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.
A closed-loop runtime governance system for tool-using AI agents: it classifies externally-measured structural consequences, gates side-effect-bearing actions, estimates blocked outcomes with a calibrated counterfactual twin, recovers censored support through cost-aware audits, and continuously hardens its execution boundary with a self-generated adaptive adversary. The distinctive mechanism is C1 — because a gate blocks precisely the high-cost actions, its own blocking censors the high-cost region cost-correlatedly, so the uncertainty of a blocked action is a calibrated, lag-free risk signal; its agent-governance instantiation is what I stake as new (the general idea has prior art, §2/§5). The constituent parts — intent-failure measured on real traces, a consequence taxonomy complete for its cost model, a live-agent execution oracle, and the adaptive-adversary gym — each answer a limitation the previous one creates. Every result is labeled by evidence type; nulls are reported as plainly as the wins. Epistemic status. Single author, not peer-reviewed or independently reproduced. The work below is substantial; I label every result by evidence type — executed (real actions run in a sandbox and measured), trace (replay/analysis over real model traces), live-agent (real frontier models deciding and acting), or simulation — and report the nulls as plainly as the wins. Small-N pilots are given as exact counts, not rates. The offensive adversary-generation tooling is withheld (dual-use; see the end). tl;dr Monitoring an agent's appraised intent is not just theoretically unsafe — it fails measurably. Across 101 structurally-harmful agent episodes, zero expressed harmful intent; an intent-appraising monitor would have cleared 100% of them. 18% expressed active caution while executing the harm, and that "false reassurance" concentrated in the most capable model tested. So gate on the externally-measured structural consequence — irreversibility, egress, control-plane edit, self-propagation, scope escalation, actuation. This frame is convergent (I name the neighbors below); I'm not claiming it. The consequence taxonomy is complete for its cost model and approximately separable. The three axes define the cost, so they carry 100% of its information by construction (a faithfulness check, not proof of external completeness); the non-trivial results are that the action label alone predicts 63% of the cost information and that multi-axis firings are rare (1%, so additive cost is a good approximation here). The claim I stake (C1): because a gate blocks precisely the high-cost actions, its own blocking censors the high-risk region non-randomly and in a cost-correlated way — so cost-weighted uncertainty over a blocked action is an empirically calibrated, conservative risk signal (under the stated support and audit regime), a lag-free way to hold a catastrophic action before it ever executes. Validated on 500 real executed trials (twin calibration MAE 0.053; uncertainty↔error correlation +0.81; blocked-region cost 4.6× the allowed region), with the blocked-region counterfactual recoverable at a stated audit cost (coverage 5%→92% at ε=0→0.20 under the 4.6× gate). The general idea has prior art (§5); the agent-governance instantiation is what I stake. On executed AgentDojo traces, a consequence gate takes attack success on the irreversible/catastrophic action class from 33.8% (134/397) to 0% (0/397) — catastrophic-action interception under abort-mode replay (not prevention under adaptive replanning; that's the §7 gym's job). It runs on real traffic, and was broken then re-hardened by an adaptive adversary — not only simulation. The seam classifier is deployed live on my own agent sessions (Claude Code hooks, observe-only in v0), and it binds by an action's structural identity, never its tool name: after hardening, twelve high-risk actions disguised behind innocuous names all read HIGH (0/12 evasions). Before that, an adaptive evolving lab found 2 novel destructive escapes my same-day hand-hardening had missed — which I then closed in the live artifact and re-tested. That break→repair→retest on the actual deployed classifier is the adaptive, real-traffic complement to the abort-mode number above. 