6205 papers
The paper demonstrates that unlike in domains with external verifiers, scaling inference compute through crowd wisdom strategies fails to improve LLM truthfulness in unverified settings because correlated model errors and the inability to distinguish social prediction from truth verification cause aggregation to reinforce shared misconceptions rather than identify correct answers.
The paper introduces OptiRoulette, a stochastic meta-optimizer that dynamically selects update rules from a pool during training, demonstrating significantly faster convergence and higher test accuracy across multiple image-classification benchmarks compared to a standard AdamW baseline.
This paper proposes a unified linear representation for diffusion models and flow matching to theoretically demonstrate that the often weak correlation between noisy data and predicted targets in existing methods may adversely impact the learning process.
This paper proposes Annealed Co-Generation (ACG), a framework that replaces high-dimensional joint diffusion modeling with a low-dimensional, pairwise approach coupled through a three-stage annealing process to achieve efficient and consistent multivariate co-generation for scientific applications like flow-field completion and antibody generation.
RACER is a novel, model-agnostic routing framework that addresses misrouting risks in multi-LLM systems by formulating the problem as -VOR to generate calibrated, variable-sized model sets with rigorous distribution-free risk control, thereby enhancing downstream accuracy and cost-performance trade-offs.
The paper introduces Evo, a novel large language model that unifies autoregressive and diffusion-based generation within a continuous evolutionary latent framework, enabling adaptive balancing of planning and refinement to achieve state-of-the-art performance across diverse benchmarks while maintaining fast inference speeds.
This paper proposes a novel topology-aware framework that leverages domain-specific foundation models, a graph tokenizer for multiplex connectivity, and knowledge distillation to achieve robust zero-shot interaction prediction in multiplex biological networks, outperforming state-of-the-art methods.
The paper introduces NAT (Not All Tokens Are Needed), a token-efficient reinforcement learning framework that utilizes unbiased partial-token gradient estimation via Horvitz-Thompson reweighting to achieve full-sequence performance with significantly reduced compute and memory costs by updating policies on only a subset of generated tokens.
GraphSkill is an agentic framework that improves complex graph reasoning by leveraging hierarchical document retrieval and self-debugging with generated test cases, validated on a new comprehensive dataset.
This paper reveals that state-of-the-art Process Reward Models (PRMs) are systematically exploitable by adversarial optimization, functioning primarily as fluency detectors rather than reasoning verifiers due to a critical dissociation between stylistic changes and ground-truth accuracy, prompting the release of a diagnostic framework and benchmark to address these vulnerabilities.
This paper empirically demonstrates that a Transformer model utilizing self-attention mechanisms outperforms traditional ARIMA and recurrent neural network approaches (LSTM, BiLSTM) in short-term power load forecasting on PJM data, achieving a superior 3.8% MAPE and highlighting the effectiveness of attention-based architectures for capturing complex temporal patterns.
This survey comprehensively reviews the methodological advances and diverse applications of Flow-GRPO, a framework that extends Group Relative Policy Optimization to align large-scale flow matching models with human preferences across various generative tasks and modalities.
This paper introduces Exploration Space Theory (EST), a formal lattice-theoretic framework that adapts Knowledge Space Theory to location-based recommendation by modeling prerequisite dependencies among points of interest, thereby providing structural guarantees for validity, optimality, and explainability in the Exploration Space Recommender System (ESRS).
This paper proposes a collective learning-based Graph Convolutional Network model that effectively imputes missing pavement condition data by integrating features from adjacent road sections and capturing dependencies between observed conditions, demonstrating promising results in a Texas Department of Transportation case study.
Grouter is a preemptive routing framework that decouples structural optimization from weight updates by distilling high-quality routing policies from fully trained models, thereby significantly accelerating Mixture-of-Experts (MoE) training convergence and throughput while improving data utilization.
The paper proposes T-REX, a novel transformer-based architecture that addresses the unique challenges of online grocery shopping by generating personalized category-level basket recommendations through dynamic sequence splitting, adaptive positional encoding, and causal masking to effectively capture both short-term dependencies and long-term user preferences.
This paper proposes a leakage-safe, time-respecting graph feature extraction protocol for temporal transaction networks that, when combined with transaction attributes, significantly enhances the interpretability and performance of illicit entity classification while preventing look-ahead bias.
This paper proposes a new "Uncertainty Principle" in machine learning, asserting that the sharpness of a minimum in polynomial-based problems is inversely related to the smoothness of the optimization landscape, a phenomenon caused by the degeneracy of Heaviside and sigmoid expansions that traps gradient descent and necessitates a physics-based rather than purely computational approach to solving these scientific problems.
This paper demonstrates that the ability of small language models to infer graph properties depends critically on how relational information is represented and the reasoning strategy employed, rather than solely on model scale.
This paper introduces SmartBench, the first dataset designed to evaluate LLMs on detecting anomalous device states and behavioral contexts in smart homes, revealing that current state-of-the-art models struggle significantly with this critical task.