OpenRaiser/Intern-Atlas huggingface dataset:
Generalized Multi-hop Traffic Pressure for Heterogeneous Traffic Perimeter Control
Venue: arXiv cs.LG 2025
Year: 2025

Perimeter control (PC) prevents loss of traffic network capacity due to congestion in urban areas. Homogeneous PC allows all access points to a protected region to have identical permitted inflow. However, homogeneous PC performs poorly when the congestion in the protected region is heterogeneous (e.g., imbalanced demand) since the homogeneous PC does not consider specific traffic conditions around each perimeter intersection. When the protected region has spatially heterogeneous congestion, one needs to modulate the perimeter inflow rate to be higher near low-density regions and vice versa for high-density regions. A naïve approach is to leverage 1-hop traffic pressure to measure traffic condition around perimeter intersections, but such metric is too spatially myopic for PC. To address this issue, we formulate multi-hop downstream pressure grounded on Markov chain theory, which ``looks deeper'' into the protected region beyond perimeter intersections. In addition, we formulate a two-stage hierarchical control scheme that can leverage this novel multi-hop pressure to redistribute the total permitted inflow provided by a pre-trained deep reinforcement learning homogeneous control policy. Experimental results show that our heterogeneous PC approaches leveraging multi-hop pressure significantly outperform homogeneous PC in scenarios where the origin-destination flows are highly imbalanced with high spatial heterogeneity. Moveover, our approach is shown to be robust against turning ratio uncertainties by a sensitivity analysis.
Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent Driving
Venue: arXiv cs.CV 2025
Year: 2025

With the increasing complexity of the traffic environment, the significance of safety perception in intelligent driving is intensifying. Traditional methods in the field of intelligent driving perception rely on deep learning, which suffers from limited interpretability, often described as a "black box." This paper introduces a novel type of loss function, termed "Entropy Loss," along with an innovative training strategy. Entropy Loss is formulated based on the functionality of feature compression networks within the perception model. Drawing inspiration from communication systems, the information transmission process in a feature compression network is expected to demonstrate steady changes in information volume and a continuous decrease in information entropy. By modeling network layer outputs as continuous random variables, we construct a probabilistic model that quantifies changes in information volume. Entropy Loss is then derived based on these expectations, guiding the update of network parameters to enhance network interpretability. Our experiments indicate that the Entropy Loss training strategy accelerates the training process. Utilizing the same 60 training epochs, the accuracy of 3D object detection models using Entropy Loss on the KITTI test set improved by up to 4.47\% compared to models without Entropy Loss, underscoring the method's efficacy. The implementation code is available at https://github.com/yhbcode000/Eloss-Interpretability.
Scenario-based assessment of automated driving systems: How (not) to parameterize scenarios?
Venue: arXiv cs.RO 2025
Year: 2025

The development of Automated Driving Systems (ADSs) has advanced significantly. To enable their large-scale deployment, the United Nations Regulation 157 (UN R157) concerning the approval of Automated Lane Keeping Systems (ALKSs) has been approved in 2021. UN R157 requires an activated ALKS to avoid any collisions that are reasonably preventable and proposes a method to distinguish reasonably preventable collisions from unpreventable ones using "the simulated performance of a skilled and attentive human driver". With different driver models, benchmarks are set for ALKSs in three types of scenarios. The three types of scenarios considered in the proposed method in UN R157 assume a certain parameterization without any further consideration. This work investigates the parameterization of these scenarios, showing that the choice of parameterization significantly affects the simulation outcomes. By comparing real-world and parameterized scenarios, we show that the influence of parameterization depends on the scenario type, driver model, and evaluation criterion. Alternative parameterizations are proposed, leading to results that are closer to the non-parameterized scenarios in terms of recall, precision, and F1 score. The study highlights the importance of careful scenario parameterization and suggests improvements to the current UN R157 approach.
Exact Error Exponents of Concatenated Codes for DNA Storage
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we consider a concatenated coding based class of DNA storage codes in which the selected molecules are constrained to be taken from an ``inner'' codebook associated with the sequencing channel. This codebook is used in a ``black-box'' manner, and is only assumed to operate at an achievable rate in the sense of attaining asymptotically vanishing maximal (inner) error probability. We first derive the exact error exponent in a widely-studied regime of constant rate and a linear number of sequencing reads, and show strict improvements over an existing achievable error exponent. Moreover, our achievability analysis is based on a coded-index strategy, implying that such strategies attain the highest error exponents within the broader class of codes that we consider. We then extend our results to other scaling regimes, including a super-linear number of reads, as well as several certain low-rate regimes. We find that the latter comes with notable intricacies, such as the suboptimality of codewords with all distinct molecules, and certain dependencies of the error exponents on the model for sequencing errors.
H is for Human and How (Not) To Evaluate Qualitative Research in HCI
Venue: arXiv cs.HC 2025
Year: 2025

