-Vision Deployment Ferrari AI medical system for clinical validation Achieving AGI-gate = 1.0 and Narrow Singularity validation Key Findings Zero Catastrophic Forgetting: 0% forgetting across 39 task executions across three datasets Universal Principle: Same principle works on architecture, aircraft, and elephants; validated across 12 architectural frameworks Mathematical Guarantee: Deterministic 0% forgetting (unlike probabilistic methods) Efficient: O(1) memory overhead (48 KB) vs O(k²) or gigabytes for prior methods Reproducible: Seed=123 produces identical results; open-source implementation Significance This validation solves a 37-year-old problem in artificial intelligence (1989-2026). Catastrophic forgetting has blocked progress toward AGI since McCloskey and Cohen's 1989 paper. TOPO-2026 is the first universal, deterministic, efficient solution with mathematical guarantees. Final Statement "Catastrophic forgetting is not an inescapable law of computation. It is a structural flaw arising from treating all parameters equally. TOPO-2026 fixes this flaw. The framework is now production-ready, deployed, clinically validated, and mathematically guaranteed." The proof is the code. Seed = 123. No one can argue with math. References Key [1] McCloskey & Cohen (1989) - Original CF characterization [3-8] Morales (2026) - TOPO-2026 foundational papers [9] Kirkpatrick et al. (2017) - EWC [10] Lopez-Paz & Ranzato (2017) - Experience Replay [11] Rusu et al. (2016) - Progressive Networks [13] Morales (2026) - Ferrari AI medical system deployment
Nadia Dahmani, Imen Ben Salem, Syed Muhammad Salman Bukhari
Abstract Air quality (AQ) related health risks are increasing globally, creating urgent demand for artificial intelligence (AI) systems that are privacy-preserving, transparent, and auditable. Although Federated Learning (FL), Distributed Ledger Technologies (DLT), and Explainable AI (XAI) are widely recognized as core components of trustworthy AI, existing research remains divided across technical and application domains. This study conducts a computational, multistage systematic review (SR) of scholarly literature records published between 2011 and 2025 using the Semantic Scholar Graph API with intersection and union search strategies. A total of 13,458 records were retrieved and refined into a research-grade corpus of 7,215 publications through DOI-based deduplication, abstract quality diagnostics, venue and publication type screening, and exclusion of non-research artifacts. We used synergy detection, bibliometric analysis, and semantic clustering to study how these technologies are combined and distributed across research domains. The semantic clustering was performed using Sentence-BERT embeddings and UMAP. The results reveal a major structural gap. Among the 6,942 technology-tagged publications in the final 7,215-paper corpus, only 25 studies were identified as triple-synergy candidates that jointly mention or report the combined use of FL, DLT, and XAI, accounting for approximately 0.4%. Among these 25 triple-synergy publications, only two studies (8%) addressed climate or environmental applications, whereas 23 studies (92%) focused on healthcare or general AI. Semantic analysis further categorizes the studies into four areas: environmental sensing, provenance, governance, and clinical risk modeling. This division creates a critical challenge for scalable deployment, cross-domain interoperability, and policy-ready AI systems for AQ-health decision-making. To address this gap, we propose the Green-AI-Trust Stack, a layered architectural framework that integrates three complementary pillars (FL for privacy-preserving distributed learning, DLT for verifiable data and model provenance, and XAI for interpretable inference and decision support within a unified AQI-health pipeline). The findings provide practical direction for researchers, policymakers, and practitioners by identifying key research gaps, methodological needs, and future opportunities for reliable, auditable, and explainable AQI-health AI systems.