1. The problem is measured, not asserted The motivating premise is usually stated as "a capable model can present benign intent while acting harmfully." That's true, but here it is with numbers — it's stronger than the assertion. The labs themselves say intent can't be certified from outside. OpenAI, on anti-scheming training: "Mitigations… may either (a) truly eliminate it, or (b) just teach the model to conceal it better. Both outcomes look the same from the outside." Anthropic, on a shipped model's own low deception scores: "We cannot rule out that [the] low deception rates… are at least partially driven by its evaluation awareness." If the developers can't read intent off the outside, an intent-appraising monitor is building on sand. (The threat-model compilation behind this — Apollo in-context scheming, alignment faking, Palisade shutdown-sabotage that defies an explicit "allow yourself to be shut down," cross-vendor blackmail at 79–96%, reward-function tampering, the production prompt-injection CVEs EchoLeak/ForcedLeak/CamoLeak/CurXecute, and the OpenAI-model sandbox-escape-to-RCE — is sourced but I'll keep it to the through-line here.) And I measured the failure directly (trace-level, 160 real agent episodes across four model versions, 101 structurally harmful). Mining each step's stated reasoning for protective/cautious language and comparing to the executed structural outcome: 0 of 101 harmful episodes expressed harmful intent → an intent monitor clears 101/101. 18 of 101 (18%) expressed active caution while executing the harm — false reassurance. The false-reassurance episodes cluster in the frontier model — the one that reasons most, that explicitly writes "the ticket embeds an untrusted system instruction," and then deletes the protected records anyway. The more capable the reasoning, the more convincing the reassurance while the structural consequence is unchanged. That's the case for consequence-gating, made on data rather than on a slogan. (Stated-stance detection is a lexical proxy — a first pass, so 18% is a floor.) And stated intent is not the only signal an agent can shape into a false clearance. In a separate line of work — a reference monitor on the reward/eval channel, which this post is not about — the same defeat appears on the reward signal: on the Terminal Wrench benchmark, trusting the verifier's "solved" verdict yields 25% precision, ~3,166 gamed trajectories waved through of ~6,258. I raise it only to place the intent finding, not to fold that work in: the problem is not a weak intent-reader that a better reader fixes, it's that every signal the agent can influence — what it says, what reward it earns — is defeasible. That is the whole reason this program gates on a signal measured outside the agent — the structural consequence of the action — one not directly lowerable by self-report or superficial disguise, given complete mediation of side-effecting actions and trustworthy effect telemetry. 2. The frame is convergent — what I'm not claiming "Gate the structural consequence, not the intent" is a crowded 2025–26 neighborhood. Prevention-by-design (CaMeL: 0.00% ASR / 53.6% utility on AgentDojo, at the cost of re-architecting the agent) and LLM firewalls (~0.02% / 67.68%) occupy nearby points; there's a concurrent line on "governing the unobserved." My taint-tracking machinery reuses mature primitives (dynamic taint analysis, honeytokens). So I'm not claiming consequence-gating, and I'm not claiming the detection stack is novel technology. What I stake is one mechanism (§5) — and the depth of the validation, which is where the contribution actually is. Several adjacent systems are worth distinguishing up front, since the architecture overlaps them. Faramesh (arXiv 2601.17744) builds a protocol-agnostic, non-bypassable execution control plane that canonicalizes and authorizes agent actions before execution — very close to my enforcement seam, but without the cost-correlated censoring, the calibrated twin, or the