Search: “inference”

Bounded Graph Clustering with Graph Neural Networks

arXiv:2512.05623v2 Announce Type: replace Abstract: In community detection, many methods require the user to specify the number of clusters in advance since an exhaustive search over all possible valu

Can Explicit Physical Feasibility Benefit VLA Learning? An Empirical Study

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DGX agent

arXiv:2604.17896v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models map multimodal inputs directly to robot actions and are typically trained through large-scale imitation learning. Wh

safetyarxiv-cs-lg

21 Apr 2026

Hardware

Capacity without conflict: A guide to multi-tenant GPU cluster design for AI-native teams

This guide addresses the design and management of multi-tenant GPU clusters optimized for AI teams, focusing on strategies to maximize resource utilization while minimizing contention and conflicts be

hardwaretogether-ai-blog

CARI4D: Category Agnostic 4D Reconstruction of Human-Object Interaction

arXiv:2512.11988v3 Announce Type: replace Abstract: Accurate capture of human-object interaction from ubiquitous sensors like RGB cameras is important for applications in human understanding, gaming,

Channel Attention-Guided Cross-Modal Knowledge Distillation for Referring Image Segmentation

arXiv:2604.16806v1 Announce Type: new Abstract: Referring image segmentation (RIS) requires accurate segmentation of target regions in images according to language descriptions, which is a cross-modal

CoDial: Interpretable Task-Oriented Dialogue Systems Through Dialogue Flow Alignment

model-releasesarxiv-cs-cl

arXiv:2506.02264v3 Announce Type: replace Abstract: Building Task-Oriented Dialogue (TOD) systems that generalize across different tasks remains a challenging problem. Data-driven approaches often str

ConforNets: Latents-Based Conformational Control in OpenFold3

arXiv:2604.18559v1 Announce Type: cross Abstract: Models from the AlphaFold (AF) family reliably predict one dominant conformation for most well-ordered proteins but struggle to capture biologically r

Tutorials

ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling

arXiv:2510.08878v3 Announce Type: replace-cross Abstract: Text-to-audio (TTA) generation with fine-grained control signals, e.g., precise timing control or intelligible speech content, has been explor

tutorialsarxiv-cs-cl

Hardware

Copy-as-Decode: Grammar-Constrained Parallel Prefill for LLM Editing

arXiv:2604.18170v1 Announce Type: new Abstract: LLMs edit text and code by autoregressively regenerating the full output, even when most tokens appear verbatim in the input. We study Copy-as-Decode, a

hardwarearxiv-cs-cl

CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit

model-releasesarxiv-cs-cl

arXiv:2510.06133v2 Announce Type: replace Abstract: Diffusion large language models (dLLMs) generate text through iterative denoising. In commonly adopted parallel decoding schemes, each step confirms

Cross-Modal Bayesian Low-Rank Adaptation for Uncertainty-Aware Multimodal Learning

model-releasesarxiv-cs-lg

arXiv:2604.16657v1 Announce Type: new Abstract: Large pre-trained language models are increasingly adapted to downstream tasks using parameter-efficient fine-tuning (PEFT), but existing PEFT methods a

Tutorials

Cross-Modal Generation: From Commodity WiFi to High-Fidelity mmWave and RFID Sensing

arXiv:2604.16558v1 Announce Type: new Abstract: AIGC has shown remarkable success in CV and NLP, and has recently demonstrated promising potential in the wireless domain. However, significant data imb

tutorialsarxiv-cs-lg

Data Compressibility Quantifies LLM Memorization

arXiv:2507.06056v4 Announce Type: replace Abstract: Large Language Models (LLMs) are known to memorize portions of their training data, sometimes even reproduce content verbatim when prompted appropri

Decisive: Guiding User Decisions with Optimal Preference Elicitation from Unstructured Documents

arXiv:2604.18122v1 Announce Type: new Abstract: Decision-making is a cognitively intensive task that requires synthesizing relevant information from multiple unstructured sources, weighing competing f

DeepRitzSplit Neural Operator for Phase-Field Models via Energy Splitting

arXiv:2604.18261v1 Announce Type: cross Abstract: The multi-scale and non-linear nature of phase-field models of solidification requires fine spatial and temporal discretization, leading to long compu

