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Daily Arxiv
Daily Arxiv
전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
본 페이지는 Google Gemini를 활용해 요약 정리하며, 비영리로 운영 됩니다.
논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.
ACCeLLiuM: Supervised Fine-Tuning for Automated OpenACC Pragma Generation
AnchDrive: Bootstrapping Diffusion Policies with Hybrid Trajectory Anchors for End-to-End Driving
Diffusion-Augmented Contrastive Learning: A Noise-Robust Encoder for Biosignal Representations
FusedANN: Convexified Hybrid ANN via Attribute-Vector Fusion
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST
A Longitudinal Randomized Control Study of Companion Chatbot Use: Anthropomorphism and Its Mediating Role on Social Impacts
TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding
Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models
Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling
Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on Math Textbook
RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation
Distribution-Aligned Decoding for Efficient LLM Task Adaptation
DivLogicEval: A Framework for Benchmarking Logical Reasoning Evaluation in Large Language Models
Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey
Constructive Conflict-Driven Multi-Agent Reinforcement Learning for Strategic Diversity
Towards a Physics Foundation Model
Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews
Positional Encoding via Token-Aware Phase Attention
Chain or tree? Re-evaluating complex reasoning from the perspective of a matrix of thought
A Two-Stage Strategy for Mitosis Detection Using Improved YOLO11x Proposals and ConvNeXt Classification
JudgeAgent: Knowledge-wise and Dynamic LLM Evaluation with Agent-as-Interviewer
Draw-In-Mind: Rebalancing Designer-Painter Roles in Unified Multimodal Models Benefits Image Editing
Scalable Option Learning in High-Throughput Environments
"She was useful, but a bit too optimistic": Augmenting Design with Interactive Virtual Personas
In-Context Algorithm Emulation in Fixed-Weight Transformers
Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling
Sparse but Wrong: Incorrect L0 Leads to Incorrect Features in Sparse Autoencoders
Conflict-Aware Soft Prompting for Retrieval-Augmented Generation
ERIS: An Energy-Guided Feature Disentanglement Framework for Out-of-Distribution Time Series Classification
StreetReaderAI: Making Street View Accessible Using Context-Aware Multimodal AI
Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
Intuition emerges in Maximum Caliber models at criticality
GTPO and GRPO-S: Token and Sequence-Level Reward Shaping with Policy Entropy
Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models
Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning
SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy
DAMR: Efficient and Adaptive Context-Aware Knowledge Graph Question Answering with LLM-Guided MCTS
Generative Logic: A New Computer Architecture for Deterministic Reasoning and Knowledge Generation
Persona-Augmented Benchmarking: Evaluating LLMs Across Diverse Writing Styles
Hierarchical Graph Neural Network for Compressed Speech Steganalysis
R-Stitch: Dynamic Trajectory Stitching for Efficient Reasoning
The Invisible Leash: Why RLVR May or May Not Escape Its Origin
APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation
LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues
Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities
KV Cache Steering for Controlling Frozen LLMs
Lightweight MSA Design Advances Protein Folding From Evolutionary Embeddings
Learn Globally, Speak Locally: Bridging the Gaps in Multilingual Reasoning
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions
Investigating Redundancy in Multimodal Large Language Models with Multiple Vision Encoders
Neural-Network solver of ideal MHD equilibria
Why Reinforcement Fine-Tuning Enables MLLMs Preserve Prior Knowledge Better: A Data Perspective
Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs
On the Necessity of Output Distribution Reweighting for Effective Class Unlearning
TAMMs: Temporal-Aware Multimodal Model for Satellite Image Change Understanding and Forecasting
