# TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference

arXiv: https://arxiv.org/abs/2501.16007 · HTML: https://mlsanity.com/paper/2501.16007/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2025.

## Built on by (most cited first, 17 of 17)

- [Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs](https://mlsanity.com/paper/2504.04715/) (2025)
- [Log Probability Tracking of LLM APIs](https://mlsanity.com/paper/2512.03816/) (2025)
- [Verifying LLM Inference to Detect Model Weight Exfiltration](https://mlsanity.com/paper/2511.02620/) (2025)
- [Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape](https://mlsanity.com/paper/2507.07765/) (2025)
- [IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation](https://mlsanity.com/paper/2602.22700/) (2026)
- [VeriLLM: A Lightweight Framework for Publicly Verifiable Decentralized Inference](https://mlsanity.com/paper/2509.24257/) (2025)
- [DiFR: Inference Verification Despite Nondeterminism](https://mlsanity.com/paper/2511.20621/) (2025)
- [Hawkeye: Reproducing GPU-Level Non-Determinism](https://mlsanity.com/paper/2603.20421/) (2026)
- [Privacy Failure in Split-LLM Training, The Returned Gradient Nullifies the Decoys](https://mlsanity.com/paper/2609.04382/) (2026)
- [Towards Anonymous Neural Network Inference](https://mlsanity.com/paper/2505.18398/) (2025)
- [TensorCommitments: A Lightweight Verifiable Inference for Language Models](https://mlsanity.com/paper/2602.12630/) (2026)
- [Privacy-Preserving Mechanisms Enable Cheap Verifiable Inference of LLMs](https://mlsanity.com/paper/2602.17223/) (2026)
- [Committed SAE-Feature Traces for Audited-Session Substitution Detection in Hosted LLMs](https://mlsanity.com/paper/2604.18179/) (2026)
- [Computational Challenges in Token Economics: Bridging Economic Theory and AI System Design](https://mlsanity.com/paper/2605.17410/) (2026)
- [Integrity of peer-to-peer distributed LLM inference under malicious nodes](https://mlsanity.com/paper/2607.19490/) (2026)
- [Privacy-Preserving AI Verification via Minimal Information Disclosure](https://mlsanity.com/paper/2608.02774/) (2026)
- [What You Sign Is Not What You Run: The Verifiability Gap in Quantized LLM Supply Chains](https://mlsanity.com/paper/2610.19498/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have proven to be very capable, but access to frontier models currently relies on inference providers. This introduces trust challenges: how can we be sure that the provider is using the model configuration they claim? We propose TOPLOC, a novel method for verifiable inference that addresses this problem. TOPLOC leverages a compact locality-sensitive hashing mechanism for intermediate activations, which can detect unauthorized modifications to models, prompts, or precision with 100% accuracy, achieving no false positives or negatives in our empirical evaluations. Our approach is robust across diverse hardware configurations, GPU types, and algebraic reorderings, which allows for validation speeds significantly faster than the original inference. By introducing a polynomial encoding scheme, TOPLOC minimizes the memory overhead of the generated proofs by 1000×, requiring only 258 bytes of storage per 32 new tokens, compared to the 262 KB requirement of storing the token embeddings directly for Llama 3.1-8B-Instruct. Our method empowers users to verify LLM inference computations efficiently, fostering greater trust and transparency in open ecosystems and laying a foundation for decentralized, verifiable and trustless AI services.