OpenRaiser/Intern-Atlas huggingface dataset:
Optimization Insights into Deep Diagonal Linear Networks
Venue: arXiv cs.LG 2026
Year: 2026

Gradient-based methods successfully train highly overparameterized models in practice, even though the associated optimization problems are markedly nonconvex. Understanding the mechanisms that make such methods effective has become a central problem in modern optimization. To investigate this question in a tractable setting, we study Deep Diagonal Linear Networks. These are multilayer architectures with a reparameterization that preserves convexity in the effective parameter, while inducing a nontrivial geometry in the optimization landscape. Under mild initialization conditions, we show that gradient flow on the layer parameters induces a mirror-flow dynamic in the effective parameter space. This structural insight yields explicit convergence guarantees, including exponential decay of the loss under a Polyak-Lojasiewicz condition, and clarifies how the parametrization and initialization scale govern the training speed. Overall, our results demonstrate that deep diagonal over parameterizations, despite their apparent complexity, can endow standard gradient methods with well-behaved and interpretable optimization dynamics.
Evaluating LLMs' Divergent Thinking Capabilities for Scientific Idea Generation with Minimal Context
Venue: arXiv cs.CL 2026
Year: 2026

While Large Language Models (LLMs) demonstrate remarkable capabilities in scientific tasks such as literature analysis and experimental design (e.g., accurately extracting key findings from papers or generating coherent experimental procedures), existing evaluation benchmarks primarily assess performance using rich contextual inputs. We introduce LiveIdeaBench, a comprehensive benchmark evaluating LLMs' scientific idea generation by assessing divergent thinking capabilities using single-keyword prompts. Drawing from Guilford's creativity theory, our benchmark employs a dynamic panel of state-of-the-art LLMs to assess generated ideas across five key dimensions: originality, feasibility, fluency, flexibility, and clarity. Through extensive experimentation with over 40 leading models across 1,180 keywords spanning 22 scientific domains, we reveal that the scientific idea generation capabilities measured by our benchmark, are poorly predicted by standard metrics of general intelligence. Our results demonstrate that models like QwQ-32B-preview achieve creative performance comparable to top-tier models such as claude-3.7-sonnet:thinking, despite significant gaps in their general intelligence scores. These findings highlight the need for specialized evaluation benchmarks for scientific idea generation and suggest that enhancing these idea generation capabilities in LLMs may require different training strategies than those used for improving general problem-solving abilities, potentially enabling a wider range of AI tools tailored for different stages of the scientific process.
Learning Randomized Reductions
Venue: arXiv cs.LG 2026
Year: 2026

A self-corrector for a function $f$ takes a black-box oracle computing $f$ that is correct on most inputs and turns it into one that is correct on every input with high probability. Self-correctors exist for any function that is randomly self-reducible (RSR), where the value $f$ at a given point $x$ can be recovered by computing $f$ on random correlated points. While RSRs enable powerful self-correction capabilities and have applications in complexity theory and cryptography, their discovery has traditionally required manual derivation by experts. We present Bitween, a method and tool for automated learning of randomized self-reductions for mathematical functions. We make two key contributions: First, we demonstrate that our learning framework based on linear regression outperforms sophisticated methods including genetic algorithms, symbolic regression, and mixed-integer linear programming for discovering RSRs from correlated samples. Second, we introduce Agentic Bitween, a neuro-symbolic approach where large language models dynamically discover novel query functions for RSR property discovery, leveraging vanilla Bitween as a tool for inference and verification, moving beyond the fixed query functions ($x+r$, $x-r$, $x \cdot r$, $x$, $r$) previously used in the literature. On RSR-Bench, our benchmark suite of 80 scientific and machine learning functions, vanilla Bitween surpasses existing symbolic methods, while Agentic Bitween discovers new RSR properties using frontier models to uncover query functions.
xFLIE: Leveraging Actionable Hierarchical Scene Representations for Autonomous Semantic-Aware Inspection Missions
Venue: arXiv cs.RO 2026
Year: 2026

