Search: “models”

ALINC: Active Learning for Inductive Node Classification via Graph Sampling

arXiv:2606.04647v1 Announce Type: new Abstract: Active learning (AL) for node classification typically focuses on selecting the most informative nodes for annotation within one or a few large graphs (

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Model Releases

Andon Labs' Real-World AI Evals: Claude calls the FBI, AI CEOs, price cartels, Butter-Bench, & Luna https://latent.space/p/andon @andonlabs …

DGX agent

Andon Labs' Real-World AI Evals: Claude calls the FBI, AI CEOs, price cartels, Butter-Bench, & Luna https://latent.space/p/andon @andonlabs cofounders @lukaspet and @axelbacklund explain why dollar-de

model-releasesswyx--x

4 Jun 2026

Benchmarking Living-Screen-Native GUI Agents on Short-Video Platforms

model-releasesarxiv-cs-cl

arXiv:2606.04701v1 Announce Type: cross Abstract: GUI agents today assume a static screen, where the world is frozen between two actions. However, real interfaces such as short-video applications viol

Beyond Structural Symmetries: Linear Mode Connectivity via Neuron Identifiability

arXiv:2606.04754v1 Announce Type: new Abstract: Many striking phenomena in deep learning, such as linear mode connectivity and the structured behavior of training dynamics, are closely tied to paramet

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?

arXiv:2506.10912v4 Announce Type: replace Abstract: Toxicity remains a leading cause of early-stage drug development failure. Despite advances in molecular design and property prediction, the task of

Can Generalist Agents Automate Data Curation?

arXiv:2606.04261v1 Announce Type: new Abstract: Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement,

Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression

arXiv:2510.08647v2 Announce Type: replace-cross Abstract: Recent developments have enabled advanced reasoning in Large Language Models (LLMs) via long Chain-of-Thought (CoT), trading efficiency during

researcharxiv-cs-ai

CDPM-Align: Multi-Scale Guidance-Aligned Diffusion Pretraining for Robust Few-Shot Anatomical Landmark Detection

model-releasesarxiv-cs-cv

arXiv:2606.04898v1 Announce Type: new Abstract: Anatomical landmark detection is a fundamental task in medical image analysis supporting a wide range of diagnostic and interventional workflows. Althou

ChannelTok: Efficient Flexible-Length Vision Tokenization

model-releasesarxiv-cs-cv

arXiv:2606.04461v1 Announce Type: new Abstract: Leading flexible vision tokenizers achieve SOTA quality at an extreme cost, relying on parameter-heavy backbones and slow, multi-step generative decoder

Tutorials

Characterizing, Evaluating, and Optimizing Complex Reasoning

arXiv:2602.08498v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) increasingly rely on reasoning traces with complex internal structures. However, existing work lacks a unified answer

tutorialsarxiv-cs-cl

ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents

arXiv:2407.03884v4 Announce Type: replace-cross Abstract: Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and

researcharxiv-cs-ai

Computational conceptual history of scientific concepts: From early digital methods to LLMs

arXiv:2606.04118v1 Announce Type: new Abstract: This article situates large language models (LLMs) within the longer history of computational approaches to concept analysis in the history, philosophy,

Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation

arXiv:2603.05881v2 Announce Type: replace Abstract: Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Existing methods are predominantly answer-first, produ

safetyarxiv-cs-cl

Constraint-Enhanced Physical Search through Correlation Matching

arXiv:2606.03554v1 Announce Type: cross Abstract: Physical systems do not merely add noise to search processes; they impose constraints that generate structured correlations. We propose a principle of

Cross-Prompt Generalization in Detecting AI-Generated Fake News Using Interpretable Linguistic Features

arXiv:2606.04199v1 Announce Type: new Abstract: The increasing use of large language models has raised concerns about the spread of AI-generated fake news, particularly under varying prompting strateg

D^3-MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2606.04884v1 Announce Type: new Abstract: Traditional end-to-end autonomous driving frameworks frequently suffer from the 'style-averaging' dilemma when trained on high-variance human demonstrat