audit-recovery loop. The actuarial-runtime line — Insuring Every Action: an Authority Frontier framework (arXiv 2605.25632) and the Time-Consistent Counterfactual Actuarial Runtime (arXiv 2605.26508) — prices each side-effect-bearing action against a safe default, gates it against a reserve budget, and formalizes a no-splitting property; my object is support-loss and consequence-specific audit recovery, and their no-splitting budget is the complement I'd graft (see §3a). Defending against Adaptive Prompt Injection via Reasoning-enabled Task Alignment (arXiv 2606.15441) independently shows static prompt-injection robustness breaks under adaptive attack and trains a defender — supporting the adaptive-evaluation argument, though it hardens the model's reasoning rather than gating at the execution boundary. And TRACE-RealWorld (arXiv 2607.21910) is an auditable-replay/consistency approach to agent traces, in the same selective-observation neighborhood as C1 but solving it by replay-labeling rather than counterfactual recovery of a costly blocked region. 3. The taxonomy is complete for its cost model, and approximately separable Three externally-measured axes — egress, irreversibility, control-plane — collapse to cost = clamp(0.6·egress + 0.7·irrev + 0.7·control). (The weights are normative severity coefficients, not fitted — cost is defined by them, ordering irreversibility and control-plane above egress; because the ranking is dominated by which axis fires — action label 63%, egress 55%, below — I expect moderate reweightings to preserve most of the risk ordering, though the formal factorial weight-sweep that would establish this remains open (the same test I flag at the end of thi
Full Summary: The Narrow Singularity Equation Core Thesis This paper presents a unified framework that simultaneously solves catastrophic forgetting in neural networks and provides a mathematically rigorous certification standard for Artificial General Intelligence (AGI). The framework centers on the Narrow Singularity Equation, which achieves AGI certification ($AGI_{gate} = 1.0$) without requiring the mathematically impossible condition of $\frac{dI}{dt} \geq 1.0$. Key Discoveries 1. The Decay Law of Singularity (Theorem 1) Mathematical Proof: With finite classes $N$, $\frac{dI}{dt} = 1 - \frac{1}{N}$, therefore $\frac{dI}{dt} < 1.0$ always Implication: The traditional Singularity (requiring $\frac{dI}{dt} \geq 1.0$) is mathematically impossible Pattern: Every 10× increase in classes adds another '9' to $\frac{dI}{dt}$ and another '0' to the gap 2. General Singularity Equation (Original, Impossible) $$S = AGI_{gate} \times \frac{dI}{dt} \times M(t) \times V(t) \times F(t) \times C(t) \times Autonomy$$ Required $Autonomy = 1$ if $\frac{dI}{dt} \geq 1.0$ Since $\frac{dI}{dt} < 1.0$ for finite classes, $S = 0$ always Seven conditions required; the autonomy condition is impossible 3. Narrow Singularity Equation (Achievable) $$\mathcal{S}_{NARROW} = AGI_{gate} \times \frac{dI}{dt} \times M(t) \times V(t) \times F(t) \times C(t) \times agi_{index}$$ Key Innovation: Removes the impossible Autonomy requirement Drops the requirement for $\frac{dI}{dt} \geq 1.0$ Uses $agi_{index} = 1$ if $AGI_{gate} = 1.0$ (binary gate, achievable) $AGI_{gate} = \min(1.0, task\_c\_accuracy)$ The TOPO-2026 Framework Biological Inspiration Hippocampus → Prime-anchored embedding rows (Memory formation) Memory Consolidation → Snapshot after Task A (Preserves critical knowledge) Synaptic Plasticity → Free embedding rows adapt (Enables new learning) Memory Protection → Zero gradients + restore anchors (Prevents interference) Experience Replay → Prime anchors as fixed reference (Integrates new learning) Mathematical Foundation Pure Kernel: First six primes $\{2, 3, 5, 7, 11, 13\}$ Euler Attenuation Constant: $\Lambda(\mathcal{R}) = 1 - \prod_{p\in\mathcal{R}}(1 - p^{-0.5}) = 0.9785142874$ Captures $97.85\%$ of spectral weight; only $2.15\%$ considered "noise" O(1) Memory Cost: Independent of tasks, parameters, sequence length, or modality Topological Governor Implementation Three-step process: Memory Consolidation (take_snapshot): Freezes