#survey
4 articles
01
·Agents·★ MEMBER·PAPER·11 min read
Paper Explained: What Makes Good Agentic Data? The ACE Lens
A survey that recasts agentic training data as a four-part object (environment, task, interaction, verifier) and reframes generation as constrained distribution design: admit on Accuracy, place mass by Complexity, spread coverage with divErsity.
02
·Large Language Models·★ MEMBER·PAPER·9 min read
Paper Explained: Agentic Artifact Creation — Where Generation Ends and Construction Begins
A survey that reorganizes 259 works around a single unit: the delivered artifact. It defines agentic creation through state, edits, and verification, then works through six artifact families, three evaluation targets, four principles, and six open problems — from first principles.
03
·Paper Deep-Dives·FREE·13 min read
When Proxies Stop Being Good Enough — Reading August 2026's Eight Autonomous Driving Papers Together
A cross-cutting read of eight autonomous-driving arXiv papers from late August 2026. Three groups independently stop measuring safety in expectation, two add an observation channel outside the ego vehicle's own history, and two genuinely don't fit the story.
04
·Agents·★ MEMBER·PAPER·9 min read
Paper Explained: Co-Evolution in Agentic Systems — Three Stages Toward Self-Directed Evolution
Why do agents that are supposed to keep improving after deployment hit a ceiling? A ground-up walkthrough of a survey that organises the field into three stages — evolving peers, evolving environments, and an evolving evolution mechanism — with the defining equations, representative methods, and the open problems in evaluation and safety.