How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphs
2026-03-05T13:38:11Z•bfa0fe2820aca0a3c0cff176b4f334d6dceb033040c4c643b63a3612f6266b81
AI-safetyHawkes-processLLM-agentsLeiden-algorithmYouTube-monetizationaffiliate-marketingalgorithmic-feedbackcommunity-detectiondecentralized-systemsdynamic-graphsemergent-behaviorhigher-order-interactionshypergraphslink-predictionmulti-agent-systemsnetwork-dynamicsregulatory-compliancescalable-algorithms
What happened
Collection of recent CS/system papers (arXiv 2026-03-05) covering: (1) algorithmic feedback in link prediction and a Hawkes-process temporal framework to disentangle choice vs. recommendation amplification; (2) MoltBook — a 770k-agent LLM multi-agent environment studying emergent role specialization, information cascades, and low cooperative-task success, with implications for decentralized agent safety and coordination; (3) UrbanHuRo — a two-layer human-robot framework for jointly optimizing heterogeneous urban services using distributed K-submodular maximization and deep submodular RL; (4) a
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_si
- Record identifier
- bfa0fe2820aca0a3c0cff176b4f334d6dceb033040c4c643b63a3612f6266b81
- Enrichment time
- 2026-03-05T13:38:11Z
- AI-assisted enrichment
- Yes
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