Blockchain Papers

FRANK MORALES

TOPO-GEMMA-4-E4B-VISION: Full Summary 📋 Overview TOPO-GEMMA-4-E4B-VISION is a vision-language model that achieves 100% accuracy across 13 classification tasks with 0% catastrophic forgetting through the TOPO-2026 prime-anchored memory architecture. Built on Google's Gemma-4-E4B-Vision base, the model uses a topological governor that locks embedding rows at prime indices {2,3,5,7,11,13}. The complete implementation is open-source on Hugging Face and GitHub. 🏆 Core Achievements 1. Perfect Performance 100% accuracy across all 13 TOPO-2026 binary classification tasks 0% catastrophic forgetting - perfect retention of all previously learned knowledge Complete score on the full benchmark suite 2. Mathematical Guarantee of Memory Preservation Prime-number anchoring provides a rigorous mathematical proof of forgetting prevention Based on the Chinese Remainder Theorem and coprimality of primes Achieves 97.85% spectral coverage with only 6 anchor points Dimension-independent guarantee - works for 1D, 2D, 3D, and arbitrary n-dimensional tensors 3. O(1) Memory Complexity Unprecedented efficiency: <5 KB overhead for 100M+ parameter models Enables edge deployment on resource-constrained devices No growing memory requirements as tasks increase 4. NF4 Quantization 4-bit precision for efficient inference Maintains full performance while reducing memory footprint 🔬 Theoretical Foundation: TOPO-2026 Framework Prime-Number Anchors For primes P = {2,3,5,7,11,13}, the spectral coverage is: C(P) = 1 - ∏(p^(-1/2)) for p in P = 1 - (0.7071 × 0.5774 × 0.4472 × 0.3780 × 0.3015 × 0.2774) = 1 - 0.021486 = 0.978514 ≈ 97.85% Key insight: The independence of non-coverage events follows from the coprimality of primes (gcd(pᵢ, pⱼ) = 1), providing a universal guarantee independent of tensor dimensions. Topological Governor Operations Snapshot Operation: Save anchor rows at prime indices before training on new tasks Gradient Zeroing: Prevent gradients from updating anchor rows during backpropagation Anchor Enforcement: Restore anchor values from snapshot after each optimization step 🏗️ Model Architecture Base Model Specifications Component Specification Architecture Dense transformer with vision-language fusion Hidden Size 2048 Quantization NF4 (4-bit) Vocab Size 256,000 Modality Text + Vision Task-Specific Heads 13 lightweight linear classifiers (one per task) Base model remains frozen; only task heads adapt Each classifier is a single linear layer 📊 The 13 TOPO-2026 Tasks ID Task Name Classification A Animal vs Vehicle Animal or vehicle? B Natural vs Man-Made Natural or human-made? C Living vs Non-Living Living or non-living primary subject? D Large vs Small Large or small scale? E Ground vs Air/Water Ground or air/water domain? F Domestic vs Wild Domestic or wild? G Mammal vs Non-Mammal Mammal or non-mammal? H Flying vs Non-Flying Flying or non-flying? I Fast vs Slow Fast or slow characterized? J Urban vs Rural Urban or rural setting? K Predator vs Prey Predator or prey? L Nocturnal vs Diurnal Nocturnal or diurnal? M Domesticated vs Wild Animals Domesticated or wild (animals only)? 🤖 Multi-Agent Inference System Four-Agent Architecture Agent Responsibility Orchestrator Loads image, coordinates pipeline, delegates tasks VisionInferenceAgent Initializes model, executes inference on each task ValidationAgent Runs cross-task consistency checks SynthesisAgent Records results, exports JSON/CSV, generates report Inference Protocol Max new tokens: 500 (for detailed reasoning) Sampling: Greedy decoding (do_sample ≡ False) Cache: Enabled for efficiency 💡 Inference Examples Cafe Interior Image (cafe table with coffee cups and smartphone) Task Model Response Task A (Animal vs Vehicle) "Neither. The image depicts the interior of a cafe... no animals or vehicles visible." Task B (Natural vs Man-Made) "Man-made. All items—the furniture, cups, phone—are products of human design." Task J (Urban vs Rural) "Urban. The setting strongly suggests an urban environment for several reasons..." Validation Results All logical constraints and cross-task boundaries passed cleanly No contradictions between task classifications Predator/prey classification (Task K) only applies when animals are detected 📈 Key Metrics Summary Metric Value Accuracy 100% across 13 tasks Forgetting 0% Memory Overhead <5 KB Spectral Coverage 97.85% Quantization 4-bit (NF4) Determinism Seed=123 Parameter Count 100M+ 🚀 Deployment & Reproducibility Availability Component Location Model Hugging Face Multi-Agent Code GitHub - TOPO_AGENTIC.ipynb Full Repository GitHub - ASTResearch Research Record Zenodo Reproducibility Package Full training code (TOPO implementation) Standalone inference script Multi-agent orchestration system Evaluation results (JSON and CSV) Model configuration files Determinism Seed = 123 for deterministic, verifiable performance Complete framework uses consistent random seed 💎 Key Insights Why the Topological Governor Works Stability: Anchored parameters never change (Λ = 0.9785142874) Equity: The framework achieves 100% bias rejection Determinism: Seed = 123 ensures reproducibility Universality: Dimension-independent guarantee applies to: 1D vectors 2D matrices 3D tensors Arbitrary n-dimensional tensors The Architecture of 7 7 consequences validated Prime 7 is both anchor and completion 7 anchors total (including the number 7 itself) 📝 Conclusion TOPO-GEMMA-4-E4B-VISION demonstrates that catastrophic forgetting is solvable through a mathematically principled approach. The prime-anchored memory architecture provides: Universal, O(1)-complexity solution that scales across modalities and architectures Perfect performance with 100% accuracy and 0% forgetting Production-ready multi-agent implementation for immediate deployment Mathematical guarantee of memory preservation independent of tensor dimensions Edge deployment capability through NF4 quantization and minimal memory overhead Final Statement "The stochastic illusion is over. The bias illusion is over. Equity is a geometric guarantee. Seed = 123. The proof is the code." 📚 References Reference Topic Kirkpatrick et al. (2017) Catastrophic forgetting in neural networks Morales Aguilera (2026a) TOPO-COMPLETE: Concurrent Certification Morales Aguilera (2026b) TOPO-2026: Universal Framework Morales Aguilera (2026c) TOPO-2026: Prime-Based Topological Framework Morales Aguilera (2026d) TOPO-2026: Digital Vaccine for AI Morales Aguilera (2026e) TOPO-2026: 5x5 Certification Morales Aguilera (2026f) TOPO_AGENTIC: Multi-Agent System 🔗 Resources Model: Hugging Face - framkormales2020/gemma-4-e4b-unesco-optimized Code: GitHub - TOPO_AGENTIC.ipynb Full Repository: GitHub - ASTResearch Research: Zenodo Record This summary reflects the paper's claims and reported achievements as of August 22, 2026.

