OpenRaiser/Intern-Atlas huggingface dataset:
Chem42: a Family of chemical Language Models for Target-aware Ligand Generation
Venue: arXiv cs.LG 2025
Year: 2025

Revolutionizing drug discovery demands more than just understanding molecular interactions - it requires generative models that can design novel ligands tailored to specific biological targets. While chemical Language Models (cLMs) have made strides in learning molecular properties, most fail to incorporate target-specific insights, restricting their ability to drive de-novo ligand generation. Chem42, a cutting-edge family of generative chemical Language Models, is designed to bridge this gap. By integrating atomic-level interactions with multimodal inputs from Prot42, a complementary protein Language Model, Chem42 achieves a sophisticated cross-modal representation of molecular structures, interactions, and binding patterns. This innovative framework enables the creation of structurally valid, synthetically accessible ligands with enhanced target specificity. Evaluations across diverse protein targets confirm that Chem42 surpasses existing approaches in chemical validity, target-aware design, and predicted binding affinity. By reducing the search space of viable drug candidates, Chem42 could accelerate the drug discovery pipeline, offering a powerful generative AI tool for precision medicine. Our Chem42 models set a new benchmark in molecule property prediction, conditional molecule generation, and target-aware ligand design. The models are publicly available at huggingface.co/inceptionai.
Extract, Match, and Score: An Evaluation Paradigm for Long Question-context-answer Triplets in Financial Analysis
Venue: arXiv cs.CL 2025
Year: 2025

The rapid advancement of large language models (LLMs) has sparked widespread adoption across diverse applications, making robust evaluation frameworks crucial for assessing their performance. While conventional evaluation metrics remain applicable for shorter texts, their efficacy diminishes when evaluating the quality of long-form answers. This limitation is particularly critical in real-world scenarios involving extended questions, extensive context, and long-form answers, such as financial analysis or regulatory compliance. In this paper, we use a practical financial use case to illustrate applications that handle "long question-context-answer triplets". We construct a real-world financial dataset comprising long triplets and demonstrate the inadequacies of traditional metrics. To address this, we propose an effective Extract, Match, and Score (EMS) evaluation approach tailored to the complexities of long-form LLMs' outputs, providing practitioners with a reliable methodology for assessing LLMs' performance in complex real-world scenarios.
Feature selection strategies for optimized heart disease diagnosis using ML and DL models
Venue: arXiv cs.LG 2025
Year: 2025

Heart disease remains one of the leading causes of morbidity and mortality worldwide, necessitating the development of effective diagnostic tools to enable early diagnosis and clinical decision-making. This study evaluates the impact of feature selection techniques Mutual Information (MI), Analysis of Variance (ANOVA), and Chi-Square on the predictive performance of various machine learning (ML) and deep learning (DL) models using a dataset of clinical indicators for heart disease. Eleven ML/DL models were assessed using metrics such as precision, recall, AUC score, F1-score, and accuracy. Results indicate that MI outperformed other methods, particularly for advanced models like neural networks, achieving the highest accuracy of 82.3% and recall score of 0.94. Logistic regression (accuracy 82.1%) and random forest (accuracy 80.99%) also demonstrated improved performance with MI. Simpler models such as Naive Bayes and decision trees achieved comparable results with ANOVA and Chi-Square, yielding accuracies of 76.45% and 75.99%, respectively, making them computationally efficient alternatives. Conversely, k Nearest Neighbors (KNN) and Support Vector Machines (SVM) exhibited lower performance, with accuracies ranging between 51.52% and 54.43%, regardless of the feature selection method. This study provides a comprehensive comparison of feature selection methods for heart disease prediction, demonstrating the critical role of feature selection in optimizing model performance. The results offer practical guidance for selecting appropriate feature selection techniques based on the chosen classification algorithm, contributing to the development of more accurate and efficient diagnostic tools for enhanced clinical decision-making in cardiology.
Machine Learning-Based Genomic Linguistic Analysis (Gene Sequence Feature Learning): A Case Study on Predicting Heavy Metal Response Genes in Rice
Venue: arXiv cs.LG 2025
Year: 2025

