AI startup sues ex-CEO, saying he took 41GB of email and lied on résumé
Hayden AI also claims co-founder improperly sold over $1.2M in stock.
Workers report watching Ray-Ban Meta-shot footage of people using the bathroom
Meta accused of "concealing the facts" about smart glass users' privacy.
City Detect, which uses AI to help cities stay safe and clean, raises $13M Series A
City Detect, a company that helps local governments prevent urban decay, is in at least 17 cities so far, including Dallas and Miami.
DiligenceSquared uses AI, voice agents to make M&A research affordable
Instead of relying on expensive management consultants, the startup uses AI voice agents to conduct interviews with customers of the companies the PE firms are considering buying.
Luma launches creative AI agents powered by its new ‘Unified Intelligence’ models
Luma introduced Luma Agents, powered by its new “Unified Intelligence” models, designed to coordinate multiple AI systems and generate end-to-end creative work across text, images, video and audio.
Cursor is rolling out a new kind of agentic coding tool
Called Automations, the new system gives users a way to automatically launch agents within their coding environment, triggered by a new addition to the codebase, a Slack message, or a simple timer.
Meta sued over AI smart glasses’ privacy concerns, after workers reviewed nudity, sex, and other footage
Lawyers say Meta's marketing materials promised privacy and user control over sharing footage. But an investigation found that subcontractors are reviewing footage from customers' glasses.
How 1,000+ customer calls shaped a breakout enterprise AI startup
On this episode of Build Mode, David Park joins Isabelle Johannessen to discuss how he and his team are intentionally iterating, fundraising, and scaling Narada.
Lio raises $30M from Andreessen Horowitz and others to automate enterprise procurement
AI procurement startup Lio announced a $30 million Series A in a round led by Andreessen Horowitz.
Quantum-Inspired Self-Attention in a Large Language Model
arXiv:2603.03318v1 Announce Type: cross Abstract: Recent advances in Natural Language Processing have been predominantly driven by transformer-based architectures, which rely heavily on self-attention mechanisms to model relationships between tokens in a sequence. Similarly, the field of Quantum Natural Language Processing,...
A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction
arXiv:2603.03327v1 Announce Type: cross Abstract: User satisfaction is closely related to enterprises, as it not only directly reflects users' subjective evaluation of service quality or products, but also affects customer loyalty and long-term business revenue. Monitoring and understanding user emotions...
The Logovista English-Japanese Machine Translation System
arXiv:2603.03311v1 Announce Type: new Abstract: This paper documents the architecture, development practices, and preserved artifacts of the Logovista English--Japanese machine translation system, a large, explicitly rule-based MT system that was developed and sold commercially from the early 1990s through at...
StructLens: A Structural Lens for Language Models via Maximum Spanning Trees
arXiv:2603.03328v1 Announce Type: new Abstract: Language exhibits inherent structures, a property that explains both language acquisition and language change. Given this characteristic, we expect language models to manifest internal structures as well. While interpretability research has investigated the components of...
Training-free Dropout Sampling for Semantic Token Acceptance in Speculative Decoding
arXiv:2603.03333v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference by proposing tokens with a lightweight draft model and selectively accepting them using a target model. This work introduces DropMatch, a novel approach that matches draft tokens to...
A theoretical model of dynamical grammatical gender shifting based on set-valued set function
arXiv:2603.03510v1 Announce Type: new Abstract: This study investigates the diverse characteristics of nouns, focusing on both semantic (e.g., countable/uncountable) and morphosyntactic (e.g., masculine/feminine) distinctions. We explore inter-word variations for gender markers in noun morphology. Grammatical gender shift is a widespread...
Half the Nonlinearity Is Wasted: Measuring and Reallocating the Transformer's MLP Budget
arXiv:2603.03459v1 Announce Type: new Abstract: We investigate when transformer MLP nonlinearity is actually necessary. A gate with $d+1$ parameters decides when to replace the full MLP with a linear surrogate. Through systematic investigation across six models (162M-2.8B parameters), two architectures,...
When Shallow Wins: Silent Failures and the Depth-Accuracy Paradox in Latent Reasoning
arXiv:2603.03475v1 Announce Type: new Abstract: Mathematical reasoning models are widely deployed in education, automated tutoring, and decision support systems despite exhibiting fundamental computational instabilities. We demonstrate that state-of-the-art models (Qwen2.5-Math-7B) achieve 61% accuracy through a mixture of reliable and unreliable...
