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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。
CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics
Ada-TransGNN: An Air Quality Prediction Model Based On Adaptive Graph Convolutional Networks
Unlearning as Ablation: Toward a Falsifiable Benchmark for Generative Scientific Discovery
Consistent Opponent Modeling of Static Opponents in Imperfect-Information Games
Finding Outliers in a Haystack: Anomaly Detection for Large Pointcloud Scenes
Agentic AI for Software: thoughts from Software Engineering community
Mind the (Language) Gap: Towards Probing Numerical and Cross-Lingual Limits of LVLMs
Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning
Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling
A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems
Generative Artificial Intelligence and Agents in Research and Teaching
CALR: Corrective Adaptive Low-Rank Decomposition for Efficient Large Language Model Layer Compression
Comparative Analysis of UAV Path Planning Algorithms for Efficient Navigation in Urban 3D Environments
Retrieval Enhanced Feedback via In-context Neural Error-book
From Confidence to Collapse in LLM Factual Robustness
On Task Vectors and Gradients
Learning in Repeated Multi-Objective Stackelberg Games with Payoff Manipulation
NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
DLLMQuant: Quantizing Diffusion-based Large Language Models
LLM-Enhanced Linear Autoencoders for Recommendation
Leveraging GNN to Enhance MEF Method in Predicting ENSO
Uncertainty-Guided Face Matting for Occlusion-Aware Face Transformation
New Kid in the Classroom: Exploring Student Perceptions of AI Coding Assistants
Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems
SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs
Apple Intelligence Foundation Language Models: Tech Report 2025
SE-VLN: A Self-Evolving Vision-Language Navigation Framework Based on Multimodal Large Language Models
Demographic-aware fine-grained classification of pediatric wrist fractures
Krul: Efficient State Restoration for Multi-turn Conversations with Dynamic Cross-layer KV Sharing
Solar Altitude Guided Scene Illumination
An Agentic System for Rare Disease Diagnosis with Traceable Reasoning
Spectra-to-Structure and Structure-to-Spectra Inference Across the Periodic Table
UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation
Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models
EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion
RePPL: Recalibrating Perplexity by Uncertainty in Semantic Propagation and Language Generation for Explainable QA Hallucination Detection
Revisiting SSL for sound event detection: complementary fusion and adaptive post-processing
Concept-Guided Interpretability via Neural Chunking
Unveiling the Landscape of LLM Deployment in the Wild: An Empirical Study
An Ontology-Driven Graph RAG for Legal Norms: A Hierarchical, Temporal, and Deterministic Approach
Prefill-level Jailbreak: A Black-Box Risk Analysis of Large Language Models
Video CLIP Model for Multi-View Echocardiography Interpretation
A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Disease Detection from Retinal Fundus Images
M$^2$IV: Towards Efficient and Fine-grained Multimodal In-Context Learning via Representation Engineering
Noise-based reward-modulated learning
Faster Parameter-Efficient Tuning with Token Redundancy Reduction
UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials
Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems
Large Language Models Badly Generalize across Option Length, Problem Types, and Irrelevant Noun Replacements
TableTalk: Scaffolding Spreadsheet Development with a Language Agent
StagFormer: Time Staggering Transformer Decoding for RunningLayers In Parallel
Provably-Safe Neural Network Training Using Hybrid Zonotope Reachability Analysis
Generative Artificial Intelligence-Supported Pentesting: A Comparison between Claude Opus, GPT-4, and Copilot
Safe Multiagent Coordination via Entropic Exploration
TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use
Cultural Dimensions of AI Perception: Charting Expectations, Risks, Benefits, Tradeoffs, and Value in Germany and China
CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers
Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI
Hierarchical Object-Oriented POMDP Planning for Object Rearrangement
