OpenRaiser/Intern-Atlas huggingface dataset:
MrM: Black-Box Membership Inference Attacks against Multimodal RAG Systems
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal retrieval-augmented generation (RAG) systems enhance large vision-language models by integrating cross-modal knowledge, enabling their increasing adoption across real-world multimodal tasks. These knowledge databases may contain sensitive information that requires privacy protection. However, multimodal RAG systems inherently grant external users indirect access to such data, making them potentially vulnerable to privacy attacks, particularly membership inference attacks (MIAs). % Existing MIA methods targeting RAG systems predominantly focus on the textual modality, while the visual modality remains relatively underexplored. To bridge this gap, we propose MrM, the first black-box MIA framework targeted at multimodal RAG systems. It utilizes a multi-object data perturbation framework constrained by counterfactual attacks, which can concurrently induce the RAG systems to retrieve the target data and generate information that leaks the membership information. Our method first employs an object-aware data perturbation method to constrain the perturbation to key semantics and ensure successful retrieval. Building on this, we design a counterfact-informed mask selection strategy to prioritize the most informative masked regions, aiming to eliminate the interference of model self-knowledge and amplify attack efficacy. Finally, we perform statistical membership inference by modeling query trials to extract features that reflect the reconstruction of masked semantics from response patterns. Experiments on two visual datasets and eight mainstream commercial visual-language models (e.g., GPT-4o, Gemini-2) demonstrate that MrM achieves consistently strong performance across both sample-level and set-level evaluations, and remains robust under adaptive defenses.
Enhancing Watermarking Quality for LLMs via Contextual Generation States Awareness
Venue: arXiv cs.CR 2025
Year: 2025

Recent advancements in watermarking techniques have enabled the embedding of secret messages into AI-generated text (AIGT), serving as an important mechanism for AIGT detection. Existing methods typically interfere with the generation processes of large language models (LLMs) to embed signals within the generated text. However, these methods often rely on heuristic rules, which can result in suboptimal token selection and a subsequent decline in the quality of the generated content. In this paper, we introduce a plug-and-play contextual generation states-aware watermarking framework (CAW) that dynamically adjusts the embedding process. It can be seamlessly integrated with various existing watermarking methods to enhance generation quality. First, CAW incorporates a watermarking capacity evaluator, which can assess the impact of embedding messages at different token positions by analyzing the contextual generation states. Furthermore, we introduce a multi-branch pre-generation mechanism to avoid the latency caused by the proposed watermarking strategy. Building on this, CAW can dynamically adjust the watermarking process based on the evaluated watermark capacity of each token, thereby minimizing potential degradation in content quality. Extensive experiments conducted on datasets across multiple domains have verified the effectiveness of our method, demonstrating superior performance compared to various baselines in terms of both detection rate and generation quality.
Compressed Feature Quality Assessment: Dataset and Baselines
Venue: arXiv cs.CV 2025
Year: 2025

The widespread deployment of large models in resource-constrained environments has underscored the need for efficient transmission of intermediate feature representations. In this context, feature coding, which compresses features into compact bitstreams, becomes a critical component for scenarios involving feature transmission, storage, and reuse. However, this compression process inevitably introduces semantic degradation that is difficult to quantify with traditional metrics. To address this, we formalize the research problem of Compressed Feature Quality Assessment (CFQA), aiming to evaluate the semantic fidelity of compressed features. To advance CFQA research, we propose the first benchmark dataset, comprising 300 original features and 12000 compressed features derived from three vision tasks and four feature codecs. Task-specific performance degradation is provided as true semantic distortion for evaluating CFQA metrics. We systematically assess three widely used metrics -- MSE, cosine similarity, and Centered Kernel Alignment (CKA) -- in terms of their ability to capture semantic degradation. Our findings demonstrate the representativeness of the proposed dataset while underscoring the need for more sophisticated metrics capable of measuring semantic distortion in compressed features. This work advances the field by establishing a foundational benchmark and providing a critical resource for the community to explore CFQA. To foster further research, we release the dataset and all associated source code at https://github.com/chansongoal/Compressed-Feature-Quality-Assessment.
Evaluating Visual Mathematics in Multimodal LLMs: A Multilingual Benchmark Based on the Kangaroo Tests
Venue: arXiv cs.AI 2025
Year: 2025

