SlotGCG uses Vulnerable Slot Score (VSS) to identify and target the most vulnerable prompt positions for adversarial token insertion, delivering 14% higher ASR than standard GCG and 42% higher against defenses.
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From clip to dino: Visual encoders shout in multi-modal large language models
21 Pith papers cite this work. Polarity classification is still indexing.
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Localization in VLMs relies on a containerization mechanism driven by object-aligned tokens and a narrow set of specialized attention heads in early-to-mid or mid-to-late layers.
PASA is an embedding-space watermarking method for LLM text that uses semantic clusters and synchronized randomness to achieve robustness against paraphrasing while remaining distortion-free.
EXPO-SQL improves Text-to-SQL by using clause-level rewards derived from execution error messages and incremental clause execution instead of uniform query-level rewards.
CognitiveBench reveals LLMs suffer representation overlap on joint cognitive tasks due to hierarchical structure; HyCoLLM in hyperbolic space fixes the mismatch and outperforms GPT-4o with far fewer parameters.
FakeReasoning is an MLLM-based framework for unified forgery detection and reasoning on AI-generated images, supported by the new MMFR-Dataset of 120K images and 378K annotations across 10 generators.
CompressKV uses Semantic Retrieval Heads to guide KV-cache token selection and layer-wise budget allocation, retaining over 97% performance with 3% cache on LongBench QA tasks.
Reweighting training emphasis toward image-negative tokens and filtering hallucinated data reduces object hallucination in LVLMs across three model variants.
MultiBreak is a large diverse multi-turn jailbreak benchmark that achieves substantially higher attack success rates on LLMs than prior datasets and reveals topic-specific vulnerabilities in multi-turn settings.
Natural-domain foundation models provide competitive and more robust priors than task-specific models for accelerated cardiac MRI reconstruction in cross-domain settings.
G-MIXER achieves state-of-the-art zero-shot composed image retrieval by using geodesic mixup to build diverse implicit candidates and MLLM-derived explicit semantics for re-ranking.
HAWK is a training-free method that prunes over 80% of visual tokens in MLLMs while retaining 96% accuracy by using head importance weights and text-guided attention to select task-relevant tokens.
CoME-VL fuses contrastive and self-supervised vision encoders via entropy-guided multi-layer aggregation and RoPE cross-attention to improve vision-language model performance on benchmarks.
SpatialStack improves 3D spatial reasoning in vision-language models by stacking and synchronizing multi-level geometric features with the language backbone.
InCoM reports 23–28 percentage-point success-rate gains in mobile manipulation benchmarks by dynamically reweighting multi-scale perception via inferred motion intent and decoupling base-arm action generation with flow matching.
A neuro-symbolic system pairing LLMs for symbolic reasoning with neural delta controllers for execution delivers over 70% step reduction and up to 8.83x speedup in language-guided planar object manipulation while remaining robust to LLM quality.
NP-LoRA fuses subject and style LoRAs via null-space projection of the content update onto the orthogonal complement of the style subspace, with a soft variant controlled by one parameter.
RA-RRG extracts key phrases with LLMs, retrieves them via multimodal similarity, and conditions report generation on them to achieve SOTA CheXbert scores and competitive RadGraph F1 on MIMIC-CXR and IU X-ray while supporting multi-view inputs.
VaaWIT proposes DSAM and VAA modules to adapt LLMs for multilingual web image translation, claiming outperformance over open-source baselines on benchmarks.
The paper demonstrates that a tailored jailbreak method for querying groups of large models can achieve up to 100% success rate in some experiments on unprotected models, revealing overlooked multi-model safety risks.
Develops a taxonomy of security interaction levels in AI/cloud infrastructure and demonstrates practical attacks exploiting isolation assumptions.
citing papers explorer
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SlotGCG: Exploiting the Positional Vulnerability in LLMs for Jailbreak Attacks
SlotGCG uses Vulnerable Slot Score (VSS) to identify and target the most vulnerable prompt positions for adversarial token insertion, delivering 14% higher ASR than standard GCG and 42% higher against defenses.
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Mechanisms of Object Localization in Vision-Language Models
Localization in VLMs relies on a containerization mechanism driven by object-aligned tokens and a narrow set of specialized attention heads in early-to-mid or mid-to-late layers.
