·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Replit’s CEO says the future of work is the ‘self-driving company’49m◆Bridging the Gap: Converting Read Text to Conversational Dialogue9h◆Sharp and Stable Label-Free Calibration Certificates from Logical Coherence9h◆Backward Compatibility in Zero-Bit Neural Audio Watermarking9h◆Disagreement-Guided Weak-to-Strong Routing9h◆One Is Not Enough: How People Use Multiple AI Models in Everyday Life9h◆L2G-SR: Learning to Guide LLM-based Symbolic Regression9h◆Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows9h◆CompMat-Bench: Benchmarking AI Agents for Computational Materials Science9h◆Trajectory-Level Speculation with Semantic Verification for Tool-Using Agents9h◆Unfragmenting Nawatl NMT: Single-Variant Specialization for Masewalkopa9h◆LLMR: Knowledge Distillation with a Large Language Model-Induced Reward9h◆PRISM: PRIvileged Sibling-guided Multi-Agent Topology Design9h◆Training Reasoning Models to Generate Diverse Scientific Ideas9h◆Linear Independence of Polynomial Compositions with Bounded Input Degree9h◆Do LLMs have core beliefs?9h◆KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents9h◆Boltzmann generators for amorphous particle systems9h◆Diffusion Models in Simulation-Based Inference: A Tutorial Review9h◆Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks9h◆Replit’s CEO says the future of work is the ‘self-driving company’49m◆Bridging the Gap: Converting Read Text to Conversational Dialogue9h◆Sharp and Stable Label-Free Calibration Certificates from Logical Coherence9h◆Backward Compatibility in Zero-Bit Neural Audio Watermarking9h◆Disagreement-Guided Weak-to-Strong Routing9h◆One Is Not Enough: How People Use Multiple AI Models in Everyday Life9h◆L2G-SR: Learning to Guide LLM-based Symbolic Regression9h◆Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows9h◆CompMat-Bench: Benchmarking AI Agents for Computational Materials Science9h◆Trajectory-Level Speculation with Semantic Verification for Tool-Using Agents9h◆Unfragmenting Nawatl NMT: Single-Variant Specialization for Masewalkopa9h◆LLMR: Knowledge Distillation with a Large Language Model-Induced Reward9h◆PRISM: PRIvileged Sibling-guided Multi-Agent Topology Design9h◆Training Reasoning Models to Generate Diverse Scientific Ideas9h◆Linear Independence of Polynomial Compositions with Bounded Input Degree9h◆Do LLMs have core beliefs?9h◆KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents9h◆Boltzmann generators for amorphous particle systems9h◆Diffusion Models in Simulation-Based Inference: A Tutorial Review9h◆Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks9h◆
News/When Harnesses Lose the Signal: Causal Evaluation of Recovery in LLM Agents
arxiv
PublishedOctober 2, 2026 at 4:00 AM

When Harnesses Lose the Signal: Causal Evaluation of Recovery in LLM Agents

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

arXiv:2610.00372v1 Announce Type: new Abstract: Large language model agents rely on external harnesses to pass information between the model and its environment and to recover from execution errors. Yet recovery is usually judged only by average task success. This hides an important tension. The sam

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
arxivBridging the Gap: Converting Read Text to Conversational Dialogue9harxivSharp and Stable Label-Free Calibration Certificates from Logical Coherence9harxivBackward Compatibility in Zero-Bit Neural Audio Watermarking9harxivDisagreement-Guided Weak-to-Strong Routing9h
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