Poisoning a single connector in MLLMs establishes a reusable latent backdoor pathway that transfers across modalities with over 95% attack success rate under bounded perturbations.
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Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
Canonical reference. 93% of citing Pith papers cite this work as background.
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Background
93% of classified citations
abstract
ChatGPT is attracting a cross-field interest as it provides a language interface with remarkable conversational competency and reasoning capabilities across many domains. However, since ChatGPT is trained with languages, it is currently not capable of processing or generating images from the visual world. At the same time, Visual Foundation Models, such as Visual Transformers or Stable Diffusion, although showing great visual understanding and generation capabilities, they are only experts on specific tasks with one-round fixed inputs and outputs. To this end, We build a system called \textbf{Visual ChatGPT}, incorporating different Visual Foundation Models, to enable the user to interact with ChatGPT by 1) sending and receiving not only languages but also images 2) providing complex visual questions or visual editing instructions that require the collaboration of multiple AI models with multi-steps. 3) providing feedback and asking for corrected results. We design a series of prompts to inject the visual model information into ChatGPT, considering models of multiple inputs/outputs and models that require visual feedback. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models. Our system is publicly available at \url{https://github.com/microsoft/visual-chatgpt}.
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representative citing papers
TerraBench is a new benchmark with 403 tasks across Earth-science domains that evaluates LLM agents on coordinating heterogeneous data using executable ReAct-style workflows and process-level metrics.
Skill-3D improves MLLM agent tool use in 3D spatial reasoning from 39% to 78% on VSI-Bench by evolving reusable scene-aware skills from aggregated trajectories stored in a scene memory.
OctoT2I uses a no-supervision PSEL loop to discover model capability frontiers and route T2I tasks, reaching 0.96 GenEval score with 90.3% speedup over Flow-GRPO.
Proposes an equation-anchored tool-use method for MLLMs that writes the pinhole back-projection equation in Chain-of-Thought and substitutes retrieved camera intrinsics and depths to achieve robustness in 3D object detection and visual grounding under rescaled intrinsics.
Image editing models fail zero-shot visual planning on abstract mazes and queen puzzles but generalize after finetuning, yet still cannot match human zero-shot efficiency.
AnchorSeg uses ordered query banks of latent reasoning tokens plus a spatial anchor token and a Token-Mask Cycle Consistency loss to achieve 67.7% gIoU and 68.1% cIoU on the ReasonSeg benchmark.
SUPERGLASSES is the first VQA benchmark built from actual smart glasses data, and SUPERLENS is an agent using automatic object detection, query decoupling, and multimodal search that outperforms GPT-4o by 2.19% on it.
FaSTA* combines LLM fast planning with A* search and inductive subroutine mining to create an efficient agent for multi-turn image editing tasks.
This survey provides the first comprehensive overview of deep multimodal learning methods designed to remain robust when some input modalities are absent.
GAIA benchmark shows humans at 92% accuracy on simple real-world questions far outperform current AI systems at 15%, proposing this gap as a key milestone for general AI.
Set-of-Mark prompting marks segmented image regions with alphanumerics and masks to let GPT-4V achieve state-of-the-art zero-shot results on referring expression comprehension and segmentation benchmarks like RefCOCOg.
VideoChat integrates video models and LLMs via a learnable interface for chat-based spatiotemporal and causal video reasoning, trained on a new video-centric instruction dataset.
LLaVA is trained on GPT-4 generated visual instruction data to achieve 85.1% relative performance to GPT-4 on synthetic multimodal tasks and 92.53% accuracy on Science QA.
S-Agent augments VLMs with spatial tools, scene and agent memory for evidence accumulation on multi-view and video tasks, and produces an 8B model via SFT on its own trajectories that beats same-scale baselines.
MedCTA is a new benchmark with 107 real-world tasks and process-aware metrics that shows frontier multimodal models remain brittle at autonomous tool selection, execution, and trajectory completion in clinical settings.
HDRAgent is the first agent-driven framework for multi-exposure HDR imaging that uses MLLM scene perception, contextual knowledge matching, and perception-distortion feedback to reduce ghosting artifacts.
MUSE is a unified agentic harness that improves off-the-shelf MLLMs on visual spatial planning, perception, multimodal reasoning, and fine-grained discrimination benchmarks through structured execution modules and verifier-guided repair without model retraining.
