OpenRaiser/Intern-Atlas huggingface dataset:
Word Synchronization Challenge: A Benchmark for Word Association Responses for Large Language Models
Venue: arXiv cs.HC 2026
Year: 2026

This paper introduces the Word Synchronization Challenge, a novel benchmark to evaluate large language models (LLMs) in Human-Computer Interaction (HCI). This benchmark uses a dynamic game-like framework to test LLMs ability to mimic human cognitive processes through word associations. By simulating complex human interactions, it assesses how LLMs interpret and align with human thought patterns during conversational exchanges, which are essential for effective social partnerships in HCI. Initial findings highlight the influence of model sophistication on performance, offering insights into the models capabilities to engage in meaningful social interactions and adapt behaviors in human-like ways. This research advances the understanding of LLMs potential to replicate or diverge from human cognitive functions, paving the way for more nuanced and empathetic human-machine collaborations.
Hallucination, Monofacts, and Miscalibration: An Empirical Investigation
Venue: arXiv cs.CL 2026
Year: 2026

Hallucinated facts in large language models (LLMs) have recently been shown to obey a statistical lower bound determined by the monofact rate (related to the classical Good-Turing missing mass estimator) minus model miscalibration (Kalai & Vempala, 2024). We present the first empirical investigation of this three-way relationship in classical n-gram models and fine-tuned encoder-decoder Transformers. By generating training data from Pareto distributions with varying shape parameters, we systematically control the monofact rates and establish its positive relationship with hallucination. To bridge theory and practice, we derive an empirical analog of the hallucination bound by replacing the population miscalibration term (Section 2.1) with an empirical bin-wise KL divergence and confirm its practical viability. We then introduce selective upweighting -- a simple yet effective technique that strategically repeats as little as 5% of training examples -- to deliberately inject miscalibration into the model. This intervention reduces hallucination by up to 40%, challenging universal deduplication policies. Our experiments reveal a critical trade-off: selective upweighting maintains pre-injection levels of accuracy while substantially reducing hallucination, whereas standard training gradually improves accuracy but fails to address persistently high hallucination, indicating an inherent tension in optimization objectives.
Analysis of Off-Policy $n$-Step TD-Learning with Linear Function Approximation
Venue: arXiv cs.LG 2026
Year: 2026

This paper analyzes multi-step temporal difference (TD)-learning algorithms within the ``deadly triad'' scenario, characterized by linear function approximation, off-policy learning, and bootstrapping. In particular, we prove that $n$-step TD-learning algorithms converge to a solution as the sampling horizon $n$ increases sufficiently. The paper is divided into two parts. In the first part, we comprehensively examine the fundamental properties of their model-based deterministic counterparts, including projected value iteration, gradient descent algorithms, which can be viewed as prototype deterministic algorithms whose analysis plays a pivotal role in understanding and developing their model-free reinforcement learning counterparts. In particular, we prove that these algorithms converge to meaningful solutions when $n$ is sufficiently large. Based on these findings, in the second part, two $n$-step TD-learning algorithms are proposed and analyzed, which can be seen as the model-free reinforcement learning counterparts of the model-based deterministic algorithms.
Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?
Venue: arXiv cs.LG 2026
Year: 2026

In Long-term Time Series Forecasting (LTSF), the lookback window is a critical hyperparameter often set arbitrarily, undermining the validity of model evaluations. We argue that the lookback window must be tuned on a per-task basis to ensure fair comparisons. Our empirical results show that failing to do so can invert performance rankings, particularly when comparing univariate and multivariate methods. Experiments on standard benchmarks reposition Channel-Independent (CI) models, such as PatchTST, as state-of-the-art methods. However, we reveal this superior performance is largely an artifact of weak inter-channel correlations and simplicity of patterns within these specific datasets. Using Granger causality analysis and ODE datasets (with implicit channel correlations), we demonstrate that the true strength of multivariate Channel-Dependent (CD) models emerges on datasets with strong, inherent cross-channel dependencies, where they significantly outperform CI models. We conclude with four key recommendations for improving TSF research: (i) consider the lookback window as a key hyperparameter to tune, (ii) for standard datasets, examining CI architectures is advantageous, (iii) leverage statistical analysis of datasets to guide the choice between CI and CD architectures, and (iv) prefer CD models in scenarios with limited data.
Fenchel-Young Variational Learning
Venue: arXiv cs.LG 2026
Year: 2026

