·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Automatic Sentence Completion Using Bengali Audio Speech with a Transformer-Based Early Speech Recognition System2h◆What Should We Remove? Evaluating the Reliability and Costs of RLVR Data Decontamination2h◆FGSS-BO: Feedback-Guided Hypothesis Management for LLM-Based Bayesian Optimization2h◆LLM-Based Weak Annotation for Cybersecurity Tweet Classification: A Teacher-Student Framework2h◆Making every stage of medical device document assessment with large language models deterministic2h◆VALCON: Benchmarking and Enhancing Value Consistency in Large Language Models via Dual-Role Variation2h◆DPA-MoE: A Runtime Precision Control Framework for MoE Inference2h◆HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding2h◆Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation2h◆OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents2h◆Writability and the Pathway: A Controlled Study of Knowledge Injection into Addressed Memory2h◆ContextLift: Breaking the Spurious Correlation Behind Conditional Emergent Misalignment2h◆Bias Mitigation in Audio-Enabled LLMs through Adversarial Training and Distillation Methods2h◆Content associations with answer selection and engagement in supply chain community question answering2h◆Beyond Context Length: Characterising the Difficulty of Long-Context Reasoning2h◆The Right Byte First: How UTF-16 Helps Multilingual Boundary Prediction2h◆Patch where the text is new: a compression view of where byte-level models should spend compute2h◆Unfragmenting Nawatl NMT: Single-Variant Specialization for Masewalkopa2h◆Training Reasoning Models to Generate Diverse Scientific Ideas2h◆OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generative World Modeling2h◆Automatic Sentence Completion Using Bengali Audio Speech with a Transformer-Based Early Speech Recognition System2h◆What Should We Remove? Evaluating the Reliability and Costs of RLVR Data Decontamination2h◆FGSS-BO: Feedback-Guided Hypothesis Management for LLM-Based Bayesian Optimization2h◆LLM-Based Weak Annotation for Cybersecurity Tweet Classification: A Teacher-Student Framework2h◆Making every stage of medical device document assessment with large language models deterministic2h◆VALCON: Benchmarking and Enhancing Value Consistency in Large Language Models via Dual-Role Variation2h◆DPA-MoE: A Runtime Precision Control Framework for MoE Inference2h◆HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding2h◆Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation2h◆OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents2h◆Writability and the Pathway: A Controlled Study of Knowledge Injection into Addressed Memory2h◆ContextLift: Breaking the Spurious Correlation Behind Conditional Emergent Misalignment2h◆Bias Mitigation in Audio-Enabled LLMs through Adversarial Training and Distillation Methods2h◆Content associations with answer selection and engagement in supply chain community question answering2h◆Beyond Context Length: Characterising the Difficulty of Long-Context Reasoning2h◆The Right Byte First: How UTF-16 Helps Multilingual Boundary Prediction2h◆Patch where the text is new: a compression view of where byte-level models should spend compute2h◆Unfragmenting Nawatl NMT: Single-Variant Specialization for Masewalkopa2h◆Training Reasoning Models to Generate Diverse Scientific Ideas2h◆OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generative World Modeling2h◆
News/K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model
arxiv
PublishedJune 16, 2026 at 4:00 AM

K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model

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

arXiv:2509.25594v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented. They are usually trained on single knowledge sources and specific to individual tasks, modalities, or organs. This fragmentation con

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
arxivAutomatic Sentence Completion Using Bengali Audio Speech with a Transformer-Based Early Speech Recognition System2harxivWhat Should We Remove? Evaluating the Reliability and Costs of RLVR Data Decontamination2harxivFGSS-BO: Feedback-Guided Hypothesis Management for LLM-Based Bayesian Optimization2harxivLLM-Based Weak Annotation for Cybersecurity Tweet Classification: A Teacher-Student Framework2h
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