Search: “inference”

VQActFlow: Vector-Quantized Action Mode Steering for Multi-Task Robot Manipulation

arXiv:2606.21600v1 Announce Type: new Abstract: Multi-task robot manipulation policies are challenging to learn from demonstration because traditionally a single network must select among qualitativel

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Tutorials

VT-DUDA: Visual Token Conditioning for Diffusion-guided Unsupervised Domain Adaptation

DGX agent

arXiv:2606.21700v1 Announce Type: new Abstract: Unsupervised domain adaptation (UDA) aims to learn a target-domain classifier from labeled source data and unlabeled target data under distribution shif

tutorialsarxiv-cs-cv

23 Jun 2026

WarPGNN: A Parametric Thermal Warpage Analysis Framework with Physics-aware Graph Neural Network

arXiv:2603.18581v2 Announce Type: replace-cross Abstract: With the advent of system-in-package (SiP) chiplet-based design and heterogeneous 2.5D/3D integration, thermal-induced warpage has become a cr

researcharxiv-cs-lg

Applications

What should a world model for agile quadrotor control actually provide? 📄 Arxiv: https://arxiv.org/pdf/2606.23444 🌐 Project: https://praty…

applicationsyann-lecun--x

What should a world model for agile quadrotor control actually provide? 📄 Arxiv: https://arxiv.org/pdf/2606.23444 🌐 Project: https://pratyaksh10.github.io/skyjepa-project-page/ 💻 Code: https://github.

When Confidence Lacks Concepts: Interpretable OOD Detection via Representation Perturbations

arXiv:2606.16196v2 Announce Type: replace-cross Abstract: Deep neural networks have achieved remarkable performance across medical imaging tasks, yet their tendency to overgeneralize under distributio

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Tutorials

Words as Difference Makers: How Large Language Models Determine Causal Structure in Text

arXiv:2606.22430v1 Announce Type: cross Abstract: Because large language models (LLMs) are impressively successful in predicting text, it appears that they must have access to a 'world model' represen

tutorialsarxiv-cs-lg

ZeroDex: Zero-Shot Long-Horizon Dexterous Manipulation via Multi-View 3D-Grounded VLM Reasoning

arXiv:2606.19340v2 Announce Type: replace Abstract: We present ZeroDex, a zero-shot framework for long-horizon dexterous manipulation that grounds language instructions into executable 3D task plans f

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Tools

A year ago this would have been an obvious closed-model task. Now GLM-5.2 can read the issue, reason through the scene, patch the code, and …

A year ago this would have been an obvious closed-model task. Now GLM-5.2 can read the issue, reason through the scene, patch the code, and keep moving on Together AI. @togethercompute + @Zai_org GLM

toolstogether-ai--x

21 Jun 2026

// Evolving Meta-Skill for Multi-Agent Systems // Can a multi-agent system get better at orchestration without touching a single weight? Aut…

// Evolving Meta-Skill for Multi-Agent Systems // Can a multi-agent system get better at orchestration without touching a single weight? Automatic MAS generation has been stuck between two bad options

agentsdair-ai--x

20 Jun 2026

Industry

We, along with @zhijianliu_ and @jianchen1799, are excited to share these models on @huggingface for the entire community to use. We look fo…

We, along with @zhijianliu_ and @jianchen1799, are excited to share these models on @huggingface for the entire community to use. We look forward to getting speculative decoding speedups into the hand

industryclem-delangue--x

20 Jun 2026

A Resource for Enthymeme Detection in Controversial Political Discourse

arXiv:2606.12186v1 Announce Type: new Abstract: Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjective.

researcharxiv-cs-cl

A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models

arXiv:2509.11575v3 Announce Type: replace Abstract: Time series reasoning treats time as a first-class axis and incorporates intermediate evidence directly into the answer. This survey defines the pro

Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models

arXiv:2606.12203v1 Announce Type: new Abstract: Large language models (LLMs) are widely used to tackle complex tasks with autonomous workflows. Recently, reusable natural language skills have emerged

APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations

model-releasesarxiv-cs-lg

arXiv:2606.11553v1 Announce Type: new Abstract: Generic time-series foundation models transfer poorly to wireless network telemetry whose signals are bursty, zero-inflated, and coupled across protocol