Latent Concept Disentanglement in Transformer-based Language Models
Personalized LLM Decoding via Contrasting Personal Preference
Exploiting Block Coordinate Descent for Cost-Effective LLM Model Training
Security Degradation in Iterative AI Code Generation -- A Systematic Analysis of the Paradox
Think With Videos For Agentic Long-Video Understanding
VidBridge-R1: Bridging QA and Captioning for RL-based Video Understanding Models with Intermediate Proxy Tasks
Position: Simulating Society Requires Simulating Thought
AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill Diversification
DriveAction: A Benchmark for Exploring Human-like Driving Decisions in VLA Models
Resisting Contextual Interference in RAG via Parametric-Knowledge Reinforcement
Dual Branch VideoMamba with Gated Class Token Fusion for Violence Detection
CapSpeech: Enabling Downstream Applications in Style-Captioned Text-to-Speech
Physics-Guided Motion Loss for Video Generation Model
Probing Neural Topology of Large Language Models
InfiMed: Low-Resource Medical MLLMs with Advancing Understanding and Reasoning
Mamba Integrated with Physics Principles Masters Long-term Chaotic System Forecasting
Model-Preserving Adaptive Rounding
DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation
SDPO: Importance-Sampled Direct Preference Optimization for Stable Diffusion Training
Spectral-inspired Operator Learning with Limited Data and Unknown Physics
BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases
Beyond the Proxy: Trajectory-Distilled Guidance for Offline GFlowNet Training
Prompting is not Enough: Exploring Knowledge Integration and Controllable Generation on Large Language Models
HD-PiSSA: High-Rank Distributed Orthogonal Adaptation
Can LLMs Alleviate Catastrophic Forgetting in Graph Continual Learning? A Systematic Study
FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models
From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning
BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation
Bottlenecked Transformers: Periodic KV Cache Consolidation for Generalised Reasoning
The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm
Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs
Learning Flexible Forward Trajectories for Masked Molecular Diffusion
Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems
Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation
UniErase: Towards Balanced and Precise Unlearning in Language Models
Octic Vision Transformers: Quicker ViTs Through Equivariance
Intentional Gesture: Deliver Your Intentions with Gestures for Speech
UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models
VocalAgent: Large Language Models for Vocal Health Diagnostics with Safety-Aware Evaluation
Learning Hierarchical Domain Models Through Environment-Grounded Interaction
Shadow-FT: Tuning Instruct Model via Training on Paired Base Model
Structured Relational Representations
Latent Veracity Inference for Identifying Errors in Stepwise Reasoning
Feature Hedging: Correlated Features Break Narrow Sparse Autoencoders
ZeroTuning: Unlocking the Initial Token's Power to Enhance Large Language Models Without Training
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From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning
Created by
Haebom
저자
Chen Shani, Liron Soffer, Dan Jurafsky, Yann LeCun, Ravid Shwartz-Ziv
개요
LLM이 압축과 의미 보존의 균형을 어떻게 맞추는지 인간과 비교하기 위해 정보 병목 현상 원리를 적용한 연구. 40개 이상의 LLM의 임베딩을 인간 범주화 벤치마크와 비교 분석하여 LLM과 인간의 차이점을 밝힘.
시사점, 한계점
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시사점:
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LLM은 인간 범주와 대체로 일치하지만, 인간 이해에 중요한 세밀한 의미 구분을 놓침.
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LLM은 "최적" 정보 이론적 효율성을 달성하는 공격적인 통계적 압축을 수행하는 반면, 인간은 맥락적 풍부함과 적응적 유연성을 우선시함.
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인코더 모델이 디코더 모델보다 인간 정렬에서 더 나은 성능을 보임.
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개념적 구조는 초기 급격한 형성과 구조적 재구성을 거쳐, 모델이 더 효율적인 인코딩을 발견함에 따라 의미 처리 과정이 심층 레이어에서 중간 네트워크 레이어로 이동하는 별개의 단계를 거쳐 개발됨.
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한계점:
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LLM은 압축을 최적화하고 인간은 적응적 유용성을 최적화하여 인공 지능과 생물학적 지능 간의 근본적인 차이점을 드러냄.
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