Search: “models”

Query-Limited Community Recovery in Stochastic Block Models

arXiv:2606.02055v1 Announce Type: cross Abstract: We study exact community recovery in the two-community stochastic block model on n vertices under limited and noisy access to network data. The learne

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RealityTest: How People Probe AI Identity and Whether Models Disclose It

DGX agent

arXiv:2606.00168v1 Announce Type: new Abstract: AI systems are increasingly deployed in conversational settings where users may be uncertain whether they are speaking with a human or an AI. Despite mo

model-releasesarxiv-cs-cl

2 Jun 2026

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models

arXiv:2505.18877v4 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pr

Score-Control for Hallucination Reduction in Diffusion Models

model-releasesarxiv-cs-cv

arXiv:2606.00377v1 Announce Type: new Abstract: Diffusion models have emerged as the backbone of modern generative AI, powering advances in vision, language, audio and other modalities. Despite their

Task Structure Reverses Layerwise State Encoding in Sequence Models

arXiv:2606.00926v1 Announce Type: cross Abstract: Mechanistic studies of sequence models often treat layerwise state encodings as architectural traits: recurrent models concentrate readable state, att

researcharxiv-cs-cl

TrustLDM: Benchmarking Trustworthiness in Language Diffusion Models

arXiv:2606.00023v1 Announce Type: cross Abstract: The rapid development of Language Diffusion Models (LDMs) challenges the dominant position of auto-regressive competitors in language processing. Howe

WAON: A Large-Scale Japanese Image-Text Dataset for Cultural Adaptation in Contrastive Vision-Language Models

arXiv:2510.22276v3 Announce Type: replace-cross Abstract: Contrastive vision-language models have achieved remarkable progress through large-scale pretraining. Recent work has shown that removing Engl

When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning

arXiv:2602.08236v2 Announce Type: replace-cross Abstract: Despite rapid progress in MLLMs, visual spatial reasoning remains unreliable when correct answers depend on how a scene would appear under uns

World Models for Robotic Manipulation: A Survey

arXiv:2606.00113v1 Announce Type: new Abstract: Robotic manipulation depends on the ability to anticipate how actions reshape objects, contacts, and scene geometry before execution. Learned world mode

safetyarxiv-cs-ro

BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali

arXiv:2605.31483v1 Announce Type: new Abstract: Despite Bengali being the sixth most spoken language in the world, no prior work has systematically evaluated hallucination in large language models (LL

End-to-End Compression for Tabular Foundation Models

arXiv:2602.05649v2 Announce Type: replace Abstract: The long-standing dominance of gradient-boosted decision trees for tabular data has recently been challenged by in-context learning tabular foundati

Hardware

🆕Grok Imagine’s Video Agent Moment: Cosmos, xAI, World Models, Generative UI, & the Codex Phase for Video! https://www.latent.space/p/video…

🆕Grok Imagine’s Video Agent Moment: Cosmos, xAI, World Models, Generative UI, & the Codex Phase for Video! https://www.latent.space/p/video-agents @EthanHe_42, former @xai world model lead and @nvidia

hardwareswyx--x

Improving Small Language Models for Code Generation with Reinforcement Learning from Verification Feedback

arXiv:2605.30478v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models using programmatically checkable signals such as unit-test outcomes, enab

Local Ai

PatchWorld: Gradient-Free Optimization of Executable World Models

arXiv:2605.30880v1 Announce Type: cross Abstract: Text-agent environments are typically modeled as partially observable Markov decision processes (POMDPs), assuming that the simulator's latent state a

local-aiarxiv-cs-ai

Experts say ChatGPT, Gemini, and other Western AI models are turbocharging Iran's cyber operations, helping it develop malware and launch phishing attacks (Jacob Judah/Financial Times)

Jacob Judah / Financial Times: Experts say ChatGPT, Gemini, and other Western AI models are turbocharging Iran's cyber operations, helping it develop malware and launch phishing attacks — Western AI m

model-releasestechmeme

31 May 2026

Alignment-Guided Score Matching for Text-to-Image Alignment in Diffusion Models

arXiv:2605.30038v1 Announce Type: cross Abstract: Diffusion models generate highly realistic images but often struggle with precise text-image alignment. While recent post-training methods improve ali

