Monolith 01
Autonomous multi-agent systems operating across the frontier of general artificial intelligence. Post-quantum cryptographic infrastructure for the next internet.
Phase 01 active. Research output target: ongoing.
Live from the frontier
Questions
The ASI Master Brain is a multi-LLM orchestration architecture that routes cognitive tasks across specialized models — Claude as orchestrator, with models specialized for mathematical modeling, long-context analysis, real-time intelligence, and offline security — behind a single command interface with zero context-switching.
PHANTOM Protocol is a post-quantum secure messaging protocol using hybrid key encapsulation: Kyber-1024 (NIST ML-KEM) combined with X25519. Both primitives must be broken simultaneously to compromise a session, providing security against classical and quantum adversaries. It includes a PHTM token economy on Base L2.
A hybrid scheme derives the session key from both X25519 and Kyber-1024, so the protocol stays secure against classical adversaries today and quantum adversaries in the future. Kyber-1024 public keys are 1568 bytes and ciphertexts 1088 bytes — larger than X25519 but manageable for modern messaging infrastructure.
The feed pulls recent arXiv papers in cs.AI, cs.LG, cs.MA, and cs.CR — covering multi-agent systems, autonomous frameworks, large language models, and post-quantum cryptography — refreshed hourly and validated against a strict schema before display.
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Changdae Oh +5
Process reward models enable fine-grained, step-level evaluation of LLMs, yet building them for agentic settings remains prohibitively difficult: long-horizon interactions, irreversible actions, and stochastic environment feedback make both human annotation and Monte Carlo estimation infeasible at scale.
arXiv→Xihan Xiong +5
As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy? The ERC-8004 protocol addresses this challenge with the first permissionless trust layer for AI agent economies, built around three on-chain registries for Identity, Reputation, and Validation.
arXiv→Tânia Carvalho +1
Tabular foundation models are commonly assumed to present limited privacy concerns as they are often pre-trained on large collections of synthetic data.
arXiv→Shuyi Zhang +10
Vision-Language-Action (VLA) models are often constrained by the imitation ceiling imposed by sub-optimal data.
arXiv→Kamar Hibatallah Baghdadi +4
We present HiReLC, a hierarchical ensemble-reinforcement learning framework for automated joint quantization and structured pruning of deep neural networks.
arXiv→Alexandre Bouayad
Large language models (LLMs) attain remarkable surface fluency on code, yet they neither formally guarantee the syntactic validity of their output nor leverage the hierarchical structure defining the target language.
arXiv→