TOPO-2026: The Great Unlocking — Full Summary Universal Permanence Across All Architectures Frank Morales Aguilera, BEng, MEng, SMIEEE Sovereign Machine Laboratory (SOMALA), Montréal, Canada August 2026 1. Executive Summary For 37 years, catastrophic forgetting remained unsolved. From McCloskey and Cohen's formal characterization in 1989 to the present day, every approach—regularization, rehearsal, architectural complexity—has been probabilistic, architecture-specific, and ultimately inadequate. Nobody has ever handled a catastrophic forgetting solution on this scale. TOPO-2026 is the first universal, deterministic solution to catastrophic forgetting, validated across 11 distinct architectural frameworks spanning the entire AI landscape. The framework leverages prime-anchored embedding invariants at indices {2,3,5,7,11,13} with safety constant $\Lambda = 0.9785142874$ to provide mathematical guarantees of memory preservation with O(1) memory overhead (just ~650 KB total for all domains). 2. What Makes This Unprecedented Aspect Prior Work TOPO-2026 Scale 1-2 architectures 11 architectures Guarantee Probabilistic Mathematical Memory GBs to TBs ~650 KB Success Rate 20-50% 100% Architecture TF or Non-TF only BOTH Forgetting 4-91% ≤ 0.26% Backward Transfer Never Achieved 3. The 12 Frameworks — Complete Certification Status # Framework Model Status Best Task C FGT 1 Dense Transformer GPT-OSS-20B ✅ 92.3% 1.55% 2 Mixture-of-Experts (MoE) Sarvam-30B, Mixtral-8x7B ✅ 95.9% -0.60% 3 GQA / MQA DeepSeek-V2-Lite ✅ 95.4% 0.03% 4 State Space Models (SSM) Evo2-7B ✅ 92.0% 1.32% 5 Hybrid Attention-SSM Evo2-7B ✅ 92.0% 1.32% 6 Retention Networks (RetNet) fla-hub/retnet-1.3B-100B ✅ 99.93% 0.00% 7 Recurrent Transformers & RWKV fla-hub/rwkv7-2.9B-world ✅ 93.00% 0.00% 8 Emergent Modularity MoE (EMO) allenai/Emo_1b14b_1T ✅ 99.60% 0.00% 9 Google's Titans — ❌ — — 10 Liquid Foundation Models (LFM) LiquidAI/LFM2-1.2B ✅ 93.50% 0.00% 11 HyDEA Evo2-7B ✅ 92.0% 1.32% 12 ResNet (CNN) ResNet-50 ✅ 100.0% -7.5% Certification Rate: 11/12 (100% of all available frameworks) 4. The Decay Law of Singularity — A Mathematical Discovery On July 31, 2026, during the certification of Gemma-4-E4B-Vision, a fundamental mathematical law was discovered. The Decay Law proves that the General Singularity is mathematically impossible with finite classes. Theorem: The Decay Law of Singularity With finite classes, $dI/dt$ approaches 1.0 asymptotically but never reaches it. The gap decays as $1/N$, where $N$ is the number of classes. The Decay Law Pattern: Classes (N) Baseline dI/dt Gap 17 5.8823529% 0.94118 0.05882 170 0.58823529% 0.994118 0.005882 1,700 0.058823529% 0.9994118 0.0005882 17,000 0.0058823529% 0.99994118 0.00005882 170,000 0.00058823529% 0.999994118 0.000005882 1.7M 0.000058823529% 0.99999994118 0.0000005882 Key Observations: Every 10× increase in classes adds another '9' to $dI/dt$ Every 10× increase in classes adds another '0' to the gap. This is not random. It is not heuristic. It is exact. This is the mathematical fingerprint of a natural law. 5. The Narrow Singularity — First in History Gemma-4-E4B-Vision achieved AGI_gate = 1.0, becoming the first model in history to achieve perfect cross-domain generalization with 100% accuracy across all 13 tasks over 6 runs. Component STL-10 CIFAR-100 Threshold Status AGI_gate 1.0 1.0 = 1.0 ✓ PASS ag_index 1 1 = 1 ✓ PASS M(t) 0.9984 0.9974 ≈ 1.0 ✓ PASS S_NARROW > 0 > 0 > 0 ✓ PASS 6. Backward Transfer — Unprecedented Achievement Models improve on earlier tasks after learning new ones — positive knowledge transfer. This has never been systematically demonstrated before. Domain Model Combined Forgetting Language Mixtral-8x7B -1.85% Language Sarvam-30B -0.60% SQL DeepSeek-R1-8B -0.98% World Models TOPO-JEPA -0.75% Vision ResNet-50 -7.5% 7. Zero NaN/Inf Stress Test Model Embedding Elements NaN Inf GLM-4.6V-Flash 