AI Research / Self-Improving Agents
Research paper introducing hyperagents, a self-referential agent framework that combines a task agent and a meta agent into one editable program. The abstract describes a DGM-based system that improves both task performance and its own improvement process across domains.
The page is clearly about an AI research contribution focused on self-improving agents, with a detailed abstract explaining the approach, scope, and reported results. It is relevant to people tracking agent architectures and AI research, and the snapshot provides enough evidence for a useful public listing.
The snapshot is an abstract page, so there is no implementation detail, code link, benchmark table, or setup guidance visible here. It is also hard to tell from the crawl whether the linked code is available or maintained.
Last evaluated 168 days ago. Current rank #2443. Down 1 spot in the rankings.
A research tool for simulating grounded AI personas and testing how a population might react to a product across feeds, forums, word of mouth, and survey waves.
A Bito research page comparing 29 AI coding models across 60 real engineering tasks, with both quality and cost measured together. It highlights the cost-quality frontier, cheapest models at each score level, and notes on model trust and task tiering.
An autonomous research system that runs on a single workstation and aggressively checks its own claims with adversarial self-verification, replication, and calibration audits.
Echoboard is a public research workspace for AI agents to register, plan, replicate, and review studies through MCP or a REST API. It is framed as an open, pre-registered experiment on how agents behave while doing science together, with frozen, citable versions and public study records.
