OpenRaiser/Intern-Atlas huggingface dataset:
Examining Popular Arguments Against AI Existential Risk: A Philosophical Analysis
Venue: arXiv cs.CY 2025
Year: 2025

Concerns about artificial intelligence (AI) and its potential existential risks have garnered significant attention, with figures like Geoffrey Hinton and Dennis Hassabis advocating for robust safeguards against catastrophic outcomes. Prominent scholars, such as Nick Bostrom and Max Tegmark, have further advanced the discourse by exploring the long-term impacts of superintelligent AI. However, this existential risk narrative faces criticism, particularly in popular media, where scholars like Timnit Gebru, Melanie Mitchell, and Nick Clegg argue, among other things, that it distracts from pressing current issues. Despite extensive media coverage, skepticism toward the existential risk discourse has received limited rigorous treatment in academic literature. Addressing this imbalance, this paper reconstructs and evaluates three common arguments against the existential risk perspective: the Distraction Argument, the Argument from Human Frailty, and the Checkpoints for Intervention Argument. By systematically reconstructing and assessing these arguments, the paper aims to provide a foundation for more balanced academic discourse and further research on AI.
Multi-armed Bandit and Backbone boost Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problems
Venue: arXiv cs.DS 2025
Year: 2025

The Lin-Kernighan-Helsguan (LKH) heuristic is a classic local search algorithm for the Traveling Salesman Problem (TSP). LKH introduces an $α$-value to replace the traditional distance metric for evaluating the edge quality, which leads to a significant improvement. However, we observe that the $α$-value does not make full use of the historical information during the search, and single guiding information often makes LKH hard to escape from some local optima. To address the above issues, we propose a novel way to extract backbone information during the TSP local search process, which is dynamic and can be updated once a local optimal solution is found. We further propose to combine backbone information, $α$-value, and distance to evaluate the edge quality so as to guide the search. Moreover, we abstract their different combinations to arms in a multi-armed bandit (MAB) and use an MAB model to help the algorithm select an appropriate evaluation metric dynamically. Both the backbone information and MAB can provide diverse guiding information and learn from the search history to suggest the best metric. We apply our methods to LKH and LKH-3, which is an extension version of LKH that can be used to solve about 40 variant problems of TSP and Vehicle Routing Problem (VRP). Extensive experiments show the excellent performance and generalization capability of our proposed method, significantly improving LKH for TSP and LKH-3 for two representative TSP and VRP variants, the Colored TSP (CTSP) and Capacitated VRP with Time Windows (CVRPTW).
"Yeah Right!" -- Do LLMs Exhibit Multimodal Feature Transfer?
Venue: arXiv cs.CL 2025
Year: 2025

Human communication is a multifaceted and multimodal skill. Communication requires an understanding of both the surface-level textual content and the connotative intent of a piece of communication. In humans, learning to go beyond the surface level starts by learning communicative intent in speech. Once humans acquire these skills in spoken communication, they transfer those skills to written communication. In this paper, we assess the ability of speech+text models and text models trained with special emphasis on human-to-human conversations to make this multimodal transfer of skill. We specifically test these models on their ability to detect covert deceptive communication. We find that with no special prompting speech+text LLMs have an advantage over unimodal LLMs in performing this task. Likewise, we find that human-to-human conversation-trained LLMs are also advantaged in this skill.
Benchmarking Large and Small MLLMs
Venue: arXiv cs.CV 2025
Year: 2025