This study explores the application of machine learning-based genetic linguistics for identifying heavy metal response genes in rice (Oryza sativa). By integrating convolutional neural networks and random forest algorithms, we developed a hybrid model capable of extracting and learning meaningful features from gene sequences, such as k-mer frequencies and physicochemical properties. The model was trained and tested on datasets of genes, achieving high predictive performance (precision: 0.89, F1-score: 0.82). RNA-seq and qRT-PCR experiments conducted on rice leaves which exposed to Hg0, revealed differential expression of genes associated with heavy metal responses, which validated the model's predictions. Co-expression network analysis identified 103 related genes, and a literature review indicated that these genes are highly likely to be involved in heavy metal-related biological processes. By integrating and comparing the analysis results with those of differentially expressed genes (DEGs), the validity of the new machine learning method was further demonstrated. This study highlights the efficacy of combining machine learning with genetic linguistics for large-scale gene prediction. It demonstrates a cost-effective and efficient approach for uncovering molecular mechanisms underlying heavy metal responses, with potential applications in developing stress-tolerant crop varieties.
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays
Venue: arXiv cs.LG 2025
Year: 2025

Approximate deep neural networks (AxDNNs) are promising for enhancing energy efficiency in real-world devices. One of the key contributors behind this enhanced energy efficiency in AxDNNs is the use of approximate multipliers. Unfortunately, the simulation of approximate multipliers does not usually scale well on CPUs and GPUs. As a consequence, this slows down the overall simulation of AxDNNs aimed at identifying the appropriate approximate multipliers to achieve high energy efficiency with a minimum accuracy loss. To address this problem, we present a novel XAI-Gen methodology, which leverages the analytical model of the emerging hardware accelerator (e.g., Google TPU v4) and explainable artificial intelligence (XAI) to precisely identify the non-critical layers for approximation and quickly discover the appropriate approximate multipliers for AxDNN layers. Our results show that XAI-Gen achieves up to 7x lower energy consumption with only 1-2% accuracy loss. We also showcase the effectiveness of the XAI-Gen approach through a neural architecture search (XAI-NAS) case study. Interestingly, XAI-NAS achieves 40\% higher energy efficiency with up to 5x less execution time when compared to the state-of-the-art NAS methods for generating AxDNNs.
A Statistical Analysis for Per-Instance Evaluation of Stochastic Optimizers: How Many Repeats Are Enough?
Venue: arXiv cs.LG 2025
Year: 2025

A key trait of stochastic optimizers is that multiple runs of the same optimizer in attempting to solve the same problem can produce different results. As a result, their performance is evaluated over several repeats, or runs, on the problem. However, the accuracy of the estimated performance metrics depends on the number of runs and should be studied using statistical tools. We present a statistical analysis of the common metrics, and develop guidelines for experiment design to measure the optimizer's performance using these metrics to a high level of confidence and accuracy. To this end, we first discuss the confidence interval of the metrics and how they are related to the number of runs of an experiment. We then derive a lower bound on the number of repeats in order to guarantee achieving a given accuracy in the metrics. Using this bound, we propose an algorithm to adaptively adjust the number of repeats needed to ensure the accuracy of the evaluated metric. Our simulation results demonstrate the utility of our analysis and how it allows us to conduct reliable benchmarking as well as hyperparameter tuning and prevent us from drawing premature conclusions regarding the performance of stochastic optimizers.
ContactFusion: Stochastic Poisson Surface Maps from Visual and Contact Sensing
Venue: arXiv cs.RO 2025
Year: 2025

