OpenRaiser/Intern-Atlas huggingface dataset:
An Evaluation-Centric Paradigm for Scientific Visualization Agents
Venue: arXiv cs.HC 2025
Year: 2025

Recent advances in multi-modal large language models (MLLMs) have enabled increasingly sophisticated autonomous visualization agents capable of translating user intentions into data visualizations. However, measuring progress and comparing different agents remains challenging, particularly in scientific visualization (SciVis), due to the absence of comprehensive, large-scale benchmarks for evaluating real-world capabilities. This position paper examines the various types of evaluation required for SciVis agents, outlines the associated challenges, provides a simple proof-of-concept evaluation example, and discusses how evaluation benchmarks can facilitate agent self-improvement. We advocate for a broader collaboration to develop a SciVis agentic evaluation benchmark that would not only assess existing capabilities but also drive innovation and stimulate future development in the field.
Voyager: An End-to-End Framework for Design-Space Exploration and Generation of DNN Accelerators
Venue: arXiv cs.AR 2025
Year: 2025

While deep neural networks (DNNs) have achieved state-of-the-art performance in fields from computer vision to natural language processing, efficiently running these computationally demanding models requires hardware accelerators. However, designing these accelerators is a time-consuming, labor-intensive process that does not scale well. While prior efforts have sought to automate DNN accelerator generation, they offer limited parameterization, cannot produce high-performance, tapeout-ready designs, provide limited support for datatypes and quantization schemes, and lack an integrated, end-to-end software compiler. This work proposes Voyager, a high-level synthesis (HLS)-based framework for design space exploration (DSE) and generation of DNN accelerators. Voyager overcomes the limitations of prior work by offering extensive configurability across technology nodes, clock frequencies, and scales, with customizable parameters such as number of processing elements, on-chip buffer sizes, and external memory bandwidth. Voyager supports a wider variety of datatypes and quantization schemes versus prior work, including both built-in floating-point, posit and integer formats, as well as user-defined formats with both per-tensor scaling and microscaling quantization. Voyager's PyTorch-based compiler efficiently maps networks end-to-end on the generated hardware, with support for quantization, fusion, and tiling. We evaluate Voyager on state-of-the-art vision and language models. Voyager enables fast DSE with full-dataset accuracy evaluation for datatypes and quantization schemes. Generated designs achieve a high utilization across models and scales, up to 99.8%, and outperform prior generators with up to 61% lower latency and 56% lower area. Compared to hand-optimized accelerators, Voyager achieves comparable performance, while offering much greater automation in design and workload mapping.
FlowRL: Matching Reward Distributions for LLM Reasoning
Venue: arXiv cs.LG 2025
Year: 2025

We propose FlowRL: matching the full reward distribution via flow balancing instead of maximizing rewards in large language model (LLM) reinforcement learning (RL). Recent advanced reasoning models adopt reward-maximizing methods (\eg, PPO and GRPO), which tend to over-optimize dominant reward signals while neglecting less frequent but valid reasoning paths, thus reducing diversity. In contrast, we transform scalar rewards into a normalized target distribution using a learnable partition function, and then minimize the reverse KL divergence between the policy and the target distribution. We implement this idea as a flow-balanced optimization method that promotes diverse exploration and generalizable reasoning trajectories. We conduct experiments on math and code reasoning tasks: FlowRL achieves a significant average improvement of $10.0\%$ over GRPO and $5.1\%$ over PPO on math benchmarks, and performs consistently better on code reasoning tasks. These results highlight reward distribution-matching as a key step toward efficient exploration and diverse reasoning in LLM reinforcement learning.
M-PACE: Mother Child Framework for Multimodal Compliance
Venue: arXiv cs.CV 2025
Year: 2025

Ensuring that multi-modal content adheres to brand, legal, or platform-specific compliance standards is an increasingly complex challenge across domains. Traditional compliance frameworks typically rely on disjointed, multi-stage pipelines that integrate separate modules for image classification, text extraction, audio transcription, hand-crafted checks, and rule-based merges. This architectural fragmentation increases operational overhead, hampers scalability, and hinders the ability to adapt to dynamic guidelines efficiently. With the emergence of Multimodal Large Language Models (MLLMs), there is growing potential to unify these workflows under a single, general-purpose framework capable of jointly processing visual and textual content. In light of this, we propose Multimodal Parameter Agnostic Compliance Engine (M-PACE), a framework designed for assessing attributes across vision-language inputs in a single pass. As a representative use case, we apply M-PACE to advertisement compliance, demonstrating its ability to evaluate over 15 compliance-related attributes. To support structured evaluation, we introduce a human-annotated benchmark enriched with augmented samples that simulate challenging real-world conditions, including visual obstructions and profanity injection. M-PACE employs a mother-child MLLM setup, demonstrating that a stronger parent MLLM evaluating the outputs of smaller child models can significantly reduce dependence on human reviewers, thereby automating quality control. Our analysis reveals that inference costs reduce by over 31 times, with the most efficient models (Gemini 2.0 Flash as child MLLM selected by mother MLLM) operating at 0.0005 per image, compared to 0.0159 for Gemini 2.5 Pro with comparable accuracy, highlighting the trade-off between cost and output quality achieved in real time by M-PACE in real life deployment over advertising data.
PRISM: Phase-enhanced Radial-based Image Signature Mapping framework for fingerprinting AI-generated images
Venue: arXiv cs.CV 2025
Year: 2025