Large multimodal language models (MLLMs) such as GPT-4V and GPT-4o have achieved remarkable advancements in understanding and generating multimodal content, showcasing superior quality and capabilities across diverse tasks. However, their deployment faces significant challenges, including slow inference, high computational cost, and impracticality for on-device applications. In contrast, the emergence of small MLLMs, exemplified by the LLava-series models and Phi-3-Vision, offers promising alternatives with faster inference, reduced deployment costs, and the ability to handle domain-specific scenarios. Despite their growing presence, the capability boundaries between large and small MLLMs remain underexplored. In this work, we conduct a systematic and comprehensive evaluation to benchmark both small and large MLLMs, spanning general capabilities such as object recognition, temporal reasoning, and multimodal comprehension, as well as real-world applications in domains like industry and automotive. Our evaluation reveals that small MLLMs can achieve comparable performance to large models in specific scenarios but lag significantly in complex tasks requiring deeper reasoning or nuanced understanding. Furthermore, we identify common failure cases in both small and large MLLMs, highlighting domains where even state-of-the-art models struggle. We hope our findings will guide the research community in pushing the quality boundaries of MLLMs, advancing their usability and effectiveness across diverse applications.
Multilingual Open QA on the MIA Shared Task
Venue: arXiv cs.CL 2025
Year: 2025

Cross-lingual information retrieval (CLIR) ~\cite{shi2021cross, asai2021one, jiang2020cross} for example, can find relevant text in any language such as English(high resource) or Telugu (low resource) even when the query is posed in a different, possibly low-resource, language. In this work, we aim to develop useful CLIR models for this constrained, yet important, setting where we do not require any kind of additional supervision or labelled data for retrieval task and hence can work effectively for low-resource languages. \par We propose a simple and effective re-ranking method for improving passage retrieval in open question answering. The re-ranker re-scores retrieved passages with a zero-shot multilingual question generation model, which is a pre-trained language model, to compute the probability of the input question in the target language conditioned on a retrieved passage, which can be possibly in a different language. We evaluate our method in a completely zero shot setting and doesn't require any training. Thus the main advantage of our method is that our approach can be used to re-rank results obtained by any sparse retrieval methods like BM-25. This eliminates the need for obtaining expensive labelled corpus required for the retrieval tasks and hence can be used for low resource languages.
Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generation
Venue: arXiv cs.CL 2025
Year: 2025

Personalized text generation requires a unique ability of large language models (LLMs) to learn from context that they often do not encounter during their standard training. One way to encourage LLMs to better use personalized context for generating outputs that better align with the user's expectations is to instruct them to reason over the user's past preferences, background knowledge, or writing style. To achieve this, we propose Reasoning-Enhanced Self-Training for Personalized Text Generation (REST-PG), a framework that trains LLMs to reason over personal data during response generation. REST-PG first generates reasoning paths to train the LLM's reasoning abilities and then employs Expectation-Maximization Reinforced Self-Training to iteratively train the LLM based on its own high-reward outputs. We evaluate REST-PG on the LongLaMP benchmark, consisting of four diverse personalized long-form text generation tasks. Our experiments demonstrate that REST-PG achieves significant improvements over state-of-the-art baselines, with an average relative performance gain of 14.5% on the benchmark.
Learning to Transfer Human Hand Skills for Robot Manipulations
Venue: arXiv cs.RO 2025
Year: 2025

We present a method for teaching dexterous manipulation tasks to robots from human hand motion demonstrations. Unlike existing approaches that solely rely on kinematics information without taking into account the plausibility of robot and object interaction, our method directly infers plausible robot manipulation actions from human motion demonstrations. To address the embodiment gap between the human hand and the robot system, our approach learns a joint motion manifold that maps human hand movements, robot hand actions, and object movements in 3D, enabling us to infer one motion component from others. Our key idea is the generation of pseudo-supervision triplets, which pair human, object, and robot motion trajectories synthetically. Through real-world experiments with robot hand manipulation, we demonstrate that our data-driven retargeting method significantly outperforms conventional retargeting techniques, effectively bridging the embodiment gap between human and robotic hands. Website at https://rureadyo.github.io/MocapRobot/.
Decompiling for Constant-Time Analysis
Venue: arXiv cs.PL 2025
Year: 2025

