Search: “models”

arXiv:2605.01394v1 Announce Type: cross Abstract: Formal specification is essential for rigorous program verification, yet writing correct specifications remains costly and difficult to automate. Alth

arXiv:2505.20740v3 Announce Type: replace Abstract: The rapid advancement of multimodal large language models (MLLMs) offers new opportunities for complex scientific challenges, yet their application

arXiv:2605.03153v1 Announce Type: cross Abstract: Static benchmarks measure a model frozen at training time. Real systems face distribution shift: new categories, paraphrased queries, drift: and must

OpenAI Group PBC is replacing the default model in ChatGPT with the launch of GPT-5.5 Instant, claiming users will notice fewer hallucinations when it’s discussing “sensitive topics” such as finance,

arXiv:2605.03399v1 Announce Type: new Abstract: Probabilistic super-resolution of high-dimensional spatial fields using diffusion models is often computationally prohibitive due to the cost of operati

arXiv:2603.05965v2 Announce Type: replace-cross Abstract: We present PROBE (PRobabilistic Occupancy BEV Encoding), a learning-free LiDAR place recognition descriptor that models each BEV cell's occupa

arXiv:2605.03784v1 Announce Type: new Abstract: Rising global food demand and growing climate pressure increase the need for sustainable, precise agricultural practices. Automated, individualized plan

arXiv:2605.02946v1 Announce Type: new Abstract: Safety alignment is critical for the responsible deployment of large language models (LLMs). As Mixture-of-Experts (MoE) architectures are increasingly

arXiv:2602.03548v3 Announce Type: replace Abstract: Large Language Models have demonstrated remarkable capabilities in open-domain dialogues. However, current methods exhibit suboptimal performance in

arXiv:2601.04025v2 Announce Type: replace Abstract: Advances in large language models (LLMs) enable many new innovations in education. However, evaluating the effectiveness of new technology requires

arXiv:2605.03907v1 Announce Type: new Abstract: Large language models can be steered at inference time through prompting or activation interventions, but activation steering methods often underperform

The Structured Output Benchmark (SOB) dataset has been released and made available on Hugging Face. This dataset, hosted by Interfaze AI, likely provides benchmark data for evaluating models' ability

arXiv:2605.00844v1 Announce Type: cross Abstract: When large language models (LLMs) are consulted as forecasting tools, the independence of individual errors -- the foundation of collective intelligen

Today we're releasing ZAYA1-8B, a reasoning MoE trained on @AMD and optimized for intelligence density. With <1B active params, it outperforms open-weight models many times its size on math and reason

arXiv:2605.02640v1 Announce Type: new Abstract: As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), is increasingly deployed in high-stakes domains, en

We’ve been working closely with the @harvey team on the launch of the Legal Agent Benchmark, a product focused on evaluating how open-weight models perform on long-horizon, real-world legal tasks. Che

arXiv:2605.02504v1 Announce Type: new Abstract: Most hallucination evaluations focus on English, leaving it unclear whether findings transfer to lower-resource languages. We investigate faithfulness h

arXiv:2605.01292v1 Announce Type: new Abstract: The growing spread of misinformation in digital media highlights the need for reliable fake news detection systems, yet progress in under-resourced lang

arXiv:2605.01643v1 Announce Type: new Abstract: We study AI alignment through the lens of law-and-economics models of deterrence and enforcement. In these models, misconduct is not treated as an exter

arXiv:2512.09169v2 Announce Type: replace-cross Abstract: We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within

arXiv:2605.01226v1 Announce Type: new Abstract: Events in spatiotemporal systems are ubiquitous, yet modeling their complex distributions remains challenging. Existing point process models often rely

arXiv:2605.00236v1 Announce Type: cross Abstract: Safety-aligned large language models rely on RLHF and instruction tuning to refuse harmful requests, yet the internal mechanisms implementing safety b

arXiv:2506.13727v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and c

arXiv:2311.10320v3 Announce Type: replace Abstract: Data collected by different modalities can provide a wealth of complementary information, such as hyperspectral image (HSI) to offer rich spectral-s

arXiv:2605.00873v1 Announce Type: cross Abstract: The rapid advancement of photorealistic Text-to-Video (T2V) generation brings in an urgent need for up-to-date evaluation methods. Existing benchmarks

