8852 papers
This paper introduces the Cultural Reference Transformation (CRT) metric to evaluate how diffusion models navigate the tension between memorization and generalization in culturally iconic contexts, revealing that model behavior depends on distinct recognition and realization mechanisms influenced by factors like data frequency, textual uniqueness, and reference popularity.
This paper introduces Soft Q-based Diffusion Finetuning (SQDF), a novel KL-regularized reinforcement learning method that employs a reparameterized policy gradient of a training-free soft Q-function, enhanced by discount factors, consistency models, and off-policy replay buffers, to effectively align diffusion models with downstream objectives while mitigating reward over-optimization and preserving sample diversity.
This paper introduces XR-DT, an Extended Reality-enhanced Digital Twin framework that integrates a novel Human-Aware Model Predictive Path Integral (HA-MPPI) controller with an attention-based trajectory prediction model to enable safe, efficient, and interpretable motion planning for mobile robots operating alongside humans.
This paper proposes a novel training framework that leverages the -divergence family to explicitly filter incorrect answers and control the precision-diversity trade-off, thereby overcoming the diversity loss inherent in standard Reinforcement Learning and achieving state-of-the-art performance on the Lean theorem-proving benchmark.
This paper proposes a point cloud-based framework for event-driven human pose estimation that leverages spatiotemporal properties through novel temporal slicing and sequencing modules alongside an edge-enhanced representation, achieving improved accuracy and efficiency on the DHP19 dataset without converting event streams into dense frames.
The paper introduces A-3PO, a method that accelerates asynchronous LLM training by 1.8x by approximating the computationally expensive proximal policy in Decoupled PPO through simple interpolation, thereby eliminating the need for extra forward passes while maintaining comparable performance.
This paper proposes a novel global sensitivity analysis method based on Individual Conditional Expectation (ICE) curves that overcomes the limitations of traditional Partial Dependence Plots (PDPs) in capturing input interactions, offering a mathematically proven, more informative metric for explainable machine learning in engineering design.
This paper addresses the optimization challenges in hyperbolic deep reinforcement learning by identifying the destabilizing effects of large-norm embeddings and introducing Hyper++, a new agent that employs feature regularization, categorical value loss, and improved layer formulations to achieve stable, faster, and superior performance compared to existing Euclidean and hyperbolic baselines.
This paper identifies and systematically studies "Tools Orchestration Privacy Risk" (TOP-R), a novel vulnerability where autonomous agents inadvertently synthesize sensitive information from non-sensitive tool fragments, and addresses it by introducing the TOP-Bench benchmark, the H-Score metric, and effective mitigation strategies that significantly improve the safety-utility trade-off.
This paper demonstrates that Cross-Attention over Self-Attention (CASA) is a highly competitive and efficient alternative to token insertion for vision-language models, offering near-constant memory costs and low latency that make it particularly suitable for long multi-image conversations and real-time video applications.
CARE (Contrastive Anchored REflection) is a failure-centric post-training framework for multimodal reasoning that enhances Group-relative Reinforcement Learning with Verifiable Rewards (RLVR) by leveraging an anchored-contrastive objective and Reflection-Guided Resampling to transform erroneous rollouts into effective supervision signals, thereby significantly improving accuracy and training stability on visual-reasoning benchmarks.
This paper introduces LLMTM, a comprehensive benchmark for evaluating Large Language Models on temporal motif analysis in dynamic graphs, and proposes a cost-effective, structure-aware dispatcher that intelligently balances high accuracy and computational expense by routing queries between standard prompting and a specialized tool-augmented agent.
This paper introduces WBC (Window-Based Comparison), a novel membership inference attack that significantly outperforms existing global-averaging methods against fine-tuned Large Language Models by exploiting localized memorization signals through a sliding window approach with sign-based aggregation.
This paper introduces a framework for finetuning Large Language Models to generate age-appropriate, factually accurate educational content across six grade levels, which significantly improves grade-level alignment by 35.64 percentage points compared to standard prompting methods.
This paper proposes a novel end-to-end audio-visual speech recognition framework that integrates speech enhancement via a Conformer-based bottleneck fusion module to implicitly refine noisy audio features without explicit mask generation, thereby preserving semantic integrity and outperforming existing mask-based methods on the LRS3 benchmark under noisy conditions.
PepEDiff is a novel zero-shot generative framework that designs peptide binders directly from protein embeddings in a continuous latent space without relying on intermediate structure prediction, thereby achieving superior sequence diversity and performance on challenging targets like TIGIT compared to state-of-the-art methods.
SpatialMem is a memory-centric system that constructs a metric-aligned 3D scaffold from casual egocentric RGB videos to enable efficient, interpretable long-horizon language grounding, retrieval, and QA by linking open-vocabulary object nodes to spatial coordinates without requiring specialized sensors.
This paper introduces CELM, the first foundation model for clinical EEG-to-language generation that leverages a large-scale dataset of 9,922 reports and 11,000 hours of recordings to achieve significant improvements in summarizing long-term EEG data and generating comprehensive clinical reports.
This paper proposes Localized In-Context Learning (L-ICL), a technique that iteratively augments instructions with targeted corrections for specific failing steps, significantly improving the ability of large language models to generate valid plans in symbolic classical planning tasks compared to traditional methods.
This paper presents a collection of case studies demonstrating how researchers successfully collaborate with Google's Gemini models to solve open problems and generate new proofs in theoretical computer science and other fields, while extracting common techniques for effective human-AI partnership in scientific discovery.