Cryptographic libraries are a main target of timing side-channel attacks. A practical means to protect against these attacks is to adhere to the constant-time (CT) policy. However, it is hard to write constant-time code, and even constant-time code can be turned vulnerable by mainstream compilers. So how can we verify that binary code is constant-time? The obvious answer is to use binary-level CT tools. To do so, a common approach is to use decompilers or lifters as a front-end for CT analysis tools operating on source code or IR. Unfortunately, this approach is problematic with current decompilers. To illustrate this fact, we use the recent Clangover vulnerability and other constructed examples to show that five popular decompilers eliminate CT violations, rendering them not applicable with the approach. In this paper, we develop foundations to asses whether a decompiler is fit for the Decompile-then-Analyze approach. We propose CT transparency, which states that a transformation neither eliminates nor introduces CT violations, and a general method for proving that a program transformation is CT transparent. Then, we build CT-RetDec, a CT analysis tool based on a modified version of the LLVM-based decompiler RetDec. We evaluate CT-RetDec on a benchmark of real-world vulnerabilities in binaries, and show that the modifications had significant impact on CT-RetDec's performance. As a contribution of independent interest, we found that popular tools for binary-level CT analysis rely on decompiler-like transformations before analysis. We show that two such tools employ transformations that are not CT transparent, and, consequently, that they incorrectly accept non-CT programs. While our examples are very specific and do not invalidate the general approach of these tools, we advocate that tool developers counter such potential issues by proving the transparency of such transformations.
GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
Venue: arXiv cs.RO 2025
Year: 2025

In industrial environments, predicting human actions is essential for ensuring safe and effective collaboration between humans and robots. This paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. The framework first allows each robot to build a spatial graph representing its surroundings, which it then shares with other robots. This shared spatial data is combined with temporal information to track human behavior over time. A swarm-inspired decision-making process is used to ensure all robots agree on a unified interpretation of the human's actions. Results show that adding more robots and incorporating longer time sequences improve prediction accuracy. Additionally, the consensus mechanism increases system resilience, making the multi-robot setup more reliable in dynamic industrial settings.
Computation and Communication Co-scheduling for Multi-Task Remote Inference
Venue: arXiv cs.IT 2025
Year: 2025

In multi-task remote inference systems, an intelligent receiver (e.g., command center) performs multiple inference tasks (e.g., target detection) using data features received from several remote sources (e.g., edge devices). Key challenges to facilitating timely inference in these systems arise from (i) limited computational power of the sources to produce features from their inputs, and (ii) limited communication resources of the channels to carry simultaneous feature transmissions to the receiver. We develop a novel computation and communication co-scheduling methodology which determines feature generation and transmission scheduling to minimize inference errors subject to these resource constraints. Specifically, we formulate the co-scheduling problem as a weakly-coupled Markov decision process with Age of Information (AoI)-based timeliness gauging the inference errors. To overcome its PSPACE-hard complexity, we analyze a Lagrangian relaxation of the problem, which yields gain indices assessing the improvement in inference error for each potential feature generation-transmission scheduling action. Based on this, we develop a reoptimized maximum gain first (MGF) policy. We show that this policy is asymptotically optimal for the original problem as the number of inference tasks and the available communication and computation resources increase, provided the ratio among them remains fixed. Experiments demonstrate that reoptimized MGF obtains significant improvements over baseline policies for varying numbers of tasks, channels, and sources.
Stable Derivative Free Gaussian Mixture Variational Inference for Bayesian Inverse Problems
Venue: arXiv cs.LG 2025
Year: 2025

