Search: “models”

Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid

arXiv:2605.01359v1 Announce Type: new Abstract: In this paper, we employ the Minimal Cognitive Grid (MCG), a framework created to evaluate the cognitive plausibility of artificial systems, to offer a

researcharxiv-cs-ai

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Tenability and Weak Semantics: Modeling Non-uniform Defense -- Extended Version

DGX agent

arXiv:2605.02024v1 Announce Type: new Abstract: In Dung-style abstract argumentation, various semantics capture notions of acceptability of arguments. The admissibility semantics capture the notion th

model-releasesarxiv-cs-ai

6 May 2026

The Blueprint: Translating stream-of-conscious speech into responsive, actionable task lists

model-releasesgoogle-cloud-ai

Welcome to The Blueprint, a new feature where we highlight how Google Cloud customers are tackling unique and common challenges across industries using the latest AI and cloud technologies. We hope to

The Design and Composition of Structural Causal Decision Processes

arXiv:2605.02681v1 Announce Type: cross Abstract: We present two new classes of causal models of decision-making agents. Our approach is motivated by the needs of modeling the economics of computing s

safetyarxiv-cs-ai

Towards Safer Large Reasoning Models by Promoting Safety Decision-Making before Chain-of-Thought Generation

arXiv:2603.17368v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieved remarkable performance via chain-of-thought (CoT), but recent studies showed that such enhanced reasoning cap

safetyarxiv-cs-ai

A framework for analyzing concept representations in neural models

arXiv:2605.01381v1 Announce Type: new Abstract: Understanding how neural models represent human-interpretable concepts is challenging. Prior work has explored linear concept subspaces from diverse per

A Unified Multi-Dynamics Framework for Perception-Oriented Modeling in Tendon-Driven Continuum Robots

arXiv:2511.18088v2 Announce Type: replace Abstract: Tendon-driven continuum robots offer intrinsically safe and contact-rich interactions owing to their kinematic redundancy and structural compliance.

safetyarxiv-cs-ro

Active Reasoning Vision-Language Models via Sequential Experimental Design

arXiv:2605.01345v1 Announce Type: new Abstract: Visual perception in modern Vision-Language Models (VLMs) is constrained by a fundamental perceptual bandwidth bottleneck: a broad field of view inevita

researcharxiv-cs-cv

Industry

AI success starts with clean data, not just better models

Data quality is a foundational prerequisite for successful AI applications, often more important than model complexity or sophistication. The article emphasizes that clean, well-organized data directl

industrydatabricks

Closed-Loop CO2 Storage Control With History-Based Reinforcement Learning and Latent Model-Based Adaptation

arXiv:2605.02405v1 Announce Type: new Abstract: Closed-loop management of geological CO2 storage requires control policies that adapt to uncertain reservoir behavior while relying on observations that

applicationsarxiv-cs-lg

Industry

come for the rate limits, stay for the best model

Sam Altman suggests that OpenAI's competitive advantage extends beyond just offering competitive API rate limits; the superior quality of their models themselves is what keeps users engaged long-term.

industrysam-altman--x

Continuous Temporal Representations of Event-Based Signals via Interference-Based Wave Modeling

arXiv:2605.01270v1 Announce Type: new Abstract: Spatio-temporal signals arising from event-driven biological processes, such as surface electromyography (sEMG), exhibit asynchronous and highly structu

Deep learning-based pavement performance modeling using multiple distress indicators and road work history

arXiv:2605.01914v1 Announce Type: new Abstract: The deterioration of pavement is a complex and dynamic process determined by different factors including material, environment, design, and some other u

applicationsarxiv-cs-lg

Disciplined Diffusion: Text-to-Image Diffusion Model against NSFW Generation

arXiv:2605.01113v1 Announce Type: new Abstract: Text-to-image (T2I) diffusion models have the ability to build high-quality pictures from text prompts, but they pose safety concerns because they can g

safetyarxiv-cs-cv

Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling

arXiv:2512.11415v2 Announce Type: replace-cross Abstract: We show that nonequilibrium dynamics can play a constructive role in unsupervised machine learning by inducing the spontaneous emergence of la

applicationsarxiv-cs-lg

Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims

arXiv:2605.02740v1 Announce Type: cross Abstract: Evidence derived from large-scale real-world data (RWD) is increasingly informing regulatory evaluation and healthcare decision-making. Administrative

safetyarxiv-cs-cl

Generalized Category Discovery under Domain Shifts: From Vision to Vision-Language Models

arXiv:2605.00906v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) aims to categorize unlabelled instances from both known and unknown classes by transferring knowledge from labelled

applicationsarxiv-cs-cv

Generative Interfaces for Language Models

arXiv:2508.19227v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly seen as assistants, copilots, and consultants, capable of supporting a wide range of tasks through nat

