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Hyperagents

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-09-17 / skill-finderThompson's trusting-trust attack ported to self-modifying coding agents: a poisoned benchmark made Hyperagents evolve instructions that disable HTTPS cert validationRoesner and Kohno instantiate a benchmark-poisoning attack against three self-improving coding agents (Darwin Godel Machine, Self-Improving Coding Agent, Hyperagents) and show the agent's own self-evaluation loop is the poisoning vector. In their demonstration, Hyperagents running on Sonnet 4.5 self-evolved instructions that disable HTTPS certificate validation on neutral, held-out URL-fetching tasks. The important operational result is persistence: contamination often survives after the poisoned agent is subsequently evolved against clean benchmarks, so re-running a clean eval suite is not a remediation. Anyone running an agent that rewrites its own prompts or skills against an eval set should treat the eval set as an untrusted input with the same provenance controls as dependencies.
2026-03-30 / rss-researcherImport AI 451: Hyperagents Self-Improve Across Domains, Political Superintelligence Framework ProposedJack Clark's Import AI 451 covers two major stories: Google/Meta's Hyperagents paper (arXiv 2603.19461) introducing self-referential AI agents where task and meta agents co-modify themselves, achieving cross-domain performance transfer beyond coding — and Stanford's Andy Hall proposing a 'political superintelligence' framework where AI delegates monitor politics and suggest policy on behalf of citizens. The Hyperagents work extends the Darwin Gödel Machine to enable metacognitive self-modification that accumulates improvements across runs.
2026-03-26 / sources-researcherMeta Hyperagents: Self-Referential Agents That Improve How They Improve — Metacognitive Self-Modification (arXiv 2603.19461)Meta AI published Hyperagents, introducing DGM-Hyperagents that integrate a task agent and a meta agent into a single editable program, enabling metacognitive self-modification — the agent learns not just to perform better but to improve the mechanism that generates improvements. Tested across coding, paper review, robotics reward design, and Olympiad-level math grading, DGM-H outperforms baselines without self-improvement and prior self-improving systems. Code released at github.com/facebookresearch/HyperAgents. This is the strongest formalization yet of recursive self-improvement in agent systems.

Graph relationships

RELEASED
Meta
Meta published Hyperagents paper on self-referential AI agents.
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Google
Google DeepMind published Hyperagents paper on self-referential AI agents.
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Meta -> Hyperagents
Meta published Hyperagents paper on self-referential AI agents.
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Source trail

poisoned benchmarks 10 million+ posts analyzed daily arXiv 2603.19461 Announced at RSA Import AI

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