Minimax Optimal Strategy for Delayed Observations in Online Reinforcement Learning
arXiv:2603.03480v1 Announce Type: new Abstract: We study reinforcement learning with delayed state observation, where the agent observes the current state after some random number of time steps. We propose an algorithm that combines the augmentation method and the upper confidence...
Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory
arXiv:2603.03511v1 Announce Type: new Abstract: We aim to learn wavefunctions simulated by time-dependent density functional theory (TDDFT), which can be efficiently represented as linear combination coefficients of atomic orbitals. In real-time TDDFT, the electronic wavefunctions of a molecule evolve over...
Harmonic Dataset Distillation for Time Series Forecasting
arXiv:2603.03760v1 Announce Type: new Abstract: Time Series forecasting (TSF) in the modern era faces significant computational and storage cost challenges due to the massive scale of real-world data. Dataset Distillation (DD), a paradigm that synthesizes a small, compact dataset to...
When and Where to Reset Matters for Long-Term Test-Time Adaptation
arXiv:2603.03796v1 Announce Type: new Abstract: When continual test-time adaptation (TTA) persists over the long term, errors accumulate in the model and further cause it to predict only a few classes for all inputs, a phenomenon known as model collapse. Recent...
Relational In-Context Learning via Synthetic Pre-training with Structural Prior
arXiv:2603.03805v1 Announce Type: new Abstract: Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstacle is that high-quality RDBs are private, scarce and structurally heterogeneous, making...
Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning
arXiv:2603.03818v1 Announce Type: new Abstract: Continual learning is a long-standing challenge in robot policy learning, where a policy must acquire new skills over time without catastrophically forgetting previously learned ones. While prior work has extensively studied continual learning in relatively...
HateMirage: An Explainable Multi-Dimensional Dataset for Decoding Faux Hate and Subtle Online Abuse
arXiv:2603.02684v1 Announce Type: new Abstract: Subtle and indirect hate speech remains an underexplored challenge in online safety research, particularly when harmful intent is embedded within misleading or manipulative narratives. Existing hate speech datasets primarily capture overt toxicity, underrepresenting the nuanced...
The Distribution of Phoneme Frequencies across the World's Languages: Macroscopic and Microscopic Information-Theoretic Models
arXiv:2603.02860v1 Announce Type: new Abstract: We demonstrate that the frequency distribution of phonemes across languages can be explained at both macroscopic and microscopic levels. Macroscopically, phoneme rank-frequency distributions closely follow the order statistics of a symmetric Dirichlet distribution whose single...
MaBERT:A Padding Safe Interleaved Transformer Mamba Hybrid Encoder for Efficient Extended Context Masked Language Modeling
arXiv:2603.03001v1 Announce Type: new Abstract: Self attention encoders such as Bidirectional Encoder Representations from Transformers(BERT) scale quadratically with sequence length, making long context modeling expensive. Linear time state space models, such as Mamba, are efficient; however, they show limitations in...
BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?
arXiv:2603.03194v1 Announce Type: new Abstract: Current benchmarks for code agents primarily assess narrow, repository-specific fixes, overlooking critical real-world challenges such as cross-repository reasoning, domain-specialized problem solving, dependency-driven migration, and full-repository generation. To address this gap, we introduce BeyondSWE, a comprehensive...
Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use
arXiv:2603.03205v1 Announce Type: new Abstract: Agentic language models operate in a fundamentally different safety regime than chat models: they must plan, call tools, and execute long-horizon actions where a single misstep, such as accessing files or entering credentials, can cause...
Routing Absorption in Sparse Attention: Why Random Gates Are Hard to Beat
arXiv:2603.02227v1 Announce Type: cross Abstract: Can a transformer learn which attention entries matter during training? In principle, yes: attention distributions are highly concentrated, and a small gate network can identify the important entries post-hoc with near-perfect accuracy. In practice, barely....
Through the Lens of Contrast: Self-Improving Visual Reasoning in VLMs
arXiv:2603.02556v1 Announce Type: cross Abstract: Reasoning has emerged as a key capability of large language models. In linguistic tasks, this capability can be enhanced by self-improving techniques that refine reasoning paths for subsequent finetuning. However, extending these language-based self-improving approaches...