VESTA introduces dynamic tool creation for VLMs that outperforms static-tool and no-tool baselines on distribution fitting, time series, and astronomy tasks in the new DAWN benchmark.
InterSketch improves long-horizon visual-textual chain-of-thought in VLMs by dynamically generating and interleaving self-correcting visual sketches with text, using a synthesized dataset plus reflection in cold-start followed by stepwise-reward RL, and reports outperforming Gemini-3-Pro on benchmar
ClueAegis introduces a heuristic-to-reasoning cognitive skill framework and ClueAegis-Bench for evidence-based synthetic image detection that outperforms end-to-end classifiers in generalization and explainability.
Maestro uses outcome-based RL to train a lightweight policy that orchestrates ensembles of frozen expert models and skills, reporting 70.1% average accuracy across ten multimodal benchmarks and outperforming GPT-5 and Gemini-2.5-Pro while generalizing to unseen components.
HierVA improves multi-step chart question answering by having a high-level manager maintain key joint contexts while specialized workers perform targeted reasoning with visual zoom-in.
RaTA-Tool retrieves suitable external tools for multimodal queries by matching generated task descriptions against tool metadata, supported by a new Hugging Face-derived dataset and DPO optimization.
citing papers explorer
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Cross-Modal Backdoors in Multimodal Large Language Models
Poisoning a single connector in MLLMs establishes a reusable latent backdoor pathway that transfers across modalities with over 95% attack success rate under bounded perturbations.
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TerraBench: Can Agents Reason Over Heterogeneous Earth-System Data?
TerraBench is a new benchmark with 403 tasks across Earth-science domains that evaluates LLM agents on coordinating heterogeneous data using executable ReAct-style workflows and process-level metrics.
-
Skill-3D: Evolving Scene-Aware Skills for Agentic 3D Spatial Reasoning
Skill-3D improves MLLM agent tool use in 3D spatial reasoning from 39% to 78% on VSI-Bench by evolving reusable scene-aware skills from aggregated trajectories stored in a scene memory.
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OctoT2I: A Self-Evolving Agentic Text-to-Image Router
OctoT2I uses a no-supervision PSEL loop to discover model capability frontiers and route T2I tasks, reaching 0.96 GenEval score with 90.3% speedup over Flow-GRPO.
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Towards Camera-Robust 3D Localization: Equation-Anchored Tool-Use for MLLMs
Proposes an equation-anchored tool-use method for MLLMs that writes the pinhole back-projection equation in Chain-of-Thought and substitutes retrieved camera intrinsics and depths to achieve robustness in 3D object detection and visual grounding under rescaled intrinsics.
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Probing Visual Planning in Image Editing Models
Image editing models fail zero-shot visual planning on abstract mazes and queen puzzles but generalize after finetuning, yet still cannot match human zero-shot efficiency.
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AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation
AnchorSeg uses ordered query banks of latent reasoning tokens plus a spatial anchor token and a Token-Mask Cycle Consistency loss to achieve 67.7% gIoU and 68.1% cIoU on the ReasonSeg benchmark.
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SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses
SUPERGLASSES is the first VQA benchmark built from actual smart glasses data, and SUPERLENS is an agent using automatic object detection, query decoupling, and multimodal search that outperforms GPT-4o by 2.19% on it.
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FaSTA$^*$: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient Multi-turn Image Editing
FaSTA* combines LLM fast planning with A* search and inductive subroutine mining to create an efficient agent for multi-turn image editing tasks.
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Deep Multimodal Learning with Missing Modality: A Survey
This survey provides the first comprehensive overview of deep multimodal learning methods designed to remain robust when some input modalities are absent.
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GAIA: a benchmark for General AI Assistants
GAIA benchmark shows humans at 92% accuracy on simple real-world questions far outperform current AI systems at 15%, proposing this gap as a key milestone for general AI.
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Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
Set-of-Mark prompting marks segmented image regions with alphanumerics and masks to let GPT-4V achieve state-of-the-art zero-shot results on referring expression comprehension and segmentation benchmarks like RefCOCOg.
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VideoChat: Chat-Centric Video Understanding
VideoChat integrates video models and LLMs via a learnable interface for chat-based spatiotemporal and causal video reasoning, trained on a new video-centric instruction dataset.
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Visual Instruction Tuning
LLaVA is trained on GPT-4 generated visual instruction data to achieve 85.1% relative performance to GPT-4 on synthetic multimodal tasks and 92.53% accuracy on Science QA.
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S-Agent: Spatial Tool-Use Elicits Reasoning for Spatial Intelligence
S-Agent augments VLMs with spatial tools, scene and agent memory for evidence accumulation on multi-view and video tasks, and produces an 8B model via SFT on its own trajectories that beats same-scale baselines.
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MedCTA: A Benchmark for Clinical Tool Agents
MedCTA is a new benchmark with 107 real-world tasks and process-aware metrics that shows frontier multimodal models remain brittle at autonomous tool selection, execution, and trajectory completion in clinical settings.
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HDRAgent: An Agentic Framework for Multi-Exposure HDR Imaging
HDRAgent is the first agent-driven framework for multi-exposure HDR imaging that uses MLLM scene perception, contextual knowledge matching, and perception-distortion feedback to reduce ghosting artifacts.
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MUSE: A Unified Agentic Harness for MLLMs
MUSE is a unified agentic harness that improves off-the-shelf MLLMs on visual spatial planning, perception, multimodal reasoning, and fine-grained discrimination benchmarks through structured execution modules and verifier-guided repair without model retraining.
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VESTA: Visual Exploration with Statistical Tool Agents
VESTA introduces dynamic tool creation for VLMs that outperforms static-tool and no-tool baselines on distribution fitting, time series, and astronomy tasks in the new DAWN benchmark.
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InterSketch: An Interleaved Reasoning Model with Self-correcting Visual Sketch and Stepwise Reward
InterSketch improves long-horizon visual-textual chain-of-thought in VLMs by dynamically generating and interleaving self-correcting visual sketches with text, using a synthesized dataset plus reflection in cold-start followed by stepwise-reward RL, and reports outperforming Gemini-3-Pro on benchmar
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ClueAegis: Heuristic-to-Reasoning Cognitive-skill Learning for Unified Evidence-based Synthetic Image Detection
ClueAegis introduces a heuristic-to-reasoning cognitive skill framework and ClueAegis-Bench for evidence-based synthetic image detection that outperforms end-to-end classifiers in generalization and explainability.
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Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles
Maestro uses outcome-based RL to train a lightweight policy that orchestrates ensembles of frozen expert models and skills, reporting 70.1% average accuracy across ten multimodal benchmarks and outperforming GPT-5 and Gemini-2.5-Pro while generalizing to unseen components.
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Hierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning
HierVA improves multi-step chart question answering by having a high-level manager maintain key joint contexts while specialized workers perform targeted reasoning with visual zoom-in.
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RaTA-Tool: Retrieval-based Tool Selection with Multimodal Large Language Models
RaTA-Tool retrieves suitable external tools for multimodal queries by matching generated task descriptions against tool metadata, supported by a new Hugging Face-derived dataset and DPO optimization.
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ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution
ToolOmni combines supervised fine-tuning on a cold-start multi-turn dataset with Decoupled Multi-Objective GRPO to enable proactive retrieval and grounded execution, yielding +10.8% higher end-to-end tool-use success and better generalization to unseen tools.
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Towards Long-horizon Agentic Multimodal Search
LMM-Searcher uses file-based visual UIDs and a fetch tool plus 12K synthesized trajectories to fine-tune a multimodal agent that scales to 100-turn horizons and reaches SOTA among open-source models on MM-BrowseComp and MMSearch-Plus.
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Grounded Reinforcement Learning for Visual Reasoning
ViGoRL introduces visually grounded RL that anchors reasoning steps to image coordinates and uses multi-turn zooming to outperform standard RL and supervised baselines on spatial and GUI reasoning benchmarks.
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TempCompass: Do Video LLMs Really Understand Videos?
TempCompass benchmark reveals that state-of-the-art Video LLMs have poor ability to perceive temporal aspects such as speed, direction, and ordering in videos.
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Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception
Mobile-Agent is a vision-centric autonomous agent that uses MLLMs to perceive UI elements, plan complex multi-step tasks, and operate mobile apps without relying on XML or system metadata, showing strong results on the introduced Mobile-Eval benchmark.
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Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
Grounded SAM integrates Grounding DINO and SAM to support text-prompted open-world detection and segmentation, achieving 48.7 mean AP on SegInW zero-shot with the base detector and huge segmenter.
