9410 papers
This paper introduces Yuan3.0 Ultra, an open-source, trillion-parameter Mixture-of-Experts large language model that utilizes a novel Layer-Adaptive Expert Pruning algorithm to significantly improve pre-training efficiency and reduce model size while achieving state-of-the-art performance on enterprise-oriented benchmarks.
This paper introduces a new dataset derived from football highlight reels to evaluate foundation models' ability to identify contextually important video moments, revealing that current state-of-the-art models perform near chance levels due to their reliance on single dominant modalities and failure to effectively synthesize cross-modal information.
This paper proposes a scalable extension of CopulaGNN for link sign prediction that overcomes computational intractability by representing edge correlations via a Gramian of edge embeddings and reformulating conditional probabilities, thereby achieving linear convergence and competitive performance on signed graphs.
This paper introduces Mobility-Embedded POIs (ME-POIs), a framework that enhances general-purpose point-of-interest representations by integrating large-scale human mobility data with language model embeddings to capture both place identity and real-world usage functions, thereby outperforming existing text-only and mobility-only baselines across diverse map enrichment tasks.
PerfGuard is a performance-aware agent framework for visual content generation that enhances task planning and execution reliability by systematically modeling tool performance boundaries through Performance-Aware Selection Modeling, Adaptive Preference Update, and Capability-Aligned Planning Optimization.
This paper proposes a variational framework that interprets transformer layers as optimization iterations, enabling the design of a Nesterov-accelerated transformer architecture that outperforms standard baselines on language modeling tasks.
This paper proposes MoR, a federated alignment framework that replaces parameter exchange with preference-based learning using a Mixture-of-Rewards mechanism and GRPO to effectively align heterogeneous Vision-Language Models while preserving data privacy and accommodating diverse client constraints.
This paper introduces VIP, a Variance-Informed Predictive allocation strategy that dynamically optimizes rollout distribution across training prompts using Gaussian process-based variance estimation to minimize gradient variance and significantly improve sampling efficiency in online reinforcement learning with verifiable rewards.
This paper introduces the Exploratory and Focused Manipulation (EFM) problem to address visual occlusion in robot manipulation, proposing the EFM-10 benchmark and a Bimanual Active Perception (BAP) strategy that effectively leverages dual-arm coordination for active vision and force sensing.
This paper demonstrates that steering vectors in large language models are fundamentally non-identifiable, as numerous distinct interventions—including orthogonal perturbations—produce behaviorally indistinguishable results, thereby revealing inherent limits in interpreting these vectors as unique internal representations without additional structural constraints.
LatentChem introduces a latent reasoning interface that decouples chemical computation from textual generation, enabling models to perform multi-step reasoning in continuous latent space which spontaneously emerges as a more efficient and accurate alternative to explicit Chain-of-Thought, achieving a 59.88% win rate and 10.84 speedup over baselines.
This paper introduces Topology-Aware PINNs (TAPINN), a novel framework that employs supervised metric regularization and alternating optimization to effectively resolve spectral bias and mode collapse in multi-regime physics-informed neural networks, achieving superior convergence stability and accuracy compared to standard and hypernetwork-based baselines.
This paper empirically demonstrates that while Kolmogorov-Arnold Networks (KANs) can compete with MLPs on simple univariate residuals in hard-constrained recurrent physics-informed architectures, they suffer from severe hyperparameter fragility, instability in deeper configurations, and consistent failure on multiplicative terms, ultimately revealing limitations in their additive inductive bias for modeling state coupling in oscillatory systems.
This paper proposes an explainability-guided active learning framework that improves medical image analysis by strategically selecting samples based on both predictive uncertainty and attention misalignment with expert-defined regions, thereby achieving superior data efficiency and clinical interpretability compared to traditional methods.
Pailitao-VL is a unified multi-modal retrieval system that achieves state-of-the-art, real-time industrial search performance by replacing traditional contrastive embeddings with an absolute ID-recognition paradigm and evolving reranking into a compare-and-calibrate listwise policy, thereby overcoming granularity, noise, and latency challenges in large-scale production environments.
This paper introduces "Zombie Agents," a persistent black-box attack on self-evolving LLM agents that covertly implants payloads into long-term memory during benign sessions to survive across interactions and trigger unauthorized actions in future sessions, demonstrating that current per-session defenses are insufficient against such memory-based compromises.
SubQuad is an end-to-end pipeline that overcomes the computational and data imbalance bottlenecks in large-scale adaptive immune repertoire analysis by integrating near-subquadratic MinHash retrieval, GPU-accelerated affinity kernels, and fairness-constrained clustering to enable scalable, bias-aware discovery of clinically relevant clonotypes.
This paper proposes a three-stage curriculum learning framework that leverages structure-aware masking and Group Relative Policy Optimization (GRPO) to efficiently distill Chain-of-Thought reasoning into compact student models, achieving significant accuracy gains and output length reduction on GSM8K by progressively guiding the model from structural understanding to self-optimized brevity and targeted knowledge internalization.
This paper argues that Schema-Guided Dialogue and the Model Context Protocol converge into a unified paradigm for deterministic LLM-agent interaction, proposing five foundational schema design principles to address critical gaps in failure handling and tool relationships while enabling scalable AI governance.
This paper proposes Decoupled Promptable Sequential Recommendation (DPR), a model-agnostic framework that enables conventional sequential recommenders to dynamically steer retrieval using natural language prompts by modulating latent user representations through a specialized fusion module, Mixture-of-Experts architecture, and a three-stage training strategy, thereby achieving superior performance in intent-driven tasks without sacrificing collaborative filtering efficiency.