Demystifying the unreasonable effectiveness of online alignment methods

arXiv:2604.17207v1 Announce Type: cross Abstract: Iterative alignment methods based on purely greedy updates are remarkably effective in practice, yet existing theoretical guarantees of (O(log T)) KL-

safetyarxiv-cs-cl

Disentangled Robot Learning via Separate Forward and Inverse Dynamics Pretraining

arXiv:2604.16391v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have shown great potential in building generalist robots, but still face a dilemma-misalignment of 2D image foreca

Do LLM-derived graph priors improve multi-agent coordination?

model-releasesarxiv-cs-lg

arXiv:2604.17191v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) is crucial for AI systems that operate collaboratively in distributed and adversarial settings, particularly i

Do LLMs Encode Functional Importance of Reasoning Tokens?

arXiv:2601.03066v2 Announce Type: replace Abstract: Large language models solve complex tasks by generating long reasoning chains, achieving higher accuracy at the cost of increased computational cost

DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models

arXiv:2604.16979v1 Announce Type: cross Abstract: High-quality and diverse multimodal data are essential for improving vision-language models (VLMs), yet existing datasets often contain noisy, redunda

safetyarxiv-cs-cl

DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization

model-releasesarxiv-cs-cl

arXiv:2604.17789v1 Announce Type: cross Abstract: The MXFP4 microscaling format, which partitions tensors into blocks of 32 elements sharing an E8M0 scaling factor, has emerged as a promising substrat

EdgeVTP: Exploration of Latency-efficient Trajectory Prediction for Edge-based Embedded Vision Applications

arXiv:2604.16783v1 Announce Type: new Abstract: Vehicle trajectory prediction is central to highway perception, but deployment on roadside edge devices necessitates bounded, deterministic end-to-end l

Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing

arXiv:2604.17838v1 Announce Type: new Abstract: Generative modeling within constrained sets is essential for scientific and engineering applications involving physical, geometric, or safety requiremen

Ego-InBetween: Generating Object State Transitions in Ego-Centric Videos

arXiv:2604.17749v1 Announce Type: new Abstract: Understanding physical transformation processes is crucial for both human cognition and artificial intelligence systems, particularly from an egocentric

EgoSound: Benchmarking Sound Understanding in Egocentric Videos

arXiv:2602.14122v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have recently achieved remarkable progress in vision-language understanding. Yet, human perception is inher

End-to-End Optimization of LLM-Driven Multi-Agent Search Systems via Heterogeneous-Group-Based Reinforcement Learning

arXiv:2506.02718v2 Announce Type: replace Abstract: Large language models (LLMs) are versatile, yet their deployment in complex real-world settings is limited by static knowledge cutoffs and the diffi

ENTIRE: Learning-based Volume Rendering Time Prediction

arXiv:2501.12119v3 Announce Type: replace-cross Abstract: We introduce ENTIRE, a novel deep learning-based approach for fast and accurate volume rendering time prediction. Predicting rendering time is

Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings

arXiv:2505.15353v3 Announce Type: replace Abstract: Log-likelihood vectors define a common space for comparing language models as probability distributions, enabling unified comparisons across heterog

Fairness Constraints in High-Dimensional Generalized Linear Models

arXiv:2604.16610v1 Announce Type: cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability. Conventional fairness

Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization

arXiv:2510.09204v2 Announce Type: replace-cross Abstract: Centralized trajectory optimization in the joint space of multiple robots allows access to a larger feasible space that can result in smoother

FlowRefiner: Flow Matching-Based Iterative Refinement for 3D Turbulent Flow Simulation

arXiv:2604.17149v1 Announce Type: cross Abstract: Accurate autoregressive prediction of 3D turbulent flows remains challenging for neural PDE solvers, as small errors in fine-scale structures can accu

Local Ai

FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models

arXiv:2604.16448v1 Announce Type: cross Abstract: As edge AI deployments scale to billions of devices running always-on, real-time compound AI pipelines, they represent a massive and largely unmanaged

local-aiarxiv-cs-lg

Follow the Path: Reasoning over Knowledge Graph Paths to Improve Large Language Model Factuality

arXiv:2505.11140v3 Announce Type: replace Abstract: We introduce fