OpenRaiser/Intern-Atlas huggingface dataset:
A Taxonomy of Real Faults in Hybrid Quantum-Classical Architectures
Venue: arXiv cs.SE 2026
Year: 2026

With the popularity of Hybrid Quantum-Classical architectures, particularly noisy intermediate-scale quantum (NISQ) architectures, comes the need for quality assurance methods tailored to their specific faults. In this study, we propose a taxonomy of faults in Hybrid Quantum-Classical architectures accompanied by a dataset of real faults in the identified categories. To achieve this, we empirically analysed open-source repositories for fixed faults. We analysed over 5000 closed issues on GitHub and pre-selected 529 of them based on rigorously defined inclusion criteria. We selected 133 faults that we labelled around symptoms and the origin of the faults. We cross-validated the classification and labels assigned to every fault between two of the authors. As a result, we introduced a taxonomy of real faults in Hybrid Quantum-Classical architectures. Subsequently, we validated the taxonomy through interviews conducted with eleven developers. The taxonomy was dynamically updated throughout the cross-validation and interview processes. The final version was validated and discussed through surveys conducted with an independent group of domain experts to ensure its relevance and to gain further insights.
Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative
Venue: arXiv cs.LG 2026
Year: 2026

While many advances in time series models focus exclusively on numerical data, research on multimodal time series, particularly those involving contextual textual information, remains in its infancy. With recent progress in large language models and time series learning, we revisit the integration of paired texts with time series through the Platonic Representation Hypothesis, which posits that representations of different modalities converge to shared spaces. In this context, we identify that time-series-paired texts may naturally exhibit periodic properties that closely mirror those of the original time series. Building on this insight, we propose a novel framework, Texts as Time Series (TaTS), which considers the time-series-paired texts to be auxiliary variables of the time series. TaTS can be plugged into any existing numerical-only time series models and effectively enable them to handle time series data with paired texts. Through extensive experiments on both multimodal time series forecasting and imputation tasks across benchmark datasets with various existing time series models, we demonstrate that TaTS can enhance multimodal predictive performance without modifying model architectures. Our Code is available at https://github.com/iDEA-iSAIL-Lab-UIUC/TaTS.
A Survey of LLM Alignment: Instruction Understanding, Intention Reasoning, and Reliable Generation
Venue: arXiv cs.HC 2026
Year: 2026

Large language models have demonstrated exceptional capabilities in understanding and generation. However, in real-world scenarios, users' natural language expressions are often inherently fuzzy, ambiguous, and uncertain, leading to challenges such as vagueness, polysemy, and contextual ambiguity. This paper focuses on three challenges in LLM-based text generation tasks: instruction understanding, intention reasoning, and reliable dialog generation. Regarding human complex instruction, LLMs have deficiencies in understanding long contexts and instructions in multi-round conversations. For intention reasoning, LLMs may have inconsistent command reasoning, difficulty reasoning about commands containing incorrect information, difficulty understanding user ambiguous language commands, and a weak understanding of user intention in commands. Besides, In terms of Reliable Dialog Generation, LLMs may have unstable generated content and unethical generation. To this end, we classify and analyze the performance of LLMs in challenging scenarios and conduct a comprehensive evaluation of existing solutions. Furthermore, we introduce benchmarks and categorize them based on the aforementioned three core challenges. Finally, we explore potential directions for future research to enhance the reliability and adaptability of LLMs in real-world applications.
From Contextual Combinatorial Semi-Bandits to Bandit List Classification: Improved Sample Complexity with Sparse Rewards
Venue: arXiv cs.LG 2026
Year: 2026

We study the problem of contextual combinatorial semi-bandits, where input contexts are mapped into subsets of size $m$ of a collection of $K$ possible actions. In each round, the learner observes the realized reward of the predicted actions. Motivated by prototypical applications of contextual bandits, we focus on the $s$-sparse regime where we assume that the sum of rewards is bounded by some value $s\ll K$. For example, in recommendation systems the number of products purchased by any customer is significantly smaller than the total number of available products. Our main result is for the $(ε,δ)$-PAC variant of the problem for which we design an algorithm that returns an $ε$-optimal policy with high probability using a sample complexity of $\tilde{O}((poly(K/m)+sm/ε^2) \log(|Π|/δ))$ where $Π$ is the underlying (finite) class and $s$ is the sparsity parameter. This bound improves upon known bounds for combinatorial semi-bandits whenever $s\ll K$, and in the regime where $s=O(1)$, the leading term is independent of $K$. Our algorithm is also computationally efficient given access to an ERM oracle for $Π$. Our framework generalizes the list multiclass classification problem with bandit feedback, which can be seen as a special case with binary reward vectors. In the special case of single-label classification corresponding to $s=m=1$, we prove an $O((K^7+1/ε^2)\log(|H|/δ))$ sample complexity bound, which improves upon recent results in this scenario. Additionally, we consider the regret minimization setting where data can be generated adversarially, and establish a regret bound of $\tilde O(|Π|+\sqrt{smT\log |Π|})$, extending the result of Erez et al. (2024) who consider the simpler single label classification setting.
λScale: Enabling Fast Scaling for Serverless Large Language Model Inference
Venue: arXiv cs.DC 2026
Year: 2026

