·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Bridging the Gap: Converting Read Text to Conversational Dialogue1h◆Truth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance1h◆RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting1h◆Witness Overlap: Directional Provenance Inside Open-Weight Model Families1h◆From Migration to Calibration: Aligning Agents with Deployment Requirements1h◆CompMat-Bench: Benchmarking AI Agents for Computational Materials Science1h◆Global Coherence: When Every Agent Is Right and the Team Is Still Wrong - A Local-to-Global Semantic Foundation for Multi-Agent Collaboration1h◆KV$^2$: A Self-Refining KV Cache1h◆Video-Time Priors for EEG-fNIRS Emotion Regression on Familiar Videos1h◆Asynchrony Is Nearly Free for Evolution Strategies on Long-Horizon LLM Agentic Tasks1h◆OTel: Open Telco AI Datasets, Benchmarks, and Models1h◆SCOPE: Certified Theorem Proving with a Language Model as the Policy Planner1h◆ParanoiaEval: Benchmarking Unnecessary Defensive Work in Agentic Coding1h◆AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery1h◆RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations1h◆LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints1h◆ReTerm: Recovery Training for Self-Improving Terminal Agents1h◆What Coding Agents Leave Unsaid: Measuring Task-State Gaps Between Execution and Reports1h◆Co-Evolving Acting and Forecasting for Language Agents1h◆Distributionally Robust Regularization of Sparse Integer Programming Trained Learning Models1h◆Bridging the Gap: Converting Read Text to Conversational Dialogue1h◆Truth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance1h◆RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting1h◆Witness Overlap: Directional Provenance Inside Open-Weight Model Families1h◆From Migration to Calibration: Aligning Agents with Deployment Requirements1h◆CompMat-Bench: Benchmarking AI Agents for Computational Materials Science1h◆Global Coherence: When Every Agent Is Right and the Team Is Still Wrong - A Local-to-Global Semantic Foundation for Multi-Agent Collaboration1h◆KV$^2$: A Self-Refining KV Cache1h◆Video-Time Priors for EEG-fNIRS Emotion Regression on Familiar Videos1h◆Asynchrony Is Nearly Free for Evolution Strategies on Long-Horizon LLM Agentic Tasks1h◆OTel: Open Telco AI Datasets, Benchmarks, and Models1h◆SCOPE: Certified Theorem Proving with a Language Model as the Policy Planner1h◆ParanoiaEval: Benchmarking Unnecessary Defensive Work in Agentic Coding1h◆AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery1h◆RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations1h◆LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints1h◆ReTerm: Recovery Training for Self-Improving Terminal Agents1h◆What Coding Agents Leave Unsaid: Measuring Task-State Gaps Between Execution and Reports1h◆Co-Evolving Acting and Forecasting for Language Agents1h◆Distributionally Robust Regularization of Sparse Integer Programming Trained Learning Models1h◆
News/Natural Code Transformation Confuses Agents in Vulnerability Analysis
arxiv
PublishedOctober 15, 2026 at 4:00 AM

Natural Code Transformation Confuses Agents in Vulnerability Analysis

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

arXiv:2610.14350v2 Announce Type: cross Abstract: Having demonstrated a strong potential for automating vulnerability discovery, AI agents can also be used to attack software systems at scale. Today's software systems need mechanisms to level the playing field by slowing down potential attacks, givi

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 Dialogue1harxivTruth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance1harxivRICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting1harxivWitness Overlap: Directional Provenance Inside Open-Weight Model Families1h
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