Search: “models”

DIVE: Embedding Compression via Self-Limiting Gradient Updates

arXiv:2605.20689v1 Announce Type: new Abstract: High-dimensional embeddings from large language models impose significant storage and computational costs on vector search systems. Recent embedding com

Divide et Calibra: Multiclass Local Calibration via Vector Quantization

DGX agent

arXiv:2605.21060v1 Announce Type: new Abstract: Accurate and well-calibrated Machine Learning (ML) models are mandatory in high-stakes settings, yet effective multiclass calibration remains challengin

model-releasesarxiv-cs-lg

21 May 2026

Evolutionary Generation of Multi-Agent Systems

arXiv:2602.06511v3 Announce Type: replace Abstract: Large language model (LLM)-based multi-agent systems (MAS) show strong promise for complex reasoning, planning, and tool-augmented tasks, but design

Federated LoRA Fine-Tuning for LLMs via Collaborative Alignment

arXiv:2605.21217v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) has emerged as a powerful tool for parameter-efficient fine-tuning of large language models (LLMs). This paper studies LoRA

Fine-grained Claim-level RAG Benchmark for Law

arXiv:2605.21071v1 Announce Type: new Abstract: The rapid progress of large language models (LLMs) is shifting semantic search toward a question-answering paradigm, where users ask questions and LLMs

Geometry-Lite: Interpretable Safety Probing via Layer-Wise Margin Geometry

arXiv:2605.20241v1 Announce Type: cross Abstract: Prompt-level safety probes for large language models use hidden-state representations to separate safe from unsafe prompts, but strong average detecti

In the next version of Claude Code: run /usage to see a breakdown of which Skills, Agents, MCPs, and Plugins are using your tokens CLI today…

model-releasesboris-cherny--x

The next version of Claude Code will introduce a `/usage` command that provides a detailed breakdown of token consumption across different components including Skills, Agents, MCPs (Model Context Prot

LEAP: A closed-loop framework for perovskite precursor additive discovery

arXiv:2605.20242v1 Announce Type: new Abstract: Efficient discovery of precursor additives is essential for improving the performance of perovskite solar cells, yet the large chemical space makes conv

Memory-Efficient Partitioned DNN Inference on Resource-Constrained Android Crowds

arXiv:2605.20723v1 Announce Type: new Abstract: Deploying large deep neural networks on memory-constrained mobile devices is a central challenge in edge ML. While compression, pruning, and quantizatio

Open source 🤝 NVIDIA

Open source 🤝 NVIDIA 👏 Congratulations to @cohere on Command A+ — a powerful new model optimized for NVIDIA Blackwell and trained using NVIDIA CUDA-X libraries. Proud to be a part of it! Learn more ⤵️

model-releasescohere--x

Preserve, Reveal, Expand: Faithful 4D Video Editing with Region-Aware Conditioning

arXiv:2605.20961v1 Announce Type: new Abstract: Existing 4D-driven video diffusion models primarily target plausible generation, but faithful 4D editing requires preserving source-observed regions whi

Q-DiT4SR: Exploration of Detail-Preserving Diffusion Transformer Quantization for Real-World Image Super-Resolution

arXiv:2602.01273v4 Announce Type: replace Abstract: Recently, Diffusion Transformers (DiTs) have emerged in Real-World Image Super-Resolution (Real-ISR) to generate high-quality textures, yet their he

Reasoning-Trace Collapse: Evaluating the Loss of Explicit Reasoning During Fine-Tuning

arXiv:2605.21127v1 Announce Type: new Abstract: Explicit reasoning models are trained to produce intermediate reasoning traces before final answers, but downstream fine-tuning is often performed on or

researcharxiv-cs-lg

Sequential Data Augmentation for Generative Recommendation

arXiv:2509.13648v3 Announce Type: replace Abstract: Generative recommendation plays a crucial role in personalized systems, predicting users' future interactions from their historical behavior sequenc

Verifiable Provenance and Watermarking for Generative AI: An Evidentiary Framework for International Operational Law and Domestic Courts

arXiv:2605.21002v1 Announce Type: cross Abstract: Generative artificial intelligence now synthesizes photorealistic imagery, audio, and video at a cost that defeats traditional forensic intuition. The

