Top AI Papers of the Week: Agent Infrastructure, Memory, Evaluation & Autodata

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Ten AI papers from June 21-28, 2026, explained through source-grounded Manim visuals.

This week’s theme: agent-native infrastructure. The papers move beyond “agents can talk” toward the harder systems questions: how
agents coordinate, remember, route work, judge outputs, evaluate science tasks, and generate useful synthetic data.

Chapters:
00:00 Overview and chapter map
00:58 Skill-MAS: evolving meta-skills for agent teams
03:48 LLM agent communication protocols
06:48 Human-like autonomy from self-play
10:16 Reliability without validity in LLM judges
12:49 Sakana Fugu: learned model orchestration
15:31 Agent-as-a-Router
18:16 Critique of Agent Model
21:18 NatureBench: coding agents vs Nature-family SOTA
24:02 Agent-native memory systems
26:32 Autodata: agentic synthetic data
29:39 Final takeaway: bounded delegation

Core takeaway: the next frontier is not just more capable agents, but inspectable agent infrastructure: protocols, memory, routing,
evaluation, and data loops that leave evidence behind.
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