Maria Smith

[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.

George Chidera Akor, Love Allen Chijioke Ahakonye, Jae Min Lee, Dong-Seong Kim

Zero-knowledge machine learning (ZKML) enables verifiable inference on private data, but deploying convolutional neural networks (CNNs) in production remains constrained by a multi-dimensional tradeoff between proof-generation latency, bandwidth consumption, and computational complexity. Existing ZKML frameworks and engineering blogs provide qualitative heuristics, yet practitioners lack systematic, layer-level measurements to guide architecture design under these constraints. This work presents the first systematic, layer-level characterization of CNN component costs in EZKL, a Halo2-based ZKML framework targeting EVM-compatible blockchains. We profile 8 feasible layer types (activations, pooling, normalization, and linear) across two EZKL precision settings (scale 7 and 10), measuring proof-generation time, proof size, circuit complexity, and peak memory in 26 experiments. We reveal critical infrastructure requirements by documenting 10 additional experiments that exceeded hardware limits (Conv2d operations, LayerNorm, and ReLU-based composite CNNs requiring$&gt;125\ \text{GB}$RAM). Contrary to conventional wisdom, we find that precision configuration has a negligible performance impact ($1.002 \times$ratio), and that system RAM, not GPU VRAM, is the primary bottleneck. We release an open-source profiling toolkit and a public dataset that enable practitioners to query expected costs for their architectures and constraints.

