·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
MJ: Multi-turn LLM Jailbreaking via Decomposed Credit Assignment4h◆Query-Focused Event Summarization: A Dataset and Benchmark4h◆FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift4h◆Safe responses matter: Output-aware safety guardrail mitigate over-refusal in MLLMs4h◆Self-Compacting Language Model Agents4h◆TreeThink: A Modular Tree Search Library for Mathematical Reasoning with LLMs4h◆Nonparametric Bayesian Inverse Reinforcement Learning with Data-Parallel Gibbs Sampling4h◆Optimizing ARDL Models for Retail Sales Forecasting and Fair Pricing4h◆Integrating Background Knowledge for Scalable Causal Discovery4h◆Multimodal Routing for Interpretable, Robust, and Auditable Clinical Prediction4h◆FAD-SA-GRU: Enhancing Hate Speech Detection in Algerian Dialect Through Feature-Augmented Self-Attention GRU Networks4h◆Vilya-1: An all-atom foundation model for macrocycle structure prediction and design4h◆Memory Savings at What Cost? A Study of Alternatives to Backpropagation4h◆funOCLUST: Clustering Functional Data with Outliers4h◆FastTPS: An Optimized Method for LLM Token Phase for AI accelerators4h◆MLPs are Hebbians: Constructing Efficient Fact-Storing MLPs for Transformers4h◆Attribution-Guided Continual Learning for Large Language Models4h◆FlashTrie: A GPU-Accelerated Constrained Beam Search for Generative Retrieval4h◆Conservation Laws for Diffusion Models4h◆Error Aware Distribution Prediction for Lightweight Implicit Neural Representations4h◆MJ: Multi-turn LLM Jailbreaking via Decomposed Credit Assignment4h◆Query-Focused Event Summarization: A Dataset and Benchmark4h◆FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift4h◆Safe responses matter: Output-aware safety guardrail mitigate over-refusal in MLLMs4h◆Self-Compacting Language Model Agents4h◆TreeThink: A Modular Tree Search Library for Mathematical Reasoning with LLMs4h◆Nonparametric Bayesian Inverse Reinforcement Learning with Data-Parallel Gibbs Sampling4h◆Optimizing ARDL Models for Retail Sales Forecasting and Fair Pricing4h◆Integrating Background Knowledge for Scalable Causal Discovery4h◆Multimodal Routing for Interpretable, Robust, and Auditable Clinical Prediction4h◆FAD-SA-GRU: Enhancing Hate Speech Detection in Algerian Dialect Through Feature-Augmented Self-Attention GRU Networks4h◆Vilya-1: An all-atom foundation model for macrocycle structure prediction and design4h◆Memory Savings at What Cost? A Study of Alternatives to Backpropagation4h◆funOCLUST: Clustering Functional Data with Outliers4h◆FastTPS: An Optimized Method for LLM Token Phase for AI accelerators4h◆MLPs are Hebbians: Constructing Efficient Fact-Storing MLPs for Transformers4h◆Attribution-Guided Continual Learning for Large Language Models4h◆FlashTrie: A GPU-Accelerated Constrained Beam Search for Generative Retrieval4h◆Conservation Laws for Diffusion Models4h◆Error Aware Distribution Prediction for Lightweight Implicit Neural Representations4h◆
News/MJ: Multi-turn LLM Jailbreaking via Decomposed Credit Assignment
arxiv
PublishedJuly 14, 2026 at 4:00 AM
—neutral

MJ: Multi-turn LLM Jailbreaking via Decomposed Credit Assignment

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

arXiv:2607.11070v1 Announce Type: new Abstract: Modern large language models (LLMs) operate in interactive multi-turn settings, making multi-turn jailbreaking a realistic threat model and an important setting for automated red teaming. A core challenge in learning multi-turn jailbreak attackers is c

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 →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivQuery-Focused Event Summarization: A Dataset and Benchmark4harxivFedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift4harxivSafe responses matter: Output-aware safety guardrail mitigate over-refusal in MLLMs4harxivSelf-Compacting Language Model Agents4h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

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

Originally published on arxiv ↗
HomeModelsNews