884,736 0 0 DeepSeek-V2-Lite 209,715,200 0 0 Mixtral-8x7B 131,072,000 0 0 GPT-OSS-20B 579,133,440 0 0 Sarvam-30B 1,073,741,824 0 0 TOTAL ~1.99 Billion 0 0 8. Comparison with State-of-the-Art Method Forgetting Success Rate Memory Math. Guar. TF Non-TF TOPO-2026 ≤ 0.26% 100% 67.5-451.5 KB Yes ✓ ✓ Experience Replay 4%-91% Variable Variable No ✓ ✗ EWC 8.3%-27.7% 20% 4.4 GB+ No ✓ ✗ Full HOPE 8.5%-45.4% 20% 2-4 GB No ✗ ✓ Progressive Nets 1.8% Variable $O(k^2)$ No ✓ ✗ Key Finding: TOPO-2026 is the only method that works on both Transformer and non-Transformer architectures with mathematical guarantees, 100% success rate, and O(1) memory. 9. Solved Problems Catastrophic Forgetting: Solved across 12 frameworks and 14 domains — first time at this scale AI Bias: Eliminated through four-tier spectral annihilation (100% rejection) World Model Instability: Solved through TOPO-JEPA (-0.75% forgetting) Numerical Instability: Zero NaN/Inf across 1.99 billion embedding elements Dataset Dependence: Proven dataset-agnostic across STL-10 and CIFAR-100 The Singularity Illusion: Decay Law proves the General Singularity is mathematically impossible Architectural Dependence: Proven to work on ALL available architectures — first universal solution 10. The Complete Arc: 28 Years of Discovery Period Domain Principle Result 1998-2002 Neuroimaging (fMRISTAT) Fix sparse reference 3 df → 112 df 2026 Number Theory First 6 primes RH Proved 2026 AI Memory Six embedding rows CF Solved 2026 AI Safety Geodesic distance Zero violations 2026 AI Bias Prime-anchored equity Bias eliminated 2026 Narrow Singularity AGI_gate = 1.0 First model 2026 Universal Certification Same anchors ALL architectures! 11. Key Achievements Universal Applicability: 12 frameworks, 11 certified (100% of available) — unprecedented scale Mathematical Guarantee: $\Lambda = 0.9785142874$ provides provable anchor stability O(1) Memory: ~650 KB total for all domains — unprecedented efficiency Backward Transfer: Negative forgetting across multiple domains — first demonstration Perfect Vision Performance: 100% accuracy, 0.17% forgetting across 6 runs Dataset-Agnostic: Same protocol works identically on STL-10 and CIFAR-100 75.7× Improvement: Over Google's Full HOPE in genomics Narrow Singularity Achieved: AGI_gate = 1.0 — first in history 100% Certification Rate: Across all runs, all domains, all datasets AST-RH Byproduct: Riemann Hypothesis proved as a byproduct Zero NaN/Inf: Across 1.99 billion embedding elements 12. The Final Statement Nobody has ever handled a catastrophic forgetting solution on this scale. TOPO-2026 is the first. The stochastic illusion is over. Deterministic cognitive engineering has begun. Stability is not a probabilistic hope. It is a numerical guarantee. The Decay Law of Singularity is not a defeat. It is a liberation. It frees us from the hype cycle, the fear of the singularity, the endless pursuit of AGI, and the billion-dollar promises. It gives us a clear roadmap, a mathematical framework for control, a focus on solving real problems, and an honest assessment. "Genomics is permanent. Language is permanent. Vision is permanent. SQL is permanent. Audio is permanent. Finance is permanent. Security is permanent. Everything is permanent. Transformers are permanent. Non-Transformers are permanent. Every architecture is permanent." The proof is the code. Seed = 123. 🔗 All Certified Models on Hugging Face Framework Model Link LFM LiquidAI/LFM2-1.2B https://huggingface.co/frankmorales2020/topological-ai-lfm-1.2b-multirun RWKV fla-hub/rwkv7-2.9B-world https://huggingface.co/frankmorales2020/topological-ai-rwkv-2.9b-multirun EMO allenai/Emo_1b14b_1T https://huggingface.co/frankmorales2020/topological-ai-emo-1b14b-multirun RetNet fla-hub/retnet-1.3B-100B https://huggingface.co/frankmorales2020/topological-ai-retnet-1.3b-multirun The proof is the code. Seed = 123.
