Verifiable Outsourced Computation

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17 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

A Compute Center Anyone Can Join: A Peer-to-Peer System for AI Inference
Nov 1, 2026Yimo Lin, Zhen ZhangDistributed Inference Verifiable Outsourced Computation
Yunyong Century (Beijing) Artificial Intelligence Technology Co., Ltd. · Tsinghua University
Efficient Scalable Oversight via Rewinding
Oct 29, 2026Ziyi Guan, Yael Tauman Kalai, Ron Rothblum +1Scalable Oversight Verifiable Outsourced Computation
MIT. · MIT and RESI. · Succinct.
count-catcher: Lean Theorems from Model Counting Certificates
Oct 26, 2026Johannes K. Fichte, Stefan SzeiderFormal Verification Verifiable Outsourced Computation
Centre de Recherche en Informatique de Lens (CRIL), UMR 8188 Université d’Artois and CNRS, Lens, France · Algorithms and Complexity Group TU Wien, Vienna, Austria
Can AI Oversight Be Zero Knowledge?
Oct 1, 2026Alessandro Chiesa, Ziyi Guan, Burcu YildizScalable Oversight Zero-Knowledge Proofs
EPFL · MIT
Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation
Sep 9, 2026Ben Merbaum, Mohammad Amin Raeisi, Wenhao Wang +3Privacy-Preserving Language Models LLM Inference
Yale University, IC3
MOSAIC: Masked Outsourcing of Secure AI Computations
Jul 31, 2026James Hsin-yu Chiang, Sheila Zingg, Kari Kostiainen +1Efficient Transformer Inference Secure Multi-Party Computation
ETH Zurich Switzerland
Agree on the Model, Verify the Inference: GKR Protocols for HND-Based Transformer Inference
Jul 23, 2026Xiaolong Liang, Juanjuan Li, Rui Qin +1Neural Network Verification Transformer Inference
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
WaveCommitments: Private and Verifiable Speech-Agent Marketplaces
Jul 16, 2026Oguzhan Baser, Kaan Kale, Sriram Vishwanath +1Privacy-Preserving Speech Processing Data Valuation
†The University of Texas at Austin, Austin, TX, USA · ‡Georgia Institute of Technology, Atlanta, GA, USA
OVIG: Optimistic Verification of AI Training Integrity via Gradient Signals
Jun 19, 2026Hongxu Su, Jianzhu Yao, Huan Zhang +2Neural Network Verification Algorithmic Auditing
HKUST (GZ), Guangzhou, China · Princeton University, Princeton, NJ, USA · University of Illinois Urbana-Champaign, Urbana, IL, USA
TrustedARI: Towards Trust-Native Agentic Routing Infrastructure for Agentic AI
Jun 14, 2026Qi Li, Zhenhua Zou, Shuo Li +2Verifiable Outsourced Computation LLM Agent Security
Tsinghua University
Trust Without Trusting: A Recomputable Trust Protocol for Autonomous Agents
May 7, 2026Lars Kersten KroehlVerifiable Outsourced Computation AI Agent Governance
MolTrust / CryptoKRI GmbH, Zurich
VeriX-Anon: A Multi-Layered Framework for Mathematically Verifiable Outsourced Target-Driven Data Anonymization
Apr 14, 2026Miit Daga, Swarna Priya RamuPrivacy Auditing Verifiable Outsourced Computation
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India
Towards Verifiable AI with Lightweight Cryptographic Proofs of Inference
Mar 19, 2026Pranay Anchuri, Matteo Campanelli, Paul Cesaretti +4Verifiable Outsourced Computation Neural Network Verification
Offchain Labs · University of Tartu, Estonia · CUNY Graduate Center +1
Trusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AI
Mar 8, 2026Heng Jin, Chaoyu Zhang, Hexuan Yu +4Verifiable Outsourced Computation LLM Auditing
Virginia Tech · Washington University in St. Louis
IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation
Feb 26, 2026Yanpei Guo, Wenjie Qu, Linyu Wu +7Verifiable Outsourced Computation LLM Auditing
National University of Singapore · Nanyang Technological University · Independent Researcher +1
Privacy-Preserving Mechanisms Enable Cheap Verifiable Inference of LLMs
Feb 19, 2026Arka Pal, Louai Zahran, William Gvozdjak +2Verifiable Outsourced Computation LLM Inference
Ritual · MIT · Columbia University
TensorCommitments: A Lightweight Verifiable Inference for Large Language Models
Feb 13, 2026Oguzhan Baser, Elahe Sadeghi, Eric Wang +5LLM Inference LLM Security
Electrical and Computer Engineering, The University of Texas at Austin · Theseus AI Labs · Electrical and Computer Engineering, McGill University +1

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A Compute Center Anyone Can Join: A Peer-to-Peer System for AI Inference

Yimo Lin, Zhen Zhang

Yunyong Century (Beijing) Artificial Intelligence Technology Co., Ltd. · Tsinghua University

Distributed Inference Verifiable Outsourced Computation

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Today, most people obtain AI inference by buying it from a small number of organizations that run large data centers, while many GPUs owned by individuals and small institutions sit idle for much of the day. Bitcoin showed that strangers can contribute resources under shared rules and receive checkable rewards, but it relies on an asymmetry: proof-of-work is expensive to produce and cheap to verify. Useful inference has no such asymmetry. We propose the Open Inference Market (OIM), which replaces the missing asymmetry with an economic mechanism. A worker delivers a result without knowing whether it will be audited; its unsettled earnings serve as collateral during a challenge window; and confirmed cheating forfeits all of it. We call this principle earnings as collateral. In a simplified model we show three things. The number of audits needed for deterrence is a constant per worker per day, independent of the worker's throughput, so the audited share falls as the network gets busier. A new worker can join with a short period of intensive auditing and a very small bond. And whether the mechanism works at all depends only on the ratio of the audit judge's false-flag rate to its detection rate. Under example parameters, about 0.3 replays per worker per day are enough to make cheating unprofitable for a rational worker. On this basis we lay out a path that hands four powers back to participants one at a time: owning compute, writing history, judging work, and releasing value. A compute center need not be a building; it can be a protocol that anyone can join.