Multimodal Large Language Models (MLLMs) promise advanced vision language capabilities, yet their effectiveness in visually presented mathematics remains underexplored. This paper analyzes the development and evaluation of MLLMs for mathematical problem solving, focusing on diagrams, multilingual text, and symbolic notation. We then assess several models, including GPT 4o, Pixtral, Qwen VL, Llama 3.2 Vision variants, and Gemini 2.0 Flash in a multilingual Kangaroo style benchmark spanning English, French, Spanish, and Catalan. Our experiments reveal four key findings. First, overall precision remains moderate across geometry, visual algebra, logic, patterns, and combinatorics: no single model excels in every topic. Second, while most models see improved accuracy with questions that do not have images, the gain is often limited; performance for some remains nearly unchanged without visual input, indicating underutilization of diagrammatic information. Third, substantial variation exists across languages and difficulty levels: models frequently handle easier items but struggle with advanced geometry and combinatorial reasoning. Notably, Gemini 2.0 Flash achieves the highest precision on image based tasks, followed by Qwen VL 2.5 72B and GPT 4o, though none approach human level performance. Fourth, a complementary analysis aimed at distinguishing whether models reason or simply recite reveals that Gemini and GPT 4o stand out for their structured reasoning and consistent accuracy. In contrast, Pixtral and Llama exhibit less consistent reasoning, often defaulting to heuristics or randomness when unable to align their outputs with the given answer options.
SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation
Venue: arXiv cs.CL 2025
Year: 2025

Text-to-SQL enables non-experts to retrieve data from databases by converting natural language queries into SQL. However, state-of-the-art text-to-SQL studies rely on the BIRD dataset, which assumes that evidence is provided along with questions. Although BIRD facilitates research advancements, it assumes that users have expertise and domain knowledge, contradicting the fundamental goal of text-to-SQL. In addition, human-generated evidence in BIRD contains defects, including missing or erroneous evidence, which affects model performance. To address this issue, we propose SEED (System for Evidence Extraction and Domain knowledge generation), an approach that automatically generates evidence to improve performance and practical usability in real-world scenarios. SEED systematically analyzes database schema, description files, and values to extract relevant information. We evaluated SEED on BIRD and Spider, demonstrating that it significantly improves SQL generation accuracy in the no-evidence scenario, and in some cases, even outperforms the setting where BIRD evidence is provided. Our results highlight that SEED-generated evidence not only bridges the gap between research and real-world deployment but also improves the adaptability and robustness of text-to-SQL models. Our code is available at https://github.com/felix01189/SEED
Conjoined Predication and Scalar Implicature
Venue: arXiv cs.CL 2025
Year: 2025

Magri (2016) investigates two puzzles arising from conjunction. Although Magri has proposed a solution to the second puzzle, the first remains unresolved. This first puzzle reveals a hidden interaction among quantification, collective/concurrent interpretation, and contextual updating dimensions that have yet to be explored. In essence, the problem is that certain forms of sentences like "Some Italians come from a warm country," when conjoined as in "(Only) Some Italians come from a warm country and are blond," sound infelicitous, even though no obvious alternative triggers a conflicting scalar implicature. In this paper, we offer a conceptual analysis of Magri's first puzzle by situating it within its original theoretical framework. We argue that the oddness arises from the collective or concurrent reading of the conjunctive predicate: in examples such as "(Only) Some Italians come from a warm country and are blond," this interpretation generates an indirect contextual contradiction. Moreover, we suggest that the pragmatic mechanisms governing scalar implicature generation extend beyond what is captured by exhaustification-based grammatical licensing accounts.
GLOS: Sign Language Generation with Temporally Aligned Gloss-Level Conditioning
Venue: arXiv cs.CV 2025
Year: 2025

Sign language generation (SLG), or text-to-sign generation, bridges the gap between signers and non-signers. Despite recent progress in SLG, existing methods still often suffer from incorrect lexical ordering and low semantic accuracy. This is primarily due to sentence-level condition, which encodes the entire sentence of the input text into a single feature vector as a condition for SLG. This approach fails to capture the temporal structure of sign language and lacks the granularity of word-level semantics, often leading to disordered sign sequences and ambiguous motions. To overcome these limitations, we propose GLOS, a sign language generation framework with temporally aligned gloss-level conditioning. First, we employ gloss-level conditions, which we define as sequences of gloss embeddings temporally aligned with the motion sequence. This enables the model to access both the temporal structure of sign language and word-level semantics at each timestep. As a result, this allows for fine-grained control of signs and better preservation of lexical order. Second, we introduce a condition fusion module, temporal alignment conditioning (TAC), to efficiently deliver the word-level semantic and temporal structure provided by the gloss-level condition to the corresponding motion timesteps. Our method, which is composed of gloss-level conditions and TAC, generates signs with correct lexical order and high semantic accuracy, outperforming prior methods on CSL-Daily and Phoenix-2014T.
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Conventional language model (LM) safety alignment relies on a reactive, disjoint procedure: attackers exploit a static model, followed by defensive fine-tuning to patch exposed vulnerabilities. This sequential approach creates a mismatch -- attackers overfit to obsolete defenses, while defenders perpetually lag behind emerging threats. To address this, we propose Self-RedTeam, an online self-play reinforcement learning algorithm where an attacker and defender agent co-evolve through continuous interaction. We cast safety alignment as a two-player zero-sum game, where a single model alternates between attacker and defender roles -- generating adversarial prompts and safeguarding against them -- while a reward LM adjudicates outcomes. This enables dynamic co-adaptation. Grounded in the game-theoretic framework of zero-sum games, we establish a theoretical safety guarantee which motivates the design of our method: if self-play converges to a Nash Equilibrium, the defender will reliably produce safe responses to any adversarial input. Empirically, Self-RedTeam uncovers more diverse attacks (+21.8% SBERT) compared to attackers trained against static defenders and achieves higher robustness on safety benchmarks (e.g., +65.5% on WildJailBreak) than defenders trained against static attackers. We further propose hidden Chain-of-Thought, allowing agents to plan privately, which boosts adversarial diversity and reduces over-refusals. Our results motivate a shift from reactive patching to proactive co-evolution in LM safety training, enabling scalable, autonomous, and robust self-improvement of LMs via multi-agent reinforcement learning (MARL).
Drive Any Mesh: 4D Latent Diffusion for Mesh Deformation from Video
Venue: arXiv cs.CV 2025
Year: 2025

