·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Perturbation-Based Data Selection for Medical Question Answering1h◆Adaptive Transfer of Representational Signatures for LLM Reliability Detection1h◆Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool1h◆Incremental Predictive Value of LLM Tutoring Interactions for Student Performance1h◆Small Edits, Shifting Decisions: Benchmarking LLM Accuracy and Reliability on French Input Variants1h◆Five Architectures, One Shared Space Cross-Model Representational Alignment Without Weight Merging1h◆Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents1h◆When the Weakest Language Keeps Moving: ARMOR-DP for Multilingual BabyLM1h◆EdgeDeID: A 0.6B Clinical De-identifier Trained Without Real Patient Data1h◆When Personas Become Harmful: Guiding LLMs with Global Citizenship Values Alignment1h◆"High-Quality" Data for Early-Stage Instruction Tuning: An Empirical Study of Data Quality Metrics1h◆Reversible Pseudonymisation of Documents Sent to External LLMs: an Open Bilingual Benchmark and an Engine1h◆Memory Tax: Dissecting the Net Value of Agent Memory1h◆The LALA Benchmark: Evaluating Literary and Linguistic Analysis in LLMs1h◆Higher Reasoning Effort Improves Reference Verifiability and Changes Reasoning Summaries in GPT-5.51h◆Synthetic linguistic data1h◆SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models1h◆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 Topics1h◆Transformer to GPT-5: Advancements, Limitations, and Future Directions in Large Language Model Design1h◆Factual Yet One-Sided: Stance Poisoning and Evidence Balancing in Retrieval-Augmented Generation1h◆Perturbation-Based Data Selection for Medical Question Answering1h◆Adaptive Transfer of Representational Signatures for LLM Reliability Detection1h◆Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool1h◆Incremental Predictive Value of LLM Tutoring Interactions for Student Performance1h◆Small Edits, Shifting Decisions: Benchmarking LLM Accuracy and Reliability on French Input Variants1h◆Five Architectures, One Shared Space Cross-Model Representational Alignment Without Weight Merging1h◆Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents1h◆When the Weakest Language Keeps Moving: ARMOR-DP for Multilingual BabyLM1h◆EdgeDeID: A 0.6B Clinical De-identifier Trained Without Real Patient Data1h◆When Personas Become Harmful: Guiding LLMs with Global Citizenship Values Alignment1h◆"High-Quality" Data for Early-Stage Instruction Tuning: An Empirical Study of Data Quality Metrics1h◆Reversible Pseudonymisation of Documents Sent to External LLMs: an Open Bilingual Benchmark and an Engine1h◆Memory Tax: Dissecting the Net Value of Agent Memory1h◆The LALA Benchmark: Evaluating Literary and Linguistic Analysis in LLMs1h◆Higher Reasoning Effort Improves Reference Verifiability and Changes Reasoning Summaries in GPT-5.51h◆Synthetic linguistic data1h◆SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models1h◆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 Topics1h◆Transformer to GPT-5: Advancements, Limitations, and Future Directions in Large Language Model Design1h◆Factual Yet One-Sided: Stance Poisoning and Evidence Balancing in Retrieval-Augmented Generation1h◆
News/Multimodal Language Models Cannot Spot Spatial Inconsistencies
arxiv
PublishedApril 6, 2026 at 4:00 AM
▼bearish

Multimodal Language Models Cannot Spot Spatial Inconsistencies

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

arXiv:2604.00799v2 Announce Type: replace-cross Abstract: Spatial consistency is a fundamental property of the visual world and a key requirement for models that aim to understand physical reality. Despite recent advances, multimodal large language models (MLLMs) often struggle to reason about 3D ge

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
03
#computer-vision#machine-learning#evaluation

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#computer-vision#machine-learning#evaluation

Related coverage

More from ARXIV
arxivPerturbation-Based Data Selection for Medical Question Answering1harxivAdaptive Transfer of Representational Signatures for LLM Reliability Detection1harxivPropasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool1harxivIncremental Predictive Value of LLM Tutoring Interactions for Student Performance1h
The Bubble Brief
MON · WED · FRI

Read computer-vision 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