AutoResearch: An Execution-Grounded Multi-Agent Framework for Reliable Research Workflow Automation
2026-07-07T07:23:54Z•596a7f2b2e87b1a222bd2fc47173de018b42a0922d23f0d63fb6f9c8a14bf198
ai-governanceai-safetycooperative-logisticscybercrimedark-web-groomingdatacenter-water-usagedigital-public-goodseducationfrontier-aiin-game-fraudknowledge-componentsllm-evaluationmoral-sensitivityphishingprogramming-educationreproducibilityresearch-automationrouting-algorithmsextortionsustainability
What happened
This collection of arXiv CS/CY papers (July 7, 2026) spans research on reliable research automation, cybercrime trends, environmental impacts of hyperscale data centers, AI governance and evaluation, moral sensitivity benchmarks for LLMs, pedagogical metrics for programming assignments, digital-public-goods assessments for AI, macro‑prudential governance proposals for frontier AI, and a solidarity-focused routing algorithm for a bicycle delivery cooperative. Highlights: AutoResearch is an execution-grounded multi-agent framework that couples sandboxed execution, iterative code repair, citation
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_cy
- Record identifier
- 596a7f2b2e87b1a222bd2fc47173de018b42a0922d23f0d63fb6f9c8a14bf198
- Enrichment time
- 2026-07-07T07:23:54Z
- AI-assisted enrichment
- Yes
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