SLA2: Sparse-Linear Attention with Learnable Routing and QAT
arXiv:2602.12675v1 Announce Type: new Abstract: Sparse-Linear Attention (SLA) combines sparse and linear attention to accelerate diffusion models and has shown strong performance in video generation. However, (i) SLA relies on a heuristic split that assigns computations to the sparse or...
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing - ACL Anthology
Episode 38: Non-intervention— past, present and future - EJIL: The Podcast!
Episode 37: The ICJ’s Advisory Opinion on Climate Obligations: Remarkable, Radical and Robust - EJIL: The Podcast!
Episode 35: Human Mobility and International Law - EJIL: The Podcast!
ASML-Mistral AI: It's the Geopolitics, Stupid - AI Now Institute
Announcements Archives - AI Now Institute
Context and Agenda for the 2025 AI Action Summit
The AI Action Summit will take place in Paris from 10-11 February 2025. Here we list the agenda and key deliverables.
The Sky's the Limit? SkyKick v Sky and Speculative Trade Mark Registration
Trade marks are registered for specific products, which defines the scope of their exclusive legal monopoly. To benefit from a broad scope, applicants increasingly overclaim. They apply for categories of products with no intention to use the mark on them...
Deals
Only the best deals on Verge-approved gadgets get the Verge Deals stamp of approval, so if you’re looking for a deal on your next gadget or gift from major retailers like Amazon, Walmart, Best Buy, Target, and more, this is...
Disney
Once the public face of squeaky-clean, harmless family entertainment, the Walt Disney Corporation has evolved into a widespread conglomerate known as much for the properties it controls as the films it produces. With subsidiaries including Marvel Studios, Lucasfilm, National Geographic,...
Have money, will travel: a16z’s hunt for the next European unicorn
According to a16z, it has eyes around the world in order to spot companies as early as local funds might.
When to Think Fast and Slow? AMOR: Entropy-Based Metacognitive Gate for Dynamic SSM-Attention Switching
arXiv:2602.13215v1 Announce Type: new Abstract: Transformers allocate uniform computation to every position, regardless of difficulty. State Space Models (SSMs) offer efficient alternatives but struggle with precise information retrieval over a long horizon. Inspired by dual-process theories of cognition (Kahneman, 2011),...
VeRA: Verified Reasoning Data Augmentation at Scale
arXiv:2602.13217v1 Announce Type: new Abstract: The main issue with most evaluation schemes today is their "static" nature: the same problems are reused repeatedly, allowing for memorization, format exploitation, and eventual saturation. To measure genuine AI progress, we need evaluation that...
The Quantization Trap: Breaking Linear Scaling Laws in Multi-Hop Reasoning
arXiv:2602.13595v1 Announce Type: new Abstract: Neural scaling laws provide a predictable recipe for AI advancement: reducing numerical precision should linearly improve computational efficiency and energy profile (E proportional to bits). In this paper, we demonstrate that this scaling law breaks...
Multimodal Consistency-Guided Reference-Free Data Selection for ASR Accent Adaptation
arXiv:2602.13263v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems often degrade on accented speech because acoustic-phonetic and prosodic shifts induce a mismatch to training data, making labeled accent adaptation costly. However, common pseudo-label selection heuristics are largely text-centric (e.g.,...
How Do Lexical Senses Correspond Between Spoken German and German Sign Language?
arXiv:2602.13790v1 Announce Type: new Abstract: Sign language lexicographers construct bilingual dictionaries by establishing word-to-sign mappings, where polysemous and homonymous words corresponding to different signs across contexts are often underrepresented. A usage-based approach examining how word senses map to signs can...
LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts
arXiv:2602.14060v1 Announce Type: new Abstract: We introduce LM-Lexicon, an innovative definition modeling approach that incorporates data clustering, semantic expert learning, and model merging using a sparse mixture-of-experts architecture. By decomposing the definition modeling task into specialized semantic domains, where small...