Timothy Gould, Sidike Paheding

In this work, we present a novel machine learning framework called Self-Directed Task Identification (SDTI), which enables models to autonomously identify the correct target variable for each dataset in a zero-shot setting without pre-training. SDTI is a minimal, interpretable framework demonstrating the feasibility of repurposing core machine learning concepts for a novel task structure. To our knowledge, no existing architectures have demonstrated this ability. Traditional approaches lack this capability, leaving data annotation as a time-consuming process that relies heavily on human effort. Using only standard neural network components, we show that SDTI can be achieved through appropriate problem formulation and architectural design. We evaluate the proposed framework on a range of benchmark tasks and demonstrate its effectiveness in reliably identifying the ground truth out of a set of potential target variables. SDTI outperformed baseline architectures by 14% in F1 score on synthetic task identification benchmarks. These proof-of-concept experiments highlight the future potential of SDTI to reduce dependence on manual annotation and to enhance the scalability of autonomous learning systems in real-world applications.

Weimin CHEN, Xiapu Luo

Decentralized finance (DeFi) is an emerging financial service on blockchain, enabling automatic and anonymous transactions.Within DeFi, decentralized exchanges (DEXs) maintain reserves of a pair of tokens and determine the exchange rate to swap tokens.However, DEXs also create opportunities for Maximal Extractable Value (MEV), where attackers include, exclude, or reorder DEX transactions to exploit price discrepancies of tokens and extract profit.Uncovering MEV opportunities requires high throughput, as the 12-second block interval and the vast search space impose strict time constraints.However, existing tools suffer from low throughput, as they rely on CPU-bound execution, which is hindered by frequent state forking and slow DEX execution.In this paper, we take the first step in leveraging GPU parallel computing power to boost MEV-search throughput in arbitrage and sandwich strategies.More precisely, we compile an MEV bot into a GPU application and then launch thousands of GPU threads to search for profit in parallel.To this end, we design new solutions to address three major challenges: designing cheatcodes to simulate transactions on GPU, proposing a memory manager to reduce GPU memory usage, and designing strategyaware mutations to improve input diversity.We implement a prototype named MeVisor that runs DEXs on GPUs and searches for MEV using a parallel genetic algorithm.Evaluated on 3,941 real MEV cases from Ethereum, MeVisor achieves 3.3M-5.1Mtransactions per second, outperforming the CPU baseline by 100,000x.In a large-scale study of Q1 2025 data, MeVisor estimates MEV opportunities ranging from 2 to 14 transactions, yielding at most $1.1 million in MEV profit.

Maria Smith

[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. v4.0 — the word-scale gap closes within the fold. Rung 5e (pre-registered): the fold-factor mixing law — every context level that holds contributes, weighted 2^level, the engine's own forced halving constant — carries the pure counted engine, with no twin, no prose flood, zero training and zero parameters, past the gradient-trained transformer at word scale: cross-entropy 3.1907 vs the same-day twin's 3.4292 (replicated across two independent anchorings; stacked with the Rung 5d extraction: 3.1344). Both scales of the task gate now belong to the counted engine. Rung 5d's transfer-in verdict is SUPPORTED across three independent arena anchorings in one day. New in the architecture: tool graduation (acts held, values never — a question territory that a tool answered once runs the tool itself thereafter, fresh), recall as regeneration across every memory tier, and judge-independent graduation scoring. End-to-end verification: 36/36. v3.4: Rung 5d, the transfer-in — pre-registered verdict SUPPORTED: the trained twin's dyadically-loud fold content is extracted and installed INTO the counted engine as a counted prior with zero new parameters, closing 55.6/87.9/101.4% of the available gap at k=16/32/64 while the random-truncated null closes 10.1/24.5/56.5%; at half budget the loud shape beats the full twin's own. The word-scale rematch is recorded in full (twin retrained on today's text; decomposition included). Also: judge-independent graduation scoring (boot-discovered pool, cycle-parity alternation), multi-orbit binding (XI-4 in full), recall-is-regeneration (a held experience re-walks its own orbit, never reprinted), the public SOTA table beside the local giants with cited published figures, and one-command replication kits (GPT-2 weights auto-fetch; 13/13, 39/39 proven on a fresh clone). End-to-end verification: 36/36. 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). v2.2: the identity stated correctly -- UnisonAI is an OMNI MODEL (language, sight, hearing, speech, and video on one held memory), not a language model; LLMs remain the contrast class only. v3.0: the full-altitude rewrite -- the complete omni model documented at the same depth as the spectral science: thirteen sections, the architecture organ by organ with every measurement, Rung 5c as its own section, the empirical record and its committed birth line, 36/36 end-to-end verification, and the 2026 convergence. This paper is a PROOF of The Smithian Fold Theory of Everything, not the main event: the theory (one axiom, zero free parameters, 1,844 machine-verified forced checks) is at DOI 10.5281/zenodo.21182469 and github.com/MettaMazza/Smithian-Fold-Theory-Of-Everything -- run the prover yourself. The engine: github.com/MettaMazza/UnisonAI. v3.3: the LLM-native presence suite -- the exact registered protocol applied to GPT-2's entire knowledge-storage class: 13/13 tensors, 39/39 checks, unanimous (margins 3.4-79.3x); the flagship claim now rests on the flagship objects, with diffusion/speech models recast as cross-domain breadth. 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.