We propose DriveAnyMesh, a method for driving mesh guided by monocular video. Current 4D generation techniques encounter challenges with modern rendering engines. Implicit methods have low rendering efficiency and are unfriendly to rasterization-based engines, while skeletal methods demand significant manual effort and lack cross-category generalization. Animating existing 3D assets, instead of creating 4D assets from scratch, demands a deep understanding of the input's 3D structure. To tackle these challenges, we present a 4D diffusion model that denoises sequences of latent sets, which are then decoded to produce mesh animations from point cloud trajectory sequences. These latent sets leverage a transformer-based variational autoencoder, simultaneously capturing 3D shape and motion information. By employing a spatiotemporal, transformer-based diffusion model, information is exchanged across multiple latent frames, enhancing the efficiency and generalization of the generated results. Our experimental results demonstrate that DriveAnyMesh can rapidly produce high-quality animations for complex motions and is compatible with modern rendering engines. This method holds potential for applications in both the gaming and filming industries.
RAPID Hand: A Robust, Affordable, Perception-Integrated, Dexterous Manipulation Platform for Generalist Robot Autonomy
Venue: arXiv cs.RO 2025
Year: 2025

This paper addresses the scarcity of low-cost but high-dexterity platforms for collecting real-world multi-fingered robot manipulation data towards generalist robot autonomy. To achieve it, we propose the RAPID Hand, a co-optimized hardware and software platform where the compact 20-DoF hand, robust whole-hand perception, and high-DoF teleoperation interface are jointly designed. Specifically, RAPID Hand adopts a compact and practical hand ontology and a hardware-level perception framework that stably integrates wrist-mounted vision, fingertip tactile sensing, and proprioception with sub-7 ms latency and spatial alignment. Collecting high-quality demonstrations on high-DoF hands is challenging, as existing teleoperation methods struggle with precision and stability on complex multi-fingered systems. We address this by co-optimizing hand design, perception integration, and teleoperation interface through a universal actuation scheme, custom perception electronics, and two retargeting constraints. We evaluate the platform's hardware, perception, and teleoperation interface. Training a diffusion policy on collected data shows superior performance over prior works, validating the system's capability for reliable, high-quality data collection. The platform is constructed from low-cost and off-the-shelf components and will be made public to ensure reproducibility and ease of adoption.
Towards Large Language Models with Self-Consistent Natural Language Explanations
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) seem to offer an easy path to interpretability: just ask them to explain their decisions. Yet, studies show that these post-hoc explanations often misrepresent the true decision process, as revealed by mismatches in feature importance. Despite growing evidence of this inconsistency, no systematic solutions have emerged, partly due to the high cost of estimating feature importance, which limits evaluations to small datasets. To address this, we introduce the Post-hoc Self-Consistency Bank (PSCB) - a large-scale benchmark of decisions spanning diverse tasks and models, each paired with LLM-generated explanations and corresponding feature importance scores. Analysis of PSCB reveals that self-consistency scores barely differ between correct and incorrect predictions. We also show that the standard metric fails to meaningfully distinguish between explanations. To overcome this limitation, we propose an alternative metric that more effectively captures variation in explanation quality. We use it to fine-tune LLMs via Direct Preference Optimization (DPO), leading to significantly better alignment between explanations and decision-relevant features, even under domain shift. Our findings point to a scalable path toward more trustworthy, self-consistent LLMs.
Generative Voice Bursts during Phone Call
Venue: arXiv cs.SD 2025
Year: 2025

