·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Perturbation-Based Data Selection for Medical Question Answering2h◆Adaptive Transfer of Representational Signatures for LLM Reliability Detection2h◆Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool2h◆Incremental Predictive Value of LLM Tutoring Interactions for Student Performance2h◆Small Edits, Shifting Decisions: Benchmarking LLM Accuracy and Reliability on French Input Variants2h◆Five Architectures, One Shared Space Cross-Model Representational Alignment Without Weight Merging2h◆Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents2h◆When the Weakest Language Keeps Moving: ARMOR-DP for Multilingual BabyLM2h◆EdgeDeID: A 0.6B Clinical De-identifier Trained Without Real Patient Data2h◆When Personas Become Harmful: Guiding LLMs with Global Citizenship Values Alignment2h◆"High-Quality" Data for Early-Stage Instruction Tuning: An Empirical Study of Data Quality Metrics2h◆Reversible Pseudonymisation of Documents Sent to External LLMs: an Open Bilingual Benchmark and an Engine2h◆Memory Tax: Dissecting the Net Value of Agent Memory2h◆The LALA Benchmark: Evaluating Literary and Linguistic Analysis in LLMs2h◆Higher Reasoning Effort Improves Reference Verifiability and Changes Reasoning Summaries in GPT-5.52h◆Synthetic linguistic data2h◆SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models2h◆A Russian, a Brazilian, and a German Walk into a Bar, Ask the Same Question-- And Get Three Different Answers: Profiling LLM Perspective Diversity on Contested Topics2h◆Transformer to GPT-5: Advancements, Limitations, and Future Directions in Large Language Model Design2h◆Factual Yet One-Sided: Stance Poisoning and Evidence Balancing in Retrieval-Augmented Generation2h◆Perturbation-Based Data Selection for Medical Question Answering2h◆Adaptive Transfer of Representational Signatures for LLM Reliability Detection2h◆Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool2h◆Incremental Predictive Value of LLM Tutoring Interactions for Student Performance2h◆Small Edits, Shifting Decisions: Benchmarking LLM Accuracy and Reliability on French Input Variants2h◆Five Architectures, One Shared Space Cross-Model Representational Alignment Without Weight Merging2h◆Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents2h◆When the Weakest Language Keeps Moving: ARMOR-DP for Multilingual BabyLM2h◆EdgeDeID: A 0.6B Clinical De-identifier Trained Without Real Patient Data2h◆When Personas Become Harmful: Guiding LLMs with Global Citizenship Values Alignment2h◆"High-Quality" Data for Early-Stage Instruction Tuning: An Empirical Study of Data Quality Metrics2h◆Reversible Pseudonymisation of Documents Sent to External LLMs: an Open Bilingual Benchmark and an Engine2h◆Memory Tax: Dissecting the Net Value of Agent Memory2h◆The LALA Benchmark: Evaluating Literary and Linguistic Analysis in LLMs2h◆Higher Reasoning Effort Improves Reference Verifiability and Changes Reasoning Summaries in GPT-5.52h◆Synthetic linguistic data2h◆SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models2h◆A Russian, a Brazilian, and a German Walk into a Bar, Ask the Same Question-- And Get Three Different Answers: Profiling LLM Perspective Diversity on Contested Topics2h◆Transformer to GPT-5: Advancements, Limitations, and Future Directions in Large Language Model Design2h◆Factual Yet One-Sided: Stance Poisoning and Evidence Balancing in Retrieval-Augmented Generation2h◆
News/Demystifying Mergeability: Interpretable Properties to Predict Model Merging Success
arxiv
PublishedApril 13, 2026 at 4:00 AM
—neutral

Demystifying Mergeability: Interpretable Properties to Predict Model Merging Success

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

arXiv:2601.22285v4 Announce Type: replace Abstract: Model merging combines knowledge from separately fine-tuned models, yet success factors remain poorly understood. While recent work treats mergeability as an intrinsic property, we show with an architecture-agnostic framework that it fundamentally

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 →
Tags
04
#machine-learning#model-merging#fine-tuning#compatibility

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-learning#model-merging#fine-tuning#compatibility

Related coverage

More from ARXIV
arxivAdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science2harxivScoped Semantic Invariance as a Foundation for LLM Reliability2harxivGEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation2harxivEstimation of Propagated Risk in LLM-based Multi-Agent Systems2h
The Bubble Brief
MON · WED · FRI

Read machine-learning 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