A critical need has emerged for generative AI: attribution methods. That is, solutions that can identify the model originating AI-generated content. This feature, generally relevant in multimodal applications, is especially sensitive in commercial settings where users subscribe to paid proprietary services and expect guarantees about the source of the content they receive. To address these issues, we introduce PRISM, a scalable Phase-enhanced Radial-based Image Signature Mapping framework for fingerprinting AI-generated images. PRISM is based on a radial reduction of the discrete Fourier transform that leverages amplitude and phase information to capture model-specific signatures. The output of the above process is subsequently clustered via linear discriminant analysis to achieve reliable model attribution in diverse settings, even if the model's internal details are inaccessible. To support our work, we construct PRISM-36K, a novel dataset of 36,000 images generated by six text-to-image GAN- and diffusion-based models. On this dataset, PRISM achieves an attribution accuracy of 92.04%. We additionally evaluate our method on four benchmarks from the literature, reaching an average accuracy of 81.60%. Finally, we evaluate our methodology also in the binary task of detecting real vs fake images, achieving an average accuracy of 88.41%. We obtain our best result on GenImage with an accuracy of 95.06%, whereas the original benchmark achieved 82.20%. Our results demonstrate the effectiveness of frequency-domain fingerprinting for cross-architecture and cross-dataset model attribution, offering a viable solution for enforcing accountability and trust in generative AI systems.
Evaluating the Limitations of Local LLMs in Solving Complex Programming Challenges
Venue: arXiv cs.SE 2025
Year: 2025

This study examines the performance of today's open-source, locally hosted large-language models (LLMs) in handling complex competitive programming tasks with extended problem descriptions and contexts. Building on the original Framework for AI-driven Code Generation Evaluation (FACE), the authors retrofit the pipeline to work entirely offline through the Ollama runtime, collapsing FACE's sprawling per-problem directory tree into a handful of consolidated JSON files, and adding robust checkpointing so multi-day runs can resume after failures. The enhanced framework generates, submits, and records solutions for the full Kattis corpus of 3,589 problems across eight code-oriented models ranging from 6.7-9 billion parameters. The submission results show that the overall pass@1 accuracy is modest for the local models, with the best models performing at approximately half the acceptance rate of the proprietary models, Gemini 1.5 and ChatGPT-4. These findings expose a persistent gap between private, cost-controlled LLM deployments and state-of-the-art proprietary services, yet also highlight the rapid progress of open models and the practical benefits of an evaluation workflow that organizations can replicate on in-house hardware.
Collective Voice: Recovered-Peer Support Mediated by An LLM-Based Chatbot for Eating Disorder Recovery
Venue: arXiv cs.HC 2025
Year: 2025

Peer recovery narratives provide unique benefits beyond professional or lay mentoring by fostering hope and sustained recovery in eating disorder (ED) contexts. Yet, such support is limited by the scarcity of peer-involved programs and potential drawbacks on recovered peers, including relapse risk. To address this, we designed RecoveryTeller, a chatbot adopting a recovered-peer persona that portrays itself as someone recovered from an ED. We examined whether such a persona can reproduce the support affordances of peer recovery narratives. We compared RecoveryTeller with a lay-mentor persona chatbot offering similar guidance but without a recovery background. We conducted a 20-day cross-over deployment study with 26 ED participants, each using both chatbots for 10 days. RecoveryTeller elicited stronger emotional resonance than a lay-mentor chatbot, yet tensions between emotional and epistemic trust led participants to view the two personas as complementary rather than substitutes. We provide design implications for mental health chatbot persona design.
Measurement and Potential Field-Based Patient Modeling for Model-Mediated Tele-ultrasound
Venue: arXiv cs.RO 2025
Year: 2025

