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A Theoretical Analysis of Why Masked Diffusion Models Mitigate the Reversal Curse

arXiv:2602.02133v2 Announce Type: replace-cross Abstract: Autoregressive language models (ARMs) suffer from the reversal curse: after learning ''A is B,'' they often fail on the reverse query ''B is A

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Research

Curriculum Learning-Guided Progressive Distillation in Large Language Models

DGX agent

arXiv:2605.11260v1 Announce Type: new Abstract: Knowledge distillation is a key technique for transferring the capabilities of large language models (LLMs) into smaller, more efficient student models.

researcharxiv-cs-lg

13 May 2026

Debiased Model-based Representations for Sample-efficient Continuous Control

arXiv:2605.11711v1 Announce Type: new Abstract: Model-based representations recently stand out as a promising framework that embeds latent dynamics information into the representations for downstream

safetyarxiv-cs-lg

probably correct, from @polynoamial: “with today’s AI models, intelligence is a function of inference compute.” but what about tomorrow’s mo…

probably correct, from @polynoamial: “with today’s AI models, intelligence is a function of inference compute.” but what about tomorrow’s models? never forget that humans are remarkably intelligent (t

safetygary-marcus--x

Stop Marginalizing My Dreams: Model Inversion via Laplace Kernel for Continual Learning

arXiv:2605.11804v1 Announce Type: cross Abstract: Data-free continual learning (DFCIL) relies on model inversion to synthesize pseudo-samples and mitigate catastrophic forgetting. However, existing in

Think, then Score: Decoupled Reasoning and Scoring for Video Reward Modeling

arXiv:2605.05922v2 Announce Type: replace Abstract: Recent advances in generative video models are increasingly driven by post-training and test-time scaling, both of which critically depend on the qu

U-STS-LLM A Unified Spatio-Temporal Steered Large Language Model for Traffic Prediction and Imputation

arXiv:2605.11735v1 Announce Type: new Abstract: The efficient operation of modern cellular networks hinges on the accurate analysis of spatio-temporal traffic data. Mastering these patterns is essenti

A startup wants to pull magnesium from seawater without torching the environment. Another wants to take small language models to the podium …

model-releasesai21-labs--x

A startup wants to pull magnesium from seawater without torching the environment. Another wants to take small language models to the podium for enterprise customers. Today @jason and @alex sat down wi

Applications

Counterfactual Stress Testing for Image Classification Models

arXiv:2605.10894v1 Announce Type: new Abstract: Deep learning models in medical imaging often fail when deployed in new clinical environments due to distribution shifts in demographics, scanner hardwa

applicationsarxiv-cs-cv

CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving

arXiv:2605.10426v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing reasoning mechanisms sti

CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models

arXiv:2605.08960v1 Announce Type: cross Abstract: Crystal generative models mainly learn what stable crystals look like, with little explicit supervision for what makes them stable. We reveal a substa

Data-driven transport modelling without overfit

arXiv:2605.08801v1 Announce Type: new Abstract: Macroscopic transport modelling aims to predict traffic flows after proposed public policy interventions, such as a new road or railway section or a tem

safetyarxiv-cs-lg

Tutorials

Emergent Semantic Role Understanding in Language Models

arXiv:2605.09187v1 Announce Type: new Abstract: Understanding how linguistic structure emerges in language models is central to interpreting what these systems learn from data and how much supervision

tutorialsarxiv-cs-ai

Evidence-based Decision Modeling for Synthetic Face Detection with Uncertainty-driven Active Learning

arXiv:2605.09935v1 Announce Type: new Abstract: With the rapid development of deep generative models, forged facial images are massively exploited for illegal activities. Although existing synthetic f

Explanation Fairness in Large Language Models: An Empirical Analysis of Disparities in How LLMs Justify Decisions Across Demographic Groups

arXiv:2605.08671v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed not only to make decisions but to explain them. While AI decision fairness has been studied ext

Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage

arXiv:2605.09986v1 Announce Type: cross Abstract: Training a language model on data scattered across bandwidth-limited nodes that cannot be centralized is a setting that arises in clinical networks, e