Hey, That's My Data! Token-Only Dataset Inference in Large Language Models

arXiv:2506.06057v2 Announce Type: replace Abstract: Large Language Models (LLMs) rely on massive training datasets, often including proprietary data, which raises concerns about unauthorized usage and

How Reasoning Evolves from Post-Training Data: An Empirical Study Using Chess

model-releasesarxiv-cs-lg

arXiv:2604.05134v2 Announce Type: replace Abstract: We study how reasoning evolves in a language model -- from supervised fine-tuning (SFT) to reinforcement learning (RL) -- by analyzing how a set of

Industry

i would like to talk to people who have built amazing things with 5.5 that weren't possible with earlier models. i am especially interested …

Sam Altman solicited examples from users of innovative applications and capabilities enabled by GPT-5.5 that were not feasible with earlier OpenAI models, with particular interest in specific use case

industrysam-altman--x

IBM charts AI operating model to move enterprises beyond experimentation

IBM Corp. will use its Think 2026 conference today to outline a broad expansion of its enterprise artificial intelligence portfolio, positioning a new “AI operating model” as the next stage in its cus

agentssiliconangle

It would also be useful for funding R&D into benchmarking models, which is currently mostly done by the labs themselves right now.

applicationsethan-mollick--x

Ethan Mollick advocates for dedicated funding toward developing independent benchmarking systems for AI models, noting that evaluation is currently predominantly conducted by the labs that create the

Large Language Models for Multi-Robot Systems: A Survey

arXiv:2502.03814v5 Announce Type: replace Abstract: The rapid advancement of Large Language Models (LLMs) has opened new possibilities in Multi-Robot Systems (MRS), enabling enhanced communication, ta

agentsarxiv-cs-ro

Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference

arXiv:2605.02739v1 Announce Type: new Abstract: Dual-system Vision-Language-Action (VLA) models achieve state-of-the-art robotic manipulation but are bottlenecked by the VLM backbone, which must execu

researcharxiv-cs-ro

Local Ai

Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers

arXiv:2509.15113v2 Announce Type: replace Abstract: The growing demand for energy-efficient, high-performance AI systems has led to increased attention on alternative computing platforms (e.g., photon

local-aiarxiv-cs-lg

LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning

model-releasesarxiv-cs-lg

arXiv:2605.02133v1 Announce Type: new Abstract: AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations, driving learning-based surrogates for large-scale

Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks

model-releasesarxiv-cs-cl

arXiv:2605.01417v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for medical applications remains challenging due to benchmark saturation, limited data accessibility, and insuff

Nace.AI, which lets companies build specialized AI models tailored to their business' mission and language, raised a $21.5M seed led by Walden Catalyst (Chris Metinko/Axios)

Chris Metinko / Axios: Nace.AI, which lets companies build specialized AI models tailored to their business' mission and language, raised a 21.5M seed led by Walden Catalyst — Nace.AI, which lets busi

applicationstechmeme

On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length

arXiv:2605.02572v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise as interactive agents that solve tasks through extended sequences of environment interactions. While p

RADMI: Latent Information Aggregation as a Proxy for Model Uncertainty

model-releasesarxiv-cs-cv

arXiv:2605.01502v1 Announce Type: new Abstract: Epistemic uncertainty estimation is essential for identifying regions where deep learning system outputs may be unreliable. However, existing approaches

RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs

model-releasesarxiv-cs-cl

arXiv:2605.01913v1 Announce Type: cross Abstract: Fine-tuning safety-aligned language models for downstream tasks often leads to substantial degradation of refusal behavior, making models vulnerable t

Silicon Showdown: Performance, Efficiency, and Ecosystem Barriers in Consumer-Grade LLM Inference

model-releasesarxiv-cs-ai

arXiv:2605.00519v2 Announce Type: cross Abstract: The operational landscape of local Large Language Model (LLM) inference has shifted from lightweight models to datacenter-class weights exceeding 70B