We present a novel architecture aimed towards incremental construction and exploitation of a hierarchical 3D scene graph representation during semantic-aware inspection missions. Inspection planning, particularly of distributed targets in previously unseen environments, presents an opportunity to exploit the semantic structure of the scene during reasoning, navigation and scene understanding. Motivated by this, we propose the 3D Layered Semantic Graph (3DLSG), a hierarchical inspection scene graph constructed in an incremental manner and organized into abstraction layers that support planning demands in real-time. To address the task of semantic-aware inspection, a mission framework, termed as Enhanced First-Look Inspect Explore (xFLIE), that tightly couples the 3DLSG with an inspection planner is proposed. We assess the performance through simulations and experimental trials, evaluating target-selection, path-planning and semantic navigation tasks over the 3DLSG model. The scenarios presented are diverse, ranging from city-scale distributed to solitary infrastructure targets in simulated worlds and subsequent outdoor and subterranean environment deployments onboard a quadrupedal robot. The proposed method successfully demonstrates incremental construction and planning over the 3DLSG representation to meet the objectives of the missions. Furthermore, the framework demonstrates successful semantic navigation tasks over the structured interface at the end of the inspection missions. Finally, we report multiple orders of magnitude reduction in path-planning time compared to conventional volumetric-map-based methods over various environment scale, demonstrating the planning efficiency and scalability of the proposed approach.
Can MLLMs generate human-like feedback in grading multimodal short answers?
Venue: arXiv cs.AI 2026
Year: 2026

In education, the traditional Automatic Short Answer Grading (ASAG) with feedback problem has focused primarily on evaluating text-only responses. However, real-world assessments often include multimodal responses containing both diagrams and text. To address this limitation, we introduce the Multimodal Short Answer Grading with Feedback (MMSAF) problem, which requires jointly evaluating textual and diagrammatic content while also providing explanatory feedback. Collecting data representative of such multimodal responses is challenging due to both scale and logistical constraints. To mitigate this, we develop an automated data generation framework that leverages LLM hallucinations to mimic common student errors, thereby constructing a dataset of 2,197 instances. We evaluate 4 Multimodal Large Language Models (MLLMs) across 3 STEM subjects, showing that MLLMs achieve accuracies of up to 62.5% in predicting answer correctness (correct/partially correct/incorrect) and up to 80.36% in assessing image relevance. This also includes a human evaluation with 9 annotators across 5 parameters, including a rubric-based approach. The rubrics also serve as a way to evaluate the feedback quality semantically rather than using overlap-based approaches. Our findings highlight which MLLMs are better suited for such tasks while also pointing out to drawbacks of the remaining MLLMs.
From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models
Venue: arXiv cs.RO 2026
Year: 2026

Our aim is to learn to solve long-horizon decision-making problems in complex robotics domains given low-level skills and a handful of short-horizon demonstrations containing sequences of images. To this end, we focus on learning abstract symbolic world models that facilitate zero-shot generalization to novel goals via planning. A critical component of such models is the set of symbolic predicates that define properties of and relationships between objects. In this work, we leverage pretrained vision-language models (VLMs) to propose a large set of visual predicates potentially relevant for decision-making, and to evaluate those predicates directly from camera images. At training time, we pass the proposed predicates and demonstrations into an optimization-based model-learning algorithm to obtain an abstract symbolic world model that is defined in terms of a compact subset of the proposed predicates. At test time, given a novel goal in a novel setting, we use the VLM to construct a symbolic description of the current world state, and then use a search-based planning algorithm to find a sequence of low-level skills that achieves the goal. We demonstrate empirically across experiments in both simulation and the real world that our method can generalize aggressively, applying its learned world model to solve problems with a wide variety of object types, arrangements, numbers of objects, and visual backgrounds, as well as novel goals and much longer horizons than those seen at training time.
Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum Disorder Detection Using 3D Video Dataset
Venue: arXiv cs.LG 2026
Year: 2026

Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental condition characterized by repetitive behaviors and impairments in social and communication skills. Despite the clear manifestation of these symptoms, many individuals with ASD remain undiagnosed. This paper proposes a methodology for ASD detection using a three-dimensional walking video dataset, leveraging supervised machine learning classification algorithms combined with nature-inspired optimization algorithms for feature extraction. The approach employs supervised classifiers to identify ASD cases, while nature-inspired optimization techniques select the most relevant features, enhanced by the use of ranking coefficients to identify initial leading particles. This strategy significantly reduces computational time, thereby improving efficiency and accuracy. Experimental evaluation with various algorithmic combinations demonstrates an exceptional classification accuracy of 100% in the best case when using the Random Forest classifier coupled with the Gravitational Search Algorithm for feature selection. The methodology's application to additional datasets promises improved robustness and generalizability. With its high accuracy and reduced computational requirements, the proposed framework offers significant contributions to both medical and academic fields, providing a foundation for future advances in ASD diagnosis.
A Novel Convolution and Attention Mechanism-based Model for 6D Object Pose Estimation
Venue: arXiv cs.CV 2026
Year: 2026

