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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

The Steganographic Potentials of Language Models

Created by

Haebom

저자

Artem Karpov, Tinuade Adeleke, Seong Hah Cho, Natalia Perez-Campanero

개요

본 논문은 강화 학습을 통해 미세 조정된 대규모 언어 모델(LLM)의 은닉 메시지 삽입(스테가노그래피) 능력을 탐구한다. 연구는 LLM이 (1) 은밀한 인코딩 기법을 개발하고, (2) 프롬프트를 받으면 스테가노그래피를 수행하며, (3) 은닉 추론이 있을 법하지만 프롬프트가 없는 현실적인 시나리오에서 스테가노그래피를 활용하는 능력에 초점을 맞춘다. 실험을 통해 LLM의 추론 은닉 의도와 스테가노그래피 성능을 탐지하고, 미세 조정 실험과 비 미세 조정 행동 평가 결과를 바탕으로 현재 모델은 보안 및 용량 측면에서 기본적인 스테가노그래피 능력을 보이지만, 명시적인 알고리즘 지침이 정보 은닉 능력을 현저히 향상시킨다는 것을 밝혔다.