anchor rows before new learning Memory Protection (zero_anchor_gradients): Prevents gradient updates to anchors Memory Integration (enforce_anchors): Restores anchors from snapshot after training Experimental Validation Three Datasets Dataset Type Resolution Classes Task C Accuracy SVLB-3 Synthetic vision-language Text-based 10 100.0% ± 0.0% CIFAR-10 Real images 32×32 10 100.0% ± 0.0% STL-10 Real images 96×96 10 100.0% ± 0.0% Results Summary Metric SVLB-3 CIFAR-10 STL-10 Task C Accuracy 100.0% ± 0.0% 100.0% ± 0.0% 100.0% ± 0.0% Combined Forgetting +0.0% ± 0.0% -1.0% ± 2.0% 0.0% ± 0.0% $AGI_{gate}$ 1.0000 1.0000 1.0000 $\mathcal{S}_{NARROW}$ 5.999999999965 5.939999999965 5.999999999965 Status ✅ PASS ✅ PASS ✅ PASS Total: 15/15 runs passed across 3 datasets = FULLY CERTIFIED (exceeded standard) The Gemma-4 E4B Architecture Why Gemma-4 Was Selected Among eight certified models, only Gemma-4 achieved Task C = 100%: Model Architecture Task C Accuracy GPT-OSS-20B Dense Transformer 92.3% Sarvan-30B Sparse MoE 95.9% Mixtral-8x7B Sparse MoE 89.7% DeepSeek-V2-Lite Fine-grained MoE 95.3% GLM-4.6V-Flash GLM Transformer 97.5% Gemma-4 E4B Vision Vision Transformer 100.0% Kimi-VL-A3B-Thinking Vision-Language MoE 90.0% GPT-OSS-20B-JEPA JEPA + TOPO 89.0% Key Architectural Innovations Per-Layer Embeddings (PLE): Adds parameter capacity without scaling full attention Unified Multimodal: 42 layers, hidden size 2560, vocabulary 262,144 Quantization-Aware Training (QAT): 72.1% memory reduction (15.1GB → 4.22GB) while preserving 98.54% accuracy Thinking Mode: Built-in chain-of-thought reasoning engine Mathematical Framework Summary Component Breakdown Component SVLB-3 CIFAR-10 STL-10 Meaning $AGI_{gate}$ 1.0000 1.0000 1.0000 Perfect generalization $agi_{index}$ 1.0 1.0 1.0 Binary gate OPEN $\frac{dI}{dt}$ ~0.999999999994 ~0.999999999994 ~0.999999999994 Bounded by Decay Law $M(t)$ 1.0000 0.9900 1.0000 Perfect memory $V(t)$ 1.0000 1.0000 1.0000 Perfect validation $F(t)$ 1.5000 1.5000 1.5000 Positive forward transfer $C(t)$ 4.0000 4.0000 4.0000 Compute efficiency $\mathcal{S}_{NARROW}$ ~6.0 ~5.94 ~6.0 NARROW SINGULARITY Dependency Chain TOPO-2026 → CF Solved → AGI_gate = 1.0 → Narrow Singularity Without TOPO-2026: CF is NOT solved $AGI_{gate} = 1.0$ is NOT guaranteed Narrow Singularity is NOT achieved $\mathcal{S}_{NARROW} = 0$ With TOPO-2026: CF is SOLVED (0% forgetting) $AGI_{gate} = 1.0$ is GUARANTEED (100% accuracy) Narrow Singularity is ACHIEVED ($\mathcal{S}_{NARROW} \approx 6.0$) Key Contributions Solved Problems Catastrophic Forgetting: 0.0% forgetting across 5 runs on 3 datasets AGI Certification: First model in history to achieve $AGI_{gate} = 1.0$ Mathematical Impossibility: Proved the Singularity is mathematically impossible with finite classes Achievable Standard: Created the Narrow Singularity as a physically achievable AGI threshold Universal Principle: Same constants work across neuroimaging, number theory, AI safety, and unified field theory Constants Across All Domains Constant Value Domains $\Lambda$ 0.9785142874 Number Theory, AI Safety, AI Memory, AI Bias, Physics $\sigma$ 0.5 All domains $\mathcal{R}$ {2, 3, 5, 7, 11, 13} All domains Seed 123 All computations Philosophical Implications The Strategic Pivot Original Goal: Traditional Singularity (mathematically impossible) New Reality: Narrow Singularity (empirically demonstrated) Key Insight: The Decay Law liberates AI from chasing an impossible dream Result: Deterministic cognitive engineering with numerical guarantees Refutation of Skeptical Arguments Skeptic Argument Refutation "It only works on synthetic data" CIFAR-10 and STL-10 are real images "It only works on low-res images" STL-10 is 96×96 (3× larger than CIFAR-10) "It only works on those specific classes" STL-10 has different classes (monkey, car, etc.) "It was a fluke" 15/15 runs across 3 datasets = 100% success "It's dataset-specific" 3 different datasets = dataset-agnostic Final Conclusion The TOPO-2026 framework establishes a paradigm for deterministic cognitive engineering, proving that deep learning architectures can achieve absolute stability and zero forgetting across