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

[2509.17196] Evolution of Concepts in Language Model Pre-Training

This article examines the evolution of concepts in language model pre-training, revealing how feature development influences performance ...

[2508.18210] Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation

This article presents a diagnostic framework for evaluating synthetic dialogue generation in contact centers, highlighting the limitation...

[2410.17587] Predicting Company Growth using Scaling Theory informed Machine Learning

The paper presents a novel Scaling-Theory-Informed Machine Learning (STIML) framework for predicting company growth by integrating struct...

[2508.03882] Simulating Cyberattacks through a Breach Attack Simulation (BAS) Platform empowered by Security Chaos Engineering (SCE)

This article presents a novel approach to simulating cyberattacks by integrating Security Chaos Engineering (SCE) into Breach Attack Simu...

[2507.16713] A Pragmatist Robot: Learning to Plan Tasks by Experiencing the Real World

The paper presents PragmaBot, a framework for robotic task planning that utilizes real-world experiences and self-reflection to enhance l...

[2507.14186] A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction

This paper presents a novel framework for predicting low-altitude network coverage using disentangled representation learning, addressing...

[2602.12247] ExtractBench: A Benchmark and Evaluation Methodology for Complex Structured Extraction

ExtractBench introduces a benchmark and evaluation framework for extracting structured data from unstructured documents like PDFs, addres...

[2602.10603] dnaHNet: A Scalable and Hierarchical Foundation Model for Genomic Sequence Learning

The paper presents dnaHNet, a novel tokenizer-free autoregressive model designed for genomic sequence learning, achieving significant eff...

[2602.04942] Privileged Information Distillation for Language Models

This paper presents methods for distilling privileged information in language models, focusing on improving performance in multi-turn env...

[2602.02201] Cardinality-Preserving Attention Channels for Graph Transformers in Molecular Property Prediction

This article presents a novel graph transformer model, incorporating cardinality-preserving attention channels, to enhance molecular prop...

[2602.00628] From Associations to Activations: Comparing Behavioral and Hidden-State Semantic Geometry in LLMs

This paper examines the relationship between behavioral and hidden-state semantic geometry in large language models (LLMs) through psycho...

[2505.07671] Benchmarking Retrieval-Augmented Generation for Chemistry

This article presents ChemRAG-Bench, a benchmark for evaluating retrieval-augmented generation (RAG) in chemistry, demonstrating signific...

[2601.09495] Parallelizable memory recurrent units

The paper introduces memory recurrent units (MRUs), a new family of RNNs that combine persistent memory with parallelizable computations,...

[2512.13228] ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data

ModSSC is an open-source Python framework designed for semi-supervised classification, enhancing reproducibility and experimentation acro...

[2502.16730] RapidPen: Fully Automated IP-to-Shell Penetration Testing with LLM-based Agents

RapidPen is a novel automated penetration testing framework that utilizes large language models to autonomously exploit vulnerabilities, ...

[2511.02077] Beyond Static Cutoffs: One-Shot Dynamic Thresholding for Diffusion Language Models

This article presents One-Shot Dynamic Thresholding (OSDT) for diffusion language models, enhancing decoding efficiency and accuracy by c...

[2510.15987] Algorithmic Primitives and Compositional Geometry of Reasoning in Language Models

The paper explores how algorithmic primitives and compositional geometry can enhance reasoning capabilities in large language models (LLM...

[2510.04008] RACE Attention: A Strictly Linear-Time Attention for Long-Sequence Training

The paper presents RACE Attention, a novel linear-time attention mechanism designed for long-sequence training, significantly improving e...

[2510.03272] Where to Add PDE Diffusion in Transformers

This paper investigates the optimal placement of PDE diffusion layers in transformer architectures, revealing that their insertion order ...

[2510.02410] OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data

OpenTSLM introduces a new family of Time Series Language Models designed to enhance reasoning over multivariate medical data, outperformi...