OpenRaiser/Intern-Atlas huggingface dataset:
MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss
Venue: arXiv cs.CV 2025
Year: 2025

Medical image synthesis is an important topic for both clinical and research applications. Recently, diffusion models have become a leading approach in this area. Despite their strengths, many existing methods struggle with (1) limited generalizability that only work for specific body regions or voxel spacings, (2) slow inference, which is a common issue for diffusion models, and (3) weak alignment with input conditions, which is a critical issue for medical imaging. MAISI, a previously proposed framework, addresses generalizability issues but still suffers from slow inference and limited condition consistency. In this work, we present MAISI-v2, the first accelerated 3D medical image synthesis framework that integrates rectified flow to enable fast and high quality generation. To further enhance condition fidelity, we introduce a novel region-specific contrastive loss to enhance the sensitivity to region of interest. Our experiments show that MAISI-v2 can achieve SOTA image quality with $33 \times$ acceleration for latent diffusion model. We also conducted a downstream segmentation experiment to show that the synthetic images can be used for data augmentation. We release our code, training details, model weights, and a GUI demo to facilitate reproducibility and promote further development within the community.
Basic interactive algorithms: Preview
Venue: arXiv cs.LO 2025
Year: 2025

This dialog paper offers a preview and provides a foretaste of an upcoming work on the axiomatization of basic interactive algorithms. The modern notion of algorithm was elucidated in the 1930s--1950s. It was axiomatized a quarter of a century ago as the notion of ``sequential algorithm'' or ``classical algorithm''; we prefer to call it ``basic algorithm" now. The axiomatization was used to show that for every basic algorithm there is a behaviorally equivalent abstract state machine. It was also used to prove the Church-Turing thesis as it has been understood by the logicians. Starting from the 1960s, the notion of algorithm has expanded -- probabilistic algorithms, quantum algorithms, etc. -- prompting introduction of a much more ambitious version of the Church-Turing thesis commonly known as the ``physical thesis.'' We emphasize the difference between the two versions of the Church-Turing thesis and illustrate how nondeterministic and probabilistic algorithms can be viewed as basic algorithms with appropriate oracles. The same view applies to quantum circuit algorithms and many other classes of algorithms.
Public support for misinformation interventions depends on perceived fairness, effectiveness, and intrusiveness
Venue: arXiv cs.CY 2025
Year: 2025

The proliferation of misinformation on social media has concerning possible consequences, such as the degradation of democratic norms. While recent research on countering misinformation has largely focused on analyzing the effectiveness of interventions, the factors associated with public support for these interventions have received little attention. We asked 1,010 American social media users to rate their support for and perceptions of ten misinformation interventions implemented by the government or social media companies. Our results indicate that the perceived fairness of the intervention is the most important factor in determining support, followed by the perceived effectiveness of that intervention and then the intrusiveness. Interventions that supported user agency and transparency, such as labeling content or fact-checking ads, were more popular than those that involved moderating or removing content or accounts. We found some demographic differences in support levels, with Democrats and women supporting interventions more and finding them more fair, more effective, and less intrusive than Republicans and men, respectively. It is critical to understand which interventions are supported and why, as public opinion can play a key role in the rollout and effectiveness of policies.
Training chord recognition models on artificially generated audio
Venue: arXiv cs.SD 2025
Year: 2025

One of the challenging problems in Music Information Retrieval is the acquisition of enough non-copyrighted audio recordings for model training and evaluation. This study compares two Transformer-based neural network models for chord sequence recognition in audio recordings and examines the effectiveness of using an artificially generated dataset for this purpose. The models are trained on various combinations of Artificial Audio Multitracks (AAM), Schubert's Winterreise Dataset, and the McGill Billboard Dataset and evaluated with three metrics: Root, MajMin and Chord Content Metric (CCM). The experiments prove that even though there are certainly differences in complexity and structure between artificially generated and human-composed music, the former can be useful in certain scenarios. Specifically, AAM can enrich a smaller training dataset of music composed by a human or can even be used as a standalone training set for a model that predicts chord sequences in pop music, if no other data is available.
Neural Field Representations of Mobile Computational Photography
Venue: arXiv cs.CV 2025
Year: 2025

