·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
JB Pritzker thinks AI could help us live to 100 — or threaten us like nukes1h◆A hacker broke into John Deere and SharkNinja with an army of AI agents1h◆Democrats think cracking down on AI could give them an edge3h◆AI researchers stranded Claude on a virtual desert island. It started to “play.”6h◆A Theoretical Analysis of Test-Driven Code Generation11h◆Scalable Adaptation of 3D Geometric Foundation Models via Weak Supervision from Internet Video11h◆Can Graph Learning Learn Circuits?11h◆Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery11h◆Trajectory-Guided Steering: Mitigating Spurious Feature Bias in Clinical Classification11h◆Sentence-Level Topic Modeling with Lexical and Contextual Evidence11h◆Referring Multi-Object Tracking with Diverse Dynamic Expressions11h◆Rarity of Singletons Matters & Supplementary Materials11h◆TANGO: RNA Topology and Geometry Co-Design11h◆Handwritten Text Recognition Lives in the High-Pixel Variance Subspace11h◆RealtimeWAM: One-Step Asynchronous World Action Models11h◆How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?11h◆R$^2$ Flow: Recursive Self-Improvement via Recursive Skill Evolution11h◆Logic-Guided Graph Denoising Diffusion Models11h◆Unit-level aggregation breaks coverage in causal foundation models11h◆Mask-Based Deep Symbolic Regression11h◆JB Pritzker thinks AI could help us live to 100 — or threaten us like nukes1h◆A hacker broke into John Deere and SharkNinja with an army of AI agents1h◆Democrats think cracking down on AI could give them an edge3h◆AI researchers stranded Claude on a virtual desert island. It started to “play.”6h◆A Theoretical Analysis of Test-Driven Code Generation11h◆Scalable Adaptation of 3D Geometric Foundation Models via Weak Supervision from Internet Video11h◆Can Graph Learning Learn Circuits?11h◆Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery11h◆Trajectory-Guided Steering: Mitigating Spurious Feature Bias in Clinical Classification11h◆Sentence-Level Topic Modeling with Lexical and Contextual Evidence11h◆Referring Multi-Object Tracking with Diverse Dynamic Expressions11h◆Rarity of Singletons Matters & Supplementary Materials11h◆TANGO: RNA Topology and Geometry Co-Design11h◆Handwritten Text Recognition Lives in the High-Pixel Variance Subspace11h◆RealtimeWAM: One-Step Asynchronous World Action Models11h◆How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?11h◆R$^2$ Flow: Recursive Self-Improvement via Recursive Skill Evolution11h◆Logic-Guided Graph Denoising Diffusion Models11h◆Unit-level aggregation breaks coverage in causal foundation models11h◆Mask-Based Deep Symbolic Regression11h◆
News/Can Failed Trajectories Reveal Where Agents Go Wrong? Agentic Failure Attribution Without Step-Level Labels
arxiv
PublishedOctober 12, 2026 at 4:00 AM

Can Failed Trajectories Reveal Where Agents Go Wrong? Agentic Failure Attribution Without Step-Level Labels

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

arXiv:2610.13105v1 Announce Type: new Abstract: Failure attribution for LLM agents aims to identify decisive steps responsible for task failure, which is critical for debugging and improving agentic systems. Existing methods rely on costly LLM prompting, step-level error annotations, or verified suc

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
arxivA Theoretical Analysis of Test-Driven Code Generation11harxivScalable Adaptation of 3D Geometric Foundation Models via Weak Supervision from Internet Video11harxivCan Graph Learning Learn Circuits?11harxivBeyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery11h
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