In critical situations, conventional mobile telephony fails to convey emergency voice messages to a callee already engaged in another call. The standard call waiting alert does not provide the urgency or content of the waiting call. This paper proposes a novel method for transmitting Generative Voice Bursts short, context aware audio messages during ongoing calls, from either preauthorized or dynamically prioritized callers. By leveraging generative AI techniques, the system automatically generates spoken messages from contextual inputs example like location, health data, images, background noise when the caller is unable to speak due to incapacitation or environmental constraints. The solution incorporates voice, text, and priority inference mechanisms, allowing high priority emergency messages to bypass conventional call waiting barriers. The approach employs models such as GPT Neo for generative text, which is synthesized into audio and delivered in configurable intervals G seconds and counts N times, ensuring minimal disruption while preserving urgency. This method holds potential for significant impact across telecom, mobile device manufacturing, and emergency communication platforms.
MoQAE: Mixed-Precision Quantization for Long-Context LLM Inference via Mixture of Quantization-Aware Experts
Venue: arXiv cs.CV 2025
Year: 2025

One of the primary challenges in optimizing large language models (LLMs) for long-context inference lies in the high memory consumption of the Key-Value (KV) cache. Existing approaches, such as quantization, have demonstrated promising results in reducing memory usage. However, current quantization methods cannot take both effectiveness and efficiency into account. In this paper, we propose MoQAE, a novel mixed-precision quantization method via mixture of quantization-aware experts. First, we view different quantization bit-width configurations as experts and use the traditional mixture of experts (MoE) method to select the optimal configuration. To avoid the inefficiency caused by inputting tokens one by one into the router in the traditional MoE method, we input the tokens into the router chunk by chunk. Second, we design a lightweight router-only fine-tuning process to train MoQAE with a comprehensive loss to learn the trade-off between model accuracy and memory usage. Finally, we introduce a routing freezing (RF) and a routing sharing (RS) mechanism to further reduce the inference overhead. Extensive experiments on multiple benchmark datasets demonstrate that our method outperforms state-of-the-art KV cache quantization approaches in both efficiency and effectiveness.
Fractional Collisions: A Framework for Risk Estimation of Counterfactual Conflicts using Autonomous Driving Behavior Simulations
Venue: arXiv cs.RO 2025
Year: 2025

We present a methodology for estimating collision risk from counterfactual simulated scenarios built on sensor data from automated driving systems (ADS) or naturalistic driving databases. Two-agent conflicts are assessed by detecting and classifying conflict type, identifying the agents' roles (initiator or responder), identifying the point of reaction of the responder, and modeling their human behavioral expectations as probabilistic counterfactual trajectories. The states are used to compute velocity differentials at collision, which when combined with crash models, estimates severity of loss in terms of probabilistic injury or property damage, henceforth called fractional collisions. The probabilistic models may also be extended to include other uncertainties associated with the simulation, features, and agents. We verify the effectiveness of the methodology in a synthetic simulation environment using reconstructed trajectories from 300+ collision and near-collision scenes sourced from VTTI's SHRP2 database and Nexar dashboard camera data. Our methodology predicted fractional collisions within 1% of ground truth collisions. We then evaluate agent-initiated collision risk of an arbitrary ADS software release by replacing the naturalistic responder in these synthetic reconstructions with an ADS simulator and comparing the outcome to human-response outcomes. Our ADS reduced naturalistic collisions by 4x and fractional collision risk by ~62%. The framework's utility is also demonstrated on 250k miles of proprietary, open-loop sensor data collected on ADS test vehicles, re-simulated with an arbitrary ADS software release. The ADS initiated conflicts that caused 0.4 injury-causing and 1.7 property-damaging fractional collisions, and the ADS improved collision risk in 96% of the agent-initiated conflicts.
Towards the Influence of Text Quantity on Writer Retrieval
Venue: arXiv cs.CV 2025
Year: 2025

This paper investigates the task of writer retrieval, which identifies documents authored by the same individual within a dataset based on handwriting similarities. While existing datasets and methodologies primarily focus on page level retrieval, we explore the impact of text quantity on writer retrieval performance by evaluating line- and word level retrieval. We examine three state-of-the-art writer retrieval systems, including both handcrafted and deep learning-based approaches, and analyze their performance using varying amounts of text. Our experiments on the CVL and IAM dataset demonstrate that while performance decreases by 20-30% when only one line of text is used as query and gallery, retrieval accuracy remains above 90% of full-page performance when at least four lines are included. We further show that text-dependent retrieval can maintain strong performance in low-text scenarios. Our findings also highlight the limitations of handcrafted features in low-text scenarios, with deep learning-based methods like NetVLAD outperforming traditional VLAD encoding.
An $O(n\log n)$ Algorithm for Single-Source Shortest Paths in Disk Graphs
Venue: arXiv cs.CG 2025
Year: 2025

We prove that the single-source shortest-path problem on disk graphs can be solved in $O(n\log n)$ time, and that it can be solved on intersection graphs of fat triangles in $O(n\log^2 n)$ time.