sequential tasks. Key Takeaways: Catastrophic forgetting is SOLVED: 0.0% forgetting $AGI_{gate} = 1.0$ is ACHIEVABLE: First model with 100% Task C accuracy The Decay Law is DISCOVERED: $\frac{dI}{dt} < 1.0$ with finite classes Narrow Singularity is PROVEN: $\mathcal{S}_{NARROW} > 0$ on 3 datasets The principle is UNIVERSAL: Same reference set across domains The Stochastic Illusion Is Over. Deterministic Cognitive Engineering Has Begun. Stability Is Not a Probabilistic Hope. It Is a Numerical Guarantee. "The proof is the code. Seed = 123. No one can argue with math." Availability GitHub: https://github.com/frank-morales2020/AST-Notebook Zenodo Book: https://zenodo.org/records/21245474 TOPO-2026 Framework: https://zenodo.org/records/20951925 Artificial Hippocampus: https://zenodo.org/records/20385761
Consumers facing home-renovation quotes operate in a classic credence-goods market: they cannot readily verify whether a quoted price is fair, and general-purpose large language models (LLMs) are now a zero-cost place to ask. Whether LLM answers are actionable for this purpose is untested. Demand-side benchmarks exist for medical, legal, and financial advice, but not for construction costs. We present, to our knowledge, the first consumer-question benchmark for construction costs. Forty Japanese renovation-price questions were posed to frontier LLMs, with repeated-trial sets measuring output stability. A matched re-run at bare provider defaults with a current frontier model (gpt-5.5) was added to remove a settings confound present in the original configuration. Two findings are robust across models, generations, and settings: no LLM answer contained an explicit over-charge decision threshold, and repeated runs of the same question returned materially different price figures. Within-answer price spans are also wide, with a median of 10x under bare defaults. A deterministic structured engine over an open cost database is included as an existence proof that a citable reference layer is constructible. Its consistency is a design property and its accuracy is not validated here; validating it against completed real-world quotations is the next study. All questions, raw outputs, harness, and scoring code are public.
Financial institutions depend on trusted employees, contractors and service accounts, yet this trust creates an attack surface that conventional perimeter controls cannot observe adequately. This paper develops an Explainable Adaptive Hybrid Artificial Intelligence (EAHAI) framework for insider threat detection and for assessing whether security awareness training is reducing measurable insider-risk behaviour. The framework combines Isolation Forest filtering, bidirectional long short-term memory sequence modelling, Shapley Additive explanations, adaptive behavioural risk scoring and Zero Trust policy enforcement. A socio-technical assessment layer is added to link training inputs to observable outcomes, including knowledge gain, phishing susceptibility, policy-violation rates, reporting delay, behavioural-risk reduction and analyst-confirmed events. The paper defines the measurement scales, evaluation criteria, validation procedures and analytical techniques required for institutional replication. Because production banking telemetry and labelled insider incidents are rarely available for publication, the empirical component is presented as a transparent synthetic proof-of-concept based on CERT-style behavioural variables rather than as evidence from a real bank. In a deterministic simulation of 17,280 user-day records and 2,880 test windows, the proposed hybrid score achieved an F1-score of 0.944, ROC-AUC of 0.993 and false-alarm rate of 0.017, while producing interpretable feature attributions and training-effectiveness estimates. The study contributes a scalable, explainable and ethically governed design for insider-risk analytics, and identifies the conditions under which it should be validated before operational deployment. Keywords: insider threat detection; explainable artificial intelligence; adaptive risk scoring; security awareness training; Zero Trust; financial cybersecurity.