Serverless computing has emerged as a compelling solution for cloud-based model inference. However, as modern large language models (LLMs) continue to grow in size, existing serverless platforms often face substantial model startup overhead. This poses a significant challenge in efficiently scaling model instances to accommodate dynamic, bursty workloads commonly observed in real-world inference services. In this paper, we introduce λScale, an efficient serverless inference system to achieve fast model scaling. The key idea behind λScale is to leverage high-speed RDMA networks between GPU nodes for fast model multicast, while enabling distributed inference execution during model transmission -- referred to as "execute-while-load". λScale proposes an efficient model scaling scheme, λPipe, which supports adaptive model multicast and dynamically constructs execution pipelines across receiving nodes for collaborative, distributed inference. Additionally, λScale supports efficient model management across GPU and host memory, allowing fast scaling for models across different storage tiers. Evaluation results show that λScale enables fast model scaling and effectively handles load spikes, achieving up to 5x tail-latency improvement and 31.3% cost reduction compared to state-of-the-art solutions on real-world LLM inference traces.
Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation
Venue: arXiv cs.LG 2026
Year: 2026

We study the reinforcement learning (RL) problem in a constrained Markov decision process (CMDP), where an agent explores the environment to maximize the expected cumulative reward while satisfying a single constraint on the expected total utility value in every episode. While this problem is well understood in the tabular setting, theoretical results for function approximation remain scarce. This paper closes the gap by proposing an RL algorithm for linear CMDPs that achieves $\tilde{\mathcal{O}}(\sqrt{K})$ regret with an episode-wise zero-violation guarantee. Furthermore, our method is computationally efficient, scaling polynomially with problem-dependent parameters while remaining independent of the state space size. Our results significantly improve upon recent linear CMDP algorithms, which either violate the constraint or incur exponential computational costs.
Interactive Virtual Games: Winners for Deep Cognitive Assessment
Venue: arXiv cs.HC 2026
Year: 2026

Studies of human cognition often rely on brief, highly controlled tasks that emphasize group-level effects but poorly capture the rich variability within and between individuals. A suite of minigames built on the novel pixelDOPA platform was designed to overcome these limitations by embedding classic cognitive task paradigms in a 3D virtual interactive environment with continuous behavior logging. Four of the minigames explore constructs that overlap established NIH Toolbox tasks, including processing speed, rule shifting, inhibitory control and working memory. Across a clinical sample of 66 participants collected outside a controlled laboratory setting, large correlations (r = 0.47-0.92) between the pixelDOPA tasks and NIH Toolbox counterparts were found. Process-informed metrics improved both task convergence and data quality. Test-retest analyses revealed high reliability (ICC = 0.71-0.85) for all minigames. Beyond endpoint metrics, movement and gaze trajectories revealed stable, idiosyncratic profiles of gameplay strategy, with unsupervised clustering differentiating participants by their navigational and viewing behaviors. These trajectory-based features showed lower within-person variability than between-person variability, facilitating participant identification across repeated sessions. Game-based tasks can therefore retain the psychometric rigor of standard cognitive assessments while providing new insights into dynamic individual-specific behaviors. By leveraging a highly engaging, fully customizable game engine, comprehensive behavioral tracking can boost the power to detect individual differences without sacrificing group-level inference. This possibility reveals a path toward cognitive measures that are both psychometrically robust and deployable in less-than-ideal settings, while capturing richer behavioral data than traditional paradigms.
Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations
Venue: arXiv cs.AI 2026
Year: 2026

Reinforcement Learning (RL) has been widely used in many applications, particularly in gaming, which serves as an excellent training ground for AI models. Google DeepMind has pioneered innovations in this field, employing reinforcement learning algorithms, including model-based, model-free, and deep Q-network approaches, to create advanced AI models such as AlphaGo, AlphaGo Zero, and MuZero. AlphaGo, the initial model, integrates supervised learning and reinforcement learning to master the game of Go, surpassing professional human players. AlphaGo Zero refines this approach by eliminating reliance on human gameplay data, instead utilizing self-play for enhanced learning efficiency. MuZero further extends these advancements by learning the underlying dynamics of game environments without explicit knowledge of the rules, achieving adaptability across various games, including complex Atari games. This paper reviews the significance of reinforcement learning applications in Atari and strategy-based games, analyzing these three models, their key innovations, training processes, challenges encountered, and improvements made. Additionally, we discuss advancements in the field of gaming, including MiniZero and multi-agent models, highlighting future directions and emerging AI models from Google DeepMind.
Corotational Hinge-based Thin Plates/Shells
Venue: arXiv cs.GR 2026
Year: 2026