Ontology-Guided Neuro-Symbolic Inference: Grounding Language Models with Mathematical Domain Knowledge
arXiv:2602.17826v1 Announce Type: new Abstract: Language models exhibit fundamental limitations -- hallucination, brittleness, and lack of formal grounding -- that are particularly problematic in high-stakes specialist fields requiring verifiable reasoning. I investigate whether formal domain ontologies can enhance language model...
Symbolic computation of conservation laws of nonlinear partial differential equations in multi‐dimensions
Abstract A direct method for the computation of polynomial conservation laws of polynomial systems of nonlinear partial differential equations (PDEs) in multi‐dimensions is presented. The method avoids advanced differential‐geometric tools. Instead, it is solely based on calculus, variational calculus, and...
ALPACA: A Reinforcement Learning Environment for Medication Repurposing and Treatment Optimization in Alzheimer's Disease
arXiv:2602.19298v1 Announce Type: new Abstract: Evaluating personalized, sequential treatment strategies for Alzheimer's disease (AD) using clinical trials is often impractical due to long disease horizons and substantial inter-patient heterogeneity. To address these constraints, we present the Alzheimer's Learning Platform for...
Bi-level RL-Heuristic Optimization for Real-world Winter Road Maintenance
arXiv:2602.24097v1 Announce Type: new Abstract: Winter road maintenance is critical for ensuring public safety and reducing environmental impacts, yet existing methods struggle to manage large-scale routing problems effectively and mostly reply on human decision. This study presents a novel, scalable...
Learning Flexible Job Shop Scheduling under Limited Buffers and Material Kitting Constraints
arXiv:2602.24180v1 Announce Type: new Abstract: The Flexible Job Shop Scheduling Problem (FJSP) originates from real production lines, while some practical constraints are often ignored or idealized in current FJSP studies, among which the limited buffer problem has a particular impact...
Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG
arXiv:2602.23410v1 Announce Type: cross Abstract: Brain foundation models have achieved remarkable advances across a wide range of neuroscience tasks. However, most existing models are limited to a single functional modality, restricting their ability to exploit complementary spatiotemporal dynamics and the...
COOL-MC: Verifying and Explaining RL Policies for Platelet Inventory Management
arXiv:2603.02396v1 Announce Type: new Abstract: Platelets expire within five days. Blood banks face uncertain daily demand and must balance ordering decisions between costly wastage from overstocking and life-threatening shortages from understocking. Reinforcement learning (RL) can learn effective ordering policies for...
SpatialText: A Pure-Text Cognitive Benchmark for Spatial Understanding in Large Language Models
arXiv:2603.03002v1 Announce Type: new Abstract: Genuine spatial reasoning relies on the capacity to construct and manipulate coherent internal spatial representations, often conceptualized as mental models, rather than merely processing surface linguistic associations. While large language models exhibit advanced capabilities across...
FEAST: Retrieval-Augmented Multi-Hierarchical Food Classification for the FoodEx2 System
arXiv:2603.03176v1 Announce Type: new Abstract: Hierarchical text classification (HTC) and extreme multi-label classification (XML) tasks face compounded challenges from complex label interdependencies, data sparsity, and extreme output dimensions. These challenges are exemplified in the European Food Safety Authority's FoodEx2 system-a...
NeuroSkill(tm): Proactive Real-Time Agentic System Capable of Modeling Human State of Mind
arXiv:2603.03212v1 Announce Type: new Abstract: Real-time proactive agentic system, capable of modeling Human State of Mind, using foundation EXG model and text embeddings model, running fully offline on the edge. Unlike all previously known systems, the NeuroSkill(tm) system leverages SKILL.md...
Agentics 2.0: Logical Transduction Algebra for Agentic Data Workflows
arXiv:2603.04241v1 Announce Type: new Abstract: Agentic AI is rapidly transitioning from research prototypes to enterprise deployments, where requirements extend to meet the software quality attributes of reliability, scalability, and observability beyond plausible text generation. We present Agentics 2.0, a lightweight,...