Over the past two decades, mobile imaging has experienced a profound transformation, with cell phones rapidly eclipsing all other forms of digital photography in popularity. Today's cell phones are equipped with a diverse range of imaging technologies - laser depth ranging, multi-focal camera arrays, and split-pixel sensors - alongside non-visual sensors such as gyroscopes, accelerometers, and magnetometers. This, combined with on-board integrated chips for image and signal processing, makes the cell phone a versatile pocket-sized computational imaging platform. Parallel to this, we have seen in recent years how neural fields - small neural networks trained to map continuous spatial input coordinates to output signals - enable the reconstruction of complex scenes without explicit data representations such as pixel arrays or point clouds. In this thesis, I demonstrate how carefully designed neural field models can compactly represent complex geometry and lighting effects. Enabling applications such as depth estimation, layer separation, and image stitching directly from collected in-the-wild mobile photography data. These methods outperform state-of-the-art approaches without relying on complex pre-processing steps, labeled ground truth data, or machine learning priors. Instead, they leverage well-constructed, self-regularized models that tackle challenging inverse problems through stochastic gradient descent, fitting directly to raw measurements from a smartphone.
Beyond Perplexity: Let the Reader Select Retrieval Summaries via Spectrum Projection Score
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have shown improved generation performance through retrieval-augmented generation (RAG) following the retriever-reader paradigm, which supplements model inputs with externally retrieved knowledge. However, prior work often evaluates RAG holistically, assessing the retriever and reader jointly, making it difficult to isolate the true contribution of retrieval, particularly given the prompt sensitivity of LLMs used as readers. We move beyond perplexity and introduce Spectrum Projection Score (SPS), a lightweight and supervision-free metric that allows the reader to gauge the semantic alignment of a retrieved summary with its hidden representation by comparing the area formed by generated tokens from the summary, and the principal directions of subspace in the reader and to measure the relevance. Building on SPS we present xCompress, an inference-time controller framework that dynamically samples, ranks, and compresses retrieval summary candidates. Extensive experiments on five QA benchmarks with four open-sourced LLMs show that SPS not only enhances performance across a range of tasks but also provides a principled perspective on the interaction between retrieval and generation.
Fast, Convex and Conditioned Network for Multi-Fidelity Vectors and Stiff Univariate Differential Equations
Venue: arXiv cs.LG 2025
Year: 2025

Accuracy in neural PDE solvers often breaks down not because of limited expressivity, but due to poor optimisation caused by ill-conditioning, especially in multi-fidelity and stiff problems. We study this issue in Physics-Informed Extreme Learning Machines (PIELMs), a convex variant of neural PDE solvers, and show that asymptotic components in governing equations can produce highly ill-conditioned activation matrices, severely limiting convergence. We introduce Shifted Gaussian Encoding, a simple yet effective activation filtering step that increases matrix rank and expressivity while preserving convexity. Our method extends the solvable range of Peclet numbers in steady advection-diffusion equations by over two orders of magnitude, achieves up to six orders lower error on multi-frequency function learning, and fits high-fidelity image vectors more accurately and faster than deep networks with over a million parameters. This work highlights that conditioning, not depth, is often the bottleneck in scientific neural solvers and that simple architectural changes can unlock substantial gains.
Affordance-Guided Dual-Armed Disassembly Teleoperation for Mating Parts
Venue: arXiv cs.RO 2025
Year: 2025

Robotic non-destructive disassembly of mating parts remains challenging due to the need for flexible manipulation and the limited visibility of internal structures. This study presents an affordance-guided teleoperation system that enables intuitive human demonstrations for dual-arm fix-and-disassemble tasks for mating parts. The system visualizes feasible grasp poses and disassembly directions in a virtual environment, both derived from the object's geometry, to address occlusions and structural complexity. To prevent excessive position tracking under load when following the affordance, we integrate a hybrid controller that combines position and impedance control into the teleoperated disassembly arm. Real-world experiments validate the effectiveness of the proposed system, showing improved task success rates and reduced object pose deviation.
Dean of LLM Tutors: Exploring Comprehensive and Automated Evaluation of LLM-generated Educational Feedback via LLM Feedback Evaluators
Venue: arXiv cs.CY 2025
Year: 2025