We present six thin plate/shell models, derived from three distinct types of curvature operators formulated within the corotational frame, for simulating both rest-flat and rest-curved triangular meshes. Each curvature operator derives a curvature expression corresponding to both a plate model and a shell model. The corotational edge-based hinge model uses an edge-based stencil to compute directional curvature, while the corotational FVM hinge model utilizes a triangle-centered stencil, applying the finite volume method (FVM) to superposition directional curvatures across edges, yielding a generalized curvature. The corotational smoothed hinge model also employs a triangle-centered stencil but transforms directional curvatures into a generalized curvature based on a quadratic surface fit. All models assume small strain and small curvature, leading to constant bending energy Hessians, which benefit implicit integrators. Through quantitative benchmarks and qualitative elastodynamic simulations with large time steps, we demonstrate the accuracy, efficiency, and stability of these models. Our contributions enhance the thin plate/shell library for use in both computer graphics and engineering applications.
MET-Bench: Multimodal Entity Tracking for Evaluating the Limitations of Vision-Language and Reasoning Models
Venue: arXiv cs.CL 2026
Year: 2026

Entity state tracking is a necessary component of world modeling that requires maintaining coherent representations of entities over time. Previous work has benchmarked entity tracking performance in purely text-based tasks. We introduce MET-Bench, a multimodal entity tracking benchmark designed to evaluate vision-language models' ability to track entity states across modalities. Using two structured domains, we assess how effectively current models integrate textual and image-based state updates. Our findings reveal a significant performance gap between text-based and image-based entity tracking. We empirically show this discrepancy primarily stems from deficits in visual reasoning rather than perception. We further show that explicit text-based reasoning strategies improve performance, yet limitations remain in long-horizon multimodal tasks. We develop a reinforcement learning method to improve performance on MET-Bench. Applying our method to open-source VLMs achieves competitive performance with advanced closed models. Our results highlight the need for improved multimodal representations and reasoning techniques to bridge the gap between textual and visual entity tracking.
AdaGC: Improving Training Stability for Large Language Model Pretraining
Venue: arXiv cs.LG 2026
Year: 2026

Loss spikes remain a persistent obstacle in large-scale language model pretraining. While previous research has attempted to identify the root cause of loss spikes by investigating individual factors, we observe that, in practice, such spikes are typically triggered by the confluence of heterogeneous factors. Empirically, loss spikes may arise from a combination of data outliers, hardware or transient computational faults, numerical precision issues, and hyperparameter settings. Regardless of the underlying cause, these spikes manifest as unstable optimizer updates, as abnormal gradients contaminate both first- and second-moment states. In this paper, we propose a principled gradient-centric remedy: AdaGC, an adaptive per-tensor gradient clipping scheme that mitigates such contamination by bounding gradient norms relative to a tensor-wise exponential moving average of their historical clipped values. AdaGC is optimizer-agnostic, introduces negligible memory overhead, and reduces communication costs compared to GlobalGC, particularly in hybrid-parallel distributed training environments. Experiments on Llama-2 7B, Mixtral 8x1B, and ERNIE 10B-A1.4B demonstrate that AdaGC robustly eliminates training instabilities, consistently reducing spike scores to zero for all models and improving downstream accuracy over GlobalGC by 1.32%, 1.27%, and 2.48%, respectively. Furthermore, AdaGC seamlessly integrates with optimizers such as Muon and Lion, consistently yielding higher average accuracy and zero spike scores.
MAnchors: Memorization-Based Acceleration of Anchors via Rule Reuse and Transformation
Venue: arXiv cs.LG 2026
Year: 2026

Anchors is a popular local model-agnostic explanation technique whose applicability is limited by its computational inefficiency. To address this limitation, we propose a memorization-based framework that accelerates Anchors while preserving explanation fidelity and interpretability. Our approach leverages the iterative nature of Anchors' algorithm which gradually refines an explanation until it is precise enough for a given input by storing and reusing intermediate results obtained during prior explanations. Specifically, we maintain a memory of low-precision, high-coverage rules and introduce a rule transformation framework to adapt them to new inputs: the horizontal transformation adapts a pre-trained explanation to the current input by replacing features, and the vertical transformation refines the general explanation until it is precise enough for the input. We evaluate our method across tabular, text, and image datasets, demonstrating that it significantly reduces explanation generation time while maintaining fidelity and interpretability, thereby enabling the practical adoption of Anchors in time-sensitive applications.
Automated Visualization Code Synthesis via Multi-Path Reasoning and Feedback-Driven Optimization
Venue: arXiv cs.SE 2026
Year: 2026