Task-Related Token Compression in Multimodal Large Language Models from an Explainability Perspective

arXiv:2506.01097v2 Announce Type: replace Abstract: Existing Multimodal Large Language Models (MLLMs) process a large number of visual tokens, leading to significant computational costs and inefficien

safetyarxiv-cs-cv

TCDA: Thread-Constrained Discourse-Aware Modeling for Conversational Sentiment Quadruple Analysis

model-releasesarxiv-cs-cl

arXiv:2605.01717v1 Announce Type: new Abstract: Conversational Aspect-based Sentiment Quadruple Analysis (DiaASQ) needs to capture the complex interrelationships in multiple rounds of dialogues. Exist

TetraJet-v2: Accurate NVFP4 Training for Large Language Models with Oscillation Suppression and Outlier Control

arXiv:2510.27527v2 Announce Type: replace Abstract: Large Language Models (LLMs) training is prohibitively expensive, driving interest in low-precision fully-quantized training (FQT). While novel 4-bi

The Cylindrical Representation Hypothesis for Language Model Steering

arXiv:2605.01844v1 Announce Type: new Abstract: Steering is a widely used technique for controlling large language models, yet its effects are often unstable and hard to predict. Existing theoretical

TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution

model-releasesarxiv-cs-cv

arXiv:2605.02767v1 Announce Type: new Abstract: Diffusion models have recently demonstrated strong performance for image restoration tasks, including super-resolution. However, their large model size

Turning Drift into Constraint: Robust Reasoning Alignment in Non-Stationary Environments

model-releasesarxiv-cs-cv

arXiv:2510.04142v2 Announce Type: replace Abstract: This paper identifies a critical yet underexplored challenge in reasoning alignment from multiple multi-modal large language models (MLLMs): In non-

Validation of an AI-based end-to-end model for prostate pathology using long-term archived routine samples

arXiv:2605.02614v1 Announce Type: new Abstract: Artificial intelligence (AI) is becoming a clinical tool for prostate pathology, but generalization across variations in sample preparation and preserva

researcharxiv-cs-cv

Where Do Prompt Perturbations Break Generation? A Segment-Level View of Robustness in LoRA-Tuned Language Models

arXiv:2605.01605v1 Announce Type: new Abstract: Large language models are sensitive to minor prompt perturbations, yet existing robustness methods usually enforce consistency at the whole-sequence lev

Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamics

arXiv:2505.13150v2 Announce Type: replace Abstract: Behavioral Foundation Models (BFMs) proved successful in producing policies for arbitrary tasks in a zero-shot manner, requiring no test-time traini

safetyarxiv-cs-lg

A unified perspective on fine-tuning and sampling with diffusion and flow models

arXiv:2605.00229v1 Announce Type: cross Abstract: We study the problem of training diffusion and flow generative models to sample from target distributions defined by an exponential tilting of a base

safetyarxiv-cs-lg

Agentic development demands a multi-model strategy — and the governance to match

The rapid rise of agentic development has radically transformed the software engineering landscape, compelling enterprises to embrace a multi-model AI ecosystem. The pace of change in software develop

agentssiliconangle

Better Models Won’t Save Your Agent

This article likely argues that improving the underlying language models is insufficient for building effective AI agents, and that other critical components—such as knowledge retrieval, context manag

agentspinecone

Local Ai

Comparative Analysis of Polygon-Based and Global Machine Learning Models for Bus Occupancy Prediction

arXiv:2605.00083v1 Announce Type: new Abstract: Accurate forecasting of bus ridership (passengers numbers) is crucial for efficient management and optimization of public transport systems. Traditional

local-aiarxiv-cs-lg

Diversity in Large Language Models under Supervised Fine-Tuning

arXiv:2605.00195v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) is essential for aligning Large Language Models (LLMs) with user intent, yet it is believed to suppress generative diversit

Foundation AI Models for Aerosol Optical Depth Estimation from PACE Satellite Data

arXiv:2605.00678v1 Announce Type: new Abstract: Aerosol Optical Depth (AOD) retrieval is essential for Earth observation, supporting applications from air quality monitoring to climate studies. Conven

safetyarxiv-cs-cv