Search: “models”

DynMuon: A Dynamic Spectral Shaping View of Muon

arXiv:2605.17109v1 Announce Type: cross Abstract: In recent years, Muon has emerged as the dominant method for training large language models, and transformers more broadly. The essential difference,

Explicit Logic Channel for Validation and Enhancement of MLLMs on Zero-Shot Tasks

DGX agent

arXiv:2603.11689v2 Announce Type: replace Abstract: Frontier Multimodal Large Language Models (MLLMs) exhibit remarkable capabilities in Visual-Language Comprehension (VLC) tasks. However, they are of

researcharxiv-cs-ai

19 May 2026

Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation

arXiv:2605.18020v1 Announce Type: new Abstract: Federated Learning (FL) algorithms implicitly assume that clients passively comply with server-side orchestration by sharing local model updates upon se

local-aiarxiv-cs-lg

FedSDR: Federated Self-Distillation with Rectification

arXiv:2605.18028v1 Announce Type: cross Abstract: Federated fine-tuning of Large Language Models faces severe statistical heterogeneity. However, existing model-level defenses often overlook the root

local-aiarxiv-cs-ai

Gemini 3.5 Flash is now available in Windsurf!

model-releasescognition-ai--x

Cognition AI announced the availability of Gemini 3.5 Flash within Windsurf, their AI development environment. This integration brings Google's faster Gemini 3.5 Flash model to Windsurf users, likely

Haptic Rendering of Fractional-Order Viscoelasticity: Passivity and Rendering Fidelity

arXiv:2605.16389v1 Announce Type: cross Abstract: Haptic rendering of viscoelastic materials that exhibit creep and stress relaxation is crucial for many applications, such as medical training with re

researcharxiv-cs-ai

High-dimensional ridge regression with random features for non-identically distributed data with a variance profile

arXiv:2504.03035v2 Announce Type: replace-cross Abstract: Random feature ridge regression is often analyzed in the high-dimensional regime under the homogeneous sampling model x_i=Sigma^{1/2}x_i', whe

In-context learning enables continental-scale subsurface temperature prediction from sparse local observations

arXiv:2605.16665v1 Announce Type: new Abstract: Continental-scale knowledge of subsurface temperature is limited by the cost and sparsity of borehole measurements, but such information is essential fo

local-aiarxiv-cs-lg

Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimization

arXiv:2602.17679v2 Announce Type: replace Abstract: Bayesian optimization (BO) is a powerful method for optimizing black-box manufacturing processes, but its performance is often limited when dealing

Latency-Aware Deep Learning Benchmark for Real-Time Cyber-Physical Attack and Fault Classification in Inverter-Dominated Power Grids

arXiv:2605.17256v1 Announce Type: cross Abstract: This work introduces a latency-aware benchmarking framework for evaluating deep learning models in power system anomaly detection using high-fidelity,

LEAF: A Living Benchmark for Event-Augmented Forecasting

arXiv:2605.16358v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly applied to forecasting. To evaluate this capability while mitigating pre-training data contamination, se

Learning more physically realistic dynamics in machine-learning based weather forecasting with latent-space constraints

arXiv:2510.04006v2 Announce Type: replace Abstract: Data-driven machine learning (ML) models are reshaping weather forecasting and have shown the potential to accelerate and surpass traditional physic

researcharxiv-cs-lg

Learning Quantifiable Visual Explanations Without Ground-Truth

arXiv:2605.18681v1 Announce Type: new Abstract: Explainable AI (XAI) techniques are increasingly important for the validation and responsible use of modern deep learning models, but are difficult to e

researcharxiv-cs-ai

LoopQ: Quantization for Recursive Transformers

arXiv:2605.16343v1 Announce Type: cross Abstract: Looped language models (LoopLMs) improve parameter efficiency by recursively reusing Transformer blocks, enabling deeper computation under a fixed mod

MetaCogAgent: A Metacognitive Multi-Agent LLM Framework with Self-Aware Task Delegation

arXiv:2605.17292v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems have shown promise for solving complex tasks through agent collaboration. However, existing frameworks as

