EigenAI: Deterministic Inference, Verifiable Results

• URL: http://arxiv.org/abs/2602.00182v1
• Date: Fri, 30 Jan 2026 04:46:22 GMT
• Title: EigenAI: Deterministic Inference, Verifiable Results
• Abstract summary: EigenAI is a verifiable AI platform built on top of the EigenLayer restaking ecosystem.<n>It combines a deterministic large-language model (LLM) inference engine with a cryptoeconomically secured optimistic re-execution protocol.
• Score: 3.231493128964671
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: EigenAI is a verifiable AI platform built on top of the EigenLayer restaking ecosystem. At a high level, it combines a deterministic large-language model (LLM) inference engine with a cryptoeconomically secured optimistic re-execution protocol so that every inference result can be publicly audited, reproduced, and, if necessary, economically enforced. An untrusted operator runs inference on a fixed GPU architecture, signs and encrypts the request and response, and publishes the encrypted log to EigenDA. During a challenge window, any watcher may request re-execution through EigenVerify; the result is then deterministically recomputed inside a trusted execution environment (TEE) with a threshold-released decryption key, allowing a public challenge with private data. Because inference itself is bit-exact, verification reduces to a byte-equality check, and a single honest replica suffices to detect fraud. We show how this architecture yields sovereign agents -- prediction-market judges, trading bots, and scientific assistants -- that enjoy state-of-the-art performance while inheriting security from Ethereum's validator base.