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PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks
PASA is an embedding-space watermarking method for LLM text that uses semantic clusters and synchronized randomness to achieve robustness against paraphrasing while remaining distortion-free.
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EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL
EXPO-SQL improves Text-to-SQL by using clause-level rewards derived from execution error messages and incremental clause execution instead of uniform query-level rewards.
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Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding
CognitiveBench reveals LLMs suffer representation overlap on joint cognitive tasks due to hierarchical structure; HyCoLLM in hyperbolic space fixes the mismatch and outperforms GPT-4o with far fewer parameters.
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Toward Generalizable Forgery Detection and Reasoning
FakeReasoning is an MLLM-based framework for unified forgery detection and reasoning on AI-generated images, supported by the new MMFR-Dataset of 120K images and 378K annotations across 10 generators.
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CompressKV: Semantic-Retrieval-Guided KV-Cache Compression for Resource-Efficient Long-Context LLM Inference
CompressKV uses Semantic Retrieval Heads to guide KV-cache token selection and layer-wise budget allocation, retaining over 97% performance with 3% cache on LongBench QA tasks.
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Reducing Object Hallucination in LVLMs via Emphasizing Image-negative Tokens
Reweighting training emphasis toward image-negative tokens and filtering hallucinated data reduces object hallucination in LVLMs across three model variants.
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MultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety
MultiBreak is a large diverse multi-turn jailbreak benchmark that achieves substantially higher attack success rates on LLMs than prior datasets and reveals topic-specific vulnerabilities in multi-turn settings.
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Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?
Natural-domain foundation models provide competitive and more robust priors than task-specific models for accelerated cardiac MRI reconstruction in cross-domain settings.
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G-MIXER: Geodesic Mixup-based Implicit Semantic Expansion and Explicit Semantic Re-ranking for Zero-Shot Composed Image Retrieval
G-MIXER achieves state-of-the-art zero-shot composed image retrieval by using geodesic mixup to build diverse implicit candidates and MLLM-derived explicit semantics for re-ranking.
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HAWK: Head Importance-Aware Visual Token Pruning in Multimodal Models
HAWK is a training-free method that prunes over 80% of visual tokens in MLLMs while retaining 96% accuracy by using head importance weights and text-guided attention to select task-relevant tokens.
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CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning
CoME-VL fuses contrastive and self-supervised vision encoders via entropy-guided multi-layer aggregation and RoPE cross-attention to improve vision-language model performance on benchmarks.
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SpatialStack: Layered Geometry-Language Fusion for 3D VLM Spatial Reasoning
SpatialStack improves 3D spatial reasoning in vision-language models by stacking and synchronizing multi-level geometric features with the language backbone.
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InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation
InCoM reports 23–28 percentage-point success-rate gains in mobile manipulation benchmarks by dynamically reweighting multi-scale perception via inferred motion intent and decoupling base-arm action generation with flow matching.
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Neuro-Symbolic Control with Large Language Models for Language-Guided Spatial Tasks
A neuro-symbolic system pairing LLMs for symbolic reasoning with neural delta controllers for execution delivers over 70% step reduction and up to 8.83x speedup in language-guided planar object manipulation while remaining robust to LLM quality.
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NP-LoRA: Null Space Projection for Subject-Style LoRA Fusion
NP-LoRA fuses subject and style LoRAs via null-space projection of the content update onto the orthogonal complement of the style subspace, with a soft variant controlled by one parameter.
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RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extraction
RA-RRG extracts key phrases with LLMs, retrieves them via multimodal similarity, and conditions report generation on them to achieve SOTA CheXbert scores and competitive RadGraph F1 on MIMIC-CXR and IU X-ray while supporting multi-view inputs.
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VaaWIT: Visual-Aware Adaptation of Large Language Models for Multilingual Web Image Translation
VaaWIT proposes DSAM and VAA modules to adapt LLMs for multilingual web image translation, claiming outperformance over open-source baselines on benchmarks.
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New Wide-Net-Casting Jailbreak Attacks Risk Large Models
The paper demonstrates that a tailored jailbreak method for querying groups of large models can achieve up to 100% success rate in some experiments on unprotected models, revealing overlooked multi-model safety risks.
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Investigating The Security of Modern AI and Cloud Infrastructure
Develops a taxonomy of security interaction levels in AI/cloud infrastructure and demonstrates practical attacks exploiting isolation assumptions.