2010 papers
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 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.
This paper introduces a unified framework that models quantization and sparsification as additive noise to derive a principled, noise-corrective gradient path, enabling the stable training of neural networks at arbitrary low precisions and sparsity levels without relying on heuristic estimators like the Straight-Through Estimator.
This paper introduces Sparse Variational Student-t Processes (SVTP), a scalable framework that extends sparse inducing point methods to Student-t processes via novel inference algorithms and natural gradient optimization, achieving superior robustness to outliers and heavy-tailed data with significantly faster convergence and lower prediction error compared to sparse Gaussian processes on large datasets.
This paper proposes CoHet, a novel algorithm that leverages Graph Neural Network-driven intrinsic rewards to enable effective decentralized learning and cooperation among heterogeneous multi-agent systems despite challenges like partial observability and reward sparsity, demonstrating superior performance over state-of-the-art methods in standard benchmarks.
The paper proposes PnLCalib, an optimization-based calibration pipeline for sports field registration that leverages a 3D soccer field model, keypoints, and a novel line-based refinement module to achieve superior accuracy and robustness in diverse broadcast scenarios compared to traditional search-based methods.
The paper introduces FinTexTS, a large-scale financial text-paired time-series dataset constructed via a novel semantic-based and multi-level pairing framework that overcomes the limitations of simple keyword matching by leveraging LLMs to align news articles with stock prices across macro, sector, related company, and target-company levels, thereby significantly improving stock price forecasting performance.
This paper argues that user trust in chatbots is often driven by interactional design choices that exploit cognitive biases rather than genuine trustworthiness, urging a reframing of chatbots as skilled salespeople and a distinction between psychological trust formation and normative trustworthiness to better calibrate user expectations.
The paper proposes UAT-LITE, an inference-time framework that injects Monte Carlo dropout into the self-attention mechanisms of pretrained transformers to estimate token-level epistemic uncertainty and modulate attention, thereby significantly improving model calibration and selective prediction performance without requiring additional training or weight modifications.
This paper introduces EigenData, a unified framework that combines a self-evolving multi-agent system for synthesizing verifiable tool-use dialogues with a verifier-based reinforcement learning recipe, enabling scalable post-training of interactive agents that achieve state-of-the-art performance on complex multi-turn benchmarks without relying on expensive human annotation.
This paper addresses the challenges of health management for spacecraft power systems in the emerging mega-constellation era by proposing the "Aligning Underlying Capabilities" principle and introducing SpaceHMchat, an open-source Human-AI collaboration framework validated on a realistic hardware platform and a new large-scale dataset to achieve high-precision, interpretable, and efficient all-in-loop health management.
This paper introduces SAGE, a novel Reinforcement Learning framework that enhances LLM-based agents' self-improvement capabilities by utilizing a skill library with sequential rollouts and skill-integrated rewards, achieving significantly higher goal completion rates and greater efficiency than existing methods on the AppWorld benchmark.
This paper demonstrates that a single-epoch, domain-adapted fine-tuning of a 350M-parameter Small Language Model (OPT-350M) can significantly outperform larger models and existing baselines in tool-calling tasks, achieving a 77.55% pass rate on ToolBench and proving that targeted training can make generative AI more cost-effective and scalable for enterprise use.
This paper proposes a communication-constrained multi-agent reinforcement learning framework that utilizes a generalized model and dual mutual information estimator to distinguish between lossy and lossless messages, thereby quantifying their impact on global rewards to enhance cooperative policy learning in complex, dynamic environments.
AlphaApollo is an agentic reasoning system that enhances foundation models' performance on complex, long-horizon tasks by orchestrating multi-turn agentic reasoning, turn-level reinforcement learning for tool-use optimization, and a propose-judge-update evolution loop with verification.
VistaWise is a cost-effective agent framework for Minecraft that leverages a cross-modal knowledge graph and a finetuned object detection model to achieve state-of-the-art performance in open-world tasks while drastically reducing the need for large-scale domain-specific training data.
This paper introduces CMASE, a novel framework that integrates generative agent-based modeling with virtual ethnography to transform researchers into embedded participants, enabling real-time social intervention, causal reconstruction of social phenomena, and predictive modeling with high empirical accuracy.
MMGraphRAG addresses the limitations of text-centric GraphRAG by introducing a novel framework that integrates visual scene graphs with text knowledge graphs via spectral clustering-based entity linking (SpecLink) and a new CMEL dataset, achieving state-of-the-art performance in multimodal reasoning and reducing hallucinations.
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 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.