arXiv:2509.20912v2 Announce Type: replace Abstract: Recent advances in multimodal language models (MLLMs) have achieved remarkable progress in vision-language reasoning, especially with the emergence of “thinking with images,” which integrates explicit visual steps into the reasoning process. While this paradigm strengthens image-based reasoning, a significant challenge remains: models may arrive at correct answers by relying on […]
LLM Personas as a Substitute for Field Experiments in Method Benchmarking
arXiv:2512.21080v3 Announce Type: replace Abstract: Field experiments (A/B tests) are often the most credible benchmark for methods (algorithms) in societal systems, but their cost and latency bottleneck rapid methodological progress. LLM-based persona simulation offers a cheap synthetic alternative, yet it is unclear whether replacing humans with personas preserves the benchmark interface that adaptive methods optimize […]
Neural Force Field: Few-shot Learning of Generalized Physical Reasoning
arXiv:2502.08987v5 Announce Type: replace-cross Abstract: Physical reasoning is a remarkable human ability that enables rapid learning and generalization from limited experience. Current AI models, despite extensive training, still struggle to achieve similar generalization, especially in Out-of-distribution (OOD) settings. This limitation stems from their inability to abstract core physical principles from observations. A key challenge is […]
Speeding up Local Optimization in Vehicle Routing with Tensor-based GPU Acceleration
arXiv:2506.17357v2 Announce Type: replace-cross Abstract: Local search plays a central role in many effective heuristic algorithms for the vehicle routing problem (VRP) and its variants. However, neighborhood exploration is known to be computationally expensive and time consuming, especially for large instances or problems with complex constraints. In this study, we explore a promising direction to […]
Deep Residual Echo State Networks: exploring residual orthogonal connections in untrained Recurrent Neural Networks
arXiv:2508.21172v2 Announce Type: replace-cross Abstract: Echo State Networks (ESNs) are a particular type of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) framework, popular for their fast and efficient learning. However, traditional ESNs often struggle with long-term information processing. In this paper, we introduce a novel class of deep untrained RNNs based on […]
Entropy Guided Dynamic Patch Segmentation for Time Series Transformers
arXiv:2509.26157v2 Announce Type: replace-cross Abstract: Patch-based transformers have emerged as efficient and improved long-horizon modeling architectures for time series modeling. Yet, existing approaches rely on temporally-agnostic patch construction, where arbitrary starting positions and fixed lengths fracture temporal coherence by splitting natural transitions across boundaries. This naive segmentation often disrupts short-term dependencies and weakens representation learning. […]
TS-PEFT: Unveiling Token-Level Redundancy in Parameter-Efficient Fine-Tuning
arXiv:2511.16147v3 Announce Type: replace-cross Abstract: Current Parameter-Efficient Fine-Tuning (PEFT) methods typically operate under an implicit assumption: Once a target module is selected, every token passing through it contributes equally to the downstream task and requires a parameter update. In this paper, we challenge this convention by revealing a pervasive token-level redundancy in the fine-tuning of […]
The Algorithmic Gaze: An Audit and Ethnography of the LAION-Aesthetics Predictor Model
arXiv:2601.09896v2 Announce Type: replace-cross Abstract: Visual generative AI models are trained using a one-size-fits-all measure of aesthetic appeal. However, what is deemed “aesthetic” is inextricably linked to personal taste and cultural values, raising the question of whose taste is represented in visual generative AI models. In this work, we study an aesthetic evaluation model–LAION Aesthetic […]
AACR-Bench: Evaluating Automatic Code Review with Holistic Repository-Level Context
arXiv:2601.19494v2 Announce Type: replace-cross Abstract: High-quality evaluation benchmarks are pivotal for deploying Large Language Models (LLMs) in Automated Code Review (ACR). However, existing benchmarks suffer from two critical limitations: first, the lack of multi-language support in repository-level contexts, which restricts the generalizability of evaluation results; second, the reliance on noisy, incomplete ground truth derived from […]
TACLer: Tailored Curriculum Reinforcement Learning for Efficient Reasoning
arXiv:2601.21711v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown remarkable performance on complex reasoning tasks, especially when equipped with long chain-of-thought (CoT) reasoning. However, eliciting long CoT typically requires large-scale reinforcement learning (RL) training, while often leading to overthinking with redundant intermediate steps. To improve learning and reasoning efficiency, while preserving or even […]
Temporal Sepsis Modeling: a Fully Interpretable Relational Way
arXiv:2601.21747v1 Announce Type: cross Abstract: Sepsis remains one of the most complex and heterogeneous syndromes in intensive care, characterized by diverse physiological trajectories and variable responses to treatment. While deep learning models perform well in the early prediction of sepsis, they often lack interpretability and ignore latent patient sub-phenotypes. In this work, we propose a […]
A Decomposable Forward Process in Diffusion Models for Time-Series Forecasting
arXiv:2601.21812v1 Announce Type: cross Abstract: We introduce a model-agnostic forward diffusion process for time-series forecasting that decomposes signals into spectral components, preserving structured temporal patterns such as seasonality more effectively than standard diffusion. Unlike prior work that modifies the network architecture or diffuses directly in the frequency domain, our proposed method alters only the diffusion […]