Large Language Models (LLMs) have become a cornerstone for automated visualization code generation, enabling users to create charts through natural language instructions. Despite improvements from techniques like few-shot prompting and query expansion, existing methods often struggle when requests are underspecified in actionable details (e.g., data preprocessing assumptions, solver or library choices, etc.), frequently necessitating manual intervention. To overcome these limitations, we propose VisPath: a Multi-Path Reasoning and Feedback-Driven Optimization Framework for Visualization Code Generation. VisPath handles underspecified queries through structured, multi-stage processing. It begins by using Chain-of-Thought (CoT) prompting to reformulate the initial user input, generating multiple extended queries in parallel to surface alternative plausible concretizations of the request. These queries then generate candidate visualization scripts, which are executed to produce diverse images. By assessing the visual quality and correctness of each output, VisPath generates targeted feedback that is aggregated to synthesize an optimal final result. Extensive experiments on MatPlotBench and Qwen-Agent Code Interpreter Benchmark show that VisPath outperforms state-of-the-art methods, providing a more reliable framework for AI-driven visualization generation.
Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens
Venue: arXiv cs.LG 2026
Year: 2026

Concept-based Models are neural networks that learn a concept extractor to map inputs to high-level concepts and an inference layer to translate these into predictions. Ensuring these modules produce interpretable concepts and behave reliably in out-of-distribution is crucial, yet the conditions for achieving this remain unclear. We study this problem by establishing a novel connection between Concept-based Models and reasoning shortcuts (RSs), a common issue where models achieve high accuracy by learning low-quality concepts, even when the inference layer is fixed and provided upfront. Specifically, we extend RSs to the more complex setting of Concept-based Models and derive theoretical conditions for identifying both the concepts and the inference layer. Our empirical results highlight the impact of RSs and show that existing methods, even combined with multiple natural mitigation strategies, often fail to meet these conditions in practice.
Sparse Autoencoder Features for Classifications and Transferability
Venue: arXiv cs.LG 2026
Year: 2026

Sparse Autoencoders (SAEs) provide potentials for uncovering structured, human-interpretable representations in Large Language Models (LLMs), making them a crucial tool for transparent and controllable AI systems. We systematically analyze SAE for interpretable feature extraction from LLMs in safety-critical classification tasks. Our framework evaluates (1) model-layer selection and scaling properties, (2) SAE architectural configurations, including width and pooling strategies, and (3) the effect of binarizing continuous SAE activations. SAE-derived features achieve macro F1 > 0.8, outperforming hidden-state and BoW baselines while demonstrating cross-model transfer from Gemma 2 2B to 9B-IT models. These features generalize in a zero-shot manner to cross-lingual toxicity detection and visual classification tasks. Our analysis highlights the significant impact of pooling strategies and binarization thresholds, showing that binarization offers an efficient alternative to traditional feature selection while maintaining or improving performance. These findings establish new best practices for SAE-based interpretability and enable scalable, transparent deployment of LLMs in real-world applications. Full repo: https://github.com/shan23chen/MOSAIC.
What Scalable Second-Order Information Knows for Pruning at Initialization
Venue: arXiv cs.LG 2026
Year: 2026

Pruning remains an effective strategy for reducing both the costs and environmental impact associated with deploying large neural networks (NNs) while maintaining performance. Classical methods, such as OBD (LeCun et al., 1989) and OBS (Hassibi et al., 1992), demonstrate that utilizing curvature information can significantly enhance the balance between network complexity and performance. However, the computation and storage of the Hessian matrix make it impractical for modern NNs, motivating the use of approximations. Recent research (Gur et al., 2018; Karakida et al., 2019) suggests that the top eigenvalues guide optimization in a small subspace, are identifiable early, and remain consistent during training. Motivated by these findings, we revisit pruning at initialization (PaI) to evaluate scalable, unbiased second-order approximations, such as the Empirical Fisher and Hutchinson diagonals. Our experiments show that these methods capture sufficient curvature information to improve the identification of critical parameters compared to first-order baselines, while maintaining linear complexity. Additionally, we empirically demonstrate that updating batch normalization statistics as a warmup phase improves the performance of data-dependent criteria and mitigates the issue of layer collapse. Notably, Hutchinson-based criteria consistently outperformed or matched existing PaI algorithms across various models (including VGG, ResNet, and ViT) and datasets (such as CIFAR-10/100, TinyImageNet, and ImageNet). Our findings suggest that scalable second-order approximations strike an effective balance between computational efficiency and accuracy, making them a valuable addition to the pruning toolkit. We make our code available.