·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
FederatedSkill: Federated Learning for Agentic Skill Evolution3h◆ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning3h◆Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models3h◆Toward a Modular Architecture for Embedded AI Agent Systems at the Edge3h◆Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems3h◆Effect of Demographic Bias on Skin Lesion Classification3h◆Perceive Before Reasoning: A Pre-Reasoning Perception Framework for Efficient and Reliable Proactive Mobile Agents3h◆Solipsistic Superintelligence is Unlikely to be Cooperative3h◆Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection3h◆Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering3h◆A Negative Result on Cross-Model Activation Transfer in a Pythia Multi-Hop Setting3h◆LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks3h◆The Reliability Gap in Benchmark Auditing: Distribution Shift and Scale as Failure Modes of Contamination Detection3h◆The Violation Situation Pattern: A Knowledge-Graph Pattern for Compliance Violations3h◆DMF: A Deterministic Memory Framework for Conversational AI Agents3h◆StepFinder: A Temporal Semantic Framework for Failure Attribution in Multi-Agent Systems3h◆A formal definition and meta-model for a machine theory of mind3h◆ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning3h◆Overlaying Governance: A Compositional Authorization Framework for Delegation and Scope in Agentic AI3h◆A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation3h◆FederatedSkill: Federated Learning for Agentic Skill Evolution3h◆ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning3h◆Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models3h◆Toward a Modular Architecture for Embedded AI Agent Systems at the Edge3h◆Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems3h◆Effect of Demographic Bias on Skin Lesion Classification3h◆Perceive Before Reasoning: A Pre-Reasoning Perception Framework for Efficient and Reliable Proactive Mobile Agents3h◆Solipsistic Superintelligence is Unlikely to be Cooperative3h◆Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection3h◆Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering3h◆A Negative Result on Cross-Model Activation Transfer in a Pythia Multi-Hop Setting3h◆LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks3h◆The Reliability Gap in Benchmark Auditing: Distribution Shift and Scale as Failure Modes of Contamination Detection3h◆The Violation Situation Pattern: A Knowledge-Graph Pattern for Compliance Violations3h◆DMF: A Deterministic Memory Framework for Conversational AI Agents3h◆StepFinder: A Temporal Semantic Framework for Failure Attribution in Multi-Agent Systems3h◆A formal definition and meta-model for a machine theory of mind3h◆ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning3h◆Overlaying Governance: A Compositional Authorization Framework for Delegation and Scope in Agentic AI3h◆A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation3h◆
News/TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment
arxiv
PublishedJune 2, 2026 at 4:00 AM

TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment

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

arXiv:2606.01755v1 Announce Type: new Abstract: Personalized large language models adapt responses to users' preferences and social attributes, but can introduce substantial universal truth inconsistencies across social groups, where some groups systematically receive less accurate responses on obje

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 →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivFederatedSkill: Federated Learning for Agentic Skill Evolution3harxivChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning3harxivThinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models3harxivToward a Modular Architecture for Embedded AI Agent Systems at the Edge3h
The Bubble Brief
WEEKLY

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

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

Originally published on arxiv ↗
HomeModelsNews