OpenRaiser/Intern-Atlas huggingface dataset:
Tabular Data: Is Deep Learning all you need?
Venue: arXiv cs.LG 2025
Year: 2025

Tabular data represent one of the most prevalent data formats in applied machine learning, largely because they accommodate a broad spectrum of real-world problems. Existing literature has studied many of the shortcomings of neural architectures on tabular data and has repeatedly confirmed the scalability and robustness of gradient-boosted decision trees across varied datasets. However, recent deep learning models have not been subjected to a comprehensive evaluation under conditions that allow for a fair comparison with existing classical approaches. This situation motivates an investigation into whether recent deep-learning paradigms outperform classical ML methods on tabular data. Our survey fills this gap by benchmarking seventeen state-of-the-art methods, spanning neural networks, classical ML and AutoML techniques. Our empirical results over 68 diverse datasets from a well-established benchmark indicate a paradigm shift, where Deep Learning methods outperform classical approaches.
ProactivePIM: Accelerating Weight-Sharing Embedding Layer with PIM for Scalable Recommendation System
Venue: arXiv cs.AR 2025
Year: 2025

Although deep learning-based personalized recommendation systems provide qualified recommendations, they strain data center resources. The main bottleneck is the embedding layer, which is highly memory-intensive due to its sparse, irregular access patterns to embeddings. Recent near-memory processing (NMP) and processing-in-memory (PIM) architectures have addressed these issues by exploiting parallelism within memory. However, as model sizes increase year by year and can exceed server capacity, inference on single-node servers becomes challenging, necessitating the integration of model compression. Various algorithms have been proposed for model size reduction, but they come at the cost of increased memory access and CPU-PIM communication. We present ProactivePIM, a PIM system tailored for weight-sharing algorithms, a family of compression methods that decompose an embedding table into compact subtables, such as QR-trick and TT-Rec. Our analysis shows that embedding layer execution with weight-sharing algorithms increases memory access and incurs CPU-PIM communication. We also find that these algorithms exhibit unique data locality characteristics, which we name intra-GnR locality. ProactivePIM accelerates weight-sharing algorithms by utilizing a heterogeneous HBM-DIMM memory architecture with integration of a two-level PIM system of base-die PIM (bd-PIM) and bank-group PIM (bg-PIM) inside the HBM. To gain further speedup, ProactivePIM prefetches embeddings with high intra-GnR locality into an SRAM cache within bg-PIM and eliminates the CPU-PIM communication through duplication of target subtables across bank groups. With additional optimization techniques, our design effectively accelerates weight-sharing algorithms, achieving 2.22x and 2.15x speedup in QR-trick and TT-Rec, respectively, compared to the baseline architecture.
SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-related tasks, capitalizing on their visual semantic comprehension and reasoning capabilities. However, their ability to detect subtle visual spoofing and forgery clues in face attack detection tasks remains underexplored. In this paper, we introduce a benchmark, SHIELD, to evaluate MLLMs for face spoofing and forgery detection. Specifically, we design true/false and multiple-choice questions to assess MLLM performance on multimodal face data across two tasks. For the face anti-spoofing task, we evaluate three modalities (i.e., RGB, infrared, and depth) under six attack types. For the face forgery detection task, we evaluate GAN-based and diffusion-based data, incorporating visual and acoustic modalities. We conduct zero-shot and few-shot evaluations in standard and chain of thought (COT) settings. Additionally, we propose a novel multi-attribute chain of thought (MA-COT) paradigm for describing and judging various task-specific and task-irrelevant attributes of face images. The findings of this study demonstrate that MLLMs exhibit strong potential for addressing the challenges associated with the security of facial recognition technology applications.
Probabilistic Shapley Value Modeling and Inference
Venue: arXiv cs.LG 2025
Year: 2025

We propose probabilistic Shapley inference (PSI), a novel probabilistic framework to model and infer sufficient statistics of feature attributions in flexible predictive models, via latent random variables whose mean recovers Shapley values. PSI enables efficient, scalable inference over input-to-output attributions, and their uncertainty, via a variational objective that jointly trains a predictive (regression or classification) model and its attribution distributions. To address the challenge of marginalizing over variable-length input feature subsets in Shapley value calculation, we introduce a masking-based neural network architecture, with a modular training and inference procedure. We evaluate PSI on synthetic and real-world datasets, showing that it achieves competitive predictive performance compared to strong baselines, while learning feature attribution distributions -- centered at Shapley values -- that reveal meaningful attribution uncertainty across data modalities.
Six Fallacies in Substituting Large Language Models for Human Participants
Venue: arXiv cs.CY 2025
Year: 2025

