2385 papers
This paper investigates emotion as a latent factor influencing LLM attention and reasoning, introducing the AURA-QA dataset and an emotional regularization framework that demonstrably improves reading comprehension performance across both emotionally varying and standard benchmarks.
This paper formalizes a macro-financial stress test arguing that rapid AI adoption creates a distribution-and-contract mismatch where AI-driven abundance fails to generate sufficient demand because economic institutions remain anchored to human labor scarcity, triggering a self-reinforcing cycle of income displacement, declining monetary velocity, and intermediary collapse that poses disproportionate risks to private credit and mortgage markets.
The paper introduces PrivPRISM, an automated framework that uses language models to detect widespread discrepancies between Google Play's simplified data safety declarations and developers' full privacy policies, revealing that over half of popular apps contain non-compliant or misleading disclosures about their data practices.
This paper presents a scalable, reinforcement learning-driven simulation framework featuring a full-body musculoskeletal model that enables the quantitative co-optimization of robotic structural design and control policies by providing direct access to internal human biomechanical metrics for interactive robotics.
This paper introduces BD-FDG, a framework that leverages Bloom's Taxonomy and structured knowledge organization to generate a high-quality, cognitively layered dataset for fine-tuning LLMs, successfully adapting the Qwen3-8B model to the complex domain of Space Situational Awareness with significant performance gains while preserving general capabilities.
BridgeDiff is a diffusion-based framework that improves virtual try-off by bridging the gap between on-body observations and flat-garment synthesis through a Garment Condition Bridge Module for robust appearance inference and a Flat Structure Constraint Module for enhanced structural stability.
The paper introduces Social-R1, a reinforcement learning framework trained on the adversarial ToMBench-Hard benchmark that aligns model reasoning with human cognition through multi-dimensional, trajectory-level supervision, enabling a 4B parameter model to outperform larger counterparts in social intelligence tasks.
This study presents a comprehensive multi-model deep learning approach that integrates pre-trained and custom neural networks with advanced data augmentation and transfer learning techniques to enhance autonomous driving capabilities by effectively addressing traffic sign classification, vehicle and lane detection, and behavioral cloning across diverse datasets.
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.