This paper proposes PoseLecTr, a graph-based encoder-decoder framework that integrates a novel Legendre convolution with attention mechanisms for six-degree-of-freedom (6-DOF) object pose estimation from monocular RGB images. Conventional learning-based approaches predominantly rely on grid-structured convolutions, which can limit their ability to model higher-order and long-range dependencies among image features, especially in cluttered or occluded scenes. PoseLecTr addresses this limitation by constructing a graph representation from image features, where spatial relationships are explicitly modeled through graph connectivity. The proposed framework incorporates a Legendre convolution layer to improve numerical stability in graph convolution, together with spatial-attention and self-attention distillation to enhance feature selection. Experiments conducted on the LINEMOD, Occluded LINEMOD, and YCB-VIDEO datasets demonstrate that our method achieves competitive performance and shows consistent improvements across a wide range of objects and scene complexities.
RaZeR: Pushing the Limits of NVFP4 Quantization with Redundant Zero Remapping
Venue: arXiv cs.LG 2026
Year: 2026

The recently introduced NVFP4 format demonstrates remarkable performance and memory benefits for quantized large language model (LLM) inference. However, we observe two types of redundancy in NVFP4 encoding: (1) The FP4 element format naturally exposes an unused quantization value due to its sign-magnitude representation that contains both positive and negative zeros. (2) The FP8 block scaling factor has an unused sign bit because it is always positive. Additionally, we find that LLM weights are more tolerant to a lower-precision block scaling factor. Based on these observations, we propose Redundant Zero Remapping (RaZeR), an enhanced numerical format that pushes the limits of NVFP4 for more accurate LLM quantization under the same memory footprint. RaZeR leverages the redundant bits of the block scaling factor to adaptively remap the redundant FP4 zero to additional quantization values with improved accuracy. To demonstrate the practicality of RaZeR, we design efficient GPU kernels for RaZeR-quantized LLM inference and propose novel hardware to natively support this. Extensive experiments validate RaZeR's superior performance for 4-bit LLM quantization. For example, relative to native NVFP4, RaZeR reduces the average perplexity loss by 34.6% and 31.2% under weight-only and weight-activation quantization, respectively. Code is available at: https://github.com/yc2367/NVFP4-RaZeR.
A New Flexible Train-Test Split Algorithm, an approach for choosing among the Hold-out, K-fold cross-validation, and Hold-out iteration
Venue: arXiv cs.LG 2026
Year: 2026

Choosing an appropriate strategy for partitioning data into training and evaluation sets is a critical step in machine learning, yet validation methods are often selected using default or conventional settings without considering their impact on generalizability and real-world performance. Common approaches such as hold-out validation or k-fold cross-validation with fixed k values are frequently applied based solely on empirical practice. To address this issue, we propose a flexible Python-based framework that systematically examines how different validation strategies affect predictive performance across seven widely used machine learning algorithms, including Decision Trees, K-Nearest Neighbors, Naive Bayes variants, Logistic Regression, calibrated linear Support Vector Machines, and histogram-based gradient boosting. The framework evaluates these methods under a wide range of validation schemes, including hold-out splits from 10% to 90%, k-fold cross-validation with k between 3 and 15, repeated hold-out, and nested cross-validation. The framework is applied to three biomedical datasets of varying size, and performance is assessed using ROC-AUC, accuracy, and the Matthews correlation coefficient. The results show that no single validation strategy consistently outperforms others across all algorithms and datasets, indicating that optimal validation depends on the interaction between the algorithm, dataset characteristics, and evaluation metric.
Evaluating Sample Utility for Efficient Data Selection by Mimicking Model Weights
Venue: arXiv cs.LG 2026
Year: 2026

Large-scale web-crawled datasets contain noise, bias, and irrelevant information, necessitating data selection techniques. Existing methods depend on hand-crafted heuristics, downstream datasets, or require expensive influence-based computations -- all of which limit scalability and introduce unwanted data dependencies. To address this, we introduce the Mimic Score, a simple and geometry-based data-quality metric that evaluates utility by measuring alignment between a sample's gradients and a target direction induced by a pre-trained reference model. This leverages readily available model weights, avoids needing validation datasets, and incurs minimal computational overheads. Building on this metric, we propose Grad-Mimic, a two-stage framework that re-weights samples online to accelerate training and aggregates sample utilities offline to construct effective data filters. Empirically, we show that using mimic scores to guide training improves data efficiency, accelerates convergence, yields consistent performance gains across six image datasets, and enhances CLIP models with 20.7% fewer training steps. Additionally, mimic score-based filters augment existing filtering techniques, enabling improved CLIP models trained with 4.7 million fewer samples.
Dataset Distillation as Pushforward Optimal Quantization
Venue: arXiv cs.LG 2026
Year: 2026