Can AI systems like large language models (LLMs) replace human participants in behavioral and psychological research? Here I critically evaluate the "replacement" perspective and identify six interpretive fallacies that undermine its validity. These fallacies are: (1) equating token prediction with human intelligence, (2) treating LLMs as the average human, (3) interpreting alignment as explanation, (4) anthropomorphizing AI systems, (5) essentializing identities, and (6) substituting model data for human evidence. Each fallacy represents a potential misunderstanding about what LLMs are and what they can tell us about human cognition. The analysis distinguishes levels of similarity between LLMs and humans, particularly functional equivalence (outputs) versus mechanistic equivalence (processes), while highlighting both technical limitations (addressable through engineering) and conceptual limitations (arising from fundamental differences between statistical and biological intelligence). For each fallacy, specific safeguards are provided to guide responsible research practices. Ultimately, the analysis supports conceptualizing LLMs as pragmatic simulation tools--useful for role-play, rapid hypothesis testing, and computational modeling (provided their outputs are validated against human data)--rather than as replacements for human participants. This framework enables researchers to leverage language models productively while respecting the fundamental differences between machine intelligence and human thought.
CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
Venue: arXiv cs.NE 2025
Year: 2025

Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Compared to conventional deep Artificial Neural Networks (ANNs), SNNs exhibit superior efficiency and capability to process temporal information. However, it remains a challenge to train SNNs due to their undifferentiable spiking mechanism. The surrogate gradients method is commonly used to train SNNs, but often comes with an accuracy disadvantage over ANNs counterpart. We link the degraded accuracy to the vanishing of gradient on the temporal dimension through the analytical and experimental study of the training process of Leaky Integrate-and-Fire (LIF) Neuron-based SNNs. Moreover, we propose the Complementary Leaky Integrate-and-Fire (CLIF) Neuron. CLIF creates extra paths to facilitate the backpropagation in computing temporal gradient while keeping binary output. CLIF is hyperparameter-free and features broad applicability. Extensive experiments on a variety of datasets demonstrate CLIF's clear performance advantage over other neuron models. Furthermore, the CLIF's performance even slightly surpasses superior ANNs with identical network structure and training conditions. The code is available at https://github.com/HuuYuLong/Complementary-LIF.
Moco: A Learnable Meta Optimizer for Combinatorial Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Relevant combinatorial optimization problems (COPs) are often NP-hard. While they have been tackled mainly via handcrafted heuristics in the past, advances in neural networks have motivated the development of general methods to learn heuristics from data. Many approaches utilize a neural network to directly construct a solution, but are limited in further improving based on already constructed solutions at inference time. Our approach, Moco, defines a lightweight solution construction procedure, guided by a single continuous vector $θ$ (called heatmap) and learns a neural network to update $θ$ for a single instance of a COP at inference time. The update is based on various features of the current search state. The training procedure is budget aware, targeting the overall best solution found during the entire search. Moco is a fully learnable meta optimizer not utilizing problem specific heuristics or requiring optimal solutions for training. We test Moco on the Traveling Salesman Problem (TSP) and Maximum Independent Set (MIS) and show that it significantly improves over other heatmap based methods.
Improved off-policy training of diffusion samplers
Venue: arXiv cs.LG 2025
Year: 2025

We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We benchmark several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks). Our results shed light on the relative advantages of existing algorithms while bringing into question some claims from past work. We also propose a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer, and show that it improves the quality of samples on a variety of target distributions. Our code for the sampling methods and benchmarks studied is made public at https://github.com/GFNOrg/gfn-diffusion as a base for future work on diffusion models for amortized inference.
Optimizing Visibility-based Search in Polygonal Domains
Venue: arXiv cs.CG 2025
Year: 2025

Given a geometric domain $P$, visibility-based search problems seek routes for one or more mobile agents ("watchmen") to move within $P$ in order to be able to see a portion (or all) of $P$, while optimizing objectives, such as the length(s) of the route(s), the size (e.g., area or volume) of the portion seen, the probability of detecting a target distributed within $P$ according to a prior distribution, etc. The classic watchman route problem seeks a shortest route for an observer, with omnidirectional vision, to see all of $P$. In this paper we study bicriteria optimization problems for a single mobile agent within a polygonal domain $P$ in the plane, with the criteria of route length and area seen. Specifically, we address the problem of computing a minimum length route that sees at least a specified area of $P$ (minimum length, for a given area quota). We also study the problem of computing a length-constrained route that sees as much area as possible. We provide hardness results and approximation algorithms. In particular, for a simple polygon $P$ we provide the first fully polynomial-time approximation scheme for the problem of computing a shortest route seeing an area quota, as well as a (slightly more efficient) polynomial dual approximation. We also consider polygonal domains $P$ (with holes) and the special case of a planar domain consisting of a union of lines. Our results yield the first approximation algorithms for computing a time-optimal search route in $P$ to guarantee some specified probability of detection of a static target within $P$, randomly distributed in $P$ according to a given prior distribution.
On Scaling LT-Coded Blockchains in Heterogeneous Networks and their Vulnerabilities to DoS Threats
Venue: arXiv cs.IT 2025
Year: 2025

