CoVUBench is the first benchmark framework for evaluating multimodal copyright unlearning in LVLMs via synthetic data, systematic variations, and a dual protocol for forgetting efficacy and utility preservation.
Scalable vision language model training via high quality data curation
10 Pith papers cite this work. Polarity classification is still indexing.
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MMSearch-R1 uses reinforcement learning to train multimodal models for on-demand multi-turn internet search with image and text tools, outperforming same-size RAG baselines and matching larger ones while cutting search calls by over 30%.
VGR introduces a visual-grounded reasoning MLLM that detects and replays image regions during inference, achieving gains on visual benchmarks with 30% fewer image tokens than the LLaVA-NeXT-7B baseline.
A new 1,248-instance paired benchmark shows 18 vision-language models drop substantially in accuracy when images contain misleading visual cues.
Circle-RoPE achieves cross-modal positional disentanglement in VLMs by mapping 2D image tokens to a cone-like annulus orthogonal to the text axis, with PTD=0 eliminating RoPE geometric bias while preserving intra-image structure via alternating geometry encoding.
The survey formalizes MLLM perception as a unified vision-language capability and traces its evolution via a new five-stage taxonomy while outlining future challenges.
ELVA uses rule-based RL rewards to rank negatives by similarity, reducing grain blindness in universal multimodal retrieval and reporting a 13.1% gain on a new multi-grain benchmark.
Modality-mutual attention (MMA) is introduced to replace causal attention in MLLMs, enabling mutual attention between image and text tokens and claiming SOTA results on 12 multimodal benchmarks with no extra parameters.
Injecting a max-reward ground-truth answer into a GRPO group whenever all sampled responses fail prevents optimization collapse and matches full fine-tuning's domain scores on three benchmark tasks.
Advanced language representations shape LLMs' schemas to improve knowledge activation and problem-solving.
citing papers explorer
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Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models
CoVUBench is the first benchmark framework for evaluating multimodal copyright unlearning in LVLMs via synthetic data, systematic variations, and a dual protocol for forgetting efficacy and utility preservation.
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MMSearch-R1: Incentivizing LMMs to Search
MMSearch-R1 uses reinforcement learning to train multimodal models for on-demand multi-turn internet search with image and text tools, outperforming same-size RAG baselines and matching larger ones while cutting search calls by over 30%.
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VGR: Visual Grounded Reasoning
VGR introduces a visual-grounded reasoning MLLM that detects and replays image regions during inference, achieving gains on visual benchmarks with 30% fewer image tokens than the LLaVA-NeXT-7B baseline.
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MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs
A new 1,248-instance paired benchmark shows 18 vision-language models drop substantially in accuracy when images contain misleading visual cues.
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Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Large Vision-Language Models
Circle-RoPE achieves cross-modal positional disentanglement in VLMs by mapping 2D image tokens to a cone-like annulus orthogonal to the text axis, with PTD=0 eliminating RoPE geometric bias while preserving intra-image structure via alternating geometry encoding.
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From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models
The survey formalizes MLLM perception as a unified vision-language capability and traces its evolution via a new five-stage taxonomy while outlining future challenges.
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ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval
ELVA uses rule-based RL rewards to rank negatives by similarity, reducing grain blindness in universal multimodal retrieval and reporting a 13.1% gain on a new multi-grain benchmark.
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Seeing is Understanding: Unlocking Causal Attention into Modality-Mutual Attention for Multimodal LLMs
Modality-mutual attention (MMA) is introduced to replace causal attention in MLLMs, enabling mutual attention between image and text tokens and claiming SOTA results on 12 multimodal benchmarks with no extra parameters.
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S-GRPO: Unified Post-Training for Large Vision-Language Models
Injecting a max-reward ground-truth answer into a GRPO group whenever all sampled responses fail prevents optimization collapse and matches full fine-tuning's domain scores on three benchmark tasks.
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Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding
Advanced language representations shape LLMs' schemas to improve knowledge activation and problem-solving.