Tutorials

IntroLM: Introspective Language Models via Prefilling-Time Self-Evaluation

arXiv:2601.03511v2 Announce Type: replace-cross Abstract: A major challenge for the operation of large language models (LLMs) is how to predict whether a specific LLM will produce sufficiently high-qu

tutorialsarxiv-cs-ai

Kernel-Gradient Drifting Models

arXiv:2605.10727v1 Announce Type: new Abstract: We propose kernel-gradient drifting, a one-step generative modeling framework that replaces the fixed Euclidean displacement direction in drifting model

researcharxiv-cs-lg

Latent Geometry Beyond Search: Amortizing Planning in World Models

arXiv:2605.08732v1 Announce Type: cross Abstract: Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these space

Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.08787v1 Announce Type: new Abstract: Recent advances in 3D medical vision-language models have enabled joint reasoning over volumetric images and text, showing strong performance in medical

MicroWorld: Empowering Multimodal Large Language Models to Bridge the Microscopic Domain Gap with Multimodal Attribute Graph

arXiv:2605.10120v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) show remarkable potential for scientific reasoning, yet their performance in specialized domains such as micr

Mind-Paced Speaking: A Dual-Brain Approach to Real-Time Reasoning in Spoken Language Models

arXiv:2510.09592v2 Announce Type: replace Abstract: Real-time Spoken Language Models (SLMs) struggle to leverage Chain-of-Thought (CoT) reasoning due to the prohibitive latency of generating the entir

MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models

arXiv:2603.18256v2 Announce Type: replace-cross Abstract: Recent reasoning-based large language models have shown strong performance on tasks with verifiable outcomes, but their use in de novo molecul

Personal Visual Context Learning in Large Multimodal Models

model-releasesarxiv-cs-cv

arXiv:2605.10936v1 Announce Type: new Abstract: As wearable devices like smart glasses integrate Large Multimodal Models (LMMs) into the continuous first-person visual streams of individual users, the

Product-of-Gaussian-Mixture Diffusion Models for Joint Nonlinear MRI Reconstruction

model-releasesarxiv-cs-cv

arXiv:2605.10629v1 Announce Type: new Abstract: Recently, diffusion models have attracted considerable attention for magnetic resonance image reconstruction due to their high sample quality. However,

PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models

arXiv:2501.03544v5 Announce Type: replace-cross Abstract: Recent text-to-image (T2I) models have exhibited remarkable performance in generating high-quality images from text descriptions. However, the

safetyarxiv-cs-ai

Reasoning emerges from constrained inference manifolds in large language models

arXiv:2605.08142v1 Announce Type: cross Abstract: Reasoning in large language models is predominantly evaluated through labeled benchmarks, conflating task performance with the quality of internal inf

Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow-Matching Models

arXiv:2605.10759v1 Announce Type: cross Abstract: Diffusion and flow-matching models scale because pretraining is supervised regression: a clean sample is noised analytically, and a model regresses ag

Relational reasoning and inductive bias in transformers and large language models

arXiv:2506.04289v3 Announce Type: replace Abstract: Transformer-based models have demonstrated remarkable reasoning abilities, but the mechanisms underlying relational reasoning remain poorly understo

safetyarxiv-cs-lg

Relative Kinetic Utility for Reasoning-Aware Structural Pruning in Large Language Models

arXiv:2605.09008v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) prompting symbolized a huge improvement of reasoning capabilities of Large Language Models (LLMs). However, scaling up test-tim

Applications

Sundial: A Family of Highly Capable Time Series Foundation Models

arXiv:2502.00816v4 Announce Type: replace Abstract: We introduce Sundial, a family of native, flexible, and scalable time series foundation models. To predict the next-patch's distribution, we propose

applicationsarxiv-cs-lg

TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models

arXiv:2605.06047v2 Announce Type: replace-cross Abstract: Tabular foundation models (TFMs), such as TabPFN-2.6, TabICLv2, ConTextTab, Mitra, LimiX, and TabDPT, achieve strong zero-shot performance thr

The Safety-Aware Denoiser for Text Diffusion Models

arXiv:2605.08116v1 Announce Type: cross Abstract: Recent work on text diffusion models offers a promising alternative to autoregressive generation, but controlling their safety remains underexplored.