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Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
Video-LLaVA creates a unified visual representation for images and videos via pre-projection alignment, enabling mutual enhancement from joint training and strong results on image and video benchmarks.
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ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models
ReWOO decouples reasoning from tool observations in augmented language models, delivering 5x token efficiency and 4% higher accuracy on multi-step reasoning benchmarks like HotpotQA.
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MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
MiniGPT-4 shows that aligning a frozen vision encoder to Vicuna via one projection layer plus a second-stage detailed-description fine-tune produces GPT-4-like vision-language abilities including detailed captions, creative writing, and instruction following.
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MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action
MM-REACT uses textual prompts to let ChatGPT collaborate with external vision experts for zero-shot multimodal reasoning and action on advanced visual tasks.
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Symbiotic-MoE: Unlocking the Synergy between Generation and Understanding
A modality-aware MoE with shared experts and progressive training claims to add image generation without catastrophic forgetting and to improve understanding scores.
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ToolGate: Token-Efficient Pre-Call Control for Tool-Augmented Vision-Language Agents
ToolGate is a pre-call controller that reduces token usage in tool-augmented VLM agents to 64-69% of baseline while preserving or slightly improving accuracy on benchmarks.
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When Think-with-Image Meets Safety: What Determines Multimodal Jailbreak Robustness?
Explicit image-tool interaction in VLMs cuts multimodal jailbreak ASR by ~30% on average; the effect is attributed to a safety-relevant shift in hidden representations rather than image semantics or text traces.
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Mind the Tool Failures: Achieving Synergistic Tool Gains for Medical Agents
A GRPO-based RL framework with probabilistic risk minimization, disagreement-aware synergy rewards, and entropy-guided sampling enables instance-level tool selection that closes the single-oracle risk gap on medical benchmarks.
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Scaling Video Understanding via Compact Latent Multi-Agent Collaboration
MACF decouples agent perception budgets from overall video length using latent token collaboration to scale video understanding in MLLMs beyond current limits.
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MIRAGE: A Micro-Interaction Relational Architecture for Grounded Exploration in Multi-Figure Artworks
MIRAGE improves VLM analysis of multi-figure art by inserting a verifiable structured representation of micro-interactions between spatial grounding and narrative output.
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Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generation
A self-reasoning agentic framework constructs a Product Narrative Framework, generates constraint-aware unified grid collages, and refines outputs via failure attribution to improve narrative coherence and aesthetics over direct prompting.
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Less Detail, Better Answers: Degradation-Driven Prompting for VQA
Degradation-Driven Prompting improves VQA by intentionally reducing image detail and using masks, lines, and examples to guide models toward essential structures.
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CAMEO: A Conditional and Quality-Aware Multi-Agent Image Editing Orchestrator
A closed-loop multi-agent image editor (CAMEO) reports ~20% higher average win rates than strong one-shot editors on anomaly insertion and pose switching.
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Token Reduction via Local and Global Contexts Optimization for Efficient Video Large Language Models
AOT reduces visual tokens in VLLMs via intra-frame and inter-frame anchors with local-global optimal transport, delivering competitive benchmark performance and efficiency gains in a training-free way.
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Thought Graph Traversal for Test-time Scaling in Chest X-ray VLLMs
A new prompting framework called Thought Graph Traversal combined with reasoning budget forcing improves test-time performance of frozen chest X-ray VLLMs on report generation benchmarks.
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Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Mini-Gemini enhances VLMs via high-resolution visual refinement, curated reasoning data, and self-guided generation to reach leading zero-shot benchmark results across 2B-34B LLMs.
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InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
InternVL scales a vision model to 6B parameters and aligns it with LLMs using web data to achieve state-of-the-art results on 32 visual-linguistic benchmarks.
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SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models
SPHINX improves multi-modal LLMs through joint mixing of weights, tasks, and visual embeddings from varied sources to achieve stronger alignment and multi-purpose capabilities.
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LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
LLaMA-Adapter V2 achieves open-ended visual instruction following in LLMs by unlocking more parameters, early fusion of visual tokens, and joint training on disjoint parameter groups with only 14M added parameters.
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Multilingual OCR-Aware Fine-Tuning and Prompt-Guided Chain-of-Thought Reasoning for Multimodal Large Language Models
An OCR-aware multilingual framework combining synthetic data generation, LoRA SFT, and visual CoT prompting improves text extraction and translation robustness in multimodal LLMs on degraded images.