Safety

Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics

arXiv:2605.18549v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) introduce new opportunities for safety monitoring through their Chain of Thought (CoT) reasoning. However, CoT is not alwa

safetyarxiv-cs-cl

My notes on Gemini 3.5 Flash - 3x the price of Gemini 3 Flash but Google are planning to use it for many of their own products https://simon…

model-releasessimon-willison--x

Google's Gemini 3.5 Flash model costs approximately 3x more than Gemini 3 Flash, despite being a newer version. Google plans to integrate Gemini 3.5 Flash into many of their own products, suggesting t

NeuSymMS: A Hybrid Neuro-Symbolic Memory System for Persistent, Self-Curating LLM Agents

arXiv:2605.17596v1 Announce Type: new Abstract: We present NeuSymMS, an adaptive memory system that enables large language model (LLM) agents to learn, remember, and reason about users across sessions

Overeager Coding Agents: Measuring Out-of-Scope Actions on Benign Tasks

arXiv:2605.18583v1 Announce Type: cross Abstract: Coding agents now run autonomously with shell, file, and network privileges. When a user issues a benign request, the agent sometimes does more than a

PaliBench: A Multi-Reference Blueprint for Classical Language Translation Benchmarks

model-releasesarxiv-cs-cl

arXiv:2605.16881v1 Announce Type: new Abstract: Digital humanities projects increasingly rely on machine translation and large language models to widen access to classical, religious, and otherwise un

PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments

arXiv:2603.23231v2 Announce Type: replace Abstract: Empowering large language models with long-term memory is crucial for building agents that adapt to users' evolving needs. Existing evaluations of t

PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media

arXiv:2605.17187v1 Announce Type: cross Abstract: Social media are shifting towards pluralism -- community-governed platforms where groups define their own norms. What violates rules in one community

Protection Is (Nearly) All You Need: Structural Protection Dominates Scoring in Globally Capped KV Eviction

model-releasesarxiv-cs-cl

arXiv:2605.18053v1 Announce Type: cross Abstract: We study KV cache eviction under a shared globally capped decode-time harness. Seven policies (LRU, H2O, SnapKV, StreamingLLM, Ada-KV, QUEST, Random)

ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification

arXiv:2510.12534v4 Announce Type: replace Abstract: The rapid growth of user-generated text across digital platforms has intensified the need for interpretable models capable of fine-grained text clas

Reference anything: Gemini Omni extends Gemini's native multimodality, allowing you to blend combinations of text, audio, image, and video i…

model-releasesgoogle-ai--x

Gemini Omni is an extension of Google's Gemini model that enhances its multimodal capabilities by enabling seamless integration of text, audio, image, and video inputs and outputs. This advancement al

SAM 2++: Tracking Anything at Any Granularity

model-releasesarxiv-cs-cv

arXiv:2510.18822v4 Announce Type: replace Abstract: Due to the varying granularity of target states across different tasks, most existing trackers are tailored to a single task, which specificity limi

SpecSem-Net: Integrating Spectral and Semantic Features for Robust AI-generated Video Detection

model-releasesarxiv-cs-cv

arXiv:2605.17311v1 Announce Type: new Abstract: The remarkable visual fidelity of recent commercial video generative models, such as Sora and Veo, renders robust AI-generated video detection increasin

Hardware

Stable Audio 3

arXiv:2605.17991v1 Announce Type: cross Abstract: Stable Audio 3 is a family of fast latent diffusion models (small, medium, large) for variable-length audio generation and editing. Since our models c

hardwarearxiv-cs-ai

Tutorials

StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flow

arXiv:2605.16486v1 Announce Type: cross Abstract: Diffusion and flow-based models are ubiquitously used for generative modelling and density estimation. They admit a deterministic probability flow ord

tutorialsarxiv-cs-lg

State-of-the-Art Claims Require State-of-the-Art Evidence

arXiv:2605.17273v1 Announce Type: cross Abstract: State-of-the-Art (SOTA) claims pervade Artificial Intelligence (AI) and Machine Learning (ML) research. These claims rest on benchmark evaluations, wh

Supervising the search process produces reliable and generalizable information-seeking agents

arXiv:2502.13957v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are transforming web search by shifting from document ranking to synthesizing answers, and are increasingly deplo

TaskGround: Structured Executable Task Inference for Full-Scene Household Reasoning

arXiv:2605.18109v1 Announce Type: new Abstract: In real home deployments, household agents must often operate from a complete household scene and a situated household request, rather than from a clean