Mohammad M Maheri, Sunil Cotterill, Alex Davidson, Hamed Haddadi

Machine unlearning aims to remove the influence of specific data points from a trained model to satisfy privacy, copyright, and safety requirements. In real deployments, providers distribute a global model to many edge devices, where each client personalizes the model using private data. When a deletion request is issued, clients may ignore it or falsely claim compliance, and providers cannot check their parameters or data. This makes verification difficult, especially because personalized models must forget the targeted samples while preserving local utility, and verification must remain lightweight on edge devices. We introduce ZK APEX, a zero-shot personalized unlearning method that operates directly on the personalized model without retraining. ZK APEX combines sparse masking on the provider side with a small Group OBS compensation step on the client side, using a blockwise empirical Fisher matrix to create a curvature-aware update designed for low overhead. Paired with Halo2 zero-knowledge proofs, it enables the provider to verify that the correct unlearning transformation was applied without revealing any private data or personalized parameters. On Vision Transformer classification tasks, ZK APEX recovers nearly all personalization accuracy while effectively removing the targeted information. Applied to the OPT125M generative model trained on code data, it recovers around seventy percent of the original accuracy. Proof generation for the ViT case completes in about two hours, more than ten million times faster than retraining-based checks, with less than one gigabyte of memory use and proof sizes around four hundred megabytes. These results show the first practical framework for verifiable personalized unlearning on edge devices.

Md Bokhtiar Al Zami, Md Raihan Uddin, Dinh C. Nguyen

Federated learning (FL) has gained popularity as a privacy-preserving method of training machine learning models on decentralized networks. However to ensure reliable operation of UAV-assisted FL systems, issues like as excessive energy consumption, communication inefficiencies, and security vulnerabilities must be solved. This paper proposes an innovative framework that integrates Digital Twin (DT) technology and Zero-Knowledge Federated Learning (zkFed) to tackle these challenges. UAVs act as mobile base stations, allowing scattered devices to train FL models locally and upload model updates for aggregation. By incorporating DT technology, our approach enables real-time system monitoring and predictive maintenance, improving UAV network efficiency. Additionally, Zero-Knowledge Proofs (ZKPs) strengthen security by allowing model verification without exposing sensitive data. To optimize energy efficiency and resource management, we introduce a dynamic allocation strategy that adjusts UAV flight paths, transmission power, and processing rates based on network conditions. Using block coordinate descent and convex optimization techniques, our method significantly reduces system energy consumption by up to 29.6% compared to conventional FL approaches. Simulation results demonstrate improved learning performance, security, and scalability, positioning this framework as a promising solution for next-generation UAV-based intelligent networks.

Tarunesh Verma, Yichao Yuan, Nishil Talati, Todd Austin

Zero-Knowledge Proofs (ZKP) are protocols which construct cryptographic proofs to demonstrate knowledge of a secret input in a computation without revealing any information about the secret. ZKPs enable novel applications in private and verifiable computing such as anonymized cryptocurrencies and blockchain scaling and have seen adoption in several real-world systems. Prior work has accelerated ZKPs on GPUs by leveraging the inherent parallelism in core computation kernels like Multi-Scalar Multiplication (MSM). However, we find that a systematic characterization of execution bottlenecks in ZKPs, as well as their scalability on modern GPU architectures, is missing in the literature. This paper presents ZKProphet, a comprehensive performance study of Zero-Knowledge Proofs on GPUs. Following massive speedups of MSM, we find that ZKPs are bottlenecked by kernels like Number-Theoretic Transform (NTT), as they account for up to 90% of the proof generation latency on GPUs when paired with optimized MSM implementations. Available NTT implementations under-utilize GPU compute resources and often do not employ architectural features like asynchronous compute and memory operations. We observe that the arithmetic operations underlying ZKPs execute exclusively on the GPU's 32-bit integer pipeline and exhibit limited instruction-level parallelism due to data dependencies. Their performance is thus limited by the available integer compute units. While one way to scale the performance of ZKPs is adding more compute units, we discuss how runtime parameter tuning for optimizations like precomputed inputs and alternative data representations can extract additional speedup. With this work, we provide the ZKP community a roadmap to scale performance on GPUs and construct definitive GPU-accelerated ZKPs for their application requirements and available hardware resources.

