Research

arXiv paper on how ideas and goals can spread through multi-agent LLM systems, including experiments on propagation, susceptibility factors, and mitigation.

A Zenodo preprint reporting a 12,160-trial black-box evaluation of GPT-5.4 outputs under two prompt conditions, with detailed token-ceiling sweeps, control/ablation trials, and hash-chained verification.

arXiv paper on self-speculating LLM agents that predict their next tool call to hide tool latency and improve next-call Hit@1 while preserving task success.

An arXiv paper on reducing LLM inference cost for web automation by compiling browser tasks into a deterministic JSON workflow and executing them without repeated model calls.

A research-oriented optimizer project claiming to cut AdamW optimizer-state memory for LLM training by 50% through factorized variance and frequency-domain compression. The repo includes a Python POC, benchmark claims, and a stated drop-in usage pattern for PyTorch training.

A GitHub-hosted research note about an agent-driven engineering workflow, comparing merged PR volume across Kungfu, Google AX, and OpenAI references.

arXiv paper proposing MANTA, a framework that lets multi-agent communication structures adapt at inference time based on collaboration traces and task needs.

A research page and preprint about using code as the runtime layer for agent systems, with a taxonomy of harness interfaces, harness mechanisms, and scaling patterns for multi-agent workflows.