From Intents to Conversations: Generating Intent-Driven Dialogues with Contrastive Learning for Multi-Turn Classification
Secure Reinforcement Learning via Shuffle Privacy Model
Overcoming label shift with target-aware federated learning
Benchmarking XAI Explanations with Human-Aligned Evaluations
HonestCyberEval: An AI Cyber Risk Benchmark for Automated Software Exploitation
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning
GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configuration
ChatGPT Doesn't Trust Chargers Fans: Guardrail Sensitivity in Context
Ego-Foresight: Self-supervised Learning of Agent-Aware Representations for Improved RL
Exploring the Robustness of Language Models for Tabular Question Answering via Attention Analysis
Learning county from pixels: corn yield prediction with attention-weighted multiple instance learning
Memory augment is All You Need for image restoration
Rethinking Distribution Shifts: Empirical Analysis and Inductive Modeling for Tabular Data
DiffBlender: Composable and Versatile Multimodal Text-to-Image Diffusion Models
Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing
ST-Raptor: LLM-Powered Semi-Structured Table Question Answering
Route-and-Execute: Auditable Model-Card Matching and Specialty-Level Deployment
LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence
Response and Prompt Evaluation to Prevent Parasocial Relationships with Chatbots
Profile-Aware Maneuvering: A Dynamic Multi-Agent System for Robust GAIA Problem Solving by AWorld
Multi-Agent LLMs as Ethics Advocates for AI-Based Systems
Feature-Guided Neighbor Selection for Non-Expert Evaluation of Model Predictions
Architecting Clinical Collaboration: Multi-Agent Reasoning Systems for Multimodal Medical VQA
MRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation
Jigsaw-Puzzles: From Seeing to Understanding to Reasoning in Vision-Language Models
The Influence of Human-inspired Agentic Sophistication in LLM-driven Strategic Reasoners
YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models
Consensus in Motion: A Case of Dynamic Rationality of Sequential Learning in Probability Aggregation
Can Large Language Models Act as Ensembler for Multi-GNNs?
Pessimistic Iterative Planning with RNNs for Robust POMDPs
Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
Integrating Large Language Model for Improved Causal Discovery
A Survey on Causal Discovery: Theory and Practice
Generative Interfaces for Language Models
Interpolating Speaker Identities in Embedding Space for Data Expansion
VibeVoice Technical Report
LSD-3D: Large-Scale 3D Driving Scene Generation with Geometry Grounding
Understanding Tool-Integrated Reasoning
Emotions as Ambiguity-aware Ordinal Representations
Real-Time Model Checking for Closed-Loop Robot Reactive Planning
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Concept-Guided Interpretability via Neural Chunking
Created by
Haebom
作者
Shuchen Wu, Stephan Alaniz, Shyamgopal Karthik, Peter Dayan, Eric Schulz, Zeynep Akata
概要
本論文は、ニューラルネットワークの内部動作を理解することの難しさを「ブラックボックス」という観点から逸脱し、ニューラルネットワークの活動パターンが訓練データの規則性を反映するという「反映仮説（Reflection Hypothesis）」を提示します。単純循環ニューラルネットワーク（RNN）と大規模言語モデル（LLM）でこの現象の証拠を提示し、認知的「チャンキング（chunking）」概念を活用して高次元神経集団のダイナミクスを解析可能な単位に分割する3つの方法（DSC、PA、UCD）を提案します。これらの方法は、ラベルの有無と神経データの次元に基づいて相補的に機能し、モデル構造に関係なく概念を暗号化する単位（単語、抽象概念、構造スキーマなど）を抽出します。抽出されたチャンクがニューラルネットワークの挙動に因果的役割を果たすことを示し、ブラックボックスと見なされる複雑な学習システムの理解を向上させる新しい解釈可能性アプローチを提示します。
Takeaways、Limitations
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Takeaways：
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ニューラルネットワークの内部挙動に対する新しい解釈可能性アプローチの提示：「反映仮説」とチャンキングベースの解釈方法の提案
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さまざまなモデル（RNN、LLM）と概念（具体的、抽象的、構造的）に適用可能な一般的な方法論を提示します。
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抽出されたチャンクがニューラルネットワークの行動に因果的に影響することを証明。
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認知科学的原理と自然言語データ構造を利用した新しい解釈可能性研究方向の提示
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Limitations：
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提案された方法の一般化性能とさまざまなデータセットへの適用性に関する追加の研究が必要です。
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「反映仮説」の普遍性と限界の詳細な検討が必要
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チャンキングの大きさと境界決定のための明確な基準を設ける必要性
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高次元データ処理の計算複雑さの問題解決策が必要です。
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