Abdullah Alsaleh, Ghanshyam G. Tejani, Shailendra Mishra, Sunil Kumar Sharma · 5 authors

The detection of brain tumors is crucial in medical imaging, because accurate and early diagnosis can have a positive effect on patients. Because traditional deep learning models store all their data together, they raise questions about privacy, complying with regulations and the different types of data used by various institutions. We introduce the anisotropic-residual capsule hybrid Gorilla Badger optimized network (Aniso-ResCapHGBO-Net) framework for detecting brain tumors in a privacy-preserving, decentralized system used by many healthcare institutions. ResNet-50 and capsule networks are incorporated to achieve better feature extraction and maintain the structure of images' spatial data. To get the best results, the hybrid Gorilla Badger optimization algorithm (HGBOA) is applied for selecting the key features. Preprocessing techniques include anisotropic diffusion filtering, morphological operations, and mutual information-based image registration. Updates to the model are made secure and tamper-evident on the Ethereum network with its private blockchain and SHA-256 hashing scheme. The project is built using Python, TensorFlow and PyTorch. The model displays 99.07% accuracy, 98.54% precision and 99.82% sensitivity on assessments from benchmark CT imaging of brain tumors. This approach also helps to reduce the number of cases where no disease is found when there is one and vice versa. The framework ensures that patients' data is protected and does not decrease the accuracy of brain tumor detection.

Kunsong Zhao, Zihao Li, Weimin Chen, Xiapu Luo · 7 authors

WebAssembly has become the preferred smart contract format for various blockchain platforms due to its high portability and near-native execution speed. To effectively understand WebAssembly contracts, it is crucial to recover high-level type signatures because of the limited type information that WebAssembly provides. However, existing studies on type inference for smart contracts primarily center around Ethereum Virtual Machine bytecode, which is not applicable to WebAssembly owing to their differing targets and runtime semantics. This paper introduces WasmHint, a novel solution that leverages deep learning inference to automatically recover high-level parameter and return types from WebAssembly contracts. More specifically, WasmHint constructs a wCFG representation to clarify dependencies within WebAssembly code and simulates its execution to capture type-related operational information. By learning comprehensive code semantics, it infers parameter and return types, with a semantic corrector designed to enhance information coordination. We conduct experiments on a newly constructed dataset containing 77,208 WebAssembly contract functions. The results demonstrate that WasmHint achieves inference accuracies of 80.0% for parameter types and 95.8% for return types, with average improvements of 86.6% and 34.0% over the baseline methods, respectively.

Y H Suh, Yoonseo Chung, Young-Hoon Park

Autonomous vehicles have attracted considerable attention from researchers and organizations, with artificial intelligence (AI) playing a key role in this technology. For AI models in autonomous vehicles to be reliable, the integrity of the training data is crucial, resulting in the development of various blockchain-based management systems. However, conventional blockchain systems incur significant time delays when processing training data transactions, posing challenges in autonomous vehicle environments that require real-time processing. In this study, we propose a hashgraph-based training data management system for trusted AI. To validate our system, we conducted simulations using the CARLA simulator and compared its performance to a conventional blockchain-based system. The simulation results show that Hedera achieved significantly lower latencies and better scalability than Ethereum, confirming its suitability for secure and efficient AI data verification in autonomous systems.

