12237 papers
This paper provides a comprehensive review and formal specification of Predictive Coding Networks (PCNs), highlighting their biological plausibility, computational efficiency through inference learning, and versatility as a probabilistic framework that extends beyond traditional backpropagation-based neural networks.
This paper argues that while Large Language Models lack the autonomy required for genuine agency due to their absence of self-generated norms, goals, and embodied interaction, they function as transformative "linguistic automata" that, through a unique human-machine coupling, enable new "midtended" forms of intentional agency.
FALCON is a unified self-supervised pretraining framework for UAV action recognition that overcomes spatial imbalance in aerial footage by combining object-aware masked autoencoding with object-centric dual-horizon future reconstruction, achieving superior accuracy and faster inference without requiring additional preprocessing at test time.
UniHR is a unified hierarchical representation learning framework that overcomes the limitations of existing methods by unifying diverse knowledge graph types into triple-based representations and employing a hierarchical structure learning module to effectively model both intra-fact semantics and inter-fact relationships for link prediction.
The paper introduces SpecFuse (referred to as SpecEM in the abstract), a training-free ensemble framework that enhances large language model performance by enabling segment-level semantic collaboration through speculative decoding and dynamically adjusting model weights via an online feedback mechanism to prioritize stronger contributors.
This survey provides a comprehensive overview of the emerging ecosystem of large language models and tools that support researchers across the scientific lifecycle, covering key tasks from literature search and idea generation to content creation, experimentation, and evaluation, while addressing associated datasets, methods, limitations, and ethical concerns.
This paper proposes a methodology to fine-tune Large Language Models for generating fluent English-Spanish code-switched text by leveraging back-translated parallel corpora, demonstrating that while traditional metrics fail to correlate with human preferences, LLM-based evaluation aligns well with human judgment and the approach significantly advances CS text generation capabilities.
This paper introduces Generative Predictive Control, a supervised learning framework that leverages flow matching and sampling-based predictive control to enable high-frequency, dynamic robotic tasks by eliminating the need for difficult-to-obtain expert demonstrations.
The paper introduces FragFM, a hierarchical framework utilizing fragment-level discrete flow matching and a stochastic fragment bag strategy to achieve efficient, scalable, and property-controllable molecular generation, validated through a new Natural Product Generation (NPGen) benchmark where it outperforms existing atom-based methods.
This paper introduces SysDPO, a framework that aligns complex, multi-component Compound AI Systems with human preferences by modeling them as Directed Acyclic Graphs and extending Direct Preference Optimization to overcome the challenges of non-differentiable interactions and the difficulty of translating system-level preferences to component levels.
This paper investigates the adversarial robustness of partitioned quantum classifiers by demonstrating that perturbations targeting circuit partitioning techniques, such as wire cutting or teleportation, are equivalent to implementing adversarial gates within intermediate layers, a relationship analyzed through both theoretical and experimental perspectives.
This paper provides a comprehensive survey of music generation research by categorizing systems across single-modal, cross-modal, and multi-modal perspectives, while examining key aspects such as representation, data alignment, datasets, evaluation methods, current challenges, and future directions.
FindAnything is an efficient, open-world mapping framework that integrates vision-language features into object-centric volumetric submaps to enable real-time, open-vocabulary semantic understanding of large-scale environments on resource-constrained robots.
This paper investigates the inherent forecasting capabilities of large language models (LLMs) by controlling for tokenizer bias through large-scale pre-training, revealing that while LLM backbones show some promise, they still struggle to consistently outperform models specifically trained on large-scale time series data.
This position paper argues that anthropomorphizing intermediate token generation as "reasoning traces" or "thoughts" is a dangerous misconception that obscures the true nature of language models, hinders their effective use, and leads to flawed research, urging the community to abandon such metaphors.
This paper adopts a survivor-centered approach to expose how a "malicious technical ecosystem" of accessible tools enables the creation of AI-generated non-consensual intimate images, while demonstrating that current governance frameworks, such as the NIST AI 100-4 report, fail to effectively regulate this landscape due to flawed underlying assumptions.
This survey provides a comprehensive overview of Federated Learning, detailing its architecture and lifecycle while addressing key challenges like data heterogeneity and privacy, exploring emerging trends and applications, and outlining future research directions for scalable and trustworthy collaborative intelligence.
This paper introduces HCT-QA, a comprehensive benchmark comprising thousands of real-world and synthetic human-centric tables with natural language question-answer pairs, designed to evaluate and improve the performance of Large Language Models and Vision Language Models in querying complex tabular data.
This paper introduces FourierSpecNet, a hybrid deep learning framework that integrates the Fourier spectral method to efficiently approximate the Boltzmann collision operator, achieving resolution-invariant learning, zero-shot super-resolution, and significant computational savings while maintaining accuracy across elastic and inelastic collision regimes.
The paper introduces Reasoning Reward Models (ReasRMs), specifically the RM-R1 family, which reformulate reward modeling as a reasoning task using a chain-of-rubrics mechanism and a two-stage training pipeline to achieve superior interpretability and performance compared to existing large-scale models.