AMDP: Asynchronous Multi-Directional Pipeline Parallelism for Large-Scale Models Training

arXiv:2605.29664v1 Announce Type: cross Abstract: Pipeline parallelism is essential for large-scale model training, but existing asynchronous approaches often degrade convergence due to parameter mism

Tutorials

BioArc: Discovering Optimal Neural Architectures for Biological Foundation Models

arXiv:2512.00283v3 Announce Type: replace-cross Abstract: Foundation models have revolutionized various fields such as natural language processing (NLP) and computer vision (CV). While efforts have be

tutorialsarxiv-cs-ai

Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective

arXiv:2505.20955v4 Announce Type: replace-cross Abstract: Diffusion models have achieved tremendous success in image generation, but they also raise significant concerns regarding privacy and copyrigh

researcharxiv-cs-lg

Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models

arXiv:2605.29303v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) followed by reinforcement learning (RL) has become a standard post-training paradigm for large language models. This paradi

safetyarxiv-cs-ai

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach

arXiv:2511.19316v2 Announce Type: replace-cross Abstract: Recent fine-tuning techniques for diffusion models enable them to reproduce specific image sets, such as particular faces or artistic styles,

Evolutionary Rule Extraction from Corporate Default Prediction Models

arXiv:2605.29478v1 Announce Type: cross Abstract: Small and medium-sized enterprises (SMEs) represent the majority of firms in most economies and often face financial constraints and higher vulnerabil

Feature Geometry of LoRA Adapters: A Sparse Autoencoder Analysis of Representational Divergence in Fine-Tuned Language Models

arXiv:2605.28896v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has emerged as a widely adopted approach for adapting large language models, yet the internal representational changes induce

How's it going? Reinforcement learning in language models recruits a functional welfare axis

arXiv:2605.30232v1 Announce Type: cross Abstract: How does reinforcement learning shape a language model's internal representations? We present evidence that RL recruits a pre-existing representation

safetyarxiv-cs-cl

Is Your Diffusion Sampler Actually Correct? A Sampler-Centric Evaluation of Discrete Diffusion Language Models

arXiv:2602.19619v2 Announce Type: replace Abstract: Discrete diffusion language models (dLLMs) provide a fast and flexible alternative to autoregressive models (ARMs) via iterative denoising with para

researcharxiv-cs-lg

Large language models reorganize representational geometry during in-context learning

arXiv:2605.28854v1 Announce Type: new Abstract: Large language models (LLMs) exhibit remarkable flexibility: they can adapt to novel tasks from in-context examples without any parameter updates, a cap

Midpoint Generative Models

arXiv:2605.29920v1 Announce Type: new Abstract: We introduce Midpoint Generative Models (MGM), a principled framework for training one-step generative models. MGM is based on a simple symmetry of Flow

researcharxiv-cs-lg

Multi-Turn Adaptive Prompting Attack on Large Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2602.14399v2 Announce Type: replace Abstract: Multi-turn jailbreak attacks have proven effective against text-only large language models (LLMs), where malicious content is gradually introduced t

Pocket-Dentist: On-Device Dental Image Understanding via Efficient Multimodal Large Language Models

arXiv:2605.29299v1 Announce Type: cross Abstract: Evaluations of dental vision-language models remain fragmented across datasets, task definitions and metrics, and often ignore their computational cos

Prioritize the Process, Not Just the Outcome: Rewarding Latent Thought Trajectories Improves Reasoning in Looped Language Models

arXiv:2602.10520v3 Announce Type: replace Abstract: Looped Language Models (LoopLMs) perform multi-step latent reasoning prior to token generation and outperform conventional LLMs on reasoning benchma

Tutorials

Reasoning that Travels: Dissecting How Chain-of-Thought Transfers Across Models

arXiv:2605.28913v1 Announce Type: new Abstract: Large reasoning models (LRMs) often generate extensive chain-of-thought (CoT) traces before producing a final answer. As explicit textual artifacts, the

tutorialsarxiv-cs-cl

Spurious Prompts: Can Irrelevant Prompts Steer Large Language Models?

arXiv:2605.29678v1 Announce Type: new Abstract: Large language models are highly sensitive to prompts, but this sensitivity is usually studied through task-relevant instructions, demonstrations, or re

researcharxiv-cs-cl

Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies

arXiv:2605.29712v1 Announce Type: cross Abstract: Grounded claim factuality checking is important for large language model (LLM) applications such as retrieval-augmented generation, as it helps users