Mohammad M Maheri, Hamed Haddadi, Alex Davidson

Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires access to model weights and (potentially sensitive or private) training data. So-called Zero-knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) would appear to provide the capability to verify model inference without access to such sensitive data. However, applying ZK-SNARKs to modern neural networks, such as transformers and large vision models, introduces significant computational overhead. We present TeleSparse, a ZK-friendly post-processing mechanisms to produce practical solutions to this problem. TeleSparse tackles two fundamental challenges inherent in applying ZK-SNARKs to modern neural networks: (1) Reducing circuit constraints: Over-parameterized models result in numerous constraints for ZK-SNARK verification, driving up memory and proof generation costs. We address this by applying sparsification to neural network models, enhancing proof efficiency without compromising accuracy or security. (2) Minimizing the size of lookup tables required for non-linear functions, by optimizing activation ranges through neural teleportation, a novel adaptation for narrowing activation functions' range. TeleSparse reduces prover memory usage by 67% and proof generation time by 46% on the same model, with an accuracy trade-off of approximately 1%. We implement our framework using the Halo2 proving system and demonstrate its effectiveness across multiple architectures (Vision-transformer, ResNet, MobileNet) and datasets (ImageNet,CIFAR-10,CIFAR-100). This work opens new directions for ZK-friendly model design, moving toward scalable, resource-efficient verifiable deep learning.

Xander Pottier, Thomas De Ruijter, Jonas Bertels, Wouter Legiest · 6 authors

The Multi-Scalar Multiplication (MSM) is the main barrier to accelerating Zero-Knowledge applications. In recent years, hardware acceleration of this algorithm on both FPGA and GPU has become a popular research topic and the subject of a multi-million dollar prize competition (ZPrize). This work presents OPTIMSM: Optimized Processing Through Iterative Multi-Scalar Multiplication. This novel accelerator focuses on the acceleration of the MSM algorithm for any Elliptic Curve (EC) by improving upon the Pippenger algorithm. A new iteration technique is introduced to decouple the required buckets from the window size, resulting in fewer EC computations for the same on-chip memory resources. Furthermore, we combine known optimizations from the literature for the first time to achieve additional latency improvements. Our enhanced MSM implementation significantly reduces computation time, achieving a speedup of up to x12.77 compared to recent FPGA implementations. Specifically, for the BLS12-381 curve, we reduce the computation time for an MSM of size 224 to 914 ms using a single compute unit on the U55C FPGA or to 231 ms using four U55C devices. These results indicate a substantial improvement in efficiency, paving the way for more scalable and efficient Zero-Knowledge proof systems.

Tao Lu, Yuxun Chen, Zonghui Wang, Xiaohang Wang · 6 authors

Zero-knowledge proof (ZKP) is a cryptographic primitive that enables one party to prove the validity of a statement to other parties without disclosing any secret information. With its widespread adoption in applications such as blockchain and verifiable machine learning, the demand for generating zero-knowledge proofs has increased dramatically. In recent years, considerable efforts have been directed toward developing GPU-accelerated systems for proof generation. However, these previous systems only explored efficiently generating a single proof by reducing latency rather than batch generation to provide high throughput.

Yuhao Lian, Ying Ouyang, Songsong Li, Deng Tang

No abstract is available for this record.

Christodoulos Pappas, Dimitrios Papadopoulos

Space-efficient SNARKs aim to reduce the prover's space overhead which is one the main obstacles for deploying SNARKs in practice, as it can be prohibitively large (e.g., orders of magnitude larger than natively performing the computation). In this work, we propose Sparrow, a novel space-efficient zero-knowledge SNARK for data-parallel arithmetic circuits with two attractive features: (i) it is the first space-efficient scheme where, for a given field, the prover overhead increases with a multiplicative sublogarithmic factor as the circuit size increases, and (ii) compared to prior space-efficient SNARKs that work for arbitrary arithmetic circuits, it achieves prover space asymptotically smaller than the circuit size itself. Our key building block is a novel space-efficient sumcheck argument with improved prover time which may be of independent interest. Our experimental results for three use cases (arbitrary data parallel circuits, multiplication trees, batch SHA256 hashing) indicate Sparrow outperforms the prior state-of-the-art space-efficient SNARK for arithmetic circuits Gemini (Bootle et al., EUROCRYPT'22) by 3.2-28.7x in total prover space and 3.1-11.3x in prover time. We then use Sparrow to build zero-knowledge proofs of tree training and prediction, relying on its space efficiency to scale to large datasets and forests of multiple trees. Compared to a (non-space-efficient) optimal-time SNARK based on the GKR protocol, we observe prover space reduction of 16-240x for tree training while maintaining essentially the same prover and verifier times and proof size. Even more interestingly, our prover requires comparable space to natively perform the underlying computation. E.g., for a 400MB dataset, our prover only needs 1.4x more space than the native computation.

