·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning4h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks4h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts4h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning4h◆FrontierChallenge: Evaluating Scientific Workflow Completion4h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier4h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising4h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic4h◆Omni Interaction Agent Technical Report4h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification4h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability4h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization4h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding4h◆Tracing Computation Density in LLMs4h◆Cultural Binding Heads in Language Models4h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training4h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models4h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning4h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection4h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation4h◆Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning4h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks4h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts4h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning4h◆FrontierChallenge: Evaluating Scientific Workflow Completion4h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier4h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising4h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic4h◆Omni Interaction Agent Technical Report4h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification4h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability4h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization4h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding4h◆Tracing Computation Density in LLMs4h◆Cultural Binding Heads in Language Models4h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training4h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models4h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning4h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection4h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation4h◆
News/Library Drift: Diagnosing and Fixing a Silent Failure Mode in Self-Evolving LLM Skill Libraries
arxiv
PublishedJuly 31, 2026 at 4:00 AM
—neutral

Library Drift: Diagnosing and Fixing a Silent Failure Mode in Self-Evolving LLM Skill Libraries

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

arXiv:2605.19576v3 Announce Type: replace Abstract: Self-evolving skill libraries face a silent failure mode we term \emph{library drift}: unbounded skill accumulation without outcome-driven lifecycle management causes retrieval degradation, false-positive injections, and performance stagnation. Rec

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
#library-drift#self-evolving-agents#governance#evaluation

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#library-drift#self-evolving-agents#governance#evaluation

Related coverage

More from ARXIV
arxivBringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning4harxivSubagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks4harxivDistribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts4harxivIn RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning4h
The Bubble Brief
WEEKLY

Read library-drift insights every Tuesday — top movers, new releases, story of the week.

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

Originally published on arxiv ↗
HomeModelsNews