·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models37m◆Humanly: A Configurable and Traceable Environment for Human-AI Collaborative Writing37m◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders37m◆Khondo: A Multimodal Benchmark for Document Packet Splitting of Bangla Forms37m◆Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA37m◆Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models37m◆On Improving Faithfulness of Podcasts from Documents37m◆Ground Truth First: A Longitudinal Evaluation Instrument for Agent Memory, and the Tenure Crossover in Memory-Architecture Rankings37m◆MoE$^2$-LoRA: When MoE Models Meet MoE-style Low-Rank Adaptation37m◆Analyzing Toxic Behavior and Its Impact on the Mastodon Community37m◆J-CoT: Chain-of-Thought in J-Space37m◆Analysing Self-Harm Representations in Language Models: a Cross-Architecture Study37m◆DWT-Fusion: A Signal-Based Framework for Training-Free LLM-Generated Text Detection37m◆Enough is as good as a feast: A Comprehensive Analysis of How Reinforcement Learning Mitigates Task Conflicts in LLMs37m◆Developing and Validating the Spanish Version of the Large Language Models Dependency Scale (LLM-D12-SP)37m◆Scaling Native Multimodal Pre-Training From Scratch37m◆Benchmarking Fine-tuning and Retrieval Strategies for a Multimodal Language Model on the NRC Reactor Operator Licensing Examination37m◆FSE: Continual Learning for Named Entity Recognition by Fast-Slow Experts37m◆MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond37m◆Dynamic Commonsense Coordination for Empathetic Response Generation37m◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models37m◆Humanly: A Configurable and Traceable Environment for Human-AI Collaborative Writing37m◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders37m◆Khondo: A Multimodal Benchmark for Document Packet Splitting of Bangla Forms37m◆Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA37m◆Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models37m◆On Improving Faithfulness of Podcasts from Documents37m◆Ground Truth First: A Longitudinal Evaluation Instrument for Agent Memory, and the Tenure Crossover in Memory-Architecture Rankings37m◆MoE$^2$-LoRA: When MoE Models Meet MoE-style Low-Rank Adaptation37m◆Analyzing Toxic Behavior and Its Impact on the Mastodon Community37m◆J-CoT: Chain-of-Thought in J-Space37m◆Analysing Self-Harm Representations in Language Models: a Cross-Architecture Study37m◆DWT-Fusion: A Signal-Based Framework for Training-Free LLM-Generated Text Detection37m◆Enough is as good as a feast: A Comprehensive Analysis of How Reinforcement Learning Mitigates Task Conflicts in LLMs37m◆Developing and Validating the Spanish Version of the Large Language Models Dependency Scale (LLM-D12-SP)37m◆Scaling Native Multimodal Pre-Training From Scratch37m◆Benchmarking Fine-tuning and Retrieval Strategies for a Multimodal Language Model on the NRC Reactor Operator Licensing Examination37m◆FSE: Continual Learning for Named Entity Recognition by Fast-Slow Experts37m◆MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond37m◆Dynamic Commonsense Coordination for Empathetic Response Generation37m◆
News/Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots
arxiv
PublishedJune 12, 2026 at 4:00 AM
—neutral

Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.12439v1 Announce Type: cross Abstract: Large language model (LLM) answer engines are increasingly used for information seeking, shifting visibility from ranked lists to synthesized answers. This enables Generative Engine Optimization (GEO), which targets LLM answer engines' evidence pool

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#optimization#governance#artificial-intelligence#search-engine

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#optimization#governance#artificial-intelligence#search-engine

Related coverage

More from ARXIV
arxivA Consensus-Based Framework for Relative Preference Evaluation of Large Language Models37marxivHumanly: A Configurable and Traceable Environment for Human-AI Collaborative Writing37marxivProbing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders37marxivKhondo: A Multimodal Benchmark for Document Packet Splitting of Bangla Forms37m
The Bubble Brief
WEEKLY

Read optimization insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews