Hand Object Interaction (HOI) generation provides an efficient solution for virtual reality simulation and embodied AI deployment.Recent studies have explored instruction-driven HOI synthesis, yet they overlooked the fine-grained interactive contact and struggled with robust generalization in data-scarce scenarios. In addition, existing datasets are frequently constrained with limited object categories and monotonous motion patterns. To address these limitations, we first integrate a large-scale HOI dataset, namely HOI-X, which contains diverse objects, rich motion patterns, and part-level contact annotations. Along with HOI-X, we propose A3fford-HOI, a fine-grained and generalizable Hand Object Interaction framework with Anatomy-Aligned Affordance Disentanglement. Specifically, we define a Disentangle-2-Interact paradigm in A3fford-HOI. Considering the various interactions of distinct hand anatomical structures, in the Disentangle stage, we devise an Anatomy-Aligned Affordance Predictor, which leverages the external plausible physical priors in 3D multimodal large language models for affordance disentanglement prediction. Then, the disentangled affordance is employed into the Interact stage, i.e. Affordance Driven Interactor. The interactor integrates textual instructions, object geometric features, and the external knowledge enhanced (MLLMs) affordance via a Diffusion Transformer (DiT) for generalizable HOI generation. Extensive experiments demonstrate that A3fford-HOI maintains superior generation quality and contact plausibility for fine-grained and generalizable hand object interaction.
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Multimodal reasoning requires a path that retains integrity over a wide range of constraints, from visual grounding to logic consistency. However, the current Process Reward Models (PRMs) focus on heuristically defined rewards that equally weigh these factors, which may lead to the concealment of individual dimension failures (such as visual hallucinations) by the dominating factors, without guaranteeing the validity of the reasoning process in general. Therefore, to overcome the limitation, the paper proposes the concept of Multimodal Multi-Dimensional Scalarization Process Reward Modeling (MMS-PRM), a paradigm specifically developed to enforce the worst dimension’s robustness in multimodal reasoning. Specifically, a hierarchical fine-grained reward space is developed to represent the multimodal risks in the reasoning tasks, and a Chebyshev-based Monte Carlo Tree Search (MCTS) algorithm is introduced, in which the primary focus during the path searching is given to the worst-performing dimension. Moreover, a curriculum-based Direct Preference Optimization (DPO) approach is developed to gradually learn the balanced reasoning skills in the policy. The experimental results show that, without the dimension collapse issue, the MMS-PRM approach significantly improves the reliability of the multimodal reasoning performance and reaches competitive results in various challenging tasks. The code is available at https://github.com/leibniz-Man/MMS-PRM
In this paper, we revisit multimodal few-shot 3D point cloud semantic segmentation (FS-PCS), identifying a conflict in "Fuse-then-Refine" paradigms: the "Plasticity-Stability Dilemma." In addition, Contrastive Language-Image Pre-training (CLIP)'s inter-class confusion can result in semantic blindness. To address these issues, we present the Decoupled-experts Arbitration Few-Shot SegNet (DA-FSS), a model that effectively distinguishes between semantic and geometric paths and mutually regularizes their gradients to achieve better generalization. DA-FSS employs the same backbone and pre-trained text encoder as the baseline MultiModal Few-Shot SegNet (MM-FSS) to generate text embeddings, which can increase cost-free modalities' utilization rate and better leverage each modality's information space. To achieve this, we propose a Parallel Expert Refinement module to generate each modal correlation. We also propose a Stacked Arbitration Module (SAM) to perform convolutional fusion and arbitrate correlations for each modality pathway. The Parallel Experts decouple two paths: a Geometric Expert maintains plasticity, and a Semantic Expert ensures stability. They are coordinated via a Decoupled Alignment Module (DAM) that transfers knowledge without propagating confusion. Experiments on popular datasets (S3DIS, ScanNet) demonstrate the superiority of DA-FSS over MM-FSS. Meanwhile, geometric boundaries, completeness, and texture differentiation are all superior to the baseline. The code is available at: https://github.com/MoWenQAQ/DA-FSS/.
Generalized vehicle trajectory prediction across diverse junctions, including urban intersections and roundabouts, remains a fundamental task in Cooperative Vehicle–Infrastructure Systems (CVIS). This study faces two key challenges: (1) Generalize across junctions with heterogeneous map semantics and traffic behavioral patterns, where the former arises from differences in road topologies and traffic regulations, and the latter reflects diverse behavioral intentions of road users; (2) Scenario-adaptive interaction modeling, where single-modality trajectory learning captures local spatio-temporal correlation, but lacks map constraint and direction-aware interaction contexts. To overcome these challenges, we propose G-VTM, a generalized vision-trajectory model. G-VTM models fine-grained behavioral patterns and relative spatial interaction from trajectory modality. At the vision modality, G-VTM captures global map semantics while modeling scenario- and direction-aware interaction based on intuitive visual perception. Experiments on multiple real-world datasets collected by unmanned aerial vehicles (UAVs) demonstrate that our method achieves strong generalized performance under heterogeneous traffic conditions. The code is provided at https://github. com/zxyhaclyon/G-VTM.
Although Large Vision-Language Models (LVLMs) have demonstrated remarkable reasoning capabilities across various downstream multimodal tasks, they are proven to be vulnerable to carefully designed adversarial examples. Existing LVLM attackers show that exploring external components of adversarial guidance (e.g., forcing adversarial alignment, resembling harmful features) can help improve adversarial effects. However, leveraging the intrinsic patterns of LVLMs to induce adversarial perturbation generation by exploring how LVLMs perceive images has not been deeply studied. Inspired by the cognitive science, in this paper, we make the first attempt to investigate the interference of adversarial perturbation from the perspectives of image phase, and find that LVLMs are sensitive to the phase-aware image structure. Motivated by this, we propose a novel LVLM attack method called BadPhase with further backdoor designs, to implant adversarial phase as triggers into any image inputs via data poisoning so as to control the LVLMs’ predictions. A textual trigger and a backdoor perturbation switcher are also introduced to activate the malicious behavior only when both triggers are present. The whole backdoor optimization is implemented at the test-time to reduce the resource reliance. Experiments on four popular LVLMs and three benchmarks demonstrate the effectiveness of our proposed method.
We present Magnol.AI Copilot, an extension of the Magnol.AI digital biomarker platform that integrates multimodal large language models (LLMs) to transform digital health technology (DHT) trial dashboards into conversational systems. Copilot augments the platform with a multi-agent orchestration layer and vision-enabled LLMs that interpret visualizations, tabular summaries, and textual metadata. The system enables natural language queries and automatic generation of contextual insights, allowing researchers to interact with wearable data through dialogue rather than static inspection. A case study with an actigraphy device demonstrates Copilot’s ability to identify nightly compliance gaps and provide contextual explanations, reducing cognitive load compared to manual dashboard review. This work presents a novel integration of IoMT infrastructure with multimodal LLMs, advancing digital biomarker research toward conversational and accessible DHT trial platforms.
VLHSA: Vision-Language Hierarchical Semantic Alignment for Jigsaw Puzzle Solving with Eroded Gaps (Student Abstract)
PDF ↗Jigsaw puzzle solving remains difficult because models must reconcile local fragment cues with global structure. Most prior work leans solely on visual signals (edge or texture coherence) and rarely exploits natural-language descriptions, which are especially helpful for puzzles with eroded gaps. We introduce a vision–language framework that uses textual context to guide assembly. At its core, the Vision–Language Hierarchical Semantic Alignment (VLHSA) module aligns image patches with text via multi-level matching—from local tokens to global summaries—within a multimodal design that couples dual visual encoders with language features for cross-modal reasoning. Across multiple datasets, the method surpasses the state of the art, including a 14.2 percentage point gain in piece accuracy; ablations identify VLHSA as the principal source of improvement. These results suggest a practical shift for jigsaw solving: augmenting vision with language to resolve ambiguous placements
Zero-Shot Vision Language Reasoning via Dual-layer Scene Graph Chain of Thoughts (Student Abstract)
PDF ↗Large Multimodal Models (LMMs) often hallucinate objects and struggle with compositional reasoning in complex visual scenes. Structured Scene Graph (SG) representations explicitly encoding objects, attributes, and relations can mitigate these issues, however finetuning risks catastrophic forgetting. Recent zero-shot approaches prompt LMMs with scene graphs, yet typically rely on a single SG generated in one step, limiting capture of holistic context and question-specific details. We introduce a Dual-Layer Scene Graph Chain-of-Thought DLSG-CoT framework that enriches reasoning by combining two structured SGs: a Global Scene Graph (G-SG) that offers comprehensive image context, and a Query-Specific Scene Graph (Q-SG) produced through a two-step process targeting information relevant to the input query. Extensive experiments demonstrate that DLSG-CoT substantially improves LMM performance on compositional and context-sensitive tasks.
This talk surveys my research agenda on advancing general-purpose visual intelligence, moving AI beyond static recognition toward active reasoning and embodied action. A central challenge is enabling AI systems to generalize reliably in low-data and long-tail regimes. I address this by combining multimodal representation learning with agentic reasoning frameworks such as PyVision, which equips vision models to dynamically generate tools for deliberate problem-solving, and ViGaL, which leverages gameplay to instill transferable cognitive skills for reasoning under scarcity. These efforts chart a trajectory from representation and generation to interactive, embodied agents, re-imagining AI as an active collaborator capable of tool use, imagination, and purposeful engagement across both digital and physical environments.
Time series analysis has progressed from traditional autoregressive models to deep learning, Transformers, and foundation models (FMs), including large language models (LLMs) and large vision models (LVMs). These advances expand model design possibilities and enable time series problem-solving across multiple modalities. This talk will provide an overview of recent developments in large FMs for time series, highlighting frameworks for transferring knowledge from other modalities to time series, and identifying the advantages of LVMs over LLMs in cross-modal knowledge transfer. I will then delve into our recent research on LVMs for time series, discussing (1) mainstream techniques for imaging time series; (2) key strengths and limitations of LVMs in time series modeling; and (3) multimodal frameworks that integrate LVMs for time series encoding. This talk will conclude with an application of LVMs to brain time series analysis in neuroscience. The aim of the talk is to review state-of-the-art (SOTA) AI techniques for time series, highlight unique challenges, and share our recent findings in this promising area.
Scaling Human-Centric Trustworthy Foundation Model via Advanced Reasoning and Agentic Frameworks
PDF ↗As foundation models grow in size and scope, crucial challenges remain in scaling their trustworthiness and adaptability to meet the diverse needs of individual users, as well as mitigating their risk of generating unhelpful, non-factual, or harmful content. To address this, we propose to reframe model reasoning through a unified paradigm of active knowledge grounding that coordinates different tools and modalities. First, to scale reasoning depth and creativity, we introduce the novel paradigm of Thinking with Images to encourage models to externalize intermediate structure and perform interleaved cross-modal advanced reasoning beyond text-centric cues. To further scale honesty and bridge knowledge gaps reliably, we develop one of the first vision-language deep research agents, WebWatcher, that actively gathers and verifies information from the web with enhanced fragmented reasoning capability. Ultimately, to scale effective and efficient human-AI collaboration, we propose AdaCtrl as a novel training mechanism for dynamically aligning model behavior with individual user preferences and difficulty awareness to adaptively allocate computational resources. Together, these three pillars of integrating advanced multimodal reasoning, autonomous discovery, and adaptive alignment form a foundational framework for advancing the frontier of next generation human-centric trustworthy AI systems.
This talk surveys my research journey toward building reliable machine learning systems that behave safely and predictably in the open world. While modern machine learning models—including foundation models (FMs)—have demonstrated unprecedented capabilities, they often suffer from reliability failures under distribution shift, leading to overconfident mispredictions, hallucinated generations, or susceptibility to adversarial prompts. My research rethinks reliability not as an afterthought, but as a first-class algorithmic principle, to be optimized alongside accuracy with minimal human supervision. The talk is organized around three key threads. To respect the allotted 20-30 minutes, the first and second parts will be briefly discussed. 1. Unknown-Aware Learning via Outlier Synthesis. I introduce a class of learning algorithms that synthesize “virtual outliers” in representation or pixel space to explicitly teach models what they don’t know. This includes the VOS, NPOS, and Dream-OOD frameworks, which shape the energy landscape around in-distribution data to avoid overconfidence on OOD. 2. Learning in the Wild with Unlabeled Data. I present theoretical insights and practical algorithms for leveraging unlabeled in-the-wild data to improve reliability. This includes SAL framework, which uses a gradient-based spectral method to separate potential outliers, and SCONE, which handles semantic and covariate shifts via constrained optimization. These results turn unlabeled data contamination into a learning signal. 3. Reliable Foundation Models. I explore reliability failures in LLMs and multimodal systems. I introduce HaloScope for hallucination detection via subspace separation on LLM representations, and TSV that performs LLM latent steering for improved hallucination detection. I will also briefly cover the LLM security and alignment, which includes VLMGuard for detecting malicious prompts in vision-language models and a data-centric paradigm for AI alignment through source-aware feedback cleaning. Throughout the talk, I highlight how representation learning, data generation, and theoretical guarantees intersect to produce scalable, label-efficient reliability methods. I will also reflect on my broader vision: designing proactive and collaborative AI systems that anticipate uncertainty and support rich human-AI interaction—especially for underrepresented communities and emerging scientific domains. This talk will be accessible to a broad AAAI audience, combining foundational algorithmic insights with real-world applications and forward-looking perspectives on the future of responsible AI.
Unlocking the Power of Large Multimodal Models for Robot Learning: Robustness, Generalization, and Opportunities
PDF ↗Large multimodal models (LMMs) have revolutionized AI by demonstrating remarkable capabilities in vision, language, audio, and other domains, particularly in understanding and generalization tasks. Yet, moving beyond passive understanding to active interaction requires embodied agents, such as robots, that can harness the capabilities of AI models to act within the physical world. My core research aims to build embodied agents that reason about and interact with the physical world with human-like commonsense. Specifically, I design algorithms and representations that enable robots to perceive their environment, reason about physical properties, and plan long-horizon actions for both manipulation and locomotion. These advances are grounded in the integration of large-scale AI models with embodied control. I organize this agenda into three stages: (1) injecting actions into LMMs to form vision–language–action (VLA) models; (2) learning from human motion and contact to enrich physical reasoning; and (3) advancing whole-body robot loco-manipulation guided by LMMs toward embodied artificial general intelligence (AGI). The talk details recent advances in leveraging LMMs for robot learning, emphasizing the promise of robust generalization across diverse environments, tasks, and modalities. I will highlight contributions at the intersection of perception, reasoning, and control, and outline open challenges and future opportunities toward enabling humanoid robots that can robustly understand, interact, and collaborate with humans in complex real-world settings.
Due to their ability of follow natural language instructions, vision-language-action (VLA) models are increasingly preva- lent in the embodied AI arena, following the widespread suc- cess of their precursors—LLMs and VLMs. In this paper, we discuss 10 principal milestones in the ongoing develop- ment of VLA models—multimodality, reasoning, data, eval- uation, cross-robkot action generalization, efficiency, whole- body coordination, safety, agents, and coordination with hu- mans. Furthermore, we discuss the emerging trends of us- ing spatial understanding, modeling world dynamics, post training, and data synthesis—all aiming to reach these mile- stones. Through these discussions, we hope to bring attention to the research avenues that may accelerate the development of VLA models into wider acceptability.
PlantTraitNet: An Uncertainty-Aware Multimodal Framework for Global-Scale Plant Trait Inference from Citizen Science Data
PDF ↗Global plant maps of plant traits, such as leaf nitrogen or plant height, are essential for understanding ecosystem processes, including the carbon and energy cycles of the Earth system. However, existing trait maps remain limited by the high cost and sparse geographic coverage of field-based measurements. Citizen science initiatives offer a largely untapped resource to overcome these limitations, with over 50 million geotagged plant photographs worldwide capturing valuable visual information on plant morphology and physiology. In this study, we introduce PlantTraitNet, a multi-modal, multi-task uncertainty-aware deep learning framework that predicts four key plant traits (plant height, leaf area, specific leaf area, and nitrogen content) from citizen science photos using weak supervision. By aggregating individual trait predictions across space, we generate global maps of trait distributions. We validate these maps against independent vegetation survey data (sPlotOpen) and benchmark them against leading global trait products. Our results show that PlantTraitNet consistently outperforms existing trait maps across all evaluated traits, demonstrating that citizen science imagery, when integrated with computer vision and geospatial AI, enables not only scalable but also more accurate global trait mapping. This approach offers a powerful new pathway for ecological research and Earth system modeling.
As automated attack techniques rapidly advance, CAPTCHAs remain a critical defense mechanism against malicious bots. However, existing CAPTCHA schemes encompass a diverse range of modalities—from static distorted text and obfuscated images to interactive clicks, sliding puzzles, and logic-based questions—yet the community still lacks a unified, large-scale, multimodal benchmark to rigorously evaluate their security robustness. To address this gap, we introduce MCA-Bench, a comprehensive and reproducible benchmarking suite that integrates heterogeneous CAPTCHA types into a single evaluation protocol. Leveraging a shared vision–language model backbone, we fine-tune specialized cracking agents for each CAPTCHA category, enabling consistent, cross-modal assessments. Extensive experiments reveal that MCA-Bench effectively maps the vulnerability spectrum of modern CAPTCHA designs under varied attack settings, and—crucially—offers the first quantitative analysis of how challenge complexity, interaction depth, and model solvability interrelate. Based on these findings, we propose three actionable design principles and identify key open challenges, laying the groundwork for systematic CAPTCHA hardening, fair benchmarking, and broader community collaboration.
Vision-language models (VLMs) have made significant strides in reasoning, yet they often struggle with complex multimodal tasks and tend to generate overly verbose outputs. A key limitation is their reliance on chain-of-thought (CoT) reasoning, despite many tasks benefiting from alternative topologies like trees or graphs. To address this, we introduce STELAR-Vision, a training framework for topology-aware reasoning. At its core is TopoAug, a synthetic data pipeline that enriches training with diverse topological structures. Using supervised fine-tuning and reinforcement learning, we post-train Qwen2VL models with both accuracy and efficiency in mind. Additionally, we propose Frugal Learning, which reduces output length with minimal accuracy loss. On MATH-V and VLM_S2H, STELAR-Vision improves accuracy by 9.7% over its base model and surpasses the larger Qwen2VL-72B-Instruct by 7.3%. On five out-of-distribution benchmarks, it outperforms Phi-4-Multimodal-Instruct by up to 28.4% and LLaMA-3.2-11B-Vision-Instruct by up to 13.2%, demonstrating strong generalization. Compared to Chain-Only training, our approach achieves 4.3% higher overall accuracy on in-distribution datasets and consistently outperforms across all OOD benchmarks.