Robust and precise robotic assembly entails insertion of constituent components. Insertion success is hindered when noise in scene understanding exceeds tolerance limits, especially when fabricated with tight tolerances. In this work, we propose ContactFusion which combines global mapping with local contact information, fusing point clouds with force sensing. Our method entails a Rejection Sampling based contact occupancy sensing procedure which estimates contact locations on the end-effector from Force/Torque sensing at the wrist. We demonstrate how to fuse contact with visual information into a Stochastic Poisson Surface Map (SPSMap) - a map representation that can be updated with the Stochastic Poisson Surface Reconstruction (SPSR) algorithm. We first validate the contact occupancy sensor in simulation and show its ability to detect the contact location on the robot from force sensing information. Then, we evaluate our method in a peg-in-hole task, demonstrating an improvement in the hole pose estimate with the fusion of the contact information with the SPSMap.
A Schwarz-Christoffel Mapping-based Framework for Sim-to-Real Transfer in Autonomous Robot Operations
Venue: arXiv cs.RO 2025
Year: 2025

Despite the remarkable acceleration of robotic development through advanced simulation technology, robotic applications are often subject to performance reductions in real-world deployment due to the inherent discrepancy between simulation and reality, often referred to as the "sim-to-real gap". This gap arises from factors like model inaccuracies, environmental variations, and unexpected disturbances. Similarly, model discrepancies caused by system degradation over time or minor changes in the system's configuration also hinder the effectiveness of the developed methodologies. Effectively closing these gaps is critical and remains an open challenge. This work proposes a lightweight conformal mapping framework to transfer control and planning policies from an expert teacher to a degraded less capable learner. The method leverages Schwarz-Christoffel Mapping (SCM) to geometrically map teacher control inputs into the learner's command space, ensuring maneuver consistency. To demonstrate its generality, the framework is applied to two representative types of control and planning methods in a path-tracking task: 1) a discretized motion primitives command transfer and 2) a continuous Model Predictive Control (MPC)-based command transfer. The proposed framework is validated through extensive simulations and real-world experiments, demonstrating its effectiveness in reducing the sim-to-real gap by closely transferring teacher commands to the learner robot.
When Less is Enough: Adaptive Token Reduction for Efficient Image Representation
Venue: arXiv cs.CV 2025
Year: 2025

Vision encoders typically generate a large number of visual tokens, providing information-rich representations but significantly increasing computational demands. This raises the question of whether all generated tokens are equally valuable or if some of them can be discarded to reduce computational costs without compromising quality. In this paper, we introduce a new method for determining feature utility based on the idea that less valuable features can be reconstructed from more valuable ones. We implement this concept by integrating an autoencoder with a Gumbel-Softmax selection mechanism, that allows identifying and retaining only the most informative visual tokens. To validate our approach, we compared the performance of the LLaVA-NeXT model, using features selected by our method with randomly selected features. We found that on OCR-based tasks, more than 50% of the visual context can be removed with minimal performance loss, whereas randomly discarding the same proportion of features significantly affects the model capabilities. Furthermore, in general-domain tasks, even randomly retaining only 30% of tokens achieves performance comparable to using the full set of visual tokens. Our results highlight a promising direction towards adaptive and efficient multimodal pruning that facilitates scalable and low-overhead inference without compromising performance.
Echoes of Power: Investigating Geopolitical Bias in US and China Large Language Models
Venue: arXiv cs.CY 2025
Year: 2025

Large Language Models (LLMs) have emerged as powerful tools for generating human-like text, transforming human-machine interactions. However, their widespread adoption has raised concerns about their potential to influence public opinion and shape political narratives. In this work, we investigate the geopolitical biases in US and Chinese LLMs, focusing on how these models respond to questions related to geopolitics and international relations. We collected responses from ChatGPT and DeepSeek to a set of geopolitical questions and evaluated their outputs through both qualitative and quantitative analyses. Our findings show notable biases in both models, reflecting distinct ideological perspectives and cultural influences. However, despite these biases, for a set of questions, the models' responses are more aligned than expected, indicating that they can address sensitive topics without necessarily presenting directly opposing viewpoints. This study highlights the potential of LLMs to shape public discourse and underscores the importance of critically assessing AI-generated content, particularly in politically sensitive contexts.
GauRast: Enhancing GPU Triangle Rasterizers to Accelerate 3D Gaussian Splatting
Venue: arXiv cs.GR 2025
Year: 2025