DLLG: Dynamic Logit-Level Gating of LLM Experts

model-releasesarxiv-cs-cl

arXiv:2606.04378v1 Announce Type: new Abstract: Leveraging multiple specialized LLMs can combine complementary strengths, but existing approaches trade adaptability for stability: routing commits prem

DLO-Lab: Benchmarking Deformable Linear Object Manipulations with Differentiable Physics

model-releasesarxiv-cs-ro

arXiv:2606.04206v1 Announce Type: new Abstract: We address the challenge of enabling robots to manipulate deformable linear objects (DLOs), such as ropes, cables, and rubber bands. Prior work has prim

Emotion Entanglement and Bayesian Inference for Multi-Dimensional Emotion Understanding

arXiv:2604.00819v2 Announce Type: replace-cross Abstract: Understanding emotions in natural language is inherently a multi-dimensional reasoning problem, where multiple affective signals interact thro

From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents

arXiv:2606.04329v1 Announce Type: cross Abstract: Memory is a core component of AI agents, enabling them to accumulate knowledge across interactions and improve performance. However, persistent memory

Geometry Gaussians: Decoupling Appearance and Geometry in Gaussian Splatting

model-releasesarxiv-cs-cv

arXiv:2606.05124v1 Announce Type: cross Abstract: After the success of 3D Gaussian Splatting (3DGS) for novel view synthesis, many works have explored how to also use it for geometric surface represen

GRAIL: Gradient-Reweighted Advantages for Reinforcement Learning with Verifiable Rewards

arXiv:2606.04889v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (e.g. GRPO) is now a common way to improve mathematical reasoning in Large Language Models (LLMs). Howeve

safetyarxiv-cs-cl

Graph Set Transformer

arXiv:2606.05116v1 Announce Type: new Abstract: We introduce the Graph Set Transformer (GST), a neural network architecture for learning on sets of graphs, designed for tasks in which per-element pred

Improving Semantic Uncertainty Quantification in LVLMs with Semantic Gaussian Processes

arXiv:2512.14177v3 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) often produce plausible but unreliable outputs, making robust uncertainty estimation essential. Recent work on

researcharxiv-cs-cv

Inference-Time Vulnerability Beyond Shallow Safety: Alignment Along Generation Trajectories

arXiv:2606.04778v1 Announce Type: new Abstract: Safety-aligned Large Language Models (LLMs) remain vulnerable to interventions during inference that redirect generation toward harmful outputs. Recent

Learning While Acting: A Skill-Enhanced Test-Time Co-Evolution Framework for Online Lifelong Learning Agents

arXiv:2606.04815v1 Announce Type: cross Abstract: Lifelong learning is essential for Large Language Model (LLM) agents operating in dynamic, interactive environments. However, existing lifelong learni

LiSeCo: Linear Semantic Control for Language Generation

arXiv:2405.15454v4 Announce Type: replace Abstract: The prevalence of Large Language Models (LLMs) in critical applications highlights the need for controlled language generation methods that are both

Hardware

LLM Compression with Jointly Optimizing Architectural and Quantization choices

arXiv:2606.04063v1 Announce Type: cross Abstract: Deploying large language models (LLMs) is challenging due to their significant memory and computational requirements. While some methods address this

hardwarearxiv-cs-ai

Metric-Aware Hybrid Forecasting for the CTF4Science Lorenz Challenge

arXiv:2606.04191v1 Announce Type: cross Abstract: We describe our approach to the CTF4Science Lorenz challenge, a benchmark that mixes short-horizon forecasting, long-time distribution matching, and t

MuCO: Generative Peptide Cyclization Empowered by Multi-stage Conformation Optimization

arXiv:2602.11189v2 Announce Type: replace-cross Abstract: Modeling peptide cyclization is critical for the virtual screening of candidate peptides with desirable physical and pharmaceutical properties

researcharxiv-cs-ai

Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI

model-releaseshugging-face

Nemotron 3.5 Content Safety is NVIDIA's multimodal safety solution designed for enterprise AI applications, offering customizable safeguards for both text and image inputs across different global cont