Related papers

• Zero-Knowledge Predicate Proofs Between AI Agents: A Measured, Cross-Protocol Gateway and the Source-Integrity Gap [0.0]
  Multi-agent AI platforms move quickly from staging to production, but the way agents establish trust remains rudimentary.<n>We take provable data minimisation between agents from proposal to running system.<n>A 32-bit threshold predicate proves in 6.2 ms and verifies in 1.0 ms with a 608-byte Bulletproofs proof.
  arXiv  Detail & Related papers  (2026-08-30T23:16:17Z)
• Bit-Exact AI Inference Verification Without Performance Tradeoffs [0.0]
  Covert adversaries can exploit unverifiable degrees of freedom in monitored computation.<n>We analyze how modern inference engines produce deterministic but non-invariant outputs.<n>We demonstrate that bitwise-precise re-computation does not require access to identical hardware.
  arXiv  Detail & Related papers  (2026-05-29T19:15:15Z)
• Agentic Witnessing: Pragmatic and Scalable TEE-Enabled Privacy-Preserving Auditing [0.0]
  We propose em Agentic Witnessing, a framework that moves verification from execution to reasoning.<n>The system is composed of three agents: a Verifier (who wants to check properties of a dataset), a Prover (who owns the dataset) and an Auditor (that inspects the dataset).<n>By isolating an LLM-based Auditor within a Trusted Execution Environment (TEE), the system enables the Verifier to query a Prover's private data via simple queries, without exposing the raw dataset.
  arXiv  Detail & Related papers  (2026-04-27T09:07:15Z)
• HadAgent: Harness-Aware Decentralized Agentic AI Serving with Proof-of-Inference Blockchain Consensus [4.693243537924961]
  We present HadAgent, a decentralized agentic AI serving system that replaces hash-based mining with Proof-of-Inference (PoI)<n>HadAgent organizes validated records into a three-lane block body with dedicated DATA, MODEL, and PROOF channels.<n>Experiments on a prototype implementation demonstrate 100% detection rate and 0% false positive rate for tampered records.
  arXiv  Detail & Related papers  (2026-04-15T14:46:29Z)
• Towards Verifiable AI with Lightweight Cryptographic Proofs of Inference [3.3323431541048385]
  We present a verification framework and protocol that replaces full proofs with a lightweight, sampling-based approach.<n>We formalize the conditions under which trace separation between functionally dissimilar models can be leveraged to argue the security of verifiable inference protocols.<n>Our approach reduces proving times by several orders of magnitude compared to state-of-the-art cryptographic proof systems.
  arXiv  Detail & Related papers  (2026-03-19T15:24:27Z)
• IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation [49.796717294455796]
  We present IMMACULATE, a practical auditing framework that detects economically motivated deviations.<n>IMMACULATE selectively audits a small fraction of requests using verifiable computation, achieving strong detection guarantees while amortizing cryptographic overhead.
  arXiv  Detail & Related papers  (2026-02-26T07:21:02Z)
• CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks [54.04030169323115]
  We introduce CREDIT, a certified ownership verification against Model Extraction Attacks (MEAs)<n>We quantify the similarity between DNN models, propose a practical verification threshold, and provide rigorous theoretical guarantees for ownership verification based on this threshold.<n>We extensively evaluate our approach on several mainstream datasets across different domains and tasks, achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-02-23T23:36:25Z)
• Symfrog-512: High-Capacity Sponge-Based AEAD Cipher (1024-bit State) [0.0]
  This submission includes a complete reference implementation together with deterministic test vectors and a reproducible benchmark suite.<n>The AEAD construction is fully specified, including domain separation, rate and capacity choices, tag generation, and the exact file format used by the reference CLI.<n>Reported performance numbers are produced by the built in benchmark tool under documented hardware and compiler settings.
  arXiv  Detail & Related papers  (2026-02-19T23:39:54Z)
• Preventing the Collapse of Peer Review Requires Verification-First AI [49.995126139461085]
  We propose truth-coupling, i.e. how tightly venue scores track latent scientific truth.<n>We formalize two forces that drive a phase transition toward proxy-sovereign evaluation.
  arXiv  Detail & Related papers  (2026-01-23T17:17:32Z)
• Proof of Trusted Execution: A Consensus Paradigm for Deterministic Blockchain Finality [0.391985484065646]
  We propose Proof of Trusted Execution (PoTE), a consensus paradigm where agreement emerges from verifiable execution rather than replicated re-execution.<n>Because the execution is deterministic and the proposer is uniquely derived from public randomness, PoTE avoids forks, eliminates slot.time bottlenecks, and commits blocks in a single round of verification.
  arXiv  Detail & Related papers  (2025-12-10T08:04:38Z)
• SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarking [58.475471437150674]
  We propose sequential watermarking for soft prompts (SWAP)<n>SWAP encodes watermarks through a specific order of defender-specified out-of-distribution classes.<n>Experiments on 11 datasets demonstrate SWAP's effectiveness, harmlessness, and robustness against potential adaptive attacks.
  arXiv  Detail & Related papers  (2025-11-05T13:48:48Z)
• VeriLLM: A Lightweight Framework for Publicly Verifiable Decentralized Inference [3.8760740008451156]
  We introduce VeriLLM, a publicly verifiable protocol for decentralized language models (LLMs) inference.<n>VeriLLM combines lightweight empirical rerunning with cryptographic commitments, allowing verifiers to validate results at approximately 1% of the underlying inference cost.<n>We show that VeriLLM achieves reliable public verifiability with minimal overhead.
  arXiv  Detail & Related papers  (2025-09-29T04:07:32Z)
• Benchmarking Fraud Detectors on Private Graph Data [70.4654745317714]
  Currently, many types of fraud are managed in part by automated detection algorithms that operate over graphs.<n>We consider the scenario where a data holder wishes to outsource development of fraud detectors to third parties.<n>Third parties submit their fraud detectors to the data holder, who evaluates these algorithms on a private dataset and then publicly communicates the results.<n>We propose a realistic privacy attack on this system that allows an adversary to de-anonymize individuals' data based only on the evaluation results.
  arXiv  Detail & Related papers  (2025-07-30T03:20:15Z)
• On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains [0.0]
  We introduce the concept of the Merkle Automaton, a cryptographically anchored, deterministic computational framework.<n>Each agent transition, memory fragment, and reasoning step is committed within a Merkle structure rooted on-chain.<n>This architecture reframes memory not as a cache but as a ledger - one whose contents are enforced by protocol, bound by cryptography, and constrained by formal logic.
  arXiv  Detail & Related papers  (2025-06-16T08:43:56Z)
• Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs [71.7892165868749]
  Commercial Large Language Model (LLM) APIs create a fundamental trust problem.<n>Users pay for specific models but have no guarantee that providers deliver them faithfully.<n>We formalize this model substitution problem and evaluate detection methods under realistic adversarial conditions.<n>We propose and evaluate the use of Trusted Execution Environments (TEEs) as one practical and robust solution.
  arXiv  Detail & Related papers  (2025-04-07T03:57:41Z)
• CryptoVampire: Automated Reasoning for the Complete Symbolic Attacker Cryptographic Model [8.838422697156195]
  We introduce the CryptoVampire cryptographic protocol verifier, which for the first time fully automates proofs of trace properties in the BC Logic. Key technical contribution is a first-order (FO) formalization of protocol properties with tailored handling of subterm relations. On the theoretical side, we restrict full FO logic with cryptographic axioms to ensure that, by losing the expressivity of the HO BC Logic, we do not lose soundness.
  arXiv  Detail & Related papers  (2023-05-20T11:26:51Z)
• Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning [64.78972193105443]
  This paper presents a novel AE detection framework, named trustworthy for predictions. performs the detection by distinguishing the AE's abnormal relation with its augmented versions. An off-the-shelf Self-Supervised Learning (SSL) model is used to extract the representation and predict the label.
  arXiv  Detail & Related papers  (2022-08-31T08:18:44Z)
• Quantum Proofs of Deletion for Learning with Errors [91.3755431537592]
  We construct the first fully homomorphic encryption scheme with certified deletion. Our main technical ingredient is an interactive protocol by which a quantum prover can convince a classical verifier that a sample from the Learning with Errors distribution in the form of a quantum state was deleted.
  arXiv  Detail & Related papers  (2022-03-03T10:07:32Z)

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

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