Dataset distillation aims to find a synthetic training set such that training on the synthetic data achieves similar performance to training on real data, with orders of magnitude less computational requirements. Existing methods can be broadly categorized as either bi-level optimization problems that have neural network training heuristics as the lower level problem, or disentangled methods that bypass the bi-level optimization by matching distributions of data. The latter method has the major advantages of speed and scalability in terms of size of both training and distilled datasets. We demonstrate that when equipped with an encoder-decoder structure, the empirically successful disentangled methods can be reformulated as an optimal quantization problem, where a finite set of points is found to approximate the underlying probability measure by minimizing the expected projection distance. In particular, we link existing disentangled dataset distillation methods to the classical optimal quantization and Wasserstein barycenter problems, demonstrating consistency of distilled datasets for diffusion-based generative priors. We propose Dataset Distillation by Optimal Quantization, based on clustering in a latent space. Compared to the previous SOTA method D\textsuperscript{4}M, we achieve better performance and inter-model generalization on the ImageNet-1K dataset with trivial additional computation, and SOTA performance in higher image-per-class settings. Using the distilled noise initializations in a stronger diffusion transformer model, we obtain SOTA distillation performance on ImageNet-1K and its subsets, outperforming diffusion guidance methods.
Conformal mapping based Physics-informed neural networks for designing neutral inclusions
Venue: arXiv cs.LG 2026
Year: 2026

We address the neutral inclusion problem with imperfect boundary conditions, focusing on designing interface functions for inclusions of arbitrary shapes. Traditional Physics-Informed Neural Networks (PINNs) struggle with this inverse problem, leading to the development of Conformal Mapping Coordinates Physics-Informed Neural Networks (CoCo-PINNs), which integrate geometric function theory with PINNs. CoCo-PINNs effectively solve forward-inverse problems by modeling the interface function through neural network training, which yields a neutral inclusion effect. This approach enhances the performance of PINNs in terms of credibility, consistency, and stability.
DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection
Venue: arXiv cs.CV 2026
Year: 2026

Out-of-distribution (OOD) detection holds significant importance across many applications. While semantic and domain-shift OOD problems are well-studied, this work focuses on covariate shifts - subtle variations in the data distribution that can degrade machine learning performance. We hypothesize that detecting these subtle shifts can improve our understanding of in-distribution boundaries, ultimately improving OOD detection. In adversarial discriminators trained with Batch Normalization (BN), real and adversarial samples form distinct domains with unique batch statistics - a property we exploit for OOD detection. We introduce DisCoPatch, an unsupervised Adversarial Variational Autoencoder (VAE) framework that harnesses this mechanism. During inference, batches consist of patches from the same image, ensuring a consistent data distribution that allows the model to rely on batch statistics. DisCoPatch uses the VAE's suboptimal outputs (generated and reconstructed) as negative samples to train the discriminator, thereby improving its ability to delineate the boundary between in-distribution samples and covariate shifts. By tightening this boundary, DisCoPatch achieves state-of-the-art results in public OOD detection benchmarks. The proposed model not only excels in detecting covariate shifts, achieving 95.5% AUROC on ImageNet-1K(-C) but also outperforms all prior methods on public Near-OOD (95.0%) benchmarks. With a compact model size of 25MB, it achieves high OOD detection performance at notably lower latency than existing methods, making it an efficient and practical solution for real-world OOD detection applications. The code is publicly available.
Uniform Membership for Hyperedge Replacement Grammars and Related Decision Problems
Venue: arXiv cs.FL 2026
Year: 2026

This paper investigates complexity of the uniform membership problem for hyperedge replacement grammars in comparison with other mildly context-sensitive grammar formalisms. It turns out that the complexity of this problem depends on how one defines a hypergraph. There are two commonly used definitions in the field, which differ in whether repetitions of attachment nodes of a hyperedge are allowed in a hypergraph or not. We show that, in general, the problem under consideration is EXPTIME-complete, even for string-generating hyperedge replacement grammars, but it is NP-complete if repetitions are not allowed. We extend the developed proof techniques in order to prove a general meta-theorem: checking whether a given hyperedge replacement grammar generates a hypergraph satisfying a non-Parikh property is EXPTIME-hard. Non-Parikh properties are those that are not preimages of properties on Parikh vectors of hypergraphs. This includes any graph property relying significantly on structure of graphs, e.g. connectivity, Eulerianity, Hamiltonianity, acyclicity. A tight upper bound is established for EXPTIME-compatible properties via Filter Theorem.