8228 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 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 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 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.
This paper applies thermodynamic and economic principles to reframe AI tokens as physical quantities with measurable costs, establishing a finite global "question budget" to argue that the critical challenge for humanity is not the volume of computable answers but the agency required to determine which questions are worth asking.
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.
The paper introduces HEARTS, a comprehensive benchmark comprising 16 real-world health datasets and 110 tasks across four reasoning capabilities, which reveals that current large language models significantly underperform specialized models in health time series analysis due to struggles with multi-step temporal reasoning and reliance on simple heuristics.
RECAP is a bio-inspired image classification method that couples untrained reservoir dynamics with a self-organizing Hebbian prototype readout to achieve robust, backpropagation-free learning capable of generalizing to corrupted inputs without prior exposure.
SR-TTT addresses the catastrophic recall failures of Test-Time Training (TTT) language models by introducing a loss-gated sparse memory mechanism that dynamically routes highly surprising tokens to an exact-attention residual cache, thereby preserving O(1) memory efficiency while enabling accurate retrieval of critical information.
This paper proposes a trust-aware federated learning framework that utilizes an Adaptive Trust Score Scaling and Filtering mechanism to secure bone healing stage interpretation in e-Health by mitigating the impact of unreliable or adversarial participants while maintaining model integrity and predictive performance.
This paper proposes a stateful, hierarchical multi-agent framework for 5G/6G Intent-Based Networking orchestration that leverages both Large and Small Language Models, demonstrating that while both achieve similar translation accuracy, Small Language Models improve the overall lifecycle completion speed by 20%.
This paper introduces ObjChangeVR, a novel framework and corresponding dataset designed to enhance object state change reasoning in virtual reality by addressing the challenges of detecting background changes without direct interaction through viewpoint-aware retrieval and cross-view reasoning.