The use of LLM tutors to provide automated educational feedback to students on student assignment submissions has received much attention in the AI in Education field. However, the stochastic nature and tendency for hallucinations in LLMs can undermine both quality of learning experience and adherence to ethical standards. To address this concern, we propose a method that uses LLM feedback evaluators (DeanLLMs) to automatically and comprehensively evaluate feedback generated by LLM tutor for submissions on university assignments before it is delivered to students. This allows low-quality feedback to be rejected and enables LLM tutors to improve the feedback they generated based on the evaluation results. We first proposed a comprehensive evaluation framework for LLM-generated educational feedback, comprising six dimensions for feedback content, seven for feedback effectiveness, and three for hallucination types. Next, we generated a virtual assignment submission dataset covering 85 university assignments from 43 computer science courses using eight commonly used commercial LLMs. We labelled and open-sourced the assignment dataset to support the fine-tuning and evaluation of LLM feedback evaluators. Our findings show that o3-pro demonstrated the best performance in zero-shot labelling of feedback while o4-mini demonstrated the best performance in few-shot labelling of feedback. Moreover, GPT-4.1 achieved human expert level performance after fine-tuning (Accuracy 79.8%, F1-score 79.4%; human average Accuracy 78.3%, F1-score 82.6%). Finally, we used our best-performance model to evaluate 2,000 assignment feedback instances generated by 10 common commercial LLMs, 200 each, to compare the quality of feedback generated by different LLMs. Our LLM feedback evaluator method advances our ability to automatically provide high-quality and reliable educational feedback to students.
Mildly Conservative Regularized Evaluation for Offline Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Offline reinforcement learning (RL) seeks to learn optimal policies from static datasets without further environment interaction. A key challenge is the distribution shift between the learned and behavior policies, leading to out-of-distribution (OOD) actions and overestimation. To prevent gross overestimation, the value function must remain conservative; however, excessive conservatism may hinder performance improvement. To address this, we propose the mildly conservative regularized evaluation (MCRE) framework, which balances conservatism and performance by combining temporal difference (TD) error with a behavior cloning term in the Bellman backup. Building on this, we develop the mildly conservative regularized Q-learning (MCRQ) algorithm, which integrates MCRE into an off-policy actor-critic framework. Experiments show that MCRQ outperforms strong baselines and state-of-the-art offline RL algorithms on benchmark datasets.
Dual prototype attentive graph network for cross-market recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Cross-market recommender systems (CMRS) aim to utilize historical data from mature markets to promote multinational products in emerging markets. However, existing CMRS approaches often overlook the potential for shared preferences among users in different markets, focusing primarily on modeling specific preferences within each market. In this paper, we argue that incorporating both market-specific and market-shared insights can enhance the generalizability and robustness of CMRS. We propose a novel approach called Dual Prototype Attentive Graph Network for Cross-Market Recommendation (DGRE) to address this. DGRE leverages prototypes based on graph representation learning from both items and users to capture market-specific and market-shared insights. Specifically, DGRE incorporates market-shared prototypes by clustering users from various markets to identify behavioural similarities and create market-shared user profiles. Additionally, it constructs item-side prototypes by aggregating item features within each market, providing valuable market-specific insights. We conduct extensive experiments to validate the effectiveness of DGRE on a real-world cross-market dataset, and the results show that considering both market-specific and market-sharing aspects in modelling can improve the generalization and robustness of CMRS.
Hybrid Game Control Envelope Synthesis
Venue: arXiv cs.PL 2025
Year: 2025

Control problems for embedded systems like cars and trains can be modeled by two-player hybrid games. Control envelopes, which are families of safe control solutions, correspond to nondeterministic winning policies of hybrid games, where each deterministic specialization of the policy is a control solution. This paper synthesizes nondeterministic winning policies for hybrid games that are as permissive as possible. It introduces subvalue maps, a compositional representation of such policies that enables verification and synthesis along the structure of the game. An inductive logical characterization in differential game logic (dGL) checks whether a subvalue map induces a sound control envelope which always induces a winning play. A policy is said to win if it always achieves the desirable outcome when the player follows it, no matter what actions the opponent plays. The maximal subvalue map, which allows the most action options while still winning, is shown to exist and satisfy a logical characterization. A family of algorithms for nondeterministic policy synthesis can be obtained from the inductive subvalue map soundness characterization. An implementation of these findings is evaluated on examples that use the expressivity of dGL to model a range of diverse control challenges.
Fourier-VLM: Compressing Vision Tokens in the Frequency Domain for Large Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language Models (VLMs) typically replace the predefined image placeholder token (<image>) in textual instructions with visual features from an image encoder, forming the input to a backbone Large Language Model (LLM). However, the large number of vision tokens significantly increases the context length, leading to high computational overhead and inference latency. While previous efforts mitigate this by selecting only important visual features or leveraging learnable queries to reduce token count, they often compromise performance or introduce substantial extra costs. In response, we propose Fourier-VLM, a simple yet efficient method that compresses visual representations in the frequency domain. Our approach is motivated by the observation that vision features output from the vision encoder exhibit concentrated energy in low-frequency components. Leveraging this, we apply a low-pass filter to the vision features using a two-dimensional Discrete Cosine Transform (DCT). Notably, the DCT is efficiently computed via the Fast Fourier Transform (FFT) operator with a time complexity of $\mathcal{O}(n\log n)$, minimizing the extra computational cost while introducing no additional parameters. Extensive experiments across various image-based benchmarks demonstrate that Fourier-VLM achieves competitive performance with strong generalizability across both LLaVA and Qwen-VL architectures. Crucially, it reduce inference FLOPs by up to 83.8% and boots generation speed by 31.2% compared to LLaVA-v1.5, highlighting the superior efficiency and practicality.
ArchXBench: A Complex Digital Systems Benchmark Suite for LLM Driven RTL Synthesis
Venue: arXiv cs.AR 2025
Year: 2025

