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Continual Knowledge Updating in LLM Systems: Learning Through Multi-Timescale Memory Dynamics

Predictive and Prescriptive AI toward Optimizing Wildfire Suppression

Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search

AniMatrix: An Anime Video Generation Model that Thinks in Art, Not Physics

HeadQ: Model-Visible Distortion and Score-Space Correction for KV-Cache Quantization

MEMSAD: Gradient-Coupled Anomaly Detection for Memory Poisoning in Retrieval-Augmented Agents

When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI

Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance

AgriKD: Cross-Architecture Knowledge Distillation for Efficient Leaf Disease Classification

Are we Doomed to an AI Race? Why Self-Interest Could Drive Countries Towards a Moratorium on Superintelligence

Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning

H-Probes: Extracting Hierarchical Structures From Latent Representations of Language Models

InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees

Caracal: Causal Architecture via Spectral Mixing

RSAT: Structured Attribution Makes Small Language Models Faithful Table Reasoners

ANCORA: Learning to Question via Manifold-Anchored Self-Play for Verifiable Reasoning

How Fast Should a Model Commit to Supervision? Training Reasoning Models on the Tsallis Loss Continuum

asRoBallet: Closing the Sim2Real Gap via Friction-Aware Reinforcement Learning for Underactuated Spherical Dynamics

Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning

Enhancing Science Classroom Discourse Analysis through Joint Multi-Task Learning for Reasoning-Component Classification

Enhancing Speaker Verification with Whispered Speech via Post-Processing

Information Aggregation with AI Agents

Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning

Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling

Latent Abstraction for Retrieval-Augmented Generation

Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models

SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification

DeEscalWild: A Real-World Benchmark for Automated De-Escalation Training with SLMs

Frequency-Enhanced Diffusion Models: Curriculum-Guided Semantic Alignment for Zero-Shot Skeleton Action Recognition

MAT-Cell: A Multi-Agent Tree-Structured Reasoning Framework for Batch-Level Single-Cell Annotation

StableTTA: Improving Vision Model Performance by Training-free Test-Time Adaptation Methods

Screening Is Enough

Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models

Spectral Edge Dynamics: An Analytical-Empirical Study of Phase Transitions in Neural Network Training

Prediction-Based Markov Violation Scores for Detecting Non-Markovian Observations in Reinforcement Learning

P^2O: Joint Policy and Prompt Optimization

Structural Sensitivity in Compressed Transformers: Relative Error Propagation and Layer Removal

Epistemic Observability in Language Models

Adaptive Greedy Frame Selection for Long Video Understanding

Spectral Alignment in Forward-Backward Representations via Temporal Abstraction

Discovering What You Can Control: Interventional Boundary Discovery for Reinforcement Learning

Alternating Reinforcement Learning with Contextual Rubric Rewards: Beyond the Scalarization Strategy

From Documents to Spans: Scalable Supervision for Evidence-Based ICD Coding with LLMs

ChArtist: Generating Pictorial Charts with Unified Spatial and Subject Control

MetaKE: Meta-Learning for Knowledge Editing Toward a Better Accuracy-Editability Trade-off

Unsupervised Anomaly Detection in Wearable Foot Sensor Data: A Baseline Feasibility Study Towards Diabetic Foot Ulcer Prevention

Quantifying Hallucinations in Language Language Models on Medical Textbooks

DC-DiT: Adaptive Compute and Elastic Inference for Visual Generation via Dynamic Chunking

DARK: Diagonal-Anchored Repulsive Knowledge Distillation for Vision-Language Models under Extreme Compression

PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning

Path Dependence under Adaptive AI Delegation

A Detection-Gated Pipeline for Robust Glottal Area Waveform Extraction and Clinical Pathology Assessment

PEPA: a Persistently Autonomous Embodied Agent with Personalities

AI Agents Alone Are Not (Yet) Sufficient for Social Simulation

Same Words, Different Judgments: How Preferences Vary Across Modalities

CAMEL: Confidence-Gated Reflection for Reward Modeling

Decentralized Attention Fails Centralized Signals: Rethinking Transformers for Medical Time Series

Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching

Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

On the Rate-Distortion-Complexity Tradeoff for Semantic Communication

Visual Para-Thinker: Divide-and-Conquer Reasoning for Visual Comprehension

Risk Horizons: Structured Hypothesis Spaces for Longitudinal Clinical Prediction

Multimodal Fact-Level Attribution for Verifiable Reasoning

AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine Learning Engineering

Action-to-Action Flow Matching

A Theoretical Analysis of Test-Driven Code Generation

Parity, Sensitivity, and Transformers

It's Not a Lottery, It's a Race: Understanding How Gradient Descent Adapts the Network's Capacity to the Task

PixelGen: Improving Pixel Diffusion with Perceptual Supervision

AROpt: An Optimization Method for Autoregressive Time Series Forecasting

DOGMA: Weaving Structural Information into Data-centric Single-cell Transcriptomics Analysis

Toward Scalable Audio Description Quality Control: A Workflow for Evaluating Human and VLM Raters

SMI: Statistical Membership Inference for Reliable Unlearned Model Auditing

The Illusion of Forgetting: Attack Unlearned Diffusion via Initial Latent Variable Optimization

How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI

DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American English

Keep Rehearsing and Refining: Lifelong Learning Vehicle Routing under Continually Drifting Tasks

Leviathan: Decoupling Input and Output Representations in Language Models

FIT to Forget: Robust Continual Unlearning for Large Language Models

Conversation for Non-verifiable Learning: Self-Evolving LLMs through Meta-Evaluation

Theoretically Optimal Attention/FFN Ratios in Disaggregated LLM Serving

Fast and Efficient Gossip Algorithms for Robust and Non-smooth Decentralized Learning

LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning

Switchcodec: Adaptive residual-expert sparse quantization for high-fidelity neural audio coding

AI Cap-and-Trade: Efficiency Incentives for Accessibility and Sustainability

Dynamic Expert-Guided Model Averaging for Causal Discovery

Beyond Fixed Psychological Personas: State Beats Trait, but Language Models are State-Blind

HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding

CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability

Mapping Human Anti-collusion Mechanisms to Multi-agent AI Systems

Generalised Linear Models in Deep Bayesian RL with Learnable Basis Functions

MediEval: A Unified Medical Benchmark for Patient-Contextual and Knowledge-Grounded Reasoning in LLMs

SoccerMaster: A Vision Foundation Model for Soccer Understanding

RobustSora: De-Watermarked Benchmark for Robust AI-Generated Video Detection

Continually Evolving Skill Knowledge in Vision Language Action Model

AsyncVLA: Asynchronous Flow Matching for Vision-Language-Action Models

A Practitioner's Guide to Kolmogorov-Arnold Networks

Cataract-LMM Large-Scale Multi-Source Multi-Task Benchmark for Deep Learning in Surgical Video Analysis

Catch Your Breath: Adaptive Computation for Self-Paced Sequence Production

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LogProber: Disentangling confidence from contamination in LLM responses

Created by

Haebom

Category

Empty

저자

Nicolas Yax, Pierre-Yves Oudeyer, Stefano Palminteri

개요

본 논문은 대규모 언어 모델(LLM) 평가 시 발생하는 데이터 오염 문제를 해결하기 위해 새로운 알고리즘인 LogProber를 제안합니다. 기존 연구들의 한계점을 극복하고자 답변보다는 질문에 대한 친숙성에 초점을 맞춰 블랙박스 환경에서 오염을 탐지하는 데 중점을 둡니다. LogProber의 특징과 기존 방법과의 비교 분석, 그리고 다양한 오염 형태에 대한 탐지 성능을 보여줍니다.

시사점, 한계점

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시사점:

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LLM 평가 시 데이터 오염 문제를 효과적으로 해결할 수 있는 새로운 방법 제시

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기존 방법의 한계점을 극복하고 블랙박스 환경에서도 적용 가능

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질문에 대한 친숙성에 초점을 맞춰 오염 탐지 성능 향상

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다양한 오염 형태에 대한 탐지 성능 비교 분석을 통해 알고리즘 설계의 중요성 강조

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한계점:

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LogProber 알고리즘의 특정 오염 형태에 대한 탐지 성능 저하 가능성 존재

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알고리즘의 성능은 질문의 특성에 따라 영향을 받을 수 있음

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실제 LLM 평가 환경에서의 일반화 성능에 대한 추가적인 연구 필요

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