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arXiv:2606.16253v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models increasingly rely on high-frequency multi-camera observations, making visual communication a major bottlen

applicationsarxiv-cs-ai

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Omni-Diffusion-Distill: Few-Step Distillation of Unified Multimodal Diffusion Large Language Models

DGX agent

arXiv:2610.10990v1 Announce Type: new Abstract: Unified multimodal diffusion large language models (dLLMs) offer a single architecture for both image generation and multimodal understanding, but their

tutorialsarxiv-cs-cv

9 Oct 2026

Rethinking the Tradeoff Between Temporal Encoding and Nonlinear Computation in Spiking Language Models

arXiv:2610.10933v1 Announce Type: cross Abstract: Spiking language models face a tradeoff between representing continuous semantic features over short temporal windows and retaining costly nonlinear a

Agents

Skill-SLM: Agent Skill-driven Small Language Models for Reliable Robot Operation

arXiv:2610.10812v1 Announce Type: new Abstract: Small language models (SLMs) have been increasingly adopted for onboard robot operation because they enable intelligent decision-making. However, existi

agentsarxiv-cs-ro

WAM-Cache: Staleness-Bounded KV Reuse for Efficient World Action Models

arXiv:2610.11401v1 Announce Type: cross Abstract: World Action Models (WAMs) enable generalist robot manipulation by conditioning an action expert on representations from a pretrained video Diffusion

Writing for the Reviewer: Defensive Writing in GPT Models

arXiv:2610.11355v1 Announce Type: new Abstract: Researchers increasingly use ChatGPT to revise their papers, and recent GPT versions often narrow or even retract the authors' claims. We call such chan

Event-Aligned Visual Action Reasoning for World Action Models

arXiv:2610.09427v1 Announce Type: new Abstract: World-Action Models (WAMs) utilize future visual prediction as an intermediate reasoning process to guide action generation. However, existing WAMs typi

tutorialsarxiv-cs-cv

HuLiGen: Human LiDAR Generation from Parametric Body Models

arXiv:2610.10196v1 Announce Type: new Abstract: LiDAR point clouds of humans are extremely expensive to collect and annotate, thus represent a scarce resource that hinders the development of human ana

Juno: Taming Predictive Latents for Vision-Language-Action Models

arXiv:2610.09940v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) predict masked or future observations in representation space, offering a natural source of predictiv

Slow Beats Fast at the Kesten-Stigum Threshold: Minimax, Fisher-Information and Belief-Propagation Characterizations of the Information-Computation Gap in Sparse Stochastic Block Models

arXiv:2610.08872v1 Announce Type: cross Abstract: We study community recovery in the sparse symmetric stochastic block model with q communities, average degree d and signal strength lambda through sta

Talking with Language Models

arXiv:2610.09064v1 Announce Type: cross Abstract: When we interact with large language models (LLMs), are we having a conversation? They are designed to invite us to treat them as intelligent interloc

researcharxiv-cs-cl

COMPASS: Finding Where Reasoning Lives in Language Models

arXiv:2610.07469v1 Announce Type: new Abstract: Explicitly eliciting reasoning substantially improves LLM performance. Existing approaches require a predefined characterization of reasoning, whether t

Mask-Guided KV Cache Eviction in Block Diffusion Language Models

arXiv:2610.06996v1 Announce Type: cross Abstract: Block diffusion language models keep a large key-value (KV) cache throughout generation and attend to it at every denoising step, limiting both memory

tutorialsarxiv-cs-ai

Massive Activation Gating Channel in Large Language Models

arXiv:2610.07661v1 Announce Type: new Abstract: Massive activations, a phenomenon in which a small number of hidden channels exhibit exceptionally large magnitudes, are pervasive in large language mod

Pseudowords as probes: Large Language Models show little of the sublexical sensitivity that governs human pseudoword processing

arXiv:2610.07936v1 Announce Type: new Abstract: Systematicity, the probabilistic mapping of form to meaning, permeates language at all levels, and sublexical cues have been shown to govern human pseud

safetyarxiv-cs-cl

Revisiting Numerical Forecasting Models for Language-Based Trajectory Prediction

arXiv:2610.07954v1 Announce Type: new Abstract: Language-based trajectory predictors represent coordinates as discrete tokens and learn auxiliary tasks such as destination and group reasoning. This fo