3D intelligence leverages rich 3D features and stands as a promising frontier in AI, with 3D rendering fundamental to many downstream applications. 3D Gaussian Splatting (3DGS), an emerging high-quality 3D rendering method, requires significant computation, making real-time execution on existing GPU-equipped edge devices infeasible. Previous efforts to accelerate 3DGS rely on dedicated accelerators that require substantial integration overhead and hardware costs. This work proposes an acceleration strategy that leverages the similarities between the 3DGS pipeline and the highly optimized conventional graphics pipeline in modern GPUs. Instead of developing a dedicated accelerator, we enhance existing GPU rasterizer hardware to efficiently support 3DGS operations. Our results demonstrate a 23$\times$ increase in processing speed and a 24$\times$ reduction in energy consumption, with improvements yielding 6$\times$ faster end-to-end runtime for the original 3DGS algorithm and 4$\times$ for the latest efficiency-improved pipeline, achieving 24 FPS and 46 FPS respectively. These enhancements incur only a minimal area overhead of 0.2\% relative to the entire SoC chip area, underscoring the practicality and efficiency of our approach for enabling 3DGS rendering on resource-constrained platforms.
Towards Automated Semantic Interpretability in Reinforcement Learning via Vision-Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Semantic interpretability in Reinforcement Learning (RL) enables transparency and verifiability of decision-making. Achieving semantic interpretability in reinforcement learning requires (1) a feature space composed of human-understandable concepts and (2) a policy that is interpretable and verifiable. However, constructing such a feature space has traditionally relied on manual human specification, which often fails to generalize to unseen environments. Moreover, even when interpretable features are available, most reinforcement learning algorithms employ black-box models as policies, thereby hindering transparency. We introduce interpretable Tree-based Reinforcement learning via Automated Concept Extraction (iTRACE), an automated framework that leverages pre-trained vision-language models (VLM) for semantic feature extraction and train a interpretable tree-based model via RL. To address the impracticality of running VLMs in RL loops, we distill their outputs into a lightweight model. By leveraging Vision-Language Models (VLMs) to automate tree-based reinforcement learning, iTRACE loosens the reliance the need for human annotation that is traditionally required by interpretable models. In addition, it addresses key limitations of VLMs alone, such as their lack of grounding in action spaces and their inability to directly optimize policies. We evaluate iTRACE across three domains: Atari games, grid-world navigation, and driving. The results show that iTRACE outperforms other interpretable policy baselines and matches the performance of black-box policies on the same interpretable feature space.
Natural Language Generation
Venue: arXiv cs.CL 2025
Year: 2025

This article provides a brief overview of the field of Natural Language Generation. The term Natural Language Generation (NLG), in its broadest definition, refers to the study of systems that verbalize some form of information through natural language. That information could be stored in a large database or knowledge graph (in data-to-text applications), but NLG researchers may also study summarisation (text-to-text) or image captioning (image-to-text), for example. As a subfield of Natural Language Processing, NLG is closely related to other sub-disciplines such as Machine Translation (MT) and Dialog Systems. Some NLG researchers exclude MT from their definition of the field, since there is no content selection involved where the system has to determine what to say. Conversely, dialog systems do not typically fall under the header of Natural Language Generation since NLG is just one component of dialog systems (the others being Natural Language Understanding and Dialog Management). However, with the rise of Large Language Models (LLMs), different subfields of Natural Language Processing have converged on similar methodologies for the production of natural language and the evaluation of automatically generated text.
SPACER: A Parallel Dataset of Speech Production And Comprehension of Error Repairs
Venue: arXiv cs.CL 2025
Year: 2025