In vision-language models (VLMs), the ability to perceive and interpret color and physical environment is crucial for achieving contextually accurate understanding and interaction. However, despite advances in multimodal modeling, there remains a significant lack of specialized datasets that rigorously evaluate a model's capacity to discern subtle color variations and spatial context---critical elements for situational comprehension and reliable deployment across real-world applications. Toward that goal, we curate MegaCoin, a high-quality, human-labeled dataset based on \emph{real} images with various contextual attributes. MegaCoin consists of two parts: MegaCoin-Instruct, which serves as a supervised fine-tuning (SFT) dataset for VLMs; and MegaCoin-Bench, an annotated test set that can be used as a stand-alone QA dataset. MegaCoin provides three annotated features for 220,000 real images: foreground color, background color, and description of an object's physical environment, constituting 660k human annotations. In addition, MegaCoin can be applied to benchmark domain generalization (DG) algorithms. We explore benchmarking DG methods in the linear probing setup for VLM and show some new insights. Last but not least, we show that VLMs, including GPT-4o, have subpar color recognition capabilities, and fine-tuning with MegaCoin can result in improved performance on visual evaluation tasks. In certain cases, MegaCoin fine-tuned small-scale open-source models such as LLaVA and Bunny can outperform closed-source GPT-4o. We hope the utilities of MegaCoin can shed light on the directions VLMs can improve and provide a more complex platform for domain generalization algorithms.
Bridging the Copyright Gap: Do Large Vision-Language Models Recognize and Respect Copyrighted Content?
PDF ↗Large vision-language models (LVLMs) have achieved remarkable advancements in multimodal reasoning tasks. However, their widespread accessibility raises critical concerns about potential copyright infringement. Will LVLMs accurately recognize and comply with copyright regulations when encountering copyrighted content (i.e., user input, retrieved documents) in the context? Failure to comply with copyright regulations may lead to serious legal and ethical consequences, particularly when LVLMs generate responses based on copyrighted materials (e.g., retrieved book experts, news reports). In this paper, we present a comprehensive evaluation of various LVLMs, examining how they handle copyrighted content – such as book excerpts, news articles, music lyrics, and code documentation when they are presented as visual inputs. To systematically measure copyright compliance, we introduce a large-scale benchmark dataset comprising 50,000 multimodal query-content pairs designed to evaluate how effectively LVLMs handle queries that could lead to copyright infringement. Given that real-world copyrighted content may or may not include a copyright notice, the dataset includes query-content pairs in two distinct scenarios: with and without a copyright notice. For the former, we extensively cover four types of copyright notices to account for different cases. Our evaluation reveals that even state-of-the-art closed-source LVLMs exhibit significant deficiencies in recognizing and respecting the copyrighted content, even when presented with the copyright notice. To solve this limitation, we introduce a novel tool-augmented defense framework for copyright compliance, which reduces infringement risks in all scenarios. Our findings underscore the importance of developing copyright-aware LVLMs to ensure the responsible and lawful use of copyrighted content.
EchoBat: Echo-Vision Enhancement and Echo-Layered Sampling for Video LLMs Hallucination Mitigation
PDF ↗Recent advancements in multimodal large language models (MLLMs) have shown remarkable progress in video understanding. However, video MLLMs (VideoMLLMs) still suffer from hallucinations, generating nonsensical or irrelevant content. This issue partly stems from over-reliance on pre-trained knowledge, sometimes neglecting the rich visual information present in the video. Additionally, many existing methods rely on uniform frame sampling, which can overlook critical visual cues. To address these challenges, we present EchoBat, a novel approach that leverages audio information as well as video temporal and logical consistency to improve preference data construction and keyframe extraction. Our method integrates Direct Preference Optimization (DPO) to mitigate hallucinations by leveraging high-quality, contextually rich preference feedback. Specifically, we use GPT-4o to generate high-quality video descriptions and integrate visually relevant segments from Whisper-derived transcripts to construct preference responses. Correspondingly, we use the reference model itself to describe the reversed video, and use GPT-4o to flashback the text and fill in the hallucination to produce non-preferred responses. This strategy enhances the model’s ability to better understand visual content and temporal, logical relationships within videos. Furthermore, we propose an echo-layered sampling strategy for keyframe extraction from videos, which can provide more precise visual supervision compared to uniform sampling. Experimental results on the three latest video hallucination benchmarks demonstrate the effectiveness of our approach.