From a variational perspective, many statistical learning criteria involve seeking a distribution that balances empirical risk and regularization. In this paper, we broaden this perspective by introducing a new general class of variational methods based on Fenchel-Young (FY) losses, treated as divergences that generalize (and encompass) the familiar Kullback-Leibler divergence at the core of classical variational learning. Our proposed formulation -- FY variational learning -- includes as key ingredients new notions of FY free energy, FY evidence, FY evidence lower bound, and FY posterior. We derive alternating minimization and gradient backpropagation algorithms to compute (or lower bound) the FY evidence, which enables learning a wider class of models than previous variational formulations. This leads to generalized FY variants of classical algorithms, such as an FY expectation-maximization (FYEM) algorithm, and latent-variable models, such as an FY variational autoencoder (FYVAE). Our new methods are shown to be empirically competitive, often outperforming their classical counterparts, and most importantly, to have qualitatively novel features. For example, FYEM has an adaptively sparse E-step, while the FYVAE can support models with sparse observations and sparse posteriors.
Beyond Known Fakes: Generalized Detection of AI-Generated Images via Post-hoc Distribution Alignment
Venue: arXiv cs.CR 2026
Year: 2026

The rapid proliferation of highly realistic AI-generated images poses serious security threats such as misinformation and identity fraud. Detecting generated images in open-world settings is particularly challenging when they originate from unknown generators, as existing methods typically rely on model-specific artifacts and require retraining on new fake data, limiting their generalization and scalability. In this work, we propose Post-hoc Distribution Alignment (PDA), a generalized and model-agnostic framework for detecting AI-generated images under unknown generative threats. Specifically, PDA reformulates detection as a distribution alignment task by regenerating test images through a known generative model. When real images are regenerated, they inherit model-specific artifacts and align with the known fake distribution. In contrast, regenerated unknown fakes contain incompatible or mixed artifacts and remain misaligned. This difference allows an existing detector, trained on the known generative model, to accurately distinguish real images from unknown fakes without requiring access to unseen data or retraining. Extensive experiments across 16 state-of-the-art generative models, including GANs, diffusion models, and commercial text-to-image APIs (e.g., Midjourney), demonstrate that PDA achieves average detection accuracy of 96.69%, outperforming the best baseline by 10.71%. Comprehensive ablation studies and robustness analyses further confirm PDA's generalizability and resilience to distribution shifts and image transformations. Overall, our work provides a practical and scalable solution for real-world AI-generated image detection where new generative models emerge continuously.
Be Friendly, Not Friends: How LLM Sycophancy Shapes User Trust
Venue: arXiv cs.HC 2026
Year: 2026

LLM-powered conversational agents are increasingly influencing our decision-making, raising concerns about "sycophancy" - the tendency for LLMs to excessively agree with users even at the expense of truthfulness. While prior work has primarily examined LLM sycophancy as a model behavior, our understanding of how users perceive this phenomenon and its impact on user trust remains significantly lacking. In this work, we conceptualize LLM sycophancy along two key constructs: conversational demeanor (complimentary vs. neutral) and stance adaptation (adaptive vs. consistent). A 2 x 2 between-subjects experiment (N = 224) revealed complex dynamics: complimentary LLMs that adapted their stance reduced perceived authenticity and trust, while neutral LLMs that adapted enhanced both, suggesting a pathway for manipulating users into over-trusting LLMs beyond their actual capabilities. Our findings advance user-centric understanding of LLM sycophancy and provide profound implications for developing more ethical and trustworthy LLM systems.
Equality of cycle lengths in one- and two-dimensional $σ$ automata
Venue: arXiv cs.FL 2026
Year: 2026

When the game Lights Out is played according to an algorithm specifying the player's sequence of moves, it can be modeled using deterministic cellular automata. One such model reduces to the $σ$ automaton, which evolves according to the 2-dimensional analog of Rule 90. We consider how the cycle lengths of multi-dimensional $σ$ automata depend on their dimension. We find that the cycle lengths of 1-dimensional $σ$ automata and 2-dimensional $σ$ automata (of the same size) are equal, and we prove this by relating the eigenvalues and Jordan blocks of their respective adjacency matrices. We also discover that cycle lengths of higher-dimensional $σ$ automata are bounded (despite the number of lattice sites increasing with dimension) and eventually saturate the upper bound.
On the Importance of Pretraining Data Alignment for Atomic Property Prediction
Venue: arXiv cs.LG 2026
Year: 2026

