·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Deezer launches an AI music detector for other streaming services1h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing5h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning5h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!5h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions5h◆The Impossibility of Eliciting Latent Knowledge5h◆Mapping Scientific Literature with Large Language Models and Topic Modeling5h◆Grounding Computer Use Agents on Human Demonstrations5h◆Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models5h◆LSTM based IoT Device Identification5h◆StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse5h◆Composing Linear Layers from Irreducibles5h◆Breaking the Ice: Analyzing Cold Start Latency in vLLM5h◆BioMamba: Domain-Adaptive Biomedical Language Models5h◆Intermittent time series forecasting: local vs global models5h◆From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoning5h◆Characterizing Software Aging in GPU-Based LLM Serving Systems5h◆Geometric Metrics and LLMs: What They Measure and When They Work5h◆Feature-Aligned Speech Watermarking for Robustness to Reconstruction Distortions5h◆Augmenting Molecular Language Models with Local $n$-gram Memory5h◆Deezer launches an AI music detector for other streaming services1h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing5h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning5h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!5h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions5h◆The Impossibility of Eliciting Latent Knowledge5h◆Mapping Scientific Literature with Large Language Models and Topic Modeling5h◆Grounding Computer Use Agents on Human Demonstrations5h◆Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models5h◆LSTM based IoT Device Identification5h◆StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse5h◆Composing Linear Layers from Irreducibles5h◆Breaking the Ice: Analyzing Cold Start Latency in vLLM5h◆BioMamba: Domain-Adaptive Biomedical Language Models5h◆Intermittent time series forecasting: local vs global models5h◆From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoning5h◆Characterizing Software Aging in GPU-Based LLM Serving Systems5h◆Geometric Metrics and LLMs: What They Measure and When They Work5h◆Feature-Aligned Speech Watermarking for Robustness to Reconstruction Distortions5h◆Augmenting Molecular Language Models with Local $n$-gram Memory5h◆
News/Principles Do Not Apply Themselves: A Hermeneutic Perspective on AI Alignment
arxiv
PublishedApril 14, 2026 at 4:00 AM
—neutral

Principles Do Not Apply Themselves: A Hermeneutic Perspective on AI Alignment

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

arXiv:2604.10673v1 Announce Type: new Abstract: AI alignment is often framed as the task of ensuring that an AI system follows a set of stated principles or human preferences, but general principles rarely determine their own application in concrete cases. When principles conflict, when they are too

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
03
#alignment#interpretability#evaluation

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#alignment#interpretability#evaluation

Related coverage

More from ARXIV
arxivMODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning5harxivPosition: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!5harxivGeneralizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions5harxivThe Impossibility of Eliciting Latent Knowledge5h
The Bubble Brief
WEEKLY

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

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

Originally published on arxiv ↗
HomeModelsNews