Coded blockchains have acquired prominence as a promising solution to reduce storage costs and facilitate scalability. Within this class, Luby Transform (LT) coded blockchains are an appealing choice for scalability owing to the availability of a wide range of low-complexity decoders. In the first part of this work, we identify that traditional LT decoders like Belief Propagation and On-the-Fly Gaussian Elimination may not be optimal for heterogeneous networks with nodes that have varying computational and download capabilities. To address this, we introduce a family of hybrid decoders for LT codes and propose optimal operating regimes for them to recover the blockchain at the lowest decoding cost. While LT coded blockchain architecture has been studied from the aspects of storage savings and scalability, not much is known in terms of its security vulnerabilities. Pointing at this research gap, in the second part, we present novel denial-of-service threats on LT coded blockchains that target nodes with specific decoding capabilities, preventing them from joining the network. Our proposed threats are non-oblivious in nature, wherein adversaries gain access to the archived blocks, and choose to execute their attack on a subset of them based on underlying coding scheme. We show that our optimized threats can achieve the same level of damage as that of blind attacks, however, with limited amount of resources. Overall, this is the first work of its kind that opens up new questions on designing coded blockchains to jointly provide storage savings, scalability and also resilience to optimized threats.
EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Features
Venue: arXiv cs.LG 2025
Year: 2025

Traditional model-free feature selection methods treat each feature independently while disregarding the interrelationships among features, which leads to relatively poor performance compared with the model-aware methods. To address this challenge, we propose an efficient model-free feature selection framework via elastic expansion and compression of the features, namely EasyFS, to achieve better performance than state-of-the-art model-aware methods while sharing the characters of efficiency and flexibility with the existing model-free methods. In particular, EasyFS expands the feature space by using the random non-linear projection network to achieve the non-linear combinations of the original features, so as to model the interrelationships among the features and discover most correlated features. Meanwhile, a novel redundancy measurement based on the change of coding rate is proposed for efficient filtering of redundant features. Comprehensive experiments on 21 different datasets show that EasyFS outperforms state-of-the art methods up to 10.9\% in the regression tasks and 5.7\% in the classification tasks while saving more than 94\% of the time.
The last Dance : Robust backdoor attack via diffusion models and bayesian approach
Venue: arXiv cs.LG 2025
Year: 2025

Diffusion models are state-of-the-art deep learning generative models that are trained on the principle of learning forward and backward diffusion processes via the progressive addition of noise and denoising. In this paper, we aim to fool audio-based DNN models, such as those from the Hugging Face framework, primarily those that focus on audio, in particular transformer-based artificial intelligence models, which are powerful machine learning models that save time and achieve results faster and more efficiently. We demonstrate the feasibility of backdoor attacks (called `BacKBayDiffMod`) on audio transformers derived from Hugging Face, a popular framework in the world of artificial intelligence research. The backdoor attack developed in this paper is based on poisoning model training data uniquely by incorporating backdoor diffusion sampling and a Bayesian approach to the distribution of poisoned data.
Understanding Contrastive Representation Learning from Positive Unlabeled (PU) Data
Venue: arXiv cs.LG 2025
Year: 2025

Pretext Invariant Representation Learning (PIRL) followed by Supervised Fine-Tuning (SFT) has become a standard paradigm for learning with limited labels. We extend this approach to the Positive Unlabeled (PU) setting, where only a small set of labeled positives and a large unlabeled pool -- containing both positives and negatives are available. We study this problem under two regimes: (i) without access to the class prior, and (ii) when the prior is known or can be estimated. We introduce Positive Unlabeled Contrastive Learning (puCL), an unbiased and variance reducing contrastive objective that integrates weak supervision from labeled positives judiciously into the contrastive loss. When the class prior is known, we propose Positive Unlabeled InfoNCE (puNCE), a prior-aware extension that re-weights unlabeled samples as soft positive negative mixtures. For downstream classification, we develop a pseudo-labeling algorithm that leverages the structure of the learned embedding space via PU aware clustering. Our framework is supported by theory; offering bias-variance analysis, convergence insights, and generalization guarantees via augmentation concentration; and validated empirically across standard PU benchmarks, where it consistently outperforms existing methods, particularly in low-supervision regimes.
Multimodal Clinical Trial Outcome Prediction with Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