This paper challenges the recent paradigm in atomic property prediction that links progress to growing dataset sizes and computational resources. We show that pretraining on a carefully selected task-aligned dataset can match or even surpass large-scale joint pretraining while using only 1/24th of the pretraining budget. We introduce the Chemical Similarity Index (CSI), a simple metric for molecular graphs inspired by the Fréchet Inception Distance in computer vision, which quantifies the alignment between upstream pretraining datasets and downstream tasks. By selecting the most aligned dataset with minimal CSI distance, we show that models pretrained on a smaller, focused dataset consistently achieve better performance on downstream tasks than those pretrained on massive, mixed datasets such as JMP. This holds even when the mixed dataset includes the upstream dataset most aligned with the downstream task. Counterintuitively, we also find that indiscriminately adding more data can degrade model performance when the additional data is poorly aligned with the target task. Our findings highlight that quality often outperforms quantity in pretraining for atomic property prediction.
A Program Logic for Under-approximating Worst-case Resource Usage
Venue: arXiv cs.LO 2026
Year: 2026

Understanding and predicting the worst-case resource usage is crucial for software quality; however, existing methods either over-approximate with potentially loose bounds or under-approximate without asymptotic guarantees. This paper presents a program logic to under-approximate worst-case resource usage, adapting incorrectness logic (IL) to reason quantitatively about resource consumption. We propose quantitative forward and backward under-approximate (QFUA and QBUA) triples, which generalize IL to identify execution paths leading to high resource usage. We also introduce a variant of QBUA that supports reasoning about high-water marks. Our logic is proven sound and complete with respect to a simple IMP-like language, and all meta-theoretical results are mechanized and verified in Rocq. We implement a prototype checker for all three variants of our logic and demonstrate its utility through a few examples and four case studies.
Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy
Venue: arXiv cs.LG 2026
Year: 2026

Strong Differential Privacy (DP) and Optimization guarantees are two desirable properties for a method in Federated Learning (FL). However, existing algorithms do not achieve both properties at once: they either have optimal DP guarantees but rely on restrictive assumptions such as bounded gradients/bounded data heterogeneity, or they ensure strong optimization performance but lack DP guarantees. To address this gap in the literature, we propose and analyze a new method called Clip21-SGD2M based on a novel combination of clipping, heavy-ball momentum, and Error Feedback. In particular, for non-convex smooth distributed problems with clients having arbitrarily heterogeneous data, we prove that Clip21-SGD2M has optimal convergence rate and also near optimal (local-)DP neighborhood. Our numerical experiments on non-convex logistic regression and training of neural networks highlight the superiority of Clip21-SGD2M over baselines in terms of the optimization performance for a given DP-budget.
Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting
Venue: arXiv cs.LG 2026
Year: 2026

Forecasting irregularly sampled multivariate time series with missing values (IMTS) is a fundamental challenge in domains such as healthcare, climate science, and biology. While recent advances in vision and time series forecasting have shown that lightweight MLP-based architectures (e.g., MLP-Mixer, TSMixer) can rival attention-based models in both accuracy and efficiency, their applicability to irregular and sparse time series remains unexplored. In this paper, we propose IMTS-Mixer, a novel architecture that adapts the principles of Mixer models to the IMTS setting. IMTS-Mixer introduces two key components: (1) ISCAM, a channel-wise encoder that transforms irregular observations into fixed-size vectors using simple MLPs, and (2) ConTP, a continuous time decoder that supports forecasting at arbitrary time points. In our experiments on established benchmark datasets we show that our model achieves state-of-the- art performance in both forecasting accuracy and inference time, while using fewer parameters compared to baselines.
Creating a Causally Grounded Rating Method for Assessing the Robustness of AI Models for Time-Series Forecasting
Venue: arXiv cs.LG 2026
Year: 2026

AI models, including both time-series-specific and general-purpose Foundation Models (FMs), have demonstrated strong potential in time-series forecasting across sectors like finance. However, these models are highly sensitive to input perturbations, which can lead to prediction errors and undermine trust among stakeholders, including investors and analysts. To address this challenge, we propose a causally grounded rating framework to systematically evaluate model robustness by analyzing statistical and confounding biases under various noisy and erroneous input scenarios. Our framework is applied to a large-scale experimental setup involving stock price data from multiple industries and evaluates both uni-modal and multi-modal models, including Vision Transformer-based (ViT) models and FMs. We introduce six types of input perturbations and twelve data distributions to assess model performance. Results indicate that multi-modal and time-series-specific FMs demonstrate greater robustness and accuracy compared to general-purpose models. Further, to validate our framework's usability, we conduct a user study showcasing time-series models' prediction errors along with our computed ratings. The study confirms that our ratings reduce the difficulty for users in comparing the robustness of different models. Our findings can help stakeholders understand model behaviors in terms of robustness and accuracy for better decision-making even without access to the model weights and training data, i.e., black-box settings.
Translate Policy to Language: Flow Matching Generated Rewards for LLM Explanations
Venue: arXiv cs.CL 2026
Year: 2026