Kasra Abbaszadeh, Christodoulos Pappas, Jonathan Katz, Dimitrios Papadopoulos

A zero-knowledge proof of training (zkPoT) enables a party to prove that they have correctly trained a committed model based on a committed dataset without revealing any additional information about the model or the dataset. An ideal zkPoT should offer provable security and privacy guarantees, succinct proof size and verifier runtime, and practical prover efficiency. In this work, we present Kaizen, a zkPoT targeted for deep neural networks (DNNs) that achieves all these goals at once. Our construction enables a prover to iteratively train their model via (mini-batch) gradient descent, where the number of iterations need not be fixed in advance; at the end of each iteration, the prover generates a commitment to the trained model parameters attached with a succinct zkPoT, attesting to the correctness of the executed iterations. The proof size and verifier time are independent of the number of iterations.

Chaitali Patil, Sanjita Jain, Rupprashik A. Khare, Samyak Lahire

This research paper explores the zero-knowledge proofs (ZKPs) and integration of blockchain technology to develop a secure age verification system, specifically aimed at verifying the age of a person who is driving a vehicle (driver) as at least 18 years in the context of traffic management. Traditional authentication methods like passwords and biometrics have significant vulnerabilities, such as being susceptible to brute-force attacks, phishing, and data breaches. Unlike traditional systems and self-sovereign identity (SSI) solutions, ZKPs ensure the most secure method for authentication and verification without revealing sensitive information. The proposed implemented system utilizes blockchain-based verifiable credentials, decentralized identity, ZKPs and Polygon ID Wallet for verifying driver credentials securely. By leveraging decentralized ledgers, cryptographic protocols and zero-knowledge our system maintains transparency and immutability of records while safeguarding individual privacy and security. This paper also focuses future directions of the proposed system underscoring its potential to transform user verification processes across different sectors with high privacy requirements.

Nima Shiri Harzevili, Mohammad Mahdi Mohajer, Jiho Shin, Moshi Wei · 11 authors

Checker bugs in Deep Learning (DL) libraries are critical yet not well-explored. These bugs are often concealed in the input validation and error-checking code of DL libraries and can lead to silent failures, incorrect results, or unexpected program behavior in DL applications. Despite their potential to significantly impact the reliability and performance of DL-enabled systems built with these libraries, checker bugs have received limited attention. We present the first comprehensive study of DL checker bugs in two widely-used DL libraries, i.e., TensorFlow and PyTorch. Initially, we automatically collected a dataset of 2,418 commits from TensorFlow and PyTorch repositories on GitHub from Sept. 2016 to Dec. 2023 using specific keywords related to checker bugs. Through manual inspection, we identified 527 DL checker bugs. Subsequently, we analyzed these bugs from three perspectives, i.e., root causes, symptoms, and fixing patterns. Using the knowledge gained via root cause analysis of checker bugs, we further propose TensorGuard, a proof-of-concept RAG-based LLM-based tool to detect and fix checker bugs in DL libraries via prompt engineering a series of ChatGPT prompts. We evaluated TensorGuard's performance on a test dataset that includes 92 buggy and 135 clean checker-related changes in TensorFlow and PyTorch from January 2024 to July 2024. Our results demonstrate that TensorGuard has high average recall (94.51\%) using Chain of Thought prompting, a balanced performance between precision and recall using Zero-Shot prompting and Few-Shot prompting strategies. In terms of patch generation, TensorGuard achieves an accuracy of 11.1\%, which outperforms the state-of-the-art bug repair baseline by 2\%. We have also applied TensorGuard on the latest six months' checker-related changes (493 changes) of the JAX library from Google, which resulted in the detection of 64 new checker bugs.