The world of AI is rapidly shifting from experimental Large Language Models to an era of Agentic AI. In the agentic era, autonomous software agents act on behalf of employees and consumers—driving a f

arXiv:2605.01291v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) are widely regarded as an energy-efficient paradigm for modeling and processing temporal and event-driven information. In

arXiv:2605.02600v1 Announce Type: new Abstract: While Large Language Models (LLMs) and Vision-Language Models (VLMs) demonstrate remarkable capabilities in high-level reasoning and semantic understand

arXiv:2601.21714v2 Announce Type: replace Abstract: The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high-precision problem-solving, requir

arXiv:2501.19201v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning has become a powerful framework for improving complex problem-solving capabilities in Multimodal Large Language Mod

arXiv:2605.01024v1 Announce Type: new Abstract: Multimodal Emotion Recognition (MER) is critical for interpreting real-world interactions. While Multimodal Large Language Models (MLLM) have shown prom

arXiv:2605.02519v1 Announce Type: new Abstract: Classic Network Intrusion Detection Systems (NIDS) often rely on manual feature engineering to extract meaningful patterns from network traffic data. Ho

arXiv:2605.01495v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding responses in external knowledge during inference. However, conve

arXiv:2510.27359v2 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning (PEFT) has emerged as a key strategy for adapting large-scale pre-trained models to downstream tasks, but existing a

arXiv:2605.00782v1 Announce Type: cross Abstract: Reliable spatial analysis in GIScience requires preserving coordinate semantics, topology, units, and geographic plausibility. Current LLM-based GIS s

arXiv:2605.02616v1 Announce Type: new Abstract: Salient Object Detection (SOD) remains an essential yet underexplored task in the era of large-scale vision models. Although foundation models like SAM

arXiv:2605.00865v1 Announce Type: cross Abstract: EEG based phoneme decoding is promising for brain computer interfaces, but many prior studies rely on within subject evaluation, small cohorts, or wea

arXiv:2510.25426v2 Announce Type: replace Abstract: The rapid advancement of Large Language Models (LLMs) is positioning language at the core of human-computer interaction (HCI). We argue that advanci

arXiv:2601.17723v2 Announce Type: replace Abstract: Implicit neural representation (INR) has become the standard approach for arbitrary-scale image super-resolution (ASSR). To date, no empirical study

Managing agents and their actions can quickly grow in complexity and introduce security risks unique to AI. To address these challenges, at Google Cloud Next we announced Agent Gateway to provide simp

This newsletter episode covers recent AI industry developments including a legal dispute between OpenAI and Elon Musk, Microsoft's involvement in AI developments, the release of DeepSeek's v4 model, a

arXiv:2605.02699v1 Announce Type: cross Abstract: Learning data-efficient object dynamics models for robotic manipulation remains challenging, especially for deformable objects. A popular approach is

arXiv:2601.01322v2 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) achieve impressive performance but are limited by the quadratic complexity of self-attention, which prevents th

arXiv:2603.00042v2 Announce Type: replace Abstract: We identify the Spectral Energy Gain in extreme model compression, where low-rank binary approximations outperform tiny-rank floating-point baseline

arXiv:2605.01047v1 Announce Type: cross Abstract: Hallucinations, outputs that sound plausible but are factually incorrect, remain an open challenge for deployed LLMs. In code generation, models frequ

arXiv:2605.02751v1 Announce Type: cross Abstract: Language models (LMs) are increasingly used in high-stakes, multi-agent settings, where following instructions and maintaining value alignment are cri