The Appeal and Reality of Recycling LoRAs with Adaptive Merging
arXiv:2602.12323v1 Announce Type: new
Abstract: The widespread availability of fine-tuned LoRA modules for open pre-trained models has led to an interest in methods that can adaptively merge LoRAs to improve performance. These methods typically include some way of selecting LoRAs from a pool and tu...
Intrinsic Credit Assignment for Long Horizon Interaction
arXiv:2602.12342v1 Announce Type: new
Abstract: How can we train agents to navigate uncertainty over long horizons? In this work, we propose {\Delta}Belief-RL, which leverages a language model's own intrinsic beliefs to reward intermediate progress. Our method utilizes the change in the probability...
GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory
arXiv:2602.12316v1 Announce Type: new
Abstract: Frontier AI systems are increasingly capable and deployed in high-stakes multi-agent environments. However, existing AI safety benchmarks largely evaluate single agents, leaving multi-agent risks such as coordination failure and conflict poorly unders...
A Theoretical Framework for Adaptive Utility-Weighted Benchmarking
arXiv:2602.12356v1 Announce Type: new
Abstract: Benchmarking has long served as a foundational practice in machine learning and, increasingly, in modern AI systems such as large language models, where shared tasks, metrics, and leaderboards offer a common basis for measuring progress and comparing ...
Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting
arXiv:2602.12389v1 Announce Type: new
Abstract: Temporal knowledge graph (TKG) forecasting requires predicting future facts by jointly modeling structural dependencies within each snapshot and temporal evolution across snapshots. However, most existing methods are stateless: they recompute entity r...
Intent-Driven Smart Manufacturing Integrating Knowledge Graphs and Large Language Models
arXiv:2602.12419v1 Announce Type: new
Abstract: The increasing complexity of smart manufacturing environments demands interfaces that can translate high-level human intents into machine-executable actions. This paper presents a unified framework that integrates instruction-tuned Large Language Mode...
Scaling Web Agent Training through Automatic Data Generation and Fine-grained Evaluation
arXiv:2602.12544v1 Announce Type: new
Abstract: We present a scalable pipeline for automatically generating high-quality training data for web agents. In particular, a major challenge in identifying high-quality training instances is trajectory evaluation - quantifying how much progress was made to...
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...