Sparse autoencoders inserted into VLMs and trained only for reconstruction can reliably detect adversarial attacks on images, including unseen domains and attack types.
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18 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
CCTVBench exposes a large gap between standard QA accuracy and contrastive consistency in traffic video reasoning for multimodal LLMs and introduces C-TCD to narrow that gap.
Introduces the UCSF-PDGM-VQA dataset of 2387 QA pairs from 473 glioma MRI studies and demonstrates that state-of-the-art VLMs exhibit modality collapse on multi-sequence 3D medical images.
BRITE benchmark reveals that leading T2V models handle static object composition well but degrade sharply on object-action binding and audio-visual synchronization for implausible prompts.
PGT generates synthetic tasks via geometric overlays on images to supply dense visual supervision, improving spatial and relational understanding in MLLMs by up to 20% on targeted benchmarks.
DMN achieves over 90% attack success rate on GPT-4o, Gemini-2.5-pro and Claude Sonnet 4 by distributing instructions, supplying multimodal evidence, and adding number chain tasks across multiple images.
OProver-32B achieves top Pass@32 scores on MiniF2F, ProverBench, and PutnamBench by combining continued pretraining with iterative agentic proving, retrieval, SFT on repairs, and RL on unresolved cases using a 6.86M-proof dataset.
PA-BDM adapts block diffusion by switching to causal intra-block denoising and dynamically committing reliable prefixes to KV cache, yielding higher accuracy and 71.6% higher throughput than a comparable baseline on document benchmarks.
Imagining in 360° decouples visual search into a single-step probabilistic semantic layout predictor and an actor, removing the need for multi-turn CoT reasoning and trajectory annotations while improving efficiency in 360° environments.
F^3A is a training-free visual token pruning router that treats pruning as task-conditioned evidence search and allocates a fixed vision token budget using question cues and frozen sparse heads without extra LLM passes.
Probabilistic programs of thought let LLMs produce many program variants from one generation by building a compact probabilistic representation of the token distribution.
PRISM interleaves VLM perception and LLM reasoning via a dynamic goal-oriented question-answer pipeline to produce sharper scene descriptions, outperforming prior image-based models on ALFWorld and Room-to-Room.
VANGUARD is a staged-training VLM framework that reports 94% ROC-AUC and 84% F1 on UCF-Crime while adding chain-of-thought reasoning and spatial grounding to video anomaly detection.
NVIDIA releases the Nemotron 3 model family with hybrid Mamba-Transformer architecture, LatentMoE, NVFP4 training, MTP layers, and multi-environment RL post-training for reasoning and agentic tasks.
citing papers explorer
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Sparse Autoencoders as Plug-and-Play Firewalls for Adversarial Attack Detection in VLMs
Sparse autoencoders inserted into VLMs and trained only for reconstruction can reliably detect adversarial attacks on images, including unseen domains and attack types.
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CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs
CCTVBench exposes a large gap between standard QA accuracy and contrastive consistency in traffic video reasoning for multimodal LLMs and introduces C-TCD to narrow that gap.
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UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation
Introduces the UCSF-PDGM-VQA dataset of 2387 QA pairs from 473 glioma MRI studies and demonstrates that state-of-the-art VLMs exhibit modality collapse on multi-sequence 3D medical images.
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BRITE: A Benchmark for Reliable and Interpretable T2V Evaluation on Implausible Scenarios
BRITE benchmark reveals that leading T2V models handle static object composition well but degrade sharply on object-action binding and audio-visual synchronization for implausible prompts.
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PGT: Procedurally Generated Tasks for improving visual grounding in MLLMs
PGT generates synthetic tasks via geometric overlays on images to supply dense visual supervision, improving spatial and relational understanding in MLLMs by up to 20% on targeted benchmarks.
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DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs
DMN achieves over 90% attack success rate on GPT-4o, Gemini-2.5-pro and Claude Sonnet 4 by distributing instructions, supplying multimodal evidence, and adding number chain tasks across multiple images.
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OProver: A Unified Framework for Agentic Formal Theorem Proving
OProver-32B achieves top Pass@32 scores on MiniF2F, ProverBench, and PutnamBench by combining continued pretraining with iterative agentic proving, retrieval, SFT on repairs, and RL on unresolved cases using a 6.86M-proof dataset.
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Prefix-Adaptive Block Diffusion for Efficient Document Recognition
PA-BDM adapts block diffusion by switching to causal intra-block denoising and dynamically committing reliable prefixes to KV cache, yielding higher accuracy and 71.6% higher throughput than a comparable baseline on document benchmarks.
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Beyond Thinking: Imagining in 360$^\circ$ for Humanoid Visual Search
Imagining in 360° decouples visual search into a single-step probabilistic semantic layout predictor and an actor, removing the need for multi-turn CoT reasoning and trajectory annotations while improving efficiency in 360° environments.
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How Many Visual Tokens Do Multimodal Language Models Need? Scaling Visual Token Pruning with F^3A
F^3A is a training-free visual token pruning router that treats pruning as task-conditioned evidence search and allocates a fixed vision token budget using question cues and frozen sparse heads without extra LLM passes.
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Probabilistic Programs of Thought
Probabilistic programs of thought let LLMs produce many program variants from one generation by building a compact probabilistic representation of the token distribution.
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PRISM: Perception Reasoning Interleaved for Sequential Decision Making
PRISM interleaves VLM perception and LLM reasoning via a dynamic goal-oriented question-answer pipeline to produce sharper scene descriptions, outperforming prior image-based models on ALFWorld and Room-to-Room.
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Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models
VANGUARD is a staged-training VLM framework that reports 94% ROC-AUC and 84% F1 on UCF-Crime while adding chain-of-thought reasoning and spatial grounding to video anomaly detection.
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NVIDIA Nemotron 3: Efficient and Open Intelligence
NVIDIA releases the Nemotron 3 model family with hybrid Mamba-Transformer architecture, LatentMoE, NVFP4 training, MTP layers, and multi-environment RL post-training for reasoning and agentic tasks.
- Occlusion-Aware Physics-Semantic Keyframe Selection for Robust Video Editing
- Bad Seeing or Bad Thinking? Rewarding Perception for Multimodal Reasoning
- Unified Pix Token And Word Token Generative Language Model
- Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models