Local Ai

StairVLA: Stage-Aware Hierarchical Action Generation for Vision-Language-Action Models

arXiv:2610.07756v1 Announce Type: new Abstract: Vision-language-action (VLA) models increasingly rely on diffusion- or flow-matching-based action heads to generate continuous robot actions. These acti

local-aiarxiv-cs-ro

SWAP: Stepwise Action Policy Routing for Vision-Language-Action Models

arXiv:2610.06926v1 Announce Type: new Abstract: Robot manipulation systems using Vision-Language-Action (VLA) model backbones typically use just one VLA for task execution. However, individual VLAs do

A Strong Baseline for Evaluating Vision Encoders in Multimodal Large Language Models

arXiv:2610.05413v1 Announce Type: new Abstract: Evaluating vision encoders requires metrics that reliably predict their downstream performance in multimodal large language models (MLLMs). Although rec

6 Oct 2026

The Numerical Linear Algebra of Large Language Models

arXiv:2610.04631v1 Announce Type: new Abstract: Numerical Linear Algebra (NLA) has consistently played a vital role in advancing science by providing tools to solve fundamental problems encountered in

6 Oct 2026

When to Switch: Reliable Action-Chunk Extension for Vision-Language-Action Models

arXiv:2610.05719v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models serve as unified policies for robotic manipulation, yet their expensive inference forces robots to pause between pol

6 Oct 2026

Hardware

Probe the Harness: Setup Checks for Stale-Data RL Comparisons in Language Models

arXiv:2610.02911v1 Announce Type: cross Abstract: Methods for training language models on stale samples are judged by comparisons against importance-corrected baselines. We show that details of the ex

hardwarearxiv-cs-cl

SCOPE-4D: Endoscopic 4D Geometry Foundation Models

arXiv:2610.02343v1 Announce Type: new Abstract: Geometric understanding supports endoscopic navigation and robotic assistance, but learning reliable endoscopic geometry faces two challenges: scarce ge

SimpleTouch: Can Vision-Language-Action Models Master Contact-Rich Manipulation Without Tactile Policy Pretraining?

arXiv:2610.02784v1 Announce Type: new Abstract: Tactile sensing provides essential contact information for robotic manipulation, yet incorporating it into pretrained vision-language-action (VLA) model

Local Ai

World-Calibrated Proposal-to-Action Flow for Vision-Language-Action Models

arXiv:2610.02323v1 Announce Type: cross Abstract: Flow-based Vision-Language-Action (VLA) policies generate action chunks by transporting samples from a task-agnostic isotropic Gaussian source. As thi

local-aiarxiv-cs-cv

Applications

Energy Time-Series Imputation with Differentially Private Diffusion Models via Clipping-Aware Objective Conditioning

arXiv:2610.00209v1 Announce Type: new Abstract: Reliable recovery of missing measurements is important for monitoring and analysis in energy time-series systems, where fine-grained measurements may co

applicationsarxiv-cs-lg

2 Oct 2026

Trajectory Stitching for Solving Inverse Problems with Flow-Based Models

arXiv:2602.08538v2 Announce Type: replace-cross Abstract: Flow-based generative models have emerged as powerful priors for solving inverse problems. One option is to directly optimize the initial late

2 Oct 2026

When Instructions Retrieve Trajectories: Diagnosing and Mitigating Generalization Failures in VLA Models

arXiv:2609.39971v1 Announce Type: cross Abstract: Vision-language-action (VLA) models can exceed 90% success on in-distribution tasks and withstand nuisance changes that preserve the required action,

safetyarxiv-cs-lg

1 Oct 2026

Anisotropic Representations Improve Planning in JEPA World Models

arXiv:2609.37441v1 Announce Type: new Abstract: Latent world models learn action-conditioned dynamics in representation space and often score candidate actions by Euclidean distance to a goal represen

tutorialsarxiv-cs-ro

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

arXiv:2406.00083v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant information from external knowledge bases to

Can Vision-Language Models Stay Helpful When Facing Implicit Risks? Intent-Privilege OPSD for Efficient Safety-Helpfulness Alignment

arXiv:2609.37837v1 Announce Type: new Abstract: Vision-Language Models (VLMs) remain vulnerable to cross-modal implicit risks: visual and textual inputs that appear benign in isolation can jointly eli

safetyarxiv-cs-cl

Agents

Cooperative Multi-Agent Vision-Language-Action Models via Reinforced Fine Tuning

arXiv:2609.36588v1 Announce Type: cross Abstract: We study reinforcement learning (RL) methods for cooperative multi-agent Vision-Language-Action (VLA) models. This problem is challenging because VLAs

agentsarxiv-cs-ai

Disentangling Spurious Correlations in Vision-Language-Action Models via Predicting Domain-Invariant Latent Lookahead

arXiv:2609.37165v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models remain brittle under visual distribution shifts, often relying on spurious correlations tied to domain-specific fact