Battery detection of XRay images using transfer learning

arXiv:2606.11779v1 Announce Type: new Abstract: The need for detecting and sorting batteries is drastically increasing for many applications. This study proves the potential of transfer learning in pr

researcharxiv-cs-cv

Beyond representational alignment with brain-guided language models for robust reasoning

arXiv:2606.11893v1 Announce Type: cross Abstract: The correspondence between large language models (LLMs) and the neural mechanisms underlying human higher-order cognition remains insufficiently chara

Breaking Entropy Bounds: Accelerating RL Training via MTP with Rejection Sampling

arXiv:2606.12370v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a key component in modern large language models, yet the rollout stage remains the key bottleneck in RL trainin

agentsarxiv-cs-cl

Can News Predict the Market? Limits of Zero-Shot Financial NLP and the Role of Explainable AI

arXiv:2606.12210v1 Announce Type: new Abstract: Can financial news reliably predict short-term stock movements? Despite advances in large language models, this question remains unresolved. We revisit

researcharxiv-cs-cl

Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development

arXiv:2602.19718v2 Announce Type: replace-cross Abstract: The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the

Context-Driven Incremental Compression for Multi-Turn Dialogue Generation

arXiv:2606.12411v1 Announce Type: new Abstract: Modern conversational agents condition on an ever-growing dialogue history at each turn, incurring redundant attention and encoding costs that grow with

researcharxiv-cs-cl

CoVar: Confidence-Variance-Guided Pseudo-Label Selection for Semi-Supervised Learning

arXiv:2601.11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable

CoVR-R:Reason-Aware Composed Video Retrieval

model-releasesarxiv-cs-cv

arXiv:2603.20190v2 Announce Type: replace Abstract: Composed Video Retrieval (CoVR) aims to find a target video given a reference video and a textual modification. Prior work assumes the modification

Debiasing Without Protected Attributes: Latent Concept Erasure from Textual Profiles

arXiv:2606.12088v1 Announce Type: new Abstract: Most fairness research in NLP assumes direct access to protected attributes such as gender, race, or nationality. In practice, however, such information

Applications

Diffusion-based Cumulative Adversarial Purification for Vision Language Models

arXiv:2506.03933v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have shown remarkable capabilities in multimodal understanding, yet their susceptibility to adversarial perturba

applicationsarxiv-cs-ai

Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite

arXiv:2606.11257v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generati

Family-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance

arXiv:2606.11620v1 Announce Type: cross Abstract: Approximate tensor-network simulators enable classical simulation of quantum circuits beyond the reach of exact methods, but selecting optimal approxi

researcharxiv-cs-lg

Fast Speech Foundation Model Distillation Using Interleaved Stacking

arXiv:2606.11766v1 Announce Type: cross Abstract: Distilling a large speech foundation model (SFM) into an efficient student model has been successfully applied to low-resource environments. Although

Tutorials

FOCUS: DLLMs Know How to Tame Their Compute Bound

arXiv:2601.23278v2 Announce Type: replace-cross Abstract: Diffusion Large Language Models (DLLMs) offer a compelling alternative to Auto-Regressive models, but their deployment is constrained by high

tutorialsarxiv-cs-cl

FreeBridge: Variational Schrodinger Bridges for Cellular Transition Dynamics

arXiv:2606.11286v1 Announce Type: cross Abstract: High-content imaging assays quantify cellular responses to chemical and genetic perturbations, yet continuous trajectories of individual cells are uno

From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data

arXiv:2606.07537v1 Announce Type: cross Abstract: Large language models hallucinate--producing fluent, confident, factually wrong outputs--with a consistency that persists across generations and scale

From Prompts to Tokens: Internalizing Causal Supervision in Vision-Language Model for Multi-Image Causal Reasoning

arXiv:2606.11745v1 Announce Type: cross Abstract: Visual causal reasoning is essential for understanding and intervening in the physical world, requiring identification of causal variables from visual