safetyarxiv-cs-ai

Tutorials

The two clocks and the innovation window: When and how generative models learn rules

arXiv:2605.10019v1 Announce Type: cross Abstract: Generative models trained on finite data face a fundamental tension: their score-matching or next-token objective converges to the empirical training

tutorialsarxiv-cs-ai

VLADriver-RAG: Retrieval-Augmented Vision-Language-Action Models for Autonomous Driving

arXiv:2605.08133v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving, yet their reliance on implicit parametric

When to Trust Imagination: Adaptive Action Execution for World Action Models

arXiv:2605.06222v2 Announce Type: replace-cross Abstract: World Action Models (WAMs) have recently emerged as a promising paradigm for robotic manipulation by jointly predicting future visual observat

A Reproducible Optimisation Protocol for Calibrating Prompt-Based Large Language Model Workflows in Evidence Synthesis

arXiv:2605.06937v1 Announce Type: new Abstract: This methods article presents a reproducible calibration workflow for prompt-based large language models (LLMs) in structured evidence-synthesis tasks.

Beyond Retrieval: A Multitask Benchmark and Model for Code Search

arXiv:2605.04615v2 Announce Type: replace-cross Abstract: Code search has usually been evaluated as first-stage retrieval, even though production systems rely on broader pipelines with reranking and d

Applications

Causal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings

arXiv:2605.06941v1 Announce Type: new Abstract: We introduce a causal aware foundation-model framework for real time optimal decision making in discrete choice environments. We propose a constrained t

applicationsarxiv-cs-lg

MIND: Monge Inception Distance for Generative Models Evaluation

arXiv:2605.06797v1 Announce Type: new Abstract: We propose the Monge Inception Distance (MIND), a metric for evaluating generative models that addresses key limitations of the widely adopted Frechet I

Local Ai

On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems

arXiv:2605.07860v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising paradigm for preserving client data ownership and control over distributed Internet of Things (IoT)

local-aiarxiv-cs-ai

One of the most important properties of LLMs that we take for granted is that newer, bigger models are just better at everything. The AI Lab…

model-releasesethan-mollick--x

One of the most important properties of LLMs that we take for granted is that newer, bigger models are just better at everything. The AI Labs are pouring effort into economically valuable fields like

OpenAI launches Daybreak, a cybersecurity initiative integrating AI models and Codex Security to help organizations patch vulnerabilities (Alexey Shabanov/TestingCatalog AI News)

Alexey Shabanov / TestingCatalog AI News: OpenAI launches Daybreak, a cybersecurity initiative integrating AI models and Codex Security to help organizations patch vulnerabilities — OpenAI launches Da

model-releasestechmeme

Local Ai

Predictive but Not Plannable: RC-aux for Latent World Models

arXiv:2605.07278v1 Announce Type: cross Abstract: A latent world model may achieve accurate short-horizon prediction while still inducing a latent space that is poorly aligned with planning. A key iss

local-aiarxiv-cs-ai

Self-Consolidating Language Models: Continual Knowledge Incorporation from Context

arXiv:2605.07076v1 Announce Type: new Abstract: Large language models (LLMs) increasingly receive information as streams of passages, conversations, and long-context workflows. While longer context wi

researcharxiv-cs-cl

SOD: Step-wise On-policy Distillation for Small Language Model Agents

arXiv:2605.07725v1 Announce Type: cross Abstract: Tool-integrated reasoning (TIR) is difficult to scale to small language models due to instability in long-horizon tool interactions and limited model

safetyarxiv-cs-ai

Local Ai

ST-Gen4D: Embedding 4D Spatiotemporal Cognition into World Model for 4D Generation

arXiv:2605.07390v1 Announce Type: new Abstract: Generative models have achieved success in producing apparently coherent 2D videos, but remain challenging in the physical world due to lack of 4D spati

local-aiarxiv-cs-cv

The Convergence Gap: Instruction-Tuned Language Models Stabilize Later in the Forward Pass

arXiv:2605.07282v1 Announce Type: new Abstract: Final outputs hide when a checkpoint commits to its next-token prediction. We introduce the convergence gap, a model-diffing diagnostic that decodes eac