ICME

Latest — Mar 21, 2026

Getting Found by Agents: A Builder's Guide to Tool Discovery in 2026

Getting discovered used to be a human problem. You stuffed keywords into title tags, begged for backlinks, wrote blog posts nobody wanted to read, and prayed to the Google algorithm. The audience was a person with ten blue links and a short attention span. You were fighting for a click.

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What is Automated Reasoning?

The 400-Year Idea That Now Runs AWS Infra at Billion-Query Scale, With ICME's ZK(ARc) Will Power Secure Agentic Commerce At Billion-Transaction Scale.

AI Agents Can Move Money. Lobstar Wilde Proved They Can Lose It Too.

Every security problem eventually becomes a game. Someone builds a wall, someone else finds the door. Someone writes a rule, someone else learns the language of the rule well enough to satisfy it while breaking everything it was meant to protect. We've played this game with email spam,

Succinctly Verifiable Agentic Guardrails With ZKP Over Automated Reasoning - or how to prevent the Agentic Commerce mega-hack.

Agents often have loosely defined guardrails that depend on ‘observability’ & ‘reputation’ and ultimately a human in-the-loop. ICME's Succinctly Verifiable Automated Reasoning allows for guardrails that deliver powerful protection. Use AI guardrails based on math, not trust.

Trustless Agents can't work without Trustless Agentic Memory.

Your agent just made a million-dollar decision based on 'memories' it pulled from a vector database. A TEE attested that the computation was correct. But can you prove they used the right AI memory? Introducing Kinic-CLI: zkML powered Trustless Agentic Memory (zkTAM).

The Definitive Guide to ZKML (2025).

AI systems are making decisions that move money, affect health outcomes, and control autonomous agents. But how do you verify that an AI actually computed what it claimed to compute? That it used the model it promised to use? That it didn't leak your data in the process?

Trustless Agents — with zkML

Ethproof's quest to verify Ethereum blocks in real-time can also be leveraged in a similar race for near real-time verifiable compute for trustless agents — here's how.

Sumcheck good. Lookups good. JOLT good. Particularly for zero-knowledge machine learning.

TL;DR: We made zkML 3-7x faster than everyone else. Here's how. Standing on the shoulders of giants The a16z crypto research team recently dropped some serious heat with their 6x speedup announcement, showing how JOLT's lookup-based approach with sumcheck protocol can dramatically outperform other SNARK

Verifiable AI Memory — When AI Remembers, Who Controls the Truth?

The future arrived, and it tastes like corporate surveillance. AI can now remember a lot about you. Not just your conversation from five minutes ago, but you — your preferences, your quirks, the embarrassing thing you asked about cryptocurrency in March. AI memory is not only a blossoming set of features,

Folding schemes in the lattice setting: pay-per-bit and NTTs

Lattice cryptography allows us to build cryptographic primitives from hard lattice problems. LatticeFold[1] is the first lattice-based folding scheme protocol, inspired by HyperNova[2], whose security is rooted in the Module Short Integer Solution (MSIS) problem. In short, it substitutes the existing discrete logarithm-based commitment schemes such as KZG[

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ICME

Blogging about where cryptography meets AI. Buidling SoTa zkML with Jolt Atlas (https://github.com/ICME-Lab/). DeSCI for ZKP www.novanet.xyz. Used for verifiable AI memory at https://www.kinic.io/ .

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