Teleoperated ultrasound can improve diagnostic medical imaging access for remote communities. Having accurate force feedback is important for enabling sonographers to apply the appropriate probe contact force to optimize ultrasound image quality. However, large time delays in communication make direct force feedback impractical. Prior work investigated using point cloud-based model-mediated teleoperation and internal potential field models to estimate contact forces and torques. We expand on this by introducing a method to update the internal potential field model of the patient with measured positions and forces for more transparent model-mediated tele-ultrasound. We first generate a point cloud model of the patient's surface and transmit this to the sonographer in a compact data structure. This is converted to a static voxelized volume where each voxel contains a potential field value. These values determine the forces and torques, which are rendered based on overlap between the voxelized volume and a point shell model of the ultrasound transducer. We solve for the potential field using a convex quadratic that combines the spatial Laplace operator with measured forces. This was evaluated on volunteer patients ($n=3$) by computing the accuracy of rendered forces. Results showed the addition of measured forces to the model reduced the force magnitude error by an average of 7.23 N and force vector angle error by an average of 9.37$^{\circ}$ compared to using only Laplace's equation.
Probabilistic Conformal Coverage Guarantees in Small-Data Settings
Venue: arXiv cs.LG 2025
Year: 2025

Conformal prediction provides distribution-free prediction sets with guaranteed marginal coverage. However, in split conformal prediction this guarantee is training-conditional only in expectation: across many calibration draws, the average coverage equals the nominal level, but the realized coverage for a single calibration set may vary substantially. This variance undermines effective risk control in practical applications. Here we introduce the Small Sample Beta Correction (SSBC), a plug-and-play adjustment to the conformal significance level that leverages the exact finite-sample distribution of conformal coverage to provide probabilistic guarantees, ensuring that with user-defined probability over the calibration draw, the deployed predictor achieves at least the desired coverage.
Speech Language Models for Under-Represented Languages: Insights from Wolof
Venue: arXiv cs.CL 2025
Year: 2025

We present our journey in training a speech language model for Wolof, an underrepresented language spoken in West Africa, and share key insights. We first emphasize the importance of collecting large-scale, spontaneous, high-quality unsupervised speech data, and show that continued pretraining HuBERT on this dataset outperforms both the base model and African-centric models on ASR. We then integrate this speech encoder into a Wolof LLM to train the first Speech LLM for this language, extending its capabilities to tasks such as speech translation. Furthermore, we explore training the Speech LLM to perform multi-step Chain-of-Thought before transcribing or translating. Our results show that the Speech LLM not only improves speech recognition but also performs well in speech translation. The models and the code will be openly shared.
Experience Level Influences User's Criteria for Avatar Animation Realism
Venue: arXiv cs.HC 2025
Year: 2025

The sense of realism in avatar animation is a widely pursued goal in social VR applications. A common approach to enhancing realism is improving the match between avatar motion and real-world human movement. However, experience with existing VR platforms may reshape users' expectations, suggesting that matching reality is not the only path to enhancing the sense of realism. This study examines how different levels of experience with a social VR platform influence users' criteria for evaluating the realism of avatar animation. Participants were shown a set of animations varying in the degree they reflected real-world motion and motion seen on the social VR platform VRChat. Results showed that users with no VRChat experience found animations recorded on VRChat unnatural and unrealistic, but experienced users in fact rated these animations as more likely to come from a real person than the motion-capture animations. Additionally, highly experienced users recognized the intent to imitate VRChat's style and noted the differences from genuine in-platform animations. All these results suggest users' expectations of and criteria for realistic animation were shaped by their experience level. The findings support the idea that realism in avatar animation does not solely depend on mimicking real-world movement. Experience with VR platforms can shape how users expect, perceive, and evaluate animation realism. This insight can inform the design of more immersive VR environments and virtual humans in the future.
SPH-Net: A Co-Attention Hybrid Model for Accurate Stock Price Prediction
Venue: arXiv cs.CE 2025
Year: 2025

