Asynchronous Verified Semantic Caching for Tiered LLM Architectures
Large language models (LLMs) now sit in the critical path of search, assistance, and agentic workflows, making semantic caching essential for reducing inference cost and latency. Production deployments typically use a tiered static-dynamic design: a static cache of curated, offline vetted responses ...
Brain inspired machines are better at math than expected
Neuromorphic computers modeled after the human brain can now solve the complex equations behind physics simulations — something once thought possible only with energy-hungry supercomputers. The breakthrough could lead to powerful, low-energy supercomputers while revealing new secrets about how our b...
Turning messy retail data into actionable insights. See how we use large language models to uncover what drives customer decisions and optimize every interaction.
Automated Optimization Modeling via a Localizable Error-Driven Perspective
arXiv:2602.11164v1 Announce Type: new
Abstract: Automated optimization modeling via Large Language Models (LLMs) has emerged as a promising approach to assist complex human decision-making. While post-training has become a pivotal technique to enhance LLMs' capabilities in this domain, its effectiv...
Spectra: Rethinking Optimizers for LLMs Under Spectral Anisotropy
arXiv:2602.11185v1 Announce Type: new
Abstract: Gradient signals in LLM training are highly anisotropic: recurrent linguistic structure concentrates energy into a small set of dominant spectral directions, while context specific information resides in a long tail. We show that this spike tail separ...
GAC-KAN: An Ultra-Lightweight GNSS Interference Classifier for GenAI-Powered Consumer Edge Devices
arXiv:2602.11186v1 Announce Type: new
Abstract: The integration of Generative AI (GenAI) into Consumer Electronics (CE)--from AI-powered assistants in wearables to generative planning in autonomous Uncrewed Aerial Vehicles (UAVs)--has revolutionized user experiences. However, these GenAI applicatio...
TDPNavigator-Placer: Thermal- and Wirelength-Aware Chiplet Placement in 2.5D Systems Through Multi-Agent Reinforcement Learning
arXiv:2602.11187v1 Announce Type: new
Abstract: The rapid growth of electronics has accelerated the adoption of 2.5D integrated circuits, where effective automated chiplet placement is essential as systems scale to larger and more heterogeneous chiplet assemblies. Existing placement methods typical...
Latent Generative Solvers for Generalizable Long-Term Physics Simulation
arXiv:2602.11229v1 Announce Type: new
Abstract: We study long-horizon surrogate simulation across heterogeneous PDE systems. We introduce Latent Generative Solvers (LGS), a two-stage framework that (i) maps diverse PDE states into a shared latent physics space with a pretrained VAE, and (ii) learns...
arXiv:2602.11298v1 Announce Type: new
Abstract: We introduce Voxtral Realtime, a natively streaming automatic speech recognition model that matches offline transcription quality at sub-second latency. Unlike approaches that adapt offline models through chunking or sliding windows, Voxtral Realtime ...
The PBSAI Governance Ecosystem: A Multi-Agent AI Reference Architecture for Securing Enterprise AI Estates
arXiv:2602.11301v1 Announce Type: new
Abstract: Enterprises are rapidly deploying large language models, retrieval augmented generation pipelines, and tool using agents into production, often on shared high performance computing clusters and cloud accelerator platforms that also support defensive a...
Completed Hyperparameter Transfer across Modules, Width, Depth, Batch and Duration
Hyperparameter tuning can dramatically impact training stability and final performance of large-scale models. Recent works on neural network parameterisations, such as μP, have enabled transfer of optimal global hyperparameters across model sizes. These works propose an empirical practice of search ...
Large Language Models Predict Functional Outcomes after Acute Ischemic Stroke
arXiv:2602.10119v1 Announce Type: new
Abstract: Accurate prediction of functional outcomes after acute ischemic stroke can inform clinical decision-making and resource allocation. Prior work on modified Rankin Scale (mRS) prediction has relied primarily on structured variables (e.g., age, NIHSS) an...
arXiv:2602.10177v1 Announce Type: new
Abstract: Recent advances in foundational models have yielded reasoning systems capable of achieving a gold-medal standard at the International Mathematical Olympiad. The transition from competition-level problem-solving to professional research, however, requi...
Signature-Kernel Based Evaluation Metrics for Robust Probabilistic and Tail-Event Forecasting
arXiv:2602.10182v1 Announce Type: new
Abstract: Probabilistic forecasting is increasingly critical across high-stakes domains, from finance and epidemiology to climate science. However, current evaluation frameworks lack a consensus metric and suffer from two critical flaws: they often assume indep...
Discovering Differences in Strategic Behavior Between Humans and LLMs
arXiv:2602.10324v1 Announce Type: new
Abstract: As Large Language Models (LLMs) are increasingly deployed in social and strategic scenarios, it becomes critical to understand where and why their behavior diverges from that of humans. While behavioral game theory (BGT) provides a framework for analy...
LiveMedBench: A Contamination-Free Medical Benchmark for LLMs with Automated Rubric Evaluation
arXiv:2602.10367v1 Announce Type: new
Abstract: The deployment of Large Language Models (LLMs) in high-stakes clinical settings demands rigorous and reliable evaluation. However, existing medical benchmarks remain static, suffering from two critical limitations: (1) data contamination, where test s...
Found-RL: foundation model-enhanced reinforcement learning for autonomous driving
arXiv:2602.10458v1 Announce Type: new
Abstract: Reinforcement Learning (RL) has emerged as a dominant paradigm for end-to-end autonomous driving (AD). However, RL suffers from sample inefficiency and a lack of semantic interpretability in complex scenarios. Foundation Models, particularly Vision-La...
MERIT Feedback Elicits Better Bargaining in LLM Negotiators
arXiv:2602.10467v1 Announce Type: new
Abstract: Bargaining is often regarded as a logical arena rather than an art or a matter of intuition, yet Large Language Models (LLMs) still struggle to navigate it due to limited strategic depth and difficulty adapting to complex human factors. Current benchm...
Abstraction Generation for Generalized Planning with Pretrained Large Language Models
arXiv:2602.10485v1 Announce Type: new
Abstract: Qualitative Numerical Planning (QNP) serves as an important abstraction model for generalized planning (GP), which aims to compute general plans that solve multiple instances at once. Recent works show that large language models (LLMs) can function as...
Mapping the Design Space of User Experience for Computer Use Agents
Large language model (LLM)-based computer use agents execute user commands by interacting with available UI elements, but little is known about how users want to interact with these agents or what design factors matter for their user experience (UX). We conducted a two-phase study to map the UX desi...
Trace Length is a Simple Uncertainty Signal in Reasoning Models
Uncertainty quantification for LLMs is a key research direction towards addressing hallucination and other issues that limit their reliable deployment. In this work, we show that reasoning trace length is a simple and useful confidence estimator in large reasoning models. Through comprehensive exper...