arXiv Artificial Intelligence

TensorCommitments: A Lightweight Verifiable Inference for Language Models

1 hour ago 02.10.2026 07:00 👁 1 views
TensorCommitments: A Lightweight Verifiable Inference for Language Models

Quick summary

arXiv:2602.12630v2 Announce Type: replace-cross Abstract: Most large language models (LLMs) run on external clouds: users send a prompt, pay for inference, and must trust that the remote GPU executes the LLM without any adversarial tampering. We critically ask how to achieve verifiable LLM inference, where a prover (the service) must convince a verifier (the client) that an inference was run correctly without rerunning the LLM. Existing cryptographic works are too slow at the LLM scale, while non-cryptographic ones require a strong verifier GPU. We propose TensorCommitments (TCs), a tensor-nat

Key takeaways

  • arXiv:2602.12630v2 Announce Type: replace-cross Abstract: Most large language models (LLMs) run on external clouds: users send a prompt, pay for inference, and must trust that the remote GPU executes the LLM without any adversarial tampering.
  • We critically ask how to achieve verifiable LLM inference, where a prover (the service) must convince a verifier (the client) that an inference was run correctly without rerunning the LLM.
  • Existing cryptographic works are too slow at the LLM scale, while non-cryptographic ones require a strong verifier GPU.

Why it matters

“TensorCommitments: A Lightweight Verifiable Inference for Language Models” exposes the compute, energy and supply-chain layer behind model competition. Capacity shifts can influence model costs, service availability and the ability of smaller companies to compete.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