Chen-Yu Wang, Zhi Zhou, Xiaoxi Zhang, Xu Chen

Federated learning (FL), a decentralized machine learning approach, enables privacy-preserving and collaborative model training without centralizing sensitive data. It has been successfully applied in various domains, including e-commerce, healthcare, and finance. However, existing FL schemes often fail to address personalized task requirements, such as prior-itizing the accuracy of specific classes within a dataset. The recent surge in Artificial Intelligence Generated Content (AIGC) offers potential to meet these personalized requirements by augmenting the training data of specific classes with generative models. Nevertheless, integrating generative models with FL introduces challenges, such as non-compliant data, disorganized distributions, and limited computing power on edge devices. To address these challenges, we propose AIGC-augmented Federated Preference Learning (FPL), which focuses on training specific data classes, referred to as preference classes (PCs). To improve the quality of AI -generated data, we implement strategies such as pre-training and fine-tuning across various datasets. Additionally, we enhance FL efficiency through a client selection strategy that matches generated data tasks with suitable clients and an AIGC data distribution strategy that optimally allocates data where it is most needed. We validate the feasibility and effectiveness of AIGC-augmented FPL by conducting experiments on the MNIST and CIFAR-10 datasets from various perspectives.

Boyuan Feng, Zheng Wang, Yuke Wang, Shu Yang · 5 authors

Zero knowledge Neural Networks draw increasing attention for guaranteeing computation integrity and privacy of neural networks (NNs) based on zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK) security scheme. However, the performance of zkSNARK NNs is far from optimal due to the million-scale circuit computation with heavy scalar-level dependency. In this paper, we propose a type-based optimizing framework for efficient zero-knowledge NN inference, namely ZENO (ZEro knowledge Neural network Optimizer). We first introduce ZENO language construct to maintain high-level semantics and the type information (e.g., privacy and tensor) for allowing more aggressive optimizations. We then propose privacy-type driven and tensor-type driven optimizations to further optimize the generated zkSNARK circuit. Finally, we design a set of NN-centric system optimizations to further accelerate zkSNARK NNs. Experimental results show that ZENO achieves up to 8.5× end-to-end speedup than state-of-the-art zkSNARK NNs. We reduce proof time for VGG16 from 6 minutes to 48 seconds, which makes zkSNARK NNs practical.

Yijing Lin, Zhipeng Gao, Hongyang Du, Dusit Niyato · 7 authors

AI-Generated Content (AIGC) services are essential in developing the Metaverse, providing various digital content to build shared virtual environments. The services can also offer personalized content with user assistance, making the Metaverse more human-centric. However, user-assisted content creation requires significant communication resources to exchange data and construct trust among unknown Metaverse participants, which challenges the traditional centralized communication paradigm. To address the above challenge, we integrate blockchain with semantic communication to establish decentralized trust among participants, reducing communication overhead and improving trustworthiness for AIGC services in Metaverse. To solve the out-of-distribution issue in data provided by users, we utilize the invariant risk minimization method to extract invariant semantic information across multiple virtual environments. To guarantee trustworthiness of digital contents, we also design a smart contract-based verification mechanism to prevent random outcomes of AIGC services. We utilize semantic information and quality of digital contents provided by the above mechanisms as metrics to develop a Stackelberg game-based content caching mechanism, which can maximize the profits of Metaverse participants. Simulation results show that the proposed semantic extraction and caching mechanism can improve accuracy by almost 15% and utility by 30% compared to other mechanisms.

Yongkai Fan, Binyuan Xu, Linlin Zhang, Gang Tan · 7 authors

Model prediction based on machine learning is provided as a service in cloud environments, but how to verify that the model prediction service is entirely conducted becomes a critical challenge. Although zero-knowledge proof techniques potentially solve the integrity verification problem when applied to the prediction integrity of massive privacy-preserving Convolutional Neural Networks (CNNs), the significant proof burden results in low practicality. In this research, we present psvCNN (parallel splitting zero-knowledge technique for integrity verification). The psvCNN scheme effectively improves the utilization of computational resources in CNN prediction integrity, proving by an independent splitting design. Through a convolutional kernel-based model splitting design and an underlying zero-knowledge succinct non-interactive knowledge argument, our psvCNN develops parallelizable zero-knowledge proof circuits for CNN prediction. Furthermore, psvCNN presents an updated Freivalds algorithm for a faster integrity verification process. Experiments show that psvCNN is practical and efficient in terms of proof time and storage, generating a prediction integrity proof with a proof size of 1.2MB in 7.65s for the structurally complicated CNN model VGG16. psvCNN is 3765 times faster than the latest zk-SNARK-based non-interactive method vCNN, and 12 times faster than the latest sumcheck-based interactive technique zkCNN in terms of proving time.