ToolMATH: A Diagnostic Benchmark for Long-Horizon Tool Use under Systematic Tool-Catalog Constraints

model-releasesarxiv-cs-cl

arXiv:2602.21265v2 Announce Type: replace Abstract: We introduce ToolMATH, a math-grounded diagnostic benchmark for evaluating long-horizon tool use under controllable tool-catalog conditions. ToolMAT

Trustworthiness in Retrieval-Augmented Generation Systems: A Survey

arXiv:2409.10102v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs). Although ex

TusoAI: Agentic Optimization for Scientific Methods

arXiv:2509.23986v2 Announce Type: replace Abstract: Scientific discovery is often slowed by the manual development of computational tools needed to analyze complex experimental data. Building such too

Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation

arXiv:2605.18740v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) still struggle with fine-grained visual understanding, where answers often depend on small but decisive evide

local-aiarxiv-cs-ai

Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs

arXiv:2605.18172v1 Announce Type: new Abstract: Leveraging the universal representations of pre-trained LLMs and MLLMs offers a promising path toward brain foundation models. However, visually-evoked

Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering

arXiv:2605.18313v1 Announce Type: cross Abstract: Small vision-language models (2-8B) are well-suited for clin- ical deployment due to privacy constraints, limited connectivity, and low-latency requir

When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents

arXiv:2601.17887v2 Announce Type: replace Abstract: Long-term memory enables large language model (LLM) agents to support personalized and sustained interactions. However, most work on personalized ag

Safety

Why Do Safety Guardrails Degrade Across Languages?

arXiv:2605.17173v1 Announce Type: cross Abstract: Large language models exhibit safety degradation in non-English languages. Standard evaluation relies on Jailbreak Success Rate (JSR), which confounds

safetyarxiv-cs-ai

Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise

arXiv:2605.15314v1 Announce Type: new Abstract: We study nonconvex stochastic optimization under the Blum-Gladyshev (mathsf{BG}-0) noise model, where the stochastic gradient variance grows quadratical

BiomedAP: A Vision-Informed Dual-Anchor Framework with Gated Cross-Modal Fusion for Robust Medical Vision-Language Adaptation

arXiv:2605.15736v1 Announce Type: cross Abstract: Biomedical Vision--Language Models (VLMs) have shown remarkable promise in few-shot medical diagnosis but face a critical bottleneck: extit{fragility

Can We Trust AI-Inferred User States. A Psychometric Framework for Validating the Reliability of Users States Classification by LLMs in Operational Environments

arXiv:2605.15734v1 Announce Type: new Abstract: The use of large language models to assess user states in conversational and adaptive systems is based on the assumption that the metrics used for such

COPRA: Conditional Parameter Adaptation with Reinforcement Learning for Video Anomaly Detection

model-releasesarxiv-cs-cv

arXiv:2605.15325v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown strong performance in video anomaly detection (VAD) while providing interpretable predictions. However, existin

Safety

ElasticDiT: Efficient Diffusion Transformers via Elastic Architecture and Sparse Attention for High-Resolution Image Generation on Mobile Devices

arXiv:2605.15684v1 Announce Type: new Abstract: The Diffusion Transformer (DiT) architecture is the state-of-the-art paradigm for high-fidelity image generation, underpinning models like Stable Diffus

safetyarxiv-cs-cv

Fully Open Meditron: An Auditable Pipeline for Clinical LLMs

arXiv:2605.16215v1 Announce Type: new Abstract: Clinical decision support systems (CDSS) require scrutable, auditable pipelines that enable rigorous, reproducible validation. Yet current LLM-based CDS

GenAI-Driven Approach to RISC-V Supply Chain Exploration

arXiv:2605.15223v1 Announce Type: cross Abstract: This paper presents an LLM-empowered workflow for RISC-V supply chain analysis, integrating Vision-Language Models (VLMs) and Model-Driven Engineering

researcharxiv-cs-ai

Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning

arXiv:2605.15411v1 Announce Type: cross Abstract: We study contextual dynamic pricing in a semiparametric scalar-index valuation model where the latent value is v_t=mu_ast(mathsf c_t)+xi_t, with an un