Speech errors are a natural part of communication, yet they rarely lead to complete communicative failure because both speakers and comprehenders can detect and correct errors. Although prior research has examined error monitoring and correction in production and comprehension separately, integrated investigation of both systems has been impeded by the scarcity of parallel data. In this study, we present SPACER, a parallel dataset that captures how naturalistic speech errors are corrected by both speakers and comprehenders. We focus on single-word substitution errors extracted from the Switchboard corpus, accompanied by speaker's self-repairs and comprehenders' responses from an offline text-editing experiment. Our exploratory analysis suggests asymmetries in error correction strategies: speakers are more likely to repair errors that introduce greater semantic and phonemic deviations, whereas comprehenders tend to correct errors that are phonemically similar to more plausible alternatives or do not fit into prior contexts. Our dataset enables future research on integrated approaches toward studying language production and comprehension.
Ordered Topological Deep Learning: a Network Modeling Case Study
Venue: arXiv cs.LG 2025
Year: 2025

Computer networks are the foundation of modern digital infrastructure, facilitating global communication and data exchange. As demand for reliable high-bandwidth connectivity grows, advanced network modeling techniques become increasingly essential to optimize performance and predict network behavior. Traditional modeling methods, such as packet-level simulators and queueing theory, have notable limitations --either being computationally expensive or relying on restrictive assumptions that reduce accuracy. In this context, the deep learning-based RouteNet family of models has recently redefined network modeling by showing an unprecedented cost-performance trade-off. In this work, we revisit RouteNet's sophisticated design and uncover its hidden connection to Topological Deep Learning (TDL), an emerging field that models higher-order interactions beyond standard graph-based methods. We demonstrate that, although originally formulated as a heterogeneous Graph Neural Network, RouteNet serves as the first instantiation of a new form of TDL. More specifically, this paper presents OrdGCCN, a novel TDL framework that introduces the notion of ordered neighbors in arbitrary discrete topological spaces, and shows that RouteNet's architecture can be naturally described as an ordered topological neural network. To the best of our knowledge, this marks the first successful real-world application of state-of-the-art TDL principles --which we confirm through extensive testbed experiments--, laying the foundation for the next generation of ordered TDL-driven applications.
Revisiting DRAM Read Disturbance: Identifying Inconsistencies Between Experimental Characterization and Device-Level Studies
Venue: arXiv cs.AR 2025
Year: 2025

Modern DRAM is vulnerable to read disturbance (e.g., RowHammer and RowPress) that significantly undermines the robust operation of the system. Repeatedly opening and closing a DRAM row (RowHammer) or keeping a DRAM row open for a long period of time (RowPress) induces bitflips in nearby unaccessed DRAM rows. Prior works on DRAM read disturbance either 1) perform experimental characterization using commercial-off-the-shelf (COTS) DRAM chips to demonstrate the high-level characteristics of the read disturbance bitflips, or 2) perform device-level simulations to understand the low-level error mechanisms of the read disturbance bitflips. In this paper, we attempt to align and cross-validate the real-chip experimental characterization results and state-of-the-art device-level studies of DRAM read disturbance. To do so, we first identify and extract the key bitflip characteristics of RowHammer and RowPress from the device-level error mechanisms studied in prior works. Then, we perform experimental characterization on 96 COTS DDR4 DRAM chips that directly match the data and access patterns studied in the device-level works. Through our experiments, we identify fundamental inconsistencies in the RowHammer and RowPress bitflip directions and access pattern dependence between experimental characterization results and the device-level error mechanisms. Based on our results, we hypothesize that either 1) the retention failure based DRAM architecture reverse-engineering methodologies do not fully work on modern DDR4 DRAM chips, or 2) existing device-level works do not fully uncover all the major read disturbance error mechanisms. We hope our findings inspire and enable future works to build a more fundamental and comprehensive understanding of DRAM read disturbance.