2098 papers
The paper introduces Logos, a compact molecular reasoning model that integrates multi-step logical reasoning with strict chemical consistency through a staged training strategy, achieving high structural accuracy and interpretability while outperforming larger language models in rational molecular design.
This paper introduces DendroNN, a novel dendrocentric neural network that leverages non-differentiable sequence detection and a rewiring phase to efficiently classify event-based spatiotemporal data, achieving competitive accuracy with up to 4x higher energy efficiency than state-of-the-art neuromorphic hardware through a dedicated asynchronous digital architecture.
The paper proposes DenoiseSplat, a feed-forward 3D Gaussian splatting method that achieves robust novel-view synthesis from noisy multi-view images by training end-to-end on a newly constructed noisy-clean benchmark without requiring 3D ground truth.
This paper demonstrates that the design of confidence scales significantly impacts LLM metacognition, revealing that standard 0–100 formats induce heavy discretization while 0–20 scales consistently improve metacognitive efficiency.
This paper challenges the Linear Representation Hypothesis by demonstrating that LLM activation spaces exhibit significant geometric distortion, leading to the proposal of "Curveball steering," a nonlinear intervention method using polynomial kernel PCA that outperforms traditional linear approaches by better respecting the intrinsic geometry of the model's feature space.
The paper proposes CLoE, a consistency-driven framework that enhances missing-modality medical image segmentation by enforcing decision-level agreement among modality experts on both global and clinically critical foreground regions, thereby improving robustness and generalization compared to state-of-the-art methods.
This paper introduces SpaceSense-Bench, a large-scale, multi-modal benchmark generated via high-fidelity Unreal Engine 5 simulations that provides 136 diverse satellite models with synchronized RGB, depth, and LiDAR data alongside dense semantic and pose annotations to address the scarcity of real-world space data and demonstrate the critical importance of dataset scale and diversity for advancing spacecraft perception and pose estimation.
This paper proposes and validates an emotion-context-aware VR interaction pipeline that integrates real-time prosody-derived emotional states into LLM-based agent dialogue, significantly improving user engagement, naturalness, and rapport compared to traditional text-only approaches.
This paper introduces TimberAgent, a Gram-guided retrieval system using Texture Resonance Retrieval (TRR) to bridge the semantic gap between user intent and low-level audio effect parameters, demonstrating superior performance in predicting editable plugin configurations through rigorous benchmarking and perceptual evaluation.
This paper introduces the Strategic Tactical Agent Reasoning (STAR) benchmark, a multi-agent framework for evaluating LLMs in zero-sum environments, which reveals a critical trade-off where reasoning-intensive models excel in turn-based settings but often underperform in real-time scenarios due to latency, highlighting the need to balance strategic depth with rapid execution.
TaSR-RAG is a taxonomy-guided framework that enhances Retrieval-Augmented Generation for multi-hop reasoning by decomposing complex queries into structured triple sub-queries and performing step-wise evidence selection through hybrid matching, thereby achieving superior accuracy and clearer reasoning traces without relying on costly graph construction.
This paper introduces Robust Regularized Policy Iteration (RRPI), a novel offline reinforcement learning framework that unifies policy-induced extrapolation and transition uncertainty by formulating robust policy optimization with a tractable KL-regularized surrogate, offering theoretical convergence guarantees and demonstrating superior performance and robustness on D4RL benchmarks.
This paper introduces TA-GGAD, a testing-time adaptive graph foundation model that addresses the cross-domain generalization challenge in anomaly detection by identifying and modeling the "Anomaly Disassortativity" issue, thereby achieving state-of-the-art performance across diverse real-world graphs with a single training phase.
This paper introduces a differentially private, zero-order optimization framework that extends dataset condensation to non-differentiable clinical models, enabling the creation of compact, privacy-preserving synthetic datasets that facilitate the democratization of clinical data sharing without compromising model utility.
This paper proposes M3GCLR, a game-theoretic contrastive learning framework that addresses limitations in existing skeleton-based action recognition methods by establishing an Infinite Skeleton-data Game model with a mini-max optimization strategy and dual-loss equilibrium optimizer to effectively handle view discrepancies, adversarial mechanisms, and augmentation perturbations, achieving state-of-the-art performance on multiple benchmarks.
The paper proposes MIL-PF, a scalable framework that combines frozen foundation model encoders with a lightweight attention-based Multiple Instance Learning head to achieve state-of-the-art mammography classification while significantly reducing computational costs and training complexity.
SPAARS is a curriculum learning framework for offline-to-online reinforcement learning that safely improves policies by initially exploring a low-dimensional latent space to ensure sample efficiency and stability, then seamlessly transitioning to raw action space to bypass decoder-induced performance ceilings, thereby achieving superior results over state-of-the-art baselines on both robotic manipulation and locomotion tasks.
This paper introduces the Pulse-Train-Resonator (PTR), a differentiable synthesis model that improves engine sound generation by directly modeling sequential exhaust pressure pulses and physical resonances rather than approximating spectral characteristics, achieving superior reconstruction accuracy and interpretability across diverse engine types.
This paper presents the ICDAR 2025 competition on end-to-end document image machine translation, detailing its dual-track structure for small and large models, participation statistics, and findings that highlight large-model approaches as a promising paradigm for handling complex document layouts.
This paper introduces the Fully Convolutional Diffusion Model (FCDM), a ConvNeXt-based architecture that achieves competitive generative performance with significantly fewer computational resources and training steps than Transformer-based counterparts, demonstrating that modern convolutional designs remain a highly efficient alternative for scaling diffusion models.