7003 papers
The paper introduces DRUPI (Dataset Condensation using Privileged Information), a framework that enhances dataset condensation by synthesizing auxiliary privileged information, such as feature or attention labels, alongside reduced data to significantly improve model training performance across various benchmarks.
LayoutDreamer is a novel framework that leverages 3D Gaussian Splatting and physics-guided scene graphs to generate high-quality, physically plausible, and controllable text-to-3D compositional scenes, achieving state-of-the-art performance in multi-object generation.
This paper introduces Astromer 2, an enhanced foundational model for light curve analysis that leverages self-supervised pre-training on 1.5 million MACHO survey observations and weighted per-sample embeddings to significantly outperform its predecessor and prior models in classification tasks, particularly when trained with limited labeled data.
This paper addresses the sensitivity of semivalue-based data valuation to utility selection by introducing a "spatial signature" framework that embeds data points into a lower-dimensional space, enabling a practical methodology to quantify and ensure the robustness of valuation results against utility variations.
This paper presents MKE-Coder, a novel framework that improves automatic ICD coding for Chinese electronic medical records by integrating multi-axial disease knowledge with a clinical evidence verification module to address challenges in information extraction and code validity.
The paper introduces LLM-Advisor, a prompt-based framework that leverages large language models as non-decisive post-processing advisors to significantly improve the cost efficiency of path planning across diverse terrains without modifying underlying planners, while addressing hallucination risks and demonstrating superior performance over zero-shot LLM approaches.
The paper introduces HyConEx, a novel deep hypernetwork-based classifier for tabular data that uniquely integrates prediction and explanation by simultaneously generating class labels and local counterfactual examples to interpret model decisions.
This paper proposes a theoretically grounded approach using category theory to introduce an "explaining functor" that structurally preserves logical entailment, thereby ensuring the consistency and fidelity of logic-based explanations for opaque AI models and overcoming the limitations of current heuristic post-hoc methods.
GateLens is a reasoning-enhanced LLM agent that utilizes Relational Algebra as a formal intermediate representation to bridge the gap between natural language and executable code, enabling fast, transparent, and highly accurate analysis of complex tabular data in automotive software release analytics without requiring few-shot examples or complex agent orchestration.
This paper critiques the prevalent reliance on fixed-threshold metrics in machine learning evaluation by advocating for a consequentialist framework that prioritizes proper scoring rules like the Brier score, supported by a new decision-theoretic mapping, a practical Python package called `briertools`, and a clipped Brier score variant to bridge the gap between theoretical utility and current practices.
This paper introduces MCP Bridge, a lightweight, LLM-agnostic RESTful proxy that enables Model Context Protocol servers to run in resource-constrained environments with enhanced security, while also presenting a fine-tuned Qwen3 model that achieves state-of-the-art performance on the MCPToolBench++ benchmark through advanced reinforcement learning techniques.
This paper introduces Stepwise Guided Policy Optimization (SGPO), a framework that enhances Group Relative Policy Optimization (GRPO) by utilizing a step-wise judge model to provide learning signals from all-negative sample groups, thereby enabling large language models to learn from incorrect reasoning and improving performance across various reasoning benchmarks.
This paper introduces MathQ-Verify, a novel five-stage pipeline that rigorously filters ill-posed or under-specified mathematical questions through format validation, formalization, contradiction detection, and completeness checks, achieving state-of-the-art performance in curating reliable datasets for training large language models.
The paper introduces UltraEdit, a training-, subject-, and memory-free approach for lifelong language model editing that achieves unprecedented scalability and efficiency by computing parameter shifts in a single step, enabling 7B models to be edited on consumer GPUs with over 2 million updates while outperforming existing methods in speed, memory usage, and accuracy.
The paper introduces Saturn, a reinforcement learning framework that leverages Boolean Satisfiability (SAT) problems to overcome scalability, verifiability, and difficulty control limitations in training large language models, resulting in significant reasoning improvements across SAT, math, and programming benchmarks.
The paper introduces Daily-Omni, a comprehensive audio-visual benchmark with 1,197 questions designed to evaluate cross-modal temporal reasoning, revealing that current multimodal large language models still struggle with alignment-critical tasks despite strong unimodal performance.
This paper presents the first systematic review of integrating foundation models into mobile service robotics, analyzing how these technologies address core challenges in perception and control, enabling applications in domestic and healthcare settings while discussing ethical implications and outlining future directions for safe, scalable, and trustworthy deployment.
The paper proposes TSRating, a novel meta-learning framework that leverages LLMs to generate quality comparisons across diverse domains and trains an efficient TSRater model to accurately and adaptively evaluate time series data quality without requiring extensive hypergradient computations.
This paper proposes CORA, a cooperative game-theoretic credit assignment method that utilizes core allocation and coalition sampling to effectively distribute global advantages among agents in multi-agent reinforcement learning, thereby overcoming the limitations of uniform sharing and enhancing coordinated optimal behavior.
The paper introduces ChannelTokenFormer, a unified Transformer-based framework that simultaneously addresses the challenges of complex inter-channel dependencies, asynchronous sampling, and missing values to achieve robust real-world multivariate time series forecasting.