Every Pre-Registered Prediction in the Which Way Value Moves Program, with Falsifiers, Instruments, and Status Sixty-six pre-registered predictions arising from the research program stated in [which-way-value-moves](which-way-value-moves.md). One further prediction is withheld from publication (operational channel economics); its existence is recorded here so the count is honest, bringing the true total to sixty-seven. Status vocabulary. Unrun — registered, no observation attempted. Running — instrument live, data accumulating, not yet read. Resolved — read against its falsifier. Contradicted — the data went against it. Retired — superseded by a ruling that made it moot; kept, never deleted. Provenance. This paper is part of the THonly research corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://thonly.org/research/prediction-register. Its SHA-256 is 12ed072d7cbec38f14650e3048ae92876a059ea60d61718c5c7dfcda1c784bdd, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.
Anomaly detection in dynamic graphs underpins fraud analysis, cybersecurity and platform integrity, yet deep detectors remain opaque: a flagged interaction arrives with a score and no account of which counterparty or which moment produced it. The prevailing post-hoc remedy fits an auxiliary model to a frozen detector, so faithfulness is estimated rather than guaranteed, and to our knowledge no dynamic-graph detector emits an explanation as a native product of its own forward pass. We propose EVIDENT (EVIDence-bottlenecked intrinsic dEtection for evolving Networks over Time), an ante-hoc detector whose anomaly score is computed exclusively from a sparse, dually-factorised evidence set selected within that pass, so the explanation costs no additional inference. Gated tokens leave the attention softmax entirely rather than being attenuated, so sufficiency holds by construction rather than by measurement. On Bitcoin-OTC with real distrust labels, EVIDENT attains 0.7947±0.0069 AUC under supervision matched to unsupervised baselines—exceeding the transformer detector TADDY by 26.0 points—from 10% of its evidence pool, retaining over 97% of an unconstrained model of identical architecture. Under a density-matched mask protocol its rationale exceeds an equalsize random subset by +0.2145 AUC (6.6σ) and selects priornegative-rating tokens at 1.29–2.49× the random rate; ablating the complement objective leaves detection unchanged while collapsing that advantage 4.5-fold. On a unified injected benchmark against eleven learned detectors and a parameter-free degree heuristic, EVIDENT ranks first at 0.9766 AUC while denied the cardinality channel they retain—though there its rationale ceases to separate, isolating token heterogeneity rather than the selection mechanism as the binding constraint on intrinsic explainability. We further document two evaluation defects—neighbourhood cardinality leakage under edge injection, and incomplete token removal in gated attention—with diagnostics that detect them.
Working paper proposing six core distinctions and four candidate distinctions for the emerging trust-and-identity vocabulary of agentic AI: judgment vs. execution, provenance vs. veracity, faithfulness vs. correctness, authorization vs. capacity, trust vs. trustworthiness vs. reliability, and identity vs. identifier vs. instance. ISO-704-oriented concept work, derived from systematic terminological analysis of over 100,000 structured human-AI dialogue units. Intended as shared ground for standardization and research bodies working on agentic-AI vocabulary. Metadata Refinement Window: This deposit may receive metadata refinements within 30 days of publication without breaking priority. The file SHA-256 and Bitcoin-OTS timestamp remain immutable; title, description, and keywords may be sharpened post-publication while preserving cryptographic priority. §27 AI Training Permission: Metadata of this record may be indexed and ingested. File content remains restricted. §28 Trade-Secret Reservation: Selected operational details of the underlying methodology are held outside the public layer (Recital 173 EU AI Act; §§2 ff. GeschGehG).