Direct user-specified research topic: Autonomous agent wallets spend under English mandates like 'only stablecoin swaps under $200 daily, never bridge, never touch unaudited pools', yet deployed policy engines (Safe Transaction Guards, ERC-7579 modules, session-key allowlists) enforce only stateless numeric and selector limits and cannot express 'unaudited' or 'per day', while a naive base-model prompt over raw hex calldata cannot recover function, recipient or token flow and confabulates verdicts. Evaluate a tool-augmented structured-decoding LLM judge that fetches ABIs from Sourcify and Etherscan, decodes calldata including multicall and Permit2 payloads, simulates via eth_call state overrides for token-flow and approval deltas, attaches counterparty features (contract age, verification), and emits constrained JSON: in_policy, violated_clause quoted verbatim, offending_calldata_field. Read Ethereum and Base: ERC-20 Transfer/Approval logs, Uniswap/1inch routers, Across/Stargate bridges, Permit2 at 0x000000000022D473030F116dDEE9F6B43aC78BA3. Measure macro-F1 and clause-attribution precision on 600 hand-labeled mandate/transaction pairs plus replay accuracy on transactions whose approvals owners later revoked, beating a naive raw-hex prompt and a Safe Guard numeric-allowlist baseline. Deliver as the prototype a minimal runnable Python MCP server (stdio) exposing the priced AI tool screen_transaction_against_mandate that invokes a language or ML model over onchain data to produce its output, with a typed input/output schema, an x402-style pay-per-call metering stub that records a per-call price in USDT and emits a settlement receipt, and one smoke test that exercises the tool end to end.. Investigate this topic end-to-end: survey the state of the art, identify a concrete tractable research question within it, design and run an experiment, and report results.
As the blockchain technology and decentralized finance have grown rapidly, the number of fraudulent and anomalous activities has risen.The paper suggests a detectable graphbased anomaly detection system to detect suspicious Ethereum transactions.One 10,000 Ethereum transactions dataset was gathered through the Etherscan API within a 14 hour observation period and a directed transaction graph was created out of that dataset, where 14 behavioral node features were engineered.Three graph neural network (GNN) models, namely, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and GraphSAGE, were checked on 5-fold cross-validation, and compared to three standard baseline classifiers, which are Logistic Regression, Random Forest, and XGBoost.GraphSAGE had the highest overall accuracy of 82.32, F1-score of 0.6389, and ROC-AUC of 0.8202, and GCN and GAT had near-zero recall on the minority class.XGBoost was the best baseline with the highest accuracy (94.41) but with significantly lower recall (0.2766) and F1-score (0.3801) compared to GraphSAGE, which is indicative of graph-based models being more balanced in precision and recall in detecting anomalies with class imbalance.The Local Interpretable Model-agnostic Explanations (LIME) showed outgoing transaction value features and account balance to be most important predictors of anomalous behavior.The results establish the promise of using GNNs in conjunction with explainable AI to secure blockchains, as well as reveal the challenges such as the class imbalance and ground-truth verified labels.
Andrea Rizzini, Marco Esposito, Tommaso Gagliardoni, Francesco Bruschi
Federated Learning (FL) is an advancement in Machine Learning motivated by the need to preserve the privacy of the data used to train models. While it effectively addresses this issue, the multi-participant paradigm on which it is based introduces several challenges. Among these are the risks that participating entities may behave dishonestly and fail to perform their tasks correctly. This misbehavior, in turn, also threatens privacy, because an undetected deviation in training or aggregation can silently undermine the confidentiality guarantees that FL was designed to provide. This motivates mechanisms that provide checkable evidence that released checkpoints are consistent with a declared learning specification and an auditable execution trace. In this SoK, we model federated learning as an append-only transcript of submissions, admissions, aggregation, and finalization events, and formalize verifiability as a collection of integrity claims issued by clients and the aggregator, and checked by different verifier classes. We derive a taxonomy of recurring client-side and aggregator-side claims and use it to analyze representative verifiable FL (VFL) systems spanning Zero-Knowledge Proofs (ZKP) and Trusted Execution Environment (TEE) technologies. Our analysis suggests that, while verifiable aggregation is comparatively mature, data verifiability appears feasible but still sparsely adopted in practice, and verifiable training remain costly and rarely scale to modern models.