VersusQ: Pairwise Margin Reasoning for Generalizable Video Quality Assessment

arXiv:2605.21130v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have shown promise for video quality assessment, but most methods still predict an absolute score for each video. Such po

Vision Transformers and Convolutional Neural Networks for Land Use Scene Classification

arXiv:2605.21268v1 Announce Type: new Abstract: Land Use Scene Classification (LUSC) from remote sensing imagery plays a critical role in environmental monitoring, urban planning, and sustainable reso

WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning

arXiv:2605.20311v1 Announce Type: new Abstract: Guided-wave structural health monitoring enables damage localization in composite plates using sparse networks of bonded piezoelectric transducers. Howe

Weight Decay Regimes in Grokking Transformers: Cheap Online Diagnostics

arXiv:2605.20441v1 Announce Type: new Abstract: Transformers trained on modular arithmetic exhibit sharp transitions between memorization, generalization, and collapse. We show that weight decay acts

WikiVQABench: A Knowledge-Grounded Visual Question Answering Benchmark from Wikipedia and Wikidata

arXiv:2605.21479v1 Announce Type: new Abstract: Visual Question Answering (VQA) benchmarks have largely emphasized perception-based tasks that can be solved from visual content alone. In contrast, man

100 things we announced at I/O 2026

Google I/O 2026 unveiled new models, agents and tools to help users build, search, create, discover, shop and get more done. Key announcements included Gemini Omni, Google Antigravity, and Universal C

model-releasesgoogle-ai

A Family of Divergence Measures for Evaluating the Reconstruction Quality of Explainable Ensemble Trees

arXiv:2605.19618v1 Announce Type: new Abstract: Validating interpretable surrogate models for ensemble learners requires measuring agreement between the ensemble's internal representation and its surr

Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment

arXiv:2506.14148v2 Announce Type: replace-cross Abstract: This paper presents a novel non-invasive object classification approach using acoustic scattering, demonstrated through a case study on hair a

Adynamical systems view of training generativemodels and the memorization phenomenon

arXiv:2605.19483v1 Announce Type: new Abstract: Using recent works of one of the authors (VSB) on collapse in generative models and two time scale dynamics in stochastic gradient descent in high dimen

researcharxiv-cs-lg

As always, 'hermes update'

Nous Research announced an update to their Hermes model, likely detailing improvements, new features, or performance enhancements to their open-source language model. The post was shared on X (formerl

researchnous-research--x

Causal Evidence for Attention Head Imbalance in Modality Conflict Hallucination

arXiv:2605.19250v1 Announce Type: new Abstract: Modality-conflict hallucination occurs when multimodal large language models (MLLMs) prioritize erroneous textual premises over contradictory visual evi

Compositional Literary Primitives in Instruction-Tuned LLMs: Cross-Architectural SAE Features for Self, Style, and Affect

arXiv:2605.18808v1 Announce Type: cross Abstract: We characterize a compositional architecture of literary primitives in two instruction-tuned large language models (Llama 3.1 8B-Instruct and Gemma 2

DecisionBench: A Benchmark for Emergent Delegation in Long-Horizon Agentic Workflows

arXiv:2605.19099v1 Announce Type: new Abstract: We introduce DecisionBench, a benchmark substrate for emergent delegation in long-horizon agentic workflows. The substrate fixes a task suite (GAIA, tau

Depth2Pose: A Pose-Based Benchmark for Monocular Depth Estimation without Ground-Truth Depth

arXiv:2605.19797v1 Announce Type: new Abstract: Monocular depth estimation has improved significantly in recent years, driven by increasingly powerful models and large-scale training data. Predicted d

Local Ai

Descriptive versus Regulatory Uncertainty in Bounded Predictive Systems

arXiv:2605.18909v1 Announce Type: new Abstract: Any system that models the world under finite representational capacity must compress; any compression entails a prior; and the prior is the system's bi

local-aiarxiv-cs-lg

Differential-Integral Neural Operator for Long-Term Turbulence Forecasting

arXiv:2509.21196v3 Announce Type: replace-cross Abstract: Accurately forecasting the long-term evolution of turbulence represents a grand challenge in scientific computing and is crucial for applicati

DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs

arXiv:2605.18915v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are vulnerable to jailbreak attacks, which can elicit harmful responses from MLLMs. Many MLLMs support multi-

DocQT: Improving Document Forgery Localization Robustness via Diverse JPEG Quantization Tables

arXiv:2605.19688v1 Announce Type: new Abstract: Document manipulation localization models achieve strong performance on public benchmarks yet fail to generalize to operational document workflows. We i

EventPrune: Cascaded Event-Assisted Token Pruning for Efficient First-Person Dynamic Spatial Reasoning

arXiv:2605.19506v1 Announce Type: new Abstract: First-person dynamic spatial reasoning requires models to track continuous motion and precise geometric structure, but the quadratic attention cost of T

Safety

Feature-Space Smoothing: Certified Robustness of Deep Representations

arXiv:2601.16200v3 Announce Type: replace-cross Abstract: Modern deep learning models exhibit strong capabilities across diverse applications, yet remain vulnerable to malicious inputs that induce err

safetyarxiv-cs-cv

Safety

Formal Skill: Programmable Runtime Skills for Efficient and Accurate LLM Agents

arXiv:2605.19604v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly act inside real workspaces, where tools and skills determine whether model reasoning becomes reliable act

safetyarxiv-cs-ai

Gemini 3.5 announce.

Google introduced Gemini 3.5, its latest family of models combining frontier intelligence with action capabilities, representing a major leap forward in building more capable, intelligent agents. The

model-releasesr-chatgpt

Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems

arXiv:2407.17200v3 Announce Type: replace-cross Abstract: Many real-world decision problems require solving, again and again, combinatorial optimization instances drawn from a common distribution. A r

GoTTA be Diverse: Rethinking Memory Policies for Test-Time Adaptation

arXiv:2605.19890v1 Announce Type: new Abstract: Test-time adaptation (TTA) enables a pre-trained model to adapt online to an unlabeled test stream under distribution shift. While most TTA research foc

HAVEN: Hierarchically Aligned Multimodal Benchmark for Unified Video Understanding

arXiv:2605.19223v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) exhibit strong performance on standard video tasks, their ability to faithfully summarize and reason over

Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training

arXiv:2605.18822v1 Announce Type: cross Abstract: Post-training has become essential for adapting large language models (LLMs) to complex downstream behaviors, including instruction following, prefere

Less Back-and-Forth: A Comparative Study of Structured Prompting

arXiv:2605.20149v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used for open-ended tasks, but underspecified prompts can lead to low-quality answers and additional interacti

Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries

arXiv:2509.22202v3 Announce Type: replace-cross Abstract: Large language models (LLMs) now play a central role in code generation, yet they continue to hallucinate, frequently inventing non-existent l

Lossless Anti-Distillation Sampling

arXiv:2605.18829v1 Announce Type: new Abstract: Frontier commercial generative models face a growing threat from distillation, whereby a distiller harvests generated responses and trains a competing m

researcharxiv-cs-lg

LWiAI Podcast #245 - TML-Interaction, Claude For Legal, Sam Altman on Stand

model-releaseslast-week-in-ai

This podcast episode covers three main topics: TML-Interaction (likely a new AI model or technical development), the application of Claude AI in legal settings and use cases, and Sam Altman's testimon

MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency

arXiv:2510.25897v2 Announce Type: replace Abstract: The default paradigm of post-training text-to-image generators includes post-hoc selection of generated images, and subsequent training with one rew

MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs

arXiv:2511.14159v2 Announce Type: replace Abstract: Evaluating the robustness of Large Vision-Language Models (LVLMs) is essential for their continued development and responsible deployment in real-wo

Next-Acceleration-Scale Prediction for Autoregressive MRI Reconstruction

arXiv:2605.19354v1 Announce Type: cross Abstract: MRI reconstruction is an inherently ill-posed inverse problem, since incomplete measurements admit many plausible solutions. This ambiguity becomes mo