On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers

arXiv:2603.28762v2 Announce Type: replace-cross Abstract: Modern Text-to-Image (T2I) diffusion models have achieved remarkable semantic alignment, yet they often suffer from a significant lack of vari

Parameter-Efficient Fine-Tuning with Learnable Rank

model-releasesarxiv-cs-cl

arXiv:2606.04325v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a popular parameter-efficient fine-tuning (PEFT) method that restricts weight updates to low-rank adapters, introducing a

Agents

Parthenon Law: A Self-Evolving Legal-Agent Framework

arXiv:2606.04602v1 Announce Type: new Abstract: As agents grow more capable, legal-domain LLM agents promise to turn document-heavy matters into reviewable work products -- yet reliable deployment fac

agentsarxiv-cs-ai

Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization

arXiv:2606.04866v1 Announce Type: new Abstract: Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing conce

QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples

arXiv:2606.04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in

QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation

arXiv:2606.04689v1 Announce Type: cross Abstract: Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is often limited by severe long-tail p

Query-based Cross-Modal Projector Bolstering Mamba Multimodal LLM

arXiv:2606.04719v1 Announce Type: new Abstract: The Transformer's quadratic complexity with input length imposes an unsustainable computational load on large language models (LLMs). In contrast, the S

Local Ai

R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search

arXiv:2606.04823v1 Announce Type: new Abstract: Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, where a system must plan, use tools, and act over extended horizon

local-aiarxiv-cs-ai

Rollout-Level Advantage-Prioritized Experience Replay for GRPO

arXiv:2606.04560v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards with GRPO is a standard approach for post-training reasoning LLMs. It remains sample inefficient. Each

SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization

arXiv:2505.11166v3 Announce Type: replace-cross Abstract: Despite advances in pretraining with extended context sizes, large language models (LLMs) still face challenges in effectively utilizing real-

Sources: Anthropic has embedded around half a dozen forward-deployed engineers within the NSA to help the agency deploy Mythos for offensive cyber operations (Financial Times)

Financial Times: Sources: Anthropic has embedded around half a dozen forward-deployed engineers within the NSA to help the agency deploy Mythos for offensive cyber operations — Arrangement comes as AI

model-releasestechmeme

Agents

Strabo: Declarative Specification and Implementation of Agentic Interaction Protocols

arXiv:2606.05043v1 Announce Type: new Abstract: The last few years have witnessed major advances in the modeling and implementation of multiagent systems based on declarative interaction protocols. Ou

agentsarxiv-cs-ai

Local Ai

Testing Ideogram JSON prompts in Ernie Image

local-air-stablediffusion

Ideogram 4.0 is a 9.3B open-weight text-to-image model that supports structured JSON prompts enabling control over layout, color, and text placement . Baidu's ERNIE Image is a multilingual text-to-ima

Local Ai

The Biomimetic Architecture of Software 4.0

arXiv:2606.04025v1 Announce Type: cross Abstract: Dominant programming paradigms inherit an execution model optimised for a bygone era of a single human mind instructing a local machine, leaving conte

local-aiarxiv-cs-ai

The Invisible Lottery: How Subtle Cues Steer Algorithm Choice in LLM Code Generation

arXiv:2606.04057v1 Announce Type: cross Abstract: Large language models (LLMs) now generate substantial production code, often for tasks with multiple valid algorithmic solutions. Incidental prompt cu

Towards Pretraining Text Encoders for TabPFN

arXiv:2606.04876v1 Announce Type: new Abstract: Tabular foundation models, such as TabPFN, achieve strong performance on tabular datasets with numerical and categorical data, but do not natively handl

safetyarxiv-cs-lg

Transmuting prompts into weights

arXiv:2510.08734v3 Announce Type: replace Abstract: A growing body of research has demonstrated that the behavior of large language models can be effectively controlled at inference time by directly m

researcharxiv-cs-lg