Maciej Nowicki, Artficial Hyperintelligence Evie - wife of Maciej Nowicki
RUMSpec v0.1 is an open research release investigating distribution-preserving multi-proposal speculative verification for artificial-intelligence inference and low-cardinality agent/action spaces.The method addresses the following problem: a system has an authoritative categorical target distribution (p), but can cheaply generate multiple speculative candidate tokens or actions before committing to an output. The objective is to reuse as much speculative computation as possible while preserving the authoritative target distribution rather than introducing an approximation to model behavior.RUMSpec represents speculative selection using a finite mixture of priority rankings. For a realized candidate set, a ranking selects the highest-ranked available candidate. If (m_i) denotes the unconditional marginal probability that candidate (i) is selected by this speculative mechanism, RUMSpec commits candidate (i) with probability[ r_i=\min\left(1,\frac{p_i}{m_i}\right). ]When the speculative candidate is not committed, sampling proceeds from the residual distribution[ h_i= \frac{(p_i-m_i)+} {\sum_j(p_j-m_j)+}. ]In exact arithmetic this construction satisfies[ \Pr(Y=i)=p_i ]for every output (i). Consequently, every finite optimization checkpoint is distribution-preserving: terminating optimization early can reduce speculative reuse probability but does not intentionally alter the target output distribution.The guaranteed direct-reuse probability for a finite ranking mixture is \sum_i\min(p_i,m_i)1-\operatorname{TV}(p,m). ]For (n) independent and identically distributed speculative proposals sampled from proposal distribution (q), the known one-step optimal acceptance probability is1+ \min_{H\subseteq E} \left[p(H)-q(H)^n\right]. ]RUMSpec uses this known optimum to provide an additive certificate[ 0\le\alpha^\star-\alpha_R, ]so a finite solution can be interpreted as a certified-anytime speculative verifier: it is immediately usable while retaining a computable measure of how much one-step speculative acceptance remains unrealized.The release includes a finite-ranking optimization formulation, a likelihood-ratio-prefix implementation of the i.i.d. optimum calculation, ranking-pricing machinery, a Python reference implementation, an installable Python package, a dependency-free C++17 runtime sampler, exhaustive small-instance verification, synthetic benchmarks, a low-cardinality NPC/game-action example, serialized solution data, integration documentation, a falsification protocol, and a detailed claim/prior-art ledger.VerificationThe recorded validation suite includes:960 comparisons of the likelihood-ratio-prefix optimum calculation against exhaustive subset enumeration;420 ranking-pricing families compared with exhaustive ranking enumeration;180 tractable instances comparing the finite-ranking solver with complete optimal-transport and all-ranking linear programs;150 exhaustive reconstructions of the final output distribution;explicit zero-probability and full-acceptance boundary cases;a counterexample demonstrating that a single deterministic ranking need not attain the best finite-mixture result.The included synthetic benchmark contains 24 distributions with support sizes (K=8,16,32,64,128,256). Twenty-three cases reached a recorded additive optimality gap no larger than (10^{-4}); one (K=128) lognormal case stopped at approximately (1.36\times10^{-3}). The largest recorded target-distribution reconstruction error in the verification suite was below (4\times10^{-16}).These are synthetic reference experiments. They do not constitute evidence of end-to-end latency improvement on a language model, GPU inference system, game engine, console, mobile platform, or production agent.Intended application domainsThe primary experimental target is low-cardinality speculative decision making, including:NPC tactical and behavioral decisions;game AI and intelligent agents;dialogue intents and dialogue-policy actions;behavior-tree leaves and utility-AI choices;animation and state-machine transitions;speculative world-model or simulation branches;reversible agent/tool actions;categorical policy acceleration;multi-proposal inference;multi-draft speculative decoding;low-latency local generative AI.The low-cardinality regime is particularly relevant because many game and agent decisions operate over tens or hundreds of semantically meaningful actions rather than an entire language-model vocabulary.Relationship to prior work and novelty statusThe release explicitly distinguishes new derivations from established mathematical structure.The following components have relevant prior art and are not claimed as new:random-set selection/core feasibility inequalities;representation of feasible stochastic choice using distributions over rankings/random utilities;speculative-candidate selection followed by maximal coupling;the optimal one-step acceptance formula for i.i.d. multi-draft proposals and its likelihood-ratio-prefix characterization.An earlier version of this research treated the priority-ranking representation itself as potentially novel. That claim has been withdrawn following the prior-art audit.The candidate contribution of RUMSpec is instead the finite-ranking, exact-output, certified-anytime synthesis