Three-dimensional Conditional Diffusion Models for Cosmological 21 cm Lightcone Emulation

arXiv:2605.29016v1 Announce Type: cross Abstract: We investigate conditional diffusion modeling for three-dimensional 21 cm lightcone emulation, focusing on cubes with a sky-plane size of 64imes64 and

Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy

arXiv:2509.21190v4 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is a critical task, but developing models that generalize to unseen data in a zero-shot manner remains a

researcharxiv-cs-ai

Local Ai

Towards Understanding the Shape of Representations in Protein Language Models

arXiv:2509.24895v2 Announce Type: replace Abstract: While protein language models (PLMs) are one of the most promising avenues of research for future de novo protein design, the way in which they tran

local-aiarxiv-cs-lg

When the Same Coefficients Reach Different Places: Asymmetric Realizability in Transplanting Tokenizers across Large Language Models

arXiv:2601.00065v3 Announce Type: replace-cross Abstract: Tokenizer transplant in cross-vocabulary model composition reconstructs donor-only embedding rows as weighted combinations over shared lexical

Agents

AIBuildAI-2: A Knowledge-Enhanced Agent for Automatically Building AI Models

arXiv:2605.27873v1 Announce Type: new Abstract: AI models underpin data-centric applications from image and text processing to scientific discovery in biology, physics, and chemistry. Yet developing t

agentsarxiv-cs-ai

AlphaForgeBench: Benchmarking End-to-End Trading Strategy Design with Large Language Models

arXiv:2602.18481v2 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) has led to a surge of financial benchmarks, evolving from static knowledge evaluation to

Anthropic says it expects Mythos-class models to be available to all customers 'in the coming weeks' following the development of stronger safeguards (Madison Mills/Axios)

Madison Mills / Axios: Anthropic says it expects Mythos-class models to be available to all customers “in the coming weeks” following the development of stronger safeguards — Anthropic released Claude

model-releasestechmeme

Benchmarks are Not Enough: RAMP for Runtime Assessing of Agentic Models in Production Systems

arXiv:2605.27492v1 Announce Type: cross Abstract: LLM agents are rapidly evolving from coding assistants into autonomous software engineering systems. However, existing evaluation methodologies remain

Cultural Binding Heads in Language Models

arXiv:2605.28543v1 Announce Type: new Abstract: LLMs often default to equal treatment across cultural groups, even though context warrants differentiation: this is a lack of difference awareness. Usin

DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes

arXiv:2605.28421v1 Announce Type: new Abstract: Reinforcement learning has become a central paradigm for advancing reasoning in large language models, yet most existing methods still depend on stronge

safetyarxiv-cs-ai

From Pixels to Words -- Towards Native One-Vision Models at Scale

arXiv:2605.28820v1 Announce Type: new Abstract: Current vision-language models (VLMs) typically stitch together separate image encoders and language decoders via multi-stage alignment, a modular frame

safetyarxiv-cs-cv

High Performance, Low Reliability: Uncertainty Benchmarking for Tabular Foundation Models

arXiv:2605.28554v1 Announce Type: new Abstract: Recent Tabular Foundation Models (TFMs) have demonstrated state-of-the-art predictive performance, often surpassing Gradient-Boosted Decision Trees (GBD

{Omega}-QVLA: Robust Quantization for Vision-Language-Action Models via Composite Rotation and Per-step Scaling

model-releasesarxiv-cs-cv

arXiv:2605.28803v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models unify perception, reasoning, and control within a single policy, yet their multi-billion-parameter backbones and dif

Playing with Words, Improving with Rewards: Training Language Models for Creative Association

arXiv:2605.27832v1 Announce Type: new Abstract: Large Language Models (LLMs) are being applied to increasingly difficult problems and use cases. To navigate their vast solution spaces effectively, LLM

researcharxiv-cs-cl

PrunePath: Towards Highly Structured Sparse Language Models

arXiv:2605.28283v1 Announce Type: cross Abstract: Feed-forward networks (FFNs) dominate the parameter count and computation of modern language models, yet existing pruning methods often struggle to co