Frozen Multimodal Embeddings for Personality and Cognitive Ability Assessment in Asynchronous Video Interviews

arXiv:2606.11930v1 Announce Type: cross Abstract: Predicting psychological traits from asynchronous video interviews (AVIs) is a challenging multimodal learning problem because labeled datasets are li

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction

arXiv:2606.11382v1 Announce Type: new Abstract: Deep learning models facilitate the discovery of molecules with tailored properties among billions of candidate compounds. However, the computational bu

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Harness In-Context Operator Learning with Chain of Operators

arXiv:2606.12318v1 Announce Type: cross Abstract: Neural operators approximate mappings between function spaces, but often generalize poorly to other operators and usually require fine-tuning or retra

How Auxiliary Reasoning Unleashes GUI Grounding in VLMs

model-releasesarxiv-cs-cv

arXiv:2509.11548v2 Announce Type: replace Abstract: Graphical user interface (GUI) grounding is a fundamental task for building GUI agents. However, general vision-language models (VLMs) struggle with

How Seemingly Inconsequential Design Choices Dictate Performance of LLMs in Pathology

model-releasesarxiv-cs-cv

arXiv:2606.12407v1 Announce Type: new Abstract: General-purpose large language models (LLMs) are routinely used as baselines when evaluating specialized pathology models on whole-slide images (WSIs).

Implicit Neural Representations of Individual Behavior

arXiv:2606.12200v1 Announce Type: cross Abstract: We study policy representation learning from unlabeled multi-policy behavioral data. Each episode is generated by a fixed policy, but policy labels ar

ISAP-3D: Identity-Slot Aligned Part-Aware 3D Generation

arXiv:2606.12099v1 Announce Type: new Abstract: Part-aware 3D generation aims to synthesize structured objects with semantically meaningful components, yet often suffers from structural ambiguity due

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Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking

arXiv:2602.10743v2 Announce Type: replace Abstract: State-space language models such as Mamba and gated linear attention (GLA) offer linear-complexity, parallelisable alternatives to transformers, but

researcharxiv-cs-lg

Latent World Recovery for Multimodal Learning with Missing Modalities

arXiv:2606.12362v1 Announce Type: cross Abstract: We study multimodal learning under missing modalities, with particular motivation from bioscience applications in which heterogeneous modalities are o

LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems

arXiv:2606.11560v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native,

agentsarxiv-cs-ai

Measuring Semantic Progress in Multi-turn Dialogue via Information Gain

arXiv:2606.12332v1 Announce Type: new Abstract: Evaluating multi-turn dialogue is challenging because quality emerges across turns rather than within individual responses. We focus on a key dimension

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Local Ai

MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning

arXiv:2606.12018v1 Announce Type: new Abstract: We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM), specifically designed for social intel

local-aiarxiv-cs-ai

Local Ai

Moving Beyond Diffusion: Hierarchy-to-Hierarchy Autoregression for fMRI-to-Image Reconstruction

arXiv:2510.22335v2 Announce Type: replace-cross Abstract: Reconstructing visual stimuli from fMRI signals is a central challenge bridging machine learning and neuroscience. Recent diffusion-based meth

local-aiarxiv-cs-ai

Multi-Agent Reasoning with Adaptive Worker Allocation for Stance Detection

arXiv:2606.11609v1 Announce Type: new Abstract: Stance detection requires identifying an author's position toward a target, often from short-form texts where stance is implicit, indirect, or rhetorica

Local Ai

MultiToP: Learning to Patch Visual Tokens to Mitigate Hallucinations in Video Large Multimodal Models

arXiv:2606.11792v1 Announce Type: cross Abstract: Video Large Multimodal Models have achieved remarkable progress in video understanding, yet they remain prone to hallucinations, where generated respo

local-aiarxiv-cs-ai

Organize then Retrieve: Hierarchical Memory Navigation for Efficient Agents

arXiv:2606.11680v1 Announce Type: new Abstract: Large language model (LLM) agents struggle with long-horizon tasks due to their inherent statelessness, requiring all task-relevant information to be en

agentsarxiv-cs-ai

PAWS: Preference Learning with Advantage-Weighted Segments

arXiv:2606.11982v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) learns policies from human trajectory-level comparisons, avoiding explicit reward design and expert demon

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