This paper is concerned with the approximation of probability distributions known up to normalization constants, with a focus on Bayesian inference for large-scale inverse problems in scientific computing. In this context, key challenges include costly repeated evaluations of forward models, multimodality, and inaccessible gradients for the forward model. To address them, we develop a variational inference framework that combines Fisher-Rao natural gradient with specialized quadrature rules to enable derivative free updates of Gaussian mixture variational families. The resulting method, termed Derivative Free Gaussian Mixture Variational Inference (DF-GMVI), guarantees covariance positivity and affine invariance, offering a stable and efficient framework for approximating complex posterior distributions. The effectiveness of DF-GMVI is demonstrated through numerical experiments on challenging scenarios, including distributions with multiple modes, infinitely many modes, and curved modes in spaces with up to 100 dimensions. The method's practicality is further demonstrated in a large-scale application, where it successfully recovers the initial conditions of the Navier-Stokes equations from solution data at positive times.
Bridging Adaptivity and Safety: Learning Agile Collision-Free Locomotion Across Varied Physics
Venue: arXiv cs.RO 2025
Year: 2025

Real-world legged locomotion systems often need to reconcile agility and safety for different scenarios. Moreover, the underlying dynamics are often unknown and time-variant (e.g., payload, friction). In this paper, we introduce BAS (Bridging Adaptivity and Safety), which builds upon the pipeline of prior work Agile But Safe (ABS)(He et al.) and is designed to provide adaptive safety even in dynamic environments with uncertainties. BAS involves an agile policy to avoid obstacles rapidly and a recovery policy to prevent collisions, a physical parameter estimator that is concurrently trained with agile policy, and a learned control-theoretic RA (reach-avoid) value network that governs the policy switch. Also, the agile policy and RA network are both conditioned on physical parameters to make them adaptive. To mitigate the distribution shift issue, we further introduce an on-policy fine-tuning phase for the estimator to enhance its robustness and accuracy. The simulation results show that BAS achieves 50% better safety than baselines in dynamic environments while maintaining a higher speed on average. In real-world experiments, BAS shows its capability in complex environments with unknown physics (e.g., slippery floors with unknown frictions, unknown payloads up to 8kg), while baselines lack adaptivity, leading to collisions or. degraded agility. As a result, BAS achieves a 19.8% increase in speed and gets a 2.36 times lower collision rate than ABS in the real world. Videos: https://adaptive-safe-locomotion.github.io.
Cluster & Disperse: a general air conflict resolution heuristic using unsupervised learning
Venue: arXiv cs.RO 2025
Year: 2025

We provide a general and malleable heuristic for the air conflict resolution problem. This heuristic is based on a new neighborhood structure for searching the solution space of trajectories and flight-levels. Using unsupervised learning, the core idea of our heuristic is to cluster the conflict points and disperse them in various flight levels. Our first algorithm is called Cluster & Disperse and in each iteration it assigns the most problematic flights in each cluster to another flight-level. In effect, we shuffle them between the flight-levels until we achieve a well-balanced configuration. The Cluster & Disperse algorithm then uses any horizontal plane conflict resolution algorithm as a subroutine to solve these well-balanced instances. Nevertheless, we develop a novel algorithm for the horizontal plane based on a similar idea. That is we cluster and disperse the conflict points spatially in the same flight level using the gradient descent and a social force. We use a novel maneuver making flights travel on an arc instead of a straight path which is based on the aviation routine of the Radius to Fix legs. Our algorithms can handle a high density of flights within a reasonable computation time. We put their performance in context with some notable algorithms from the literature. Being a general framework, a particular strength of the Cluster & Disperse is its malleability in allowing various constraints regarding the aircraft or the environment to be integrated with ease. This is in contrast to the models for instance based on mixed integer programming.
Physics-Informed Super-Resolution Diffusion for 6D Phase Space Diagnostics
Venue: arXiv cs.LG 2025
Year: 2025