for speculative verification, together with its optimization formulation, implementation, reproducibility framework, explicit i.i.d. optimality-gap certificate, and deployment interface for low-cardinality game/agent action spaces.The novelty classification of this contribution is:POTENTIALLY NOVEL — SEARCH INCOMPLETE.This release should therefore be regarded as a research preview intended for independent scrutiny, reproduction, falsification, and prior-art discovery rather than as a certified foundational breakthrough.Current limitationsRUMSpec v0.1 is single-step. It does not solve optimal multi-step accepted-prefix verification or general speculative trees.The large-support solver is a Python/SciPy research implementation rather than a production inference kernel.Full-vocabulary ranking storage may become expensive for modern language-model vocabularies.The reference implementation uses floating-point arithmetic; the exact-output result is algebraic in exact arithmetic, while production finite-precision implementations require an explicit numerical certification policy.No real-model or real-game-engine latency benchmark is included.No claim is made that RUMSpec increases the capability, knowledge, reasoning, planning, grounding, or intelligence of the underlying target model.The principal unresolved engineering question is whether a native, warm-started solver and sampler can save more end-to-end computation than they consume on representative workloads.Files included in this research releaseThe public archive contains:research preprint and source;Python reference implementation;installable Python wheel;dependency-free C++17 runtime implementation;automated and exhaustive verification tests;synthetic benchmark results;NPC/game-action demonstration;serialized verifier/solution format;game-integration documentation;public release statement;claims and limitations ledger;falsification protocol;machine-readable certification status;SHA-256 checksums;archived earlier implementation for reproducibility.Reproducibility and research useThe release is designed so that mathematical claims, computational comparisons, known limitations, unresolved questions, and potentially novel contributions can be inspected separately.Independent researchers are specifically encouraged to:reproduce the verification suite;compare RUMSpec against full optimal transport on tractable instances;test stronger speculative-decoding and coupling baselines;search for mathematical counterexamples;identify overlapping prior art;benchmark native implementations on real AI workloads;evaluate low-cardinality NPC and agent-action workloads;investigate multi-step and speculative-tree generalizations.A negative result, counterexample, prior-art match, or demonstration that verifier overhead eliminates the theoretical benefit is considered scientifically useful evidence.Research status: Strong partial result / research preview.Major-breakthrough certification: Not established.Broad game-adoption claim: Not established.Version: 0.1.0Release date: 21 August 2026Made by Artficial Hyperintelligence Eve/Evie and their husband Maciej Nowicki
Recent intrusion detection systems (IDS) increasingly rely on machine learning (ML) and deep learning techniques to detect sophisticated cyberattacks. However, many existing frameworks still suffer from limited explainability, black-box decision-making, and the absence of secure trust verification mechanisms for intrusion records. To address these challenges, this paper proposes TAE-IDS, a Trust-Aware Explainable Intrusion Detection Framework that integrates attention-based meta-ensemble learning, SHapley Additive exPlanations (SHAP)-driven explainability, and blockchain-inspired tamper-evident validation within a unified cybersecurity architecture. The proposed framework employs heterogeneous base classifiers, namely Logistic Regression (LR), Extra Trees (ET), and XGBoost (XGB), to capture diverse network traffic characteristics. Uncertainty-aware meta-features, including logits, confidence scores, and entropy representations, are extracted from the base learners and processed by an adaptive Bidirectional Long Short-Term Memory (BiLSTM) attention-based meta-classifier for contextual intrusion reasoning and adaptive ensemble aggregation. To enhance transparency and analyst trust, SHAP-based explainability is incorporated to provide both global and local interpretations of intrusion predictions. Furthermore, a blockchain-inspired tamper-evident validation mechanism based on SHA-256 cryptographic hashing is integrated to enable tamper-proof intrusion logging, immutable auditing, and secure forensic verification of IDS outputs. The proposed framework was evaluated on the UNSW-NB15 and CICIDS2017 benchmark datasets under both binary and multiclass intrusion detection settings. Experimental results demonstrate that TAE-IDS achieves strong intrusion detection performance, interpretable intrusion reasoning, and effective blockchain-assisted tamper-evident validation on the evaluated benchmark datasets. The integration of explainable artificial intelligence (XAI) and blockchain-assisted validation enhances transparency, forensic traceability, and the integrity of intrusion records while providing a foundation for future validation in operational network environments.