Prediction of stock price movements presents a formidable challenge in financial analytics due to the inherent volatility, non-stationarity, and nonlinear characteristics of market data. This paper introduces SPH-Net (Stock Price Prediction Hybrid Neural Network), an innovative deep learning framework designed to enhance the accuracy of time series forecasting in financial markets. The proposed architecture employs a novel co-attention mechanism that initially processes temporal patterns through a Vision Transformer, followed by refined feature extraction via an attention mechanism, thereby capturing both global and local dependencies in market data. To rigorously evaluate the model's performance, we conduct comprehensive experiments on eight diverse stock datasets: AMD, Ebay, Facebook, FirstService Corp, Tesla, Google, Mondi ADR, and Matador Resources. Each dataset is standardized using six fundamental market indicators: Open, High, Low, Close, Adjusted Close, and Volume, representing a complete set of features for comprehensive market analysis. Experimental results demonstrate that SPH-Net consistently outperforms existing stock prediction models across all evaluation metrics. The model's superior performance stems from its ability to effectively capture complex temporal patterns while maintaining robustness against market noise. By significantly improving prediction accuracy in financial time series analysis, SPH-Net provides valuable decision-support capabilities for investors and financial analysts, potentially enabling more informed investment strategies and risk assessment in volatile market conditions.
CoopQ: Cooperative Game Inspired Layerwise Mixed Precision Quantization for LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) promise impressive capabilities, yet their multi-billion-parameter scale makes on-device or low-resource deployment prohibitive. Mixed-precision quantization offers a compelling solution, but existing methods struggle when the average precision drops below four bits, as they rely on isolated, layer-specific metrics that overlook critical inter-layer interactions affecting overall performance. To address these limitations, we first frame the mixed-precision quantization problem as a cooperative game among layers and introduce Shapley-based Progressive Quantization Estimation (SPQE) to efficiently obtain accurate Shapley estimates of layer sensitivities and inter-layer interactions. Leveraging the SPQE estimates, we propose Cooperative Game Inspired Mixed-Precision Quantization (CoopQ) which translates these Shapley estimates into a binary quadratic optimization formulation, assigning either 2 or 4-bit precision to layers under strict memory constraints. Comprehensive experiments conducted on Llama-3, Gemma-2, and Qwen-3 models across three independent PTQ backends (Quanto, HQQ, GPTQ) demonstrate CoopQ's scalability and consistently superior performance compared to methods relying solely on isolated metrics. Across average precisions spanning 4 bit down to 2 bit, CoopQ cuts Perplexity by 20 - 80 % relative to the best baseline, with the margin growing as the bit-width tightens.
Comparing Computational Pathology Foundation Models using Representational Similarity Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Foundation models are increasingly developed in computational pathology (CPath) given their promise in facilitating many downstream tasks. While recent studies have evaluated task performance across models, less is known about the structure and variability of their learned representations. Here, we systematically analyze the representational spaces of six CPath foundation models using techniques popularized in computational neuroscience. The models analyzed span vision-language contrastive learning (CONCH, PLIP, KEEP) and self-distillation (UNI (v2), Virchow (v2), Prov-GigaPath) approaches. Through representational similarity analysis using H&E image patches from TCGA, we find that UNI2 and Virchow2 have the most distinct representational structures, whereas Prov-Gigapath has the highest average similarity across models. Having the same training paradigm (vision-only vs. vision-language) did not guarantee higher representational similarity. The representations of all models showed a high slide-dependence, but relatively low disease-dependence. Stain normalization decreased slide-dependence for all models by a range of 5.5% (CONCH) to 20.5% (PLIP). In terms of intrinsic dimensionality, vision-language models demonstrated relatively compact representations, compared to the more distributed representations of vision-only models. These findings highlight opportunities to improve robustness to slide-specific features, inform model ensembling strategies, and provide insights into how training paradigms shape model representations. Our framework is extendable across medical imaging domains, where probing the internal representations of foundation models can support their effective development and deployment.
SmolRGPT: Efficient Spatial Reasoning for Warehouse Environments with 600M Parameters
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in vision-language models (VLMs) have enabled powerful multimodal reasoning, but state-of-the-art approaches typically rely on extremely large models with prohibitive computational and memory requirements. This makes their deployment challenging in resource-constrained environments such as warehouses, robotics, and industrial applications, where both efficiency and robust spatial understanding are critical. In this work, we present SmolRGPT, a compact vision-language architecture that explicitly incorporates region-level spatial reasoning by integrating both RGB and depth cues. SmolRGPT employs a three-stage curriculum that progressively align visual and language features, enables spatial relationship understanding, and adapts to task-specific datasets. We demonstrate that with only 600M parameters, SmolRGPT achieves competitive results on challenging warehouse spatial reasoning benchmarks, matching or exceeding the performance of much larger alternatives. These findings highlight the potential for efficient, deployable multimodal intelligence in real-world settings without sacrificing core spatial reasoning capabilities. The code of the experimentation will be available at: https://github.com/abtraore/SmolRGPT
FRAUDGUESS: Spotting and Explaining New Types of Fraud in Million-Scale Financial Data
Venue: arXiv cs.LG 2025
Year: 2025

Given a set of financial transactions (who buys from whom, when, and for how much), as well as prior information from buyers and sellers, how can we find fraudulent transactions? If we have labels for some transactions for known types of fraud, we can build a classifier. However, we also want to find new types of fraud, still unknown to the domain experts ('Detection'). Moreover, we also want to provide evidence to experts that supports our opinion ('Justification'). In this paper, we propose FRAUDGUESS, to achieve two goals: (a) for 'Detection', it spots new types of fraud as micro-clusters in a carefully designed feature space; (b) for 'Justification', it uses visualization and heatmaps for evidence, as well as an interactive dashboard for deep dives. FRAUDGUESS is used in real life and is currently considered for deployment in an Anonymous Financial Institution (AFI). Thus, we also present the three new behaviors that FRAUDGUESS discovered in a real, million-scale financial dataset. Two of these behaviors are deemed fraudulent or suspicious by domain experts, catching hundreds of fraudulent transactions that would otherwise go un-noticed.