The clinical trial is a pivotal and costly process, often spanning multiple years and requiring substantial financial resources. Therefore, the development of clinical trial outcome prediction models aims to exclude drugs likely to fail and holds the potential for significant cost savings. Recent data-driven attempts leverage deep learning methods to integrate multimodal data for predicting clinical trial outcomes. However, these approaches rely on manually designed modal-specific encoders, which limits both the extensibility to adapt new modalities and the ability to discern similar information patterns across different modalities. To address these issues, we propose a multimodal mixture-of-experts (LIFTED) approach for clinical trial outcome prediction. Specifically, LIFTED unifies different modality data by transforming them into natural language descriptions. Then, LIFTED constructs unified noise-resilient encoders to extract information from modal-specific language descriptions. Subsequently, a sparse Mixture-of-Experts framework is employed to further refine the representations, enabling LIFTED to identify similar information patterns across different modalities and extract more consistent representations from those patterns using the same expert model. Finally, a mixture-of-experts module is further employed to dynamically integrate different modality representations for prediction, which gives LIFTED the ability to automatically weigh different modalities and pay more attention to critical information. The experiments demonstrate that LIFTED significantly enhances performance in predicting clinical trial outcomes across all three phases compared to the best baseline, showcasing the effectiveness of our proposed key components.
Random Deterministic Automata With One Added Transition
Venue: arXiv cs.FL 2025
Year: 2025

Every language recognized by a non-deterministic finite automaton can be recognized by a deterministic automaton, at the cost of a potential increase of the number of states, which in the worst case can go from $n$ states to $2^n$ states. In this article, we investigate this classical result in a probabilistic setting where we take a deterministic automaton with $n$ states uniformly at random and add just one random transition. These automata are almost deterministic in the sense that only one state has a non-deterministic choice when reading an input letter. In our model, each state has a fixed probability to be final. We prove that for any $d\geq 1$, with non-negligible probability the minimal (deterministic) automaton of the language recognized by such an automaton has more than $n^d$ states; as a byproduct, the expected size of its minimal automaton grows faster than any polynomial. Our result also holds when each state is final with some probability that depends on $n$, as long as it is not too close to $0$ and $1$, at distance at least $Ω(\frac1{\sqrt{n}})$ to be precise, therefore allowing models with a sublinear number of final states in expectation.
On coloring graphs with well-distributed edge density
Venue: arXiv cs.DM 2025
Year: 2025

In this paper, we introduce a class of graphs which we call average hereditary graphs. Many graphs that occur in the usual graph theory applications belong to this class of graphs. Many popular types of graphs fall under this class, such as regular graphs, trees and other popular classes of graphs. The paper aims to explore some interesting properties regarding colorings average hereditary graphs. We prove a new upper bound for the chromatic number of a graph in terms of its maximum average degree and show that this bound is an improvement on previous bounds. From this, we show a relationship between the average degree and the chromatic number of an average hereditary graph. We then show that even with new bound, the graph 3-coloring problem remains NP-hard when the input is restricted to average hereditary graphs. We provide an equivalent condition for a graph to be average hereditary, through which we show that we can decide if a given graph is average hereditary in polynomial time.
TREET: TRansfer Entropy Estimation via Transformers
Venue: arXiv cs.IT 2025
Year: 2025

Transfer entropy (TE) is an information theoretic measure that reveals the directional flow of information between processes, providing valuable insights for a wide range of real-world applications. This work proposes Transfer Entropy Estimation via Transformers (TREET), a novel attention-based approach for estimating TE for stationary processes. The proposed approach employs Donsker-Varadhan representation to TE and leverages the attention mechanism for the task of neural estimation. We propose a detailed theoretical and empirical study of the TREET, comparing it to existing methods on a dedicated estimation benchmark. To increase its applicability, we design an estimated TE optimization scheme that is motivated by the functional representation lemma, and use it to estimate the capacity of communication channels with memory, which is a canonical optimization problem in information theory. We further demonstrate how an optimized TREET can be used to estimate underlying densities, providing experimental results. Finally, we apply TREET to feature analysis of patients with Apnea, demonstrating its applicability to real-world physiological data. Our work, applied with state-of-the-art deep learning methods, opens a new door for communication problems which are yet to be solved.