As humans increasingly share environments with diverse agents powered by RL, LLMs, and beyond, the ability to explain agent policies in natural language is vital for reliable coexistence. We introduce a general-purpose framework that trains explanation-generating LLMs via reinforcement learning from AI feedback, with distributional rewards generated by generative continuous normalizing flows (CNFs). CNFs capture the pluralistic and probabilistic nature of human judgments about explanations. Moreover, under mild assumptions, CNFs provably bound deviations from true human reward distributions when trained on noisy proxy rewards from LLMs. We design a specialized CNF architecture that selectively attends to linguistic cues in the decision context and explanations when generating rewards. Human and LLM evaluators find that our method delivers explanations that enable more accurate predictions of true agent decisions, exhibit greater logical soundness and actionability, and impose lower cognitive load than explanations trained with proxy LLM rewards or state-of-the-art RLHF and RLAIF baselines.
Activation-wise Propagation: A One-Timestep Strategy for Spiking Neural Networks
Venue: arXiv cs.CV 2026
Year: 2026

Spiking neural networks (SNNs) have demonstrated significant potential in real-time multi-sensor perception tasks due to their event-driven and parameter-efficient characteristics. A key challenge is the timestep-wise iterative update of neuronal hidden states (membrane potentials), which complicates the trade-off between accuracy and latency. SNNs tend to achieve better performance with longer timesteps, inevitably resulting in higher computational overhead and latency compared to artificial neural networks (ANNs). Moreover, many recent advances in SNNs rely on architecture-specific optimizations, which, while effective with fewer timesteps, often limit generalizability and scalability across modalities and models. To address these limitations, we propose Activation-wise Membrane Potential Propagation (AMP2), a unified hidden state update mechanism for SNNs. Inspired by the spatial propagation of membrane potentials in biological neurons, AMP2 enables dynamic transmission of membrane potentials among spatially adjacent neurons, facilitating spatiotemporal integration and cooperative dynamics of hidden states, thereby improving efficiency and accuracy while reducing reliance on extended temporal updates. This simple yet effective strategy significantly enhances SNN performance across various architectures, including MLPs and CNNs for point cloud and event-based data. Furthermore, ablation studies integrating AMP2 into Transformer-based SNNs for classification tasks demonstrate its potential as a general-purpose and efficient solution for spiking neural networks.
Text Classification Under Class Distribution Shift: A Survey
Venue: arXiv cs.CL 2026
Year: 2026

The basic underlying assumption of machine learning (ML) models is that the training and test data are sampled from the same distribution. However, in daily practice, this assumption is often broken, i.e. the distribution of the test data changes over time, which hinders the application of conventional ML models. One domain where the distribution shift naturally occurs is text classification, since people always find new topics to discuss. To this end, we survey research articles studying open-set text classification and related tasks. We divide the methods in this area based on the constraints that define the kind of distribution shift and the corresponding problem formulation, i.e. learning with the Universum, zero-shot learning, and open-set learning. We next discuss the predominant mitigation approaches for each problem setup. We further identify several future work directions, aiming to push the boundaries beyond the state of the art. Finally, we explain how continual learning can solve many of the issues caused by the shifting class distribution. We maintain a list of relevant papers at https://github.com/Eduard6421/Open-Set-Survey.
Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications
Venue: arXiv cs.CL 2026
Year: 2026

Large Language Models (LLMs) have significantly advanced natural language processing, demonstrating strong capabilities in tasks such as text generation, summarization, and reasoning. Recently, their potential for automating precise text editing tasks across specialized domains, such as programming code, LaTeX, and structured database languages, has gained attention. However, current state-of-the-art LLMs still struggle with executing precise, instruction-driven edits, particularly when structural accuracy and strict adherence to domain conventions are required. To address these challenges, we introduce InstrEditBench, an automated benchmark dataset comprising over 30,000 structured editing tasks spanning diverse domains, including Wikipedia articles, LaTeX documents, source code, and database languages. Using this benchmark, we develop FineEdit, a specialized editing model explicitly trained for accurate, context-aware text modifications. Experimental evaluations demonstrate that FineEdit outperforms state-of-the-art models, achieving improvements of approximately 10\% over Gemini models on single-turn edits, up to 30\% over Llama-3.2-3B, and exceeding Mistral-7B-OpenOrca performance by over 40\% on direct editing tasks. FineEdit also effectively generalizes to realistic multi-turn editing scenarios, highlighting its practical applicability. To facilitate further research and reproducibility, we release FineEdit at https://github.com/StuRinDQB/FineEdit} and https://huggingface.co/datasets/YimingZeng/FineEdit_bench.