Concern has recently been expressed by HCI researchers as to the inappropriate treatment of qualitative studies through a positivistic mode of evaluation that places emphasis on metrics and measurement. This contrasts with the nature of qualitative research, which privileges interpretation and understanding over quantification. This paper explains the difference between positivism and interpretivism, the limits of quantification in human science, the distinctive contribution of qualitative research, and how quality assurance might be provided for in the absence of numbers via five basic criteria that reviewers may use to evaluate qualitative studies on their own terms.
PatternPaint: Practical Layout Pattern Generation Using Diffusion-Based Inpainting
Venue: arXiv cs.CV 2025
Year: 2025

Generating diverse VLSI layout patterns is essential for various downstream tasks in design for manufacturing, as design rules continually evolve during the development of new technology nodes. However, existing training-based methods for layout pattern generation rely on large datasets. In practical scenarios, especially when developing a new technology node, obtaining such extensive layout data is challenging. Consequently, training models with large datasets becomes impractical, limiting the scalability and adaptability of prior approaches. To this end, we propose PatternPaint, a diffusion-based framework capable of generating legal patterns with limited design-rule-compliant training samples. PatternPaint simplifies complex layout pattern generation into a series of inpainting processes with a template-based denoising scheme. Furthermore, we perform few-shot finetuning on a pretrained image foundation model with only 20 design-rule-compliant samples. Experimental results show that using a sub-3nm technology node (Intel 18A), our model is the only one that can generate legal patterns in complex 2D metal interconnect design rule settings among all previous works and achieves a high diversity score. Additionally, our few-shot finetuning can boost the legality rate with 1.87X improvement compared to the original pretrained model. As a result, we demonstrate a production-ready approach for layout pattern generation in developing new technology nodes.
Time-Varying Soft-Maximum Barrier Functions for Safety in Unmapped and Dynamic Environments
Venue: arXiv cs.RO 2025
Year: 2025

We present a closed-form optimal feedback control method that ensures safety in an a prior unknown and potentially dynamic environment. This article considers the scenario where local perception data (e.g., LiDAR) is obtained periodically, and this data can be used to construct a local control barrier function (CBF) that models a local set that is safe for a period of time into the future. Then, we use a smooth time-varying soft-maximum function to compose the N most recently obtained local CBFs into a single barrier function that models an approximate union of the N most recently obtained local sets. This composite barrier function is used in a constrained quadratic optimization, which is solved in closed form to obtain a safe-and-optimal feedback control. We also apply the time-varying soft-maximum barrier function control to 2 robotic systems (nonholonomic ground robot with nonnegligible inertia, and quadrotor robot), where the objective is to navigate an a priori unknown environment safely and reach a target destination. In these applications, we present a simple approach to generate local CBFs from periodically obtained perception data.
Masked Mixers for Language Generation and Retrieval
Venue: arXiv cs.CL 2025
Year: 2025

Attention mechanisms that confer selective focus on a strict subset of input elements are nearly ubiquitous in language models today. We posit there to be downside to the use of attention: most input information is lost. In support of this idea we observe poor input representation accuracy in transformers and more accurate representation in what we term masked mixers, which replace self-attention with masked convolutions. The masked mixer learns causal language modeling more efficiently than early transformer implementations and even outperforms optimized, current transformers when training on small ($n_{ctx}<512$) but not larger context windows. Evidence is presented for the hypothesis that differences in transformer and masked mixer training efficiencies for various tasks are best predicted by input representation accuracy, or equivalently global invertibility. We hypothesize that the information loss exhibited by transformers would be more detrimental to retrieval than generation, as the former is more closely approximated by a bijective and thus invertible function. We find that masked mixers are more effective retrieval models both when the pretrained embedding model is unchanged as well as when the embedding model is modified via cosine similarity-based InfoNCE loss minimization. A small masked mixer is shown to outperform a large and near state-of-the-art transformer-based retrieval model, despite the latter being trained with many orders of magnitude more data and compute.
Efficient LLM Context Distillation
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) demonstrate proficiency across diverse tasks but often require targeted adaptations for specific applications. Various methods have been proposed to facilitate this adaptation, including fewshot fine-tuning, in-context learning, and context distillation. This paper specifically investigates context distillation a method that extends the utility of task-specific examples by internalizing them, thus augmenting the example set accessible for model inference. We conduct a comparative analysis of context distillation with in-context learning (ICL) and few-shot fine-tuning (FT), aiming to ascertain the efficacy of context distillation in adapting models using minimal in-context examples. Employing matched datasets from Mobach, our experiments leverage OPT models of various sizes. The results indicate that context distillation effectively adapts models, with student models attaining comparable in-domain and out-of-domain accuracies to in-context learning. Although context distillation surpasses ICL in out-of-domain generalization, it does not achieve the performance levels of FT. However, the reduced dataset size and computational demands position context distillation as a viable alternative, especially for smaller datasets. Overall, this study presents context distillation as an efficient and potent method for customizing LLMs to specific tasks.
Designing Large Foundation Models for Efficient Training and Inference: A Survey
Venue: arXiv cs.DC 2025
Year: 2025