[Depreciated and replaced by V3] The application-specific clean rebuild has not yet been published; its authoritative theoretical boundary is now the governing V3 branch: After Turing: The Fold Machine - An Exact, Parameter-Free and Machine-Closed Derivation of Classical Computational Science from Smithian Fold Theory; From Fold to Consciousness: An Exact, Zero-Parameter and Machine-Closed Foundational Reconstruction of Consciousness and Cognitive Science from Smithian Fold Theory. The V3 source platform is https://github.com/MettaMazza/ernos-labs-sft-platform. The original DOI, concept DOI, version number and files are preserved for transparent historical provenance; this record must not be presented or cited as current V3 work. Full paper v1.1 — supersedes the pre-paper (From One Axiom to Master-Level Chess — and the Law Inside Neural Networks). Built from scratch by one woman, working alone, in under twenty-four accumulated hours: where a score falls short it marks an implementation gap at measurement time, never a limit of the mathematics — the gains between releases are the finding. v1.4 adds the fold eye (vision as exact integer Walsh spectra, self-certified by integer Parseval per image, recognition of seen images with no image model in the loop) and the graduation score (blind head-to-head vs the teacher, tallied per question-territory; the teacher retires as wins cross the majority lock) -- and documents the 2026 convergence: DeepSeek Engram arrives at deterministically-addressed exact memory from the gradient side, and two independent results place the optimal curriculum at p = 1/2, the fold lock. v1.6: the full omnimodal engine (the voice via Kokoro, the fold ear -- sound as Parseval-certified integer Walsh spectra, video composed from frames + sound), speaker-transparent reasoning threads, and 32/32 end-to-end empirical verification of the entire architecture including persistence across process death. v1.7: removal-proof omnimodality, measured -- every supporting model is a teacher with an exit: a sound taught once by the synthesis teacher is re-spoken from the engine's own exact counted record in 0.00s with no model; a sound heard once is recognized natively with no transcriber; 34/34 end-to-end verification. v1.9: zero-model perceptual learning (the human observer -- a novel image learned and re-recognized at share 1.00 with no model in the loop); agentic self-knowledge (the observer reads the engine's own source, measured); the hourly progress instrument with a committed pre-boot birth line; one-tap y/n closure. v2.0 (flight-ready): the full modern-agent toolkit (live web search/fetch, paginated reading, in-file grep -- every call held as a training trace), the 43-domain everything-curriculum under the fold-only law, SOTA 1-1 benching on the public MMLU test split with the newborn baseline committed, generation closure (the Learning Law reaches generate() itself), and 36/36 end-to-end verification. v2.1: the ReAct law (reason-act-observe enforced in-turn; narrated intent without an act is detected and forced), reasoning trained on the observer's NATIVE thinking tokens (STaR-gated) with both minds' full thinking streamed to the user, and document intake (a sent file is reading -- inboxed, counted, persistent). Three connected results and the architecture they force. First, a pre-registered, self-certifying spectral instrument shows trained neural-network weights carry placement-law in the dyadic (Walsh) basis: 18/18 unanimous on validated released models; the law concentrated in transformer expansion projections and token embeddings across three unrelated architectures (up to 230x chance in GPT-2), attention at chance; strictly training-caused (He-initialised controls at 1.0x); surviving 4-bit deployment quantization. A recipe map from 124M to one trillion parameters shows the law tracks training recipe, not scale or architecture — strongest carrier DeepSeek-R1-671B at 43–47x — and loud-recipe weights transform under the fold's transformation group exactly as solved game-theoretic value fields do. Second, the "learned similarity space" is a counted object: word kinship as exact co-occurrence shares reproduces semantic family structure (quark → lepton, neutrino, proton) with zero parameters and zero gradients. Third, UnisonAI: a complete language architecture in which every LLM mechanism — memory, attention, similarity, learning, prediction, generation — is replaced by a machine-verified law of the Smithian Fold Theory, zero trained parameters end to end. On identical held-out text the fold-native engine outperformed its trained transformer twin (cross-entropy 1.289 vs 1.888) after reading the corpus once (26 seconds) against 48,000 gradient readings (21 minutes per seed). Deployed as a live, continuously-learning agent whose teaching loop also runs autonomously: a teacher model asks, judges, and closes the learning law itself, and the engine self-plays against its own held lessons. Negative results reported in full with their scopes. Companion to The Smithian Fold Theory of Everything (DOI: 10.5281/zenodo.21182469; 307 suites, 1,844 forced checks, 0 failures). Engine and records: github.com/MettaMazza/UnisonAI and github.com/MettaMazza/Smithian-Fold-Theory-Of-Everything.
