TCD-Arena is a new customizable testing framework that runs millions of experiments to map how 33 different assumption violations affect time series causal discovery methods and shows ensembles can boost overall robustness.
Jina clip: Your clip model is also your text retriever
8 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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2026 8verdicts
UNVERDICTED 8representative citing papers
MARVEL reaches 37.9 nDCG@10 on the MM-BRIGHT benchmark by combining LLM query expansion, a reasoning-enhanced dense retriever, and GPT-4o CoT reranking, beating prior multimodal encoders by 10.3 points.
GELATO extends frozen Jina Embeddings v5 text models with locked non-text encoders, training only connectors to produce competitive multimodal embeddings while preserving exact text performance.
Multimodal embedding distance predicts typographic attack success rate across VLMs, and optimizing it under bounded perturbations on surrogates exposes two co-occurring failure modes of lost readability and reduced safety refusals.
MACCO applies cross-modal masked reconstruction of compositional concepts with inter- and intra-modal auxiliary objectives to improve visio-linguistic compositionality in VLMs.
Text-image embedding distance negatively correlates with typographic attack success rates (r = -0.71 to -0.93) on VLMs, with font size and image degradations strongly modulating effectiveness.
BRIDGE reaches 29.7 nDCG@10 on MM-BRIGHT by RL-aligning multimodal queries to text and using a reasoning retriever, beating multimodal encoders and, when combined with Nomic-Vision, exceeding the best text-only retriever at 33.3.
ReasonCLIP-58M applies continual pretraining with visually grounded reasoning captions on 58M examples to improve CLIP-style models on commonsense and compositional reasoning tasks.
citing papers explorer
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TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations
TCD-Arena is a new customizable testing framework that runs millions of experiments to map how 33 different assumption violations affect time series causal discovery methods and shows ensembles can boost overall robustness.
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MARVEL: Multimodal Adaptive Reasoning-intensiVe Expand-rerank and retrievaL
MARVEL reaches 37.9 nDCG@10 on the MM-BRIGHT benchmark by combining LLM query expansion, a reasoning-enhanced dense retriever, and GPT-4o CoT reranking, beating prior multimodal encoders by 10.3 points.
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jina-embeddings-v5-omni: Geometry-preserving Embeddings via Locked Aligned Towers
GELATO extends frozen Jina Embeddings v5 text models with locked non-text encoders, training only connectors to produce competitive multimodal embeddings while preserving exact text performance.
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One Perturbation, Two Failure Modes: Probing VLM Safety via Embedding-Guided Typographic Perturbations
Multimodal embedding distance predicts typographic attack success rate across VLMs, and optimizing it under bounded perturbations on surrogates exposes two co-occurring failure modes of lost readability and reduced safety refusals.
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Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality
MACCO applies cross-modal masked reconstruction of compositional concepts with inter- and intra-modal auxiliary objectives to improve visio-linguistic compositionality in VLMs.
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Reading Between the Pixels: Linking Text-Image Embedding Alignment to Typographic Attack Success on Vision-Language Models
Text-image embedding distance negatively correlates with typographic attack success rates (r = -0.71 to -0.93) on VLMs, with font size and image degradations strongly modulating effectiveness.
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BRIDGE: Multimodal-to-Text Retrieval via Reinforcement-Learned Query Alignment
BRIDGE reaches 29.7 nDCG@10 on MM-BRIGHT by RL-aligning multimodal queries to text and using a reasoning retriever, beating multimodal encoders and, when combined with Nomic-Vision, exceeding the best text-only retriever at 33.3.
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ReasonCLIP-58M: Visually Grounded Commonsense Reasoning Supervision for CLIP
ReasonCLIP-58M applies continual pretraining with visually grounded reasoning captions on 58M examples to improve CLIP-style models on commonsense and compositional reasoning tasks.