Modern SoC datapaths include deeply pipelined, domain-specific accelerators, but their RTL implementation and verification are still mostly done by hand. While large language models (LLMs) exhibit advanced code-generation abilities for programming languages like Python, their application to Verilog-like RTL remains in its nascent stage. This is reflected in the simple arithmetic and control circuits currently used to evaluate generative capabilities in existing benchmarks. In this paper, we introduce ArchXBench, a six-level benchmark suite that encompasses complex arithmetic circuits and other advanced digital subsystems drawn from domains such as cryptography, image processing, machine learning, and signal processing. Architecturally, some of these designs are purely combinational, others are multi-cycle or pipelined, and many require hierarchical composition of modules. For each benchmark, we provide a problem description, design specification, and testbench, enabling rapid research in the area of LLM-driven agentic approaches for complex digital systems design. Using zero-shot prompting with Claude Sonnet 4, GPT 4.1, o4-mini-high, and DeepSeek R1 under a pass@5 criterion, we observed that o4-mini-high successfully solves the largest number of benchmarks, 16 out of 30, spanning Levels 1, 2, and 3. From Level 4 onward, however, all models consistently fail, highlighting a clear gap in the capabilities of current state-of-the-art LLMs and prompting/agentic approaches.
RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation
Venue: arXiv cs.HC 2025
Year: 2025

Retrieval-Augmented Generation (RAG) systems have emerged as a promising solution to enhance large language models (LLMs) by integrating external knowledge retrieval with generative capabilities. While significant advancements have been made in improving retrieval accuracy and response quality, a critical challenge remains that the internal knowledge integration and retrieval-generation interactions in RAG workflows are largely opaque. This paper introduces RAGTrace, an interactive evaluation system designed to analyze retrieval and generation dynamics in RAG-based workflows. Informed by a comprehensive literature review and expert interviews, the system supports a multi-level analysis approach, ranging from high-level performance evaluation to fine-grained examination of retrieval relevance, generation fidelity, and cross-component interactions. Unlike conventional evaluation practices that focus on isolated retrieval or generation quality assessments, RAGTrace enables an integrated exploration of retrieval-generation relationships, allowing users to trace knowledge sources and identify potential failure cases. The system's workflow allows users to build, evaluate, and iterate on retrieval processes tailored to their specific domains of interest. The effectiveness of the system is demonstrated through case studies and expert evaluations on real-world RAG applications.
Towards MR-Based Trochleoplasty Planning
Venue: arXiv cs.CV 2025
Year: 2025

To treat Trochlear Dysplasia (TD), current approaches rely mainly on low-resolution clinical Magnetic Resonance (MR) scans and surgical intuition. The surgeries are planned based on surgeons experience, have limited adoption of minimally invasive techniques, and lead to inconsistent outcomes. We propose a pipeline that generates super-resolved, patient-specific 3D pseudo-healthy target morphologies from conventional clinical MR scans. First, we compute an isotropic super-resolved MR volume using an Implicit Neural Representation (INR). Next, we segment femur, tibia, patella, and fibula with a multi-label custom-trained network. Finally, we train a Wavelet Diffusion Model (WDM) to generate pseudo-healthy target morphologies of the trochlear region. In contrast to prior work producing pseudo-healthy low-resolution 3D MR images, our approach enables the generation of sub-millimeter resolved 3D shapes compatible for pre- and intraoperative use. These can serve as preoperative blueprints for reshaping the femoral groove while preserving the native patella articulation. Furthermore, and in contrast to other work, we do not require a CT for our pipeline - reducing the amount of radiation. We evaluated our approach on 25 TD patients and could show that our target morphologies significantly improve the sulcus angle (SA) and trochlear groove depth (TGD). The code and interactive visualization are available at https://wehrlimi.github.io/sr-3d-planning/.
E-React: Towards Emotionally Controlled Synthesis of Human Reactions
Venue: arXiv cs.CV 2025
Year: 2025

Emotion serves as an essential component in daily human interactions. Existing human motion generation frameworks do not consider the impact of emotions, which reduces naturalness and limits their application in interactive tasks, such as human reaction synthesis. In this work, we introduce a novel task: generating diverse reaction motions in response to different emotional cues. However, learning emotion representation from limited motion data and incorporating it into a motion generation framework remains a challenging problem. To address the above obstacles, we introduce a semi-supervised emotion prior in an actor-reactor diffusion model to facilitate emotion-driven reaction synthesis. Specifically, based on the observation that motion clips within a short sequence tend to share the same emotion, we first devise a semi-supervised learning framework to train an emotion prior. With this prior, we further train an actor-reactor diffusion model to generate reactions by considering both spatial interaction and emotional response. Finally, given a motion sequence of an actor, our approach can generate realistic reactions under various emotional conditions. Experimental results demonstrate that our model outperforms existing reaction generation methods. The code and data will be made publicly available at https://ereact.github.io/