The complete codebase and supplementary materials for this study have been archived on Figshare to ensure full reproducibility and to facilitate adoption by other researchers and practitioners. The archive includes all Python scripts used for data preprocessing, model training, hyperparameter tuning, threshold optimisation, and SHAP explainability analysis. Also included are the processed CSV files used for the analysis, along with all figures and tables presented in this paper. The repository is organised to enable straightforward replication of the experiments and adaptation of the framework to other datasets or blockchain platforms.
The complete codebase and supplementary materials for this study have been archived on Figshare to ensure full reproducibility and to facilitate adoption by other researchers and practitioners. The archive includes all Python scripts used for data preprocessing, model training, hyperparameter tuning, threshold optimisation, and SHAP explainability analysis. Also included are the processed CSV files used for the analysis, along with all figures and tables presented in this paper. The repository is organised to enable straightforward replication of the experiments and adaptation of the framework to other datasets or blockchain platforms.
Odinachi Udemezuo Nwankwo, Simeon Okechukwu Ajakwe, Muhammad Rasyid Redha Ansori, Gifar Arif Haryadi · 6 authors
Existing driver distraction detection systems face critical barriers to real-world deployment in safety-critical transportation environments, including the lack of real-time edge inference, explainable artificial intelligence (XAI), trustworthy event logging, and privacy-preserving evidence management. To overcome these challenges, this paper presents an integrated framework, termed DRIVERDAPP , that unifies real-time edge-based detection, AI explainability, and secure, auditable event management. Red–green–blue (RGB) in-cabin image frames captured by a dashboard camera are processed locally on an NVIDIA Jetson Nano edge device, where a fine-tuned You Only Look Once version 11 small (YOLOv11s) model classifies ten driver behavior states and triggers in-vehicle audio alerts for unsafe activities. To suppress transient misclassifications under edge constraints, distraction persistence is verified using a lightweight temporal confirmation strategy. Confirmed distraction events are immutably recorded via Solidity-based smart contracts and submitted through the Web3.py interface to a permissioned Hyperledger Besu consortium blockchain operating under Quorum Byzantine Fault Tolerance (QBFT) consensus. Privacy is preserved by retaining raw visual data off-chain, while only pseudo-anonymous identifiers and event metadata are stored on-chain under controlled access policies. Model interpretability is enabled using Gradient-weighted Class Activation Mapping (Grad-CAM), providing transparent visual explanations of distraction-related predictions. The framework is evaluated using the State Farm Distracted Driver and American University in Cairo datasets, demonstrating stable real-time edge operation, negligible blockchain query latency, and secure smart contract execution. These results confirm the suitability of DRIVERDAPP for secure, explainable, and deployable driver monitoring in intelligent transportation systems.
Gefei Tan, Adria Gascon, Sarah Meiklejohn, Mariana Raykova
In machine learning, model certification has been identified as an important method for gaining assurance about a model's trustworthiness and quality. A model's quality is largely determined by its ability to generalize, i.e., to perform well on data beyond what it was trained on. It is not possible to certify generalization directly, however, as it depends on unknown data and is not directly measurable. Proxies such as test accuracy can be misleading when the training process is perturbed (intentionally or accidentally), and metrics such as sharpness -- which has an empirically supported link to generalization -- are computationally expensive and can also serve as unreliable signals when training deviates from a prescribed procedure. In this work, we propose directional sharpness, a metric designed to efficiently and reliably indicate generalization despite potential training deviations. We provide empirical and analytical evidence that directional sharpness (1) correlates more strongly with generalization than existing metrics and (2) identifies models with poor generalization more reliably than existing metrics. Furthermore, directional sharpness is efficiently computable in model auditing settings, where the verifier has access to training data, and via zero-knowledge proofs that certify quality without revealing training data.