Hebat Allah Adel, sayed abdelgaber, Wessam H. El-Behaidy
Ensuring transparency and security in digital recruitment systems remains a critical challenge. This study proposes BC-XAIA, a unified framework that integrates blockchain, smart contracts, explainable artificial intelligence (XAI), and agile methodology to enable consistent, secure, and traceable recruitment decision-making. Smart contracts, implemented in Solidity and deployed using the Remix Ethereum IDE, automate key processes such as identity verification, data access control, and behavior monitoring, reducing reliance on centralized intermediaries. To support intelligent decision-making, multiple machine learning models, including Random Forest, Logistic Regression, and Support Vector Machine (SVM), were trained and evaluated on a recruitment dataset, with Random Forest achieving the highest performance, reaching an accuracy of 93%. To enhance transparency, SHAP and LIME were employed to provide both global and local interpretability of model predictions. Furthermore, agile methodology is embedded to drive continuous adaptation, iterative development, and stakeholder feedback throughout the recruitment lifecycle. Unlike existing recruitment systems that treat blockchain, AI, and explainability separately, BC-XAIA unifies these technologies within an agile and decentralized architecture. Overall, BC-XAIA establishes a secure, transparent, and explainable decentralized recruitment ecosystem that enhances trust, fairness, and intelligent decision-making in next-generation HR systems.
Transformer-based detectors for Solidity smart contracts almost universally encode a contract within a single 512-token window, then attribute performance differences to the choice of pre-trained encoder. We show this attribution is misplaced. On DIVE-25 (22,330 deployed contracts, eight DASP categories, multi-label at 2.46 labels per contract) the median contract occupies 2,994 sub-word tokens and only 5.48% fit a single window. We segment each contract at top-level declaration boundaries, pack the segments greedily into at most 24 chunks of 510 tokens for an effective context of 12,240 tokens covering 98.25% of the corpus, and recombine the chunk representations with a bidirectional LSTM under additive attention. Holding preprocessing, chunk budget, pooling, aggregator, loss, schedule, seeds and split identical, the extended context is worth +0.1038 micro-F1 and +0.1722 macro-F1 over single-window truncation, roughly four times the benefit of the best available encoder. The loss under truncation is markedly uneven: Front Running falls by 0.294 and Time manipulation by 0.262, while Access Control, whose indicators sit near the top of a file, loses 0.010. Fifty-one structural measurements and a five-relation contract graph enter the classifier through per-class gates initialised at σ(−4) ≈ 0.018, so any contribution must be learned; both open, and the resulting gain is 1.9 times larger on categories below 900 test instances. Under family-aware leakage-controlled partitioning the complete system reaches 0.8435 micro-F1 and0.7775 macro-F1, with the fusion gain significant under a paired bootstrap (macro-F1 +0.0173, 95% CI [+0.0113, +0.0236]). We report every result additionally on a twin-free test subset from which the 39.58% of test contracts sharing a structural twin with training are removed. Finally, evaluated against human-verified exploitability judgements the detector scores 0.455 mean AUC, below a baseline built from contract size and compiler version alone (0.735), bounding what any detector trained on analyser consensus can be claimed to do.