E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models

arXiv:2609.37533v1 Announce Type: new Abstract: Masked diffusion models (MDMs) generate sequences by progressively unmasking several tokens per denoising step, but their reverse process is typically f

researcharxiv-cs-cl

ER-JEPA: Experience Replay Improves Joint-Embedding Predictive Learning in Language Models

arXiv:2609.36952v1 Announce Type: cross Abstract: Large language models (LLMs) excel at token-level generation but may learn undesirable abstract semantics and lack comprehensive perception. LLM-JEPA

Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps

arXiv:2602.12624v2 Announce Type: replace-cross Abstract: Diffusion-based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited

FP2: Equipping Robotic Foundation Models with Force Control

arXiv:2609.37433v1 Announce Type: new Abstract: Robotic foundation models (RFMs) are increasingly capable of general-purpose manipulation, yet reliable physical interaction remains challenging in cont

Reliable Parallel Decoding in Masked Diffusion Language Models

arXiv:2609.36452v1 Announce Type: cross Abstract: Masked diffusion language models (MDLMs) can generate text efficiently by predicting multiple masked tokens in parallel, but predictions from the same

Signatures of semantic search in the activations of large language models

arXiv:2609.35599v2 Announce Type: replace Abstract: When recalling lists of concepts (e.g., animals) during the semantic fluency task (SFT), both humans and large language models (LLMs) organise their

Similar Choices, Different Attention: Cross-Modal Associations in Humans and Vision-Language Models

arXiv:2609.36475v1 Announce Type: cross Abstract: Cross-modal associations are systematic pairings of features across modalities, such as the association of 'bouba' with round shapes and 'kiki' with s

Staircase Policy: Streaming Inference for World-Action Models with Large Action Chunks

arXiv:2609.36471v1 Announce Type: cross Abstract: World-Action Models (WAMs) improve robotic manipulation by conditioning action generation on predicted future observations, but future prediction adds

Applications

The Canonical Order Problem: When Large Language Models Are Unreliable Knowledge Bases for Multi-Valued Relations

arXiv:2609.36209v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as knowledge bases (KBs) due to the vast amount of knowledge they acquire during pre-training. While

applicationsarxiv-cs-cl

Time-Anchored Diffusion Language Models: Latent-Space Caching for Fast Generation

arXiv:2609.37924v1 Announce Type: cross Abstract: Recent work on anchored diffusion language models improves denoising by shaping an intermediate latent space with supervised important-token targets.

researcharxiv-cs-cl

Weeding Out Bad Seeds: Initial-Noise-Robust Unlearning for Text-to-Image Diffusion Models

arXiv:2609.37537v1 Announce Type: new Abstract: Machine unlearning has emerged as a critical post-hoc safety measure to erase sensitive concepts from Text-to-Image (T2I) models without prohibitive ret

Adaptive Latent Capacity for World Models

arXiv:2609.32921v1 Announce Type: new Abstract: We introduce Adaptive LeWorldModel (ALeWM), a world model based on a joint-embedding predictive architecture (JEPA) that learns to concentrate predictiv

Are Vision-Language-Action Models Robust to One-Step Observation Perturbations?

arXiv:2609.32550v1 Announce Type: cross Abstract: Understanding the safety risks of vision-language-action (VLA) models is essential for their deployment in the physical world. Existing safety researc

safetyarxiv-cs-lg

CAST: Reconstruction-Coupled Acceleration of Interactive World Models

arXiv:2609.34144v1 Announce Type: new Abstract: Interactive world models must respond quickly to controls while preserving scene consistency. Existing acceleration methods can miss heterogeneous contr

Diverse Sampling in Diffusion Models with Divergence-Free Particle Guidance

arXiv:2605.06553v2 Announce Type: replace Abstract: Modern generative models can produce high-quality samples, but independent samples under the same condition often yields highly similar outputs. Par

safetyarxiv-cs-lg