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Daily Arxiv
Daily Arxiv
世界中で発行される人工知能関連の論文をまとめるページです。
このページはGoogle Geminiを活用して要約し、非営利で運営しています。
論文の著作権は著者および関連機関にあり、共有する際は出典を明記してください。
Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation
Invited Paper: Feature-to-Classifier Co-Design for Mixed-Signal Smart Flexible Wearables for Healthcare at the Extreme Edge
Robustness is Important: Limitations of LLMs for Data Fitting
CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics
BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens
CE-RS-SBCIT A Novel Channel Enhanced Hybrid CNN Transformer with Residual, Spatial, and Boundary-Aware Learning for Brain Tumor MRI Analysis
PlantVillageVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science
THEME: Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics
Trust but Verify! A Survey on Verification Design for Test-time Scaling
Quantized Neural Networks for Microcontrollers: A Comprehensive Review of Methods, Platforms, and Applications
Documenting Deployment with Fabric: A Repository of Real-World AI Governance
Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
Region-Level Context-Aware Multimodal Understanding
ETTRL: Balancing Exploration and Exploitation in LLM Test-Time Reinforcement Learning Via Entropy Mechanism
Mask & Match: Learning to Recognize Handwritten Math with Self-Supervised Attention
Adaptive Duration Model for Text Speech Alignment
SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs
Time-RA: Towards Time Series Reasoning for Anomaly with LLM Feedback
Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning
Single Domain Generalization for Multimodal Cross-Cancer Prognosis via Dirac Rebalancer and Distribution Entanglement
Interpretable Mnemonic Generation for Kanji Learning via Expectation-Maximization
Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspective
BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models
Scientifically-Interpretable Reasoning Network (ScIReN): Discovering Hidden Relationships in the Carbon Cycle and Beyond
A Hybrid Artificial Intelligence Method for Estimating Flicker in Power Systems
Beyond Frequency: The Role of Redundancy in Large Language Model Memorization
TrueGL: A Truthful, Reliable, and Unified Engine for Grounded Learning in Full-Stack Search
Unified Path Planner with Adaptive Safety and Optimality
FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Models
WebInject: Prompt Injection Attack to Web Agents
Towards Embodiment Scaling Laws in Robot Locomotion
SPIN-ODE: Stiff Physics-Informed Neural ODE for Chemical Reaction Rate Estimation
DDaTR: Dynamic Difference-aware Temporal Residual Network for Longitudinal Radiology Report Generation
Latent Adaptive Planner for Dynamic Manipulation
MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness
SAGA: A Security Architecture for Governing AI Agentic Systems
Towards Understanding Camera Motions in Any Video
Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction
DeepTrans: Deep Reasoning Translation via Reinforcement Learning
A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Disease Detection from Retinal Fundus Images
Decentralized Domain Generalization with Style Sharing: Formal Model and Convergence Analysis
FROG: Fair Removal on Graphs
DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
LLM Test Generation via Iterative Hybrid Program Analysis
Toxicity Begets Toxicity: Unraveling Conversational Chains in Political Podcasts
Retrieval-Augmented Machine Translation with Unstructured Knowledge
ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning
RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis
Categorical Data Clustering via Value Order Estimated Distance Metric Learning
Guiding a diffusion model using sliding windows
A Collaborative Content Moderation Framework for Toxicity Detection based on Conformalized Estimates of Annotation Disagreement
Mamba State-Space Models Are Lyapunov-Stable Learners
Alice's Adventures in a Differentiable Wonderland - Volume I, A Tour of the Land
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty
Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation
What Breaks Knowledge Graph based RAG? Empirical Insights into Reasoning under Incomplete Knowledge
QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges
AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture
Compression versus Accuracy: A Hierarchy of Lifted Models
TrustGeoGen: Formal-Verified Data Engine for Trustworthy Multi-modal Geometric Problem Solving
Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness
Transforming Wearable Data into Personal Health Insights using Large Language Model Agents
Policy Expansion for Bridging Offline-to-Online Reinforcement Learning
The Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning
DynaMark: A Reinforcement Learning Framework for Dynamic Watermarking in Industrial Machine Tool Controllers
TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank
MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction
Going over Fine Web with a Fine-Tooth Comb: Technical Report of Indexing Fine Web for Problematic Content Search and Retrieval
PiCSAR: Probabilistic Confidence Selection And Ranking
Benchmarking GPT-5 in Radiation Oncology: Measurable Gains, but Persistent Need for Expert Oversight
Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering
Reasoning-Intensive Regression
Neural Network Acceleration on MPSoC board: Integrating SLAC's SNL, Rogue Software and Auto-SNL
Developer Insights into Designing AI-Based Computer Perception Tools
CAD2DMD-SET: Synthetic Generation Tool of Digital Measurement Device CAD Model Datasets for fine-tuning Large Vision-Language Models
OptMark: Robust Multi-bit Diffusion Watermarking via Inference Time Optimization
Entropy-Based Non-Invasive Reliability Monitoring of Convolutional Neural Networks
Why Stop at Words? Unveiling the Bigger Picture スルー Line-Level OCR
Harnessing IoT and Generative AI for Weather-Adaptive Learning in Climate Resilience Education
QZhou-Embedding Technical Report
Physics-Informed Spectral Modeling for Hyperspectral Imaging
Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning
A Survey on Current Trends and Recent Advances in Text Anonymization
NSPDI-SNN: An efficient lightweight SNN based on nonlinear synaptic pruning and dendritic integration
Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
EZ-Sort: Efficient Pairwise Comparison via Zero-Shot CLIP-Based Pre-Ordering and Human-in-the-Loop Sorting
What Data is Really Necessary? A Feasibility Study of Inference Data Minimization for Recommender Systems
Complete Gaussian Splats from a Single Image with Denoising Diffusion Models
On the Hardness of Learning GNN-based SAT Solvers: The Role of Graph Ricci Curvature
ELV-Halluc: Benchmarking Semantic Aggregation Hallucinations in Long Video Understanding
Priors Matter: Addressing Misspecification in Bayesian Deep Q-Learning
HSFN: Hierarchical Selection for Fake News Detection building Heterogeneous Ensemble
Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards
Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration
Diffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction
MedShift: Implicit Conditional Transport for X-Ray Domain Adaptation
The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management
Med-RewardBench: Benchmarking Reward Models and Judges for Medical Multimodal Large Language Models
Benchmarking the State of Networks with a Low-Cost Method Based on Reservoir Computing
DRASP: A Dual-Resolution Attentive Statistics Pooling Framework for Automatic MOS Prediction
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PiCSAR: Probabilistic Confidence Selection And Ranking
Created by
Haebom
作者
Joshua Ong Jun Leang, Zheng Zhao, Aryo Pradipta Gema, Sohee Yang, Wai-Chung Kwan, Xuanli He, Wenda Li, Pasquale Minervini, Eleonora Giunchiglia, Shay B. Cohen
概要
本論文では、大規模言語モデル（LLM）と大規模推論モデル（LRM）の精度を向上させるBest-of-nサンプリング手法を改善するProbabilistic Confidence Selection And Ranking（PiCSAR）方法を提案します。 PiCSARは、正解へのアクセスなしで正確な推論プロセスを識別できるスコアリング関数を設計する問題を解決するために、推論プロセスと最終回答の組み合わせ対数尤度を使用して各候補製品を採点する簡単で訓練を必要としない方法です。この結合対数尤度は、推論信頼性と回答信頼性に自然に分解されます。さまざまなベンチマークで従来の方法より優れた性能を示し（MATH500で+10.18、AIME2025で+9.81向上）、20の比較のうち16から少なくとも2倍少ないサンプルでも優れた性能を達成します。分析の結果、正確な推論プロセスははるかに高い推論と回答の信頼性を示し、PiCSARの効果を支持します。
Takeaways、Limitations
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Takeaways：
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Best-of-nサンプリングの効率を大幅に改善する新しい採点法PiCSAR提案
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正解なしで正確な推論プロセスを効果的に識別可能。
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さまざまなベンチマークにおける従来の方法と比較して優れた性能と効率性を証明
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推論信頼性と回答信頼性分析によるPiCSARの有効性を実証
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Limitations：
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提示されたベンチマーク以外の他の種類の問題またはモデルの一般化性能は、さらなる研究が必要です。
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PiCSARの信頼度計算方式が特定の問題タイプに偏る可能性があります。
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複雑な推論プロセスを伴う問題に対するPiCSARの性能限界の確認が必要
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