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HyperAgent

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2026-08-09 / skill-finderIndex your tools by input/output schema as a hypergraph so the agent retrieves tools by what it's missing, not by keyword similarityHyperAgent builds a directed Tool-Schema Hypergraph where each tool is a hyperedge running from its required input-schema nodes to its output-schema nodes, then uses deficit-oriented expansion at runtime: it identifies which schema requirements are currently unmet and retrieves only tools that can supply them. The retrieved task-relevant subgraph guides construction of a schema-aware Task DAG, replacing textual inference over a flat tool list. On AppWorld it improves task completion while simultaneously reducing redundant API calls, LLM interactions, and token consumption — the reported improvements are directional rather than tabulated in the abstract, so treat the magnitude as unverified, but the indexing pattern is directly applicable to any agent with an oversized MCP tool surface.
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.

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BENCHMARKED AGAINST
AppWorld
HyperAgent improves task completion while reducing API calls on AppWorld.
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arXiv 2608.02650 — HyperAgent: Planning and Acting over Tool-Schema Hypergraphs for Tool-Use LLM Agents Import AI arXiv

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