Natural Language Processing

McKinsey's AI Lie Explains What's Happening to Work

Everyone thinks McKinsey just built 25,000 AI experts. They didn't. They took a 35-year-old internal database, put a natural language int...

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Walmart CEO reportedly brags that company's in-app AI agent is making people spend 35% more money

[2602.15323] Unforgeable Watermarks for Language Models via Robust Signatures

The paper presents a novel watermarking scheme for language models that ensures unforgeability and recoverability, enhancing content prov...

[2602.15580] How Vision Becomes Language: A Layer-wise Information-Theoretic Analysis of Multimodal Reasoning

This paper analyzes how multimodal Transformers integrate visual and linguistic information, revealing a layer-wise evolution of predicti...

[2602.15277] Accelerating Large-Scale Dataset Distillation via Exploration-Exploitation Optimization

This paper presents Exploration-Exploitation Distillation (E^2D), a method for efficient large-scale dataset distillation that balances a...

[2602.15553] RUVA: Personalized Transparent On-Device Graph Reasoning

The paper presents RUVA, a novel architecture for personalized on-device graph reasoning that enhances user control over AI-generated con...

[2602.15274] When Remembering and Planning are Worth it: Navigating under Change

This article explores how various memory types enhance spatial navigation in changing environments, highlighting the efficiency of agents...

[2602.15156] Panini: Continual Learning in Token Space via Structured Memory

The paper presents Panini, a continual learning framework for language models that enhances efficiency and accuracy by integrating experi...

[2602.15711] Random Wavelet Features for Graph Kernel Machines

This paper introduces randomized spectral node embeddings for graph kernel machines, enhancing node similarity estimation while improving...

[2602.15676] Relative Geometry of Neural Forecasters: Linking Accuracy and Alignment in Learned Latent Geometry

This paper explores how neural networks represent latent geometry in forecasting complex dynamical systems, linking model alignment with ...

[2602.15595] Multi-Objective Coverage via Constraint Active Search

This paper introduces a novel algorithm, MOC-CAS, for solving the multi-objective coverage problem, enhancing efficiency in applications ...

[2602.15572] Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model

This paper presents a neural network-based framework for parameter estimation in agent-based models (ABMs) of the labor market, demonstra...

[2602.15481] LLM-as-Judge on a Budget

The paper presents a novel approach to efficiently evaluate large language models (LLMs) under budget constraints, utilizing multi-armed ...

[2602.15499] ExLipBaB: Exact Lipschitz Constant Computation for Piecewise Linear Neural Networks

The paper presents ExLipBaB, a method for exact computation of Lipschitz constants in piecewise linear neural networks, addressing limita...

[2602.15438] Logit Distance Bounds Representational Similarity

This paper explores the relationship between logit distance and representational similarity in discriminative models, demonstrating that ...

[2602.15407] Fairness over Equality: Correcting Social Incentives in Asymmetric Sequential Social Dilemmas

This paper explores how asymmetric conditions in Sequential Social Dilemmas affect cooperation dynamics in Multi-Agent Reinforcement Lear...

[2602.15405] Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits

This paper presents a novel framework for joint signal enhancement and classification using coupled diffusion models, improving accuracy ...

[2602.15393] Doubly Stochastic Mean-Shift Clustering

The paper presents Doubly Stochastic Mean-Shift (DSMS), an innovative clustering algorithm that enhances standard Mean-Shift methods by i...

[2602.15380] Fractional-Order Federated Learning

The paper introduces Fractional-Order Federated Averaging (FOFedAvg), a novel federated learning approach that enhances model training ef...

[2602.15344] ER-MIA: Black-Box Adversarial Memory Injection Attacks on Long-Term Memory-Augmented Large Language Models

The paper presents ER-MIA, a framework for black-box adversarial memory injection attacks on long-term memory-augmented large language mo...

[2602.15332] Directional Reasoning Trajectory Change (DRTC): Identifying Critical Trace Segments in Reasoning Models

The paper introduces Directional Reasoning Trajectory Change (DRTC), a framework for interpreting long-horizon reasoning in language mode...

[2602.15327] Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

The paper discusses prescriptive scaling laws for language models, focusing on how compute budgets affect downstream accuracy and the sta...