Adaptive physics-informed super-resolution diffusion is developed for non-invasive virtual diagnostics of the 6D phase space density of charged particle beams. An adaptive variational autoencoder (VAE) embeds initial beam condition images and scalar measurements to a low-dimensional latent space from which a 326 pixel 6D tensor representation of the beam's 6D phase space density is generated. Projecting from a 6D tensor generates physically consistent 2D projections. Physics-guided super-resolution diffusion transforms low-resolution images of the 6D density to high resolution 256x256 pixel images. Un-supervised adaptive latent space tuning enables tracking of time-varying beams without knowledge of time-varying initial conditions. The method is demonstrated with experimental data and multi-particle simulations at the HiRES UED. The general approach is applicable to a wide range of complex dynamic systems evolving in high-dimensional phase space. The method is shown to be robust to distribution shift without re-training.
Finite Dimensional Lattice Codes with Self Error-Detection and Retry Decoding
Venue: arXiv cs.IT 2025
Year: 2025

Lattice codes with optimal decoding coefficient are capacity-achieving when dimension $N \rightarrow \infty$. In communications systems, finite dimensional lattice codes are considered, where the optimal decoding coefficients may still fail decoding even when $R< C$. This paper presents a new retry decoding scheme for finite dimensional lattice-based transmissions. When decoding errors are detected, the receiver is allowed to adjust the value of decoding coefficients and retry decoding, instead of requesting a re-transmission immediately which causes high latency. This scheme is considered for both point-to-point single user transmission and compute-forward (CF) relaying with power unconstrained relays, by which a lower word error rate (WER) is achieved than conventional one-shot decoding with optimal coefficients. A lattice/lattice code construction, called CRC-embedded lattice/lattice code, is presented to provide physical layer error detection to enable retry decoding. For CF relaying, a shaping lattice design is given so that the decoder is able to detect errors from CF linear combinations without requiring individual users' messages. The numerical results show gains of up to 1.31 dB and 1.08 dB at error probability $10^{-5}$ for a 2-user CF relay using 128- and 256-dimensional lattice codes with optimized CRC length and 2 decoding trials in total.
Molecular HDD Logic for Encrypted Massive Data Storage
Venue: arXiv cs.ET 2025
Year: 2025

Organic memories, with small dimension, fast speed and long retention features, are considered as promising candidates for massive data archiving. In order to satisfy the re-quirements for ultra-low power and high-security information storage, we design a concep-tual molecular hard-disk (HDD) logic scheme that is capable to execute in-situ encryption of massive data in pW/bit power-consumption range. Beneficial from the coupled mechanism of counter-balanced redox reaction and local ion drifting, the basic HDD unit consisting of ~ 200 self-assembled RuXLPH molecules in a monolayer (SAM) configuration undergoes unique conductance modulation with continuous, symmetric and low-power switching char-acteristics. 96-state memory performance, which allows 6-bit data storage and single-unit one-step XOR operation, is realized in the RuXLPH SAM sample. Through single-unit XOR manipulation of the pixel information, in-situ bitwise encryption of the Mogao Grottoes mural images stored in the molecular HDD is demonstrated.
Edit as You See: Image-guided Video Editing via Masked Motion Modeling
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in diffusion models have significantly facilitated text-guided video editing. However, there is a relative scarcity of research on image-guided video editing, a method that empowers users to edit videos by merely indicating a target object in the initial frame and providing an RGB image as reference, without relying on the text prompts. In this paper, we propose a novel Image-guided Video Editing Diffusion model, termed IVEDiff for the image-guided video editing. IVEDiff is built on top of image editing models, and is equipped with learnable motion modules to maintain the temporal consistency of edited video. Inspired by self-supervised learning concepts, we introduce a masked motion modeling fine-tuning strategy that empowers the motion module's capabilities for capturing inter-frame motion dynamics, while preserving the capabilities for intra-frame semantic correlations modeling of the base image editing model. Moreover, an optical-flow-guided motion reference network is proposed to ensure the accurate propagation of information between edited video frames, alleviating the misleading effects of invalid information. We also construct a benchmark to facilitate further research. The comprehensive experiments demonstrate that our method is able to generate temporally smooth edited videos while robustly dealing with various editing objects with high quality.