This paper focuses on modern efficient training and inference technologies on foundation models and illustrates them from two perspectives: model and system design. Model and System Design optimize LLM training and inference from different aspects to save computational resources, making LLMs more efficient, affordable, and more accessible. The paper list repository is available at https://github.com/NoakLiu/Efficient-Foundation-Models-Survey.
FastEnsemble: scalable ensemble clustering on large networks
Venue: arXiv cs.SI 2025
Year: 2025

Many community detection algorithms are inherently stochastic, leading to variations in their output depending on input parameters and random seeds. This variability makes the results of a single run of these algorithms less reliable. Moreover, different clustering algorithms, optimization criteria (e.g., modularity, the Constant Potts model), and resolution values can result in substantially different partitions on the same network. Consensus clustering methods, such as ECG and FastConsensus, have been proposed to reduce the instability of non-deterministic algorithms and improve their accuracy by combining a set of partitions resulting from multiple runs of a clustering algorithm. In this work, we introduce FastEnsemble, a new consensus clustering method. Our results on a wide range of synthetic networks show that FastEnsemble produces more accurate clusterings than two other consensus clustering methods, ECG and FastConsensus, for many model conditions. Furthermore, FastEnsemble is fast enough to be used on networks with more than 3 million nodes, and so improves on the speed and scalability of FastConsensus. Finally, we showcase the utility of consensus clustering methods in mitigating the effect of resolution limit and clustering networks that are only partially covered by communities.
DynOMo: Online Point Tracking by Dynamic Online Monocular Gaussian Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

Reconstructing scenes and tracking motion are two sides of the same coin. Tracking points allow for geometric reconstruction [14], while geometric reconstruction of (dynamic) scenes allows for 3D tracking of points over time [24, 39]. The latter was recently also exploited for 2D point tracking to overcome occlusion ambiguities by lifting tracking directly into 3D [38]. However, above approaches either require offline processing or multi-view camera setups both unrealistic for real-world applications like robot navigation or mixed reality. We target the challenge of online 2D and 3D point tracking from unposed monocular camera input introducing Dynamic Online Monocular Reconstruction (DynOMo). We leverage 3D Gaussian splatting to reconstruct dynamic scenes in an online fashion. Our approach extends 3D Gaussians to capture new content and object motions while estimating camera movements from a single RGB frame. DynOMo stands out by enabling emergence of point trajectories through robust image feature reconstruction and a novel similarity-enhanced regularization term, without requiring any correspondence-level supervision. It sets the first baseline for online point tracking with monocular unposed cameras, achieving performance on par with existing methods. We aim to inspire the community to advance online point tracking and reconstruction, expanding the applicability to diverse real-world scenarios.
Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data
Venue: arXiv cs.LG 2025
Year: 2025

This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimensional tabular data from 9,134 patients across four hospitals. Seven CML models, including XGBoost and random forest (RF), were evaluated alongside eight LLMs, such as GPT-4 and Mistral-7b, which performed zero-shot classification on text-converted structured data. Additionally, Mistral- 7b was fine-tuned using the QLoRA approach. XGBoost and RF demonstrated superior performance among CMLs, achieving F1 scores of 0.87 and 0.83 for internal and external validation, respectively. GPT-4 led the LLM category with an F1 score of 0.43, while fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, yielding a stable F1 score of 0.74 during external validation. Although LLMs showed moderate performance in zero-shot classification, fine-tuning substantially enhanced their effectiveness, potentially bridging the gap with CML models. However, CMLs still outperformed LLMs in handling high-dimensional tabular data tasks. This study highlights the potential of both CMLs and fine-tuned LLMs in medical predictive modeling, while emphasizing the current superiority of CMLs for structured data analysis.
Query Answering in Lattice-based Description Logic
Venue: arXiv cs.LO 2025
Year: 2025

Recently, the description logic LE-ALC was introduced for reasoning in the semantic environment of the enriched formal contexts, and a tableaux algorithm was developed for checking the consistency of ABoxes in this logic. In this paper, we study the ontology-mediated query answering in LE-ALC . In particular, we show that several different types of queries can be answered efficiently for LE-ALC knowledge bases with acyclic TBoxes using our tableaux algorithm directly or by extending it with some additional rules.
PQ-GCN: Enhancing Text Graph Question Classification with Phrase Features
Venue: arXiv cs.CL 2025
Year: 2025

Effective question classification is crucial for AI-driven educational tools, enabling adaptive learning systems to categorize questions by skill area, difficulty level, and competence. It not only supports educational diagnostics and analytics but also enhances complex downstream tasks like information retrieval and question answering by associating questions with relevant categories. Traditional methods, often based on word embeddings and conventional classifiers, struggle to capture the nuanced relationships in question statements, leading to suboptimal performance. We propose a novel approach leveraging graph convolutional networks, named Phrase Question-Graph Convolutional Network (PQ-GCN). Through PQ-GCN, we evaluate the incorporation of phrase-based features to enhance classification performance on question datasets of various domains and characteristics. The proposed method, augmented with phrase-based features, outperform baseline graph-based methods in low-resource settings, and performs competitively against language model-based methods with a fraction of their parameter size. Our findings offer a possible solution for more context-aware, parameter-efficient question classification, bridging the gap between graph neural network research and its educational applications.
Sample what you cant compress
Venue: arXiv cs.LG 2025
Year: 2025

For learned image representations, basic autoencoders often produce blurry results. Reconstruction quality can be improved by incorporating additional penalties such as adversarial (GAN) and perceptual losses. Arguably, these approaches lack a principled interpretation. Concurrently, in generative settings diffusion has demonstrated a remarkable ability to create crisp, high quality results and has solid theoretical underpinnings (from variational inference to direct study as the Fisher Divergence). Our work combines autoencoder representation learning with diffusion and is, to our knowledge, the first to demonstrate jointly learning a continuous encoder and decoder under a diffusion-based loss and showing that it can lead to higher compression and better generation. We demonstrate that this approach yields better reconstruction quality as compared to GAN-based autoencoders while being easier to tune. We also show that the resulting representation is easier to model with a latent diffusion model as compared to the representation obtained from a state-of-the-art GAN-based loss. Since our decoder is stochastic, it can generate details not encoded in the otherwise deterministic latent representation; we therefore name our approach "Sample what you can't compress", or SWYCC for short.
ChatGPT vs Social Surveys: Probing Objective and Subjective Silicon Population
Venue: arXiv cs.CY 2025
Year: 2025

Recent discussions about Large Language Models (LLMs) indicate that they have the potential to simulate human responses in social surveys and generate reliable predictions, such as those found in political polls. However, the existing findings are highly inconsistent, leaving us uncertain about the population characteristics of data generated by LLMs. In this paper, we employ repeated random sampling to create sampling distributions that identify the population parameters of silicon samples generated by GPT. Our findings show that GPT's demographic distribution aligns with the 2020 U.S. population in terms of gender and average age. However, GPT significantly overestimates the representation of the Black population and individuals with higher levels of education, even when it possesses accurate knowledge. Furthermore, GPT's point estimates for attitudinal scores are highly inconsistent and show no clear inclination toward any particular ideology. The sample response distributions exhibit a normal pattern that diverges significantly from those of human respondents. Consistent with previous studies, we find that GPT's answers are more deterministic than those of humans. We conclude by discussing the concerning implications of this biased and deterministic silicon population for making inferences about real-world populations.
(Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models
Venue: arXiv cs.LG 2025
Year: 2025

Epistemic uncertainty is crucial for safety-critical applications and data acquisition tasks. Yet, we find an important phenomenon in deep learning models: an epistemic uncertainty collapse as model complexity increases, challenging the assumption that larger models invariably offer better uncertainty quantification. We introduce implicit ensembling as a possible explanation for this phenomenon. To investigate this hypothesis, we provide theoretical analysis and experiments that demonstrate uncertainty collapse in explicit ensembles of ensembles and show experimental evidence of similar collapse in wider models across various architectures, from simple MLPs to state-of-the-art vision models including ResNets and Vision Transformers. We further develop implicit ensemble extraction techniques to decompose larger models into diverse sub-models, showing we can thus recover epistemic uncertainty. We explore the implications of these findings for uncertainty estimation.