·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Optimal Correction Sets for Provable Redress of Contestable Causal Discovery4h◆AOS-Bench: Can LLM Agents Manage Dynamic Workloads?4h◆InferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants4h◆Propensities Please: Exposing Alignment-Faking LLMs using Synthetic Document Finetuning4h◆Isharah-Selfie: Continuous Sign Language Recognition Dataset for One-handed Signing4h◆NewsBench: Expert-Grounded Evaluation of Epistemic Quality in AI News Reporting4h◆AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science4h◆Scoped Semantic Invariance as a Foundation for LLM Reliability4h◆ThinkBit: Search Codewords, Recalibrate Scales, Select by Reasoning4h◆GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation4h◆Estimation of Propagated Risk in LLM-based Multi-Agent Systems4h◆Multimodal Requirements Traceability Link Recovery in Industrial Settings4h◆The Price of Scale in Proactive Memory Recall4h◆AgentProofBench: Benchmarking Reusable Verifiers for Web Agents4h◆GUIFlow: Temporal Redundancy-Aware Incremental Inference for GUI Agents4h◆The Watchtower Imperative: Lifespan-Calibrated Evaluation for Frontier AI4h◆Do MLLMs Really See It: Reinforcing Visual Attention in Multimodal LLMs4h◆XPress: Parallel Refinement for Diffusion Drafters in Speculative Decoding4h◆The Value of Memory in Language Agents4h◆HyScale: Scaling Long-Context Training for Learned Sparse Attention4h◆Optimal Correction Sets for Provable Redress of Contestable Causal Discovery4h◆AOS-Bench: Can LLM Agents Manage Dynamic Workloads?4h◆InferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants4h◆Propensities Please: Exposing Alignment-Faking LLMs using Synthetic Document Finetuning4h◆Isharah-Selfie: Continuous Sign Language Recognition Dataset for One-handed Signing4h◆NewsBench: Expert-Grounded Evaluation of Epistemic Quality in AI News Reporting4h◆AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science4h◆Scoped Semantic Invariance as a Foundation for LLM Reliability4h◆ThinkBit: Search Codewords, Recalibrate Scales, Select by Reasoning4h◆GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation4h◆Estimation of Propagated Risk in LLM-based Multi-Agent Systems4h◆Multimodal Requirements Traceability Link Recovery in Industrial Settings4h◆The Price of Scale in Proactive Memory Recall4h◆AgentProofBench: Benchmarking Reusable Verifiers for Web Agents4h◆GUIFlow: Temporal Redundancy-Aware Incremental Inference for GUI Agents4h◆The Watchtower Imperative: Lifespan-Calibrated Evaluation for Frontier AI4h◆Do MLLMs Really See It: Reinforcing Visual Attention in Multimodal LLMs4h◆XPress: Parallel Refinement for Diffusion Drafters in Speculative Decoding4h◆The Value of Memory in Language Agents4h◆HyScale: Scaling Long-Context Training for Learned Sparse Attention4h◆
News/Evolving to Match Capabilities: Improving Harness Self-Evolution via Skill Adaptation
arxiv
PublishedOctober 27, 2026 at 4:00 AM

Evolving to Match Capabilities: Improving Harness Self-Evolution via Skill Adaptation

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

arXiv:2610.33024v1 Announce Type: cross Abstract: Harness self-evolution offers a practical route for LLM agents to improve from execution experience without updating model parameters. Its effectiveness depends on whether models can successfully act on the guidance provided by their harnesses. Howev

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

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

Your email is used only to send The Bubble Brief. Privacy policy

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
arxivOptimal Correction Sets for Provable Redress of Contestable Causal Discovery4harxivAOS-Bench: Can LLM Agents Manage Dynamic Workloads?4harxivInferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants4harxivPropensities Please: Exposing Alignment-Faking LLMs using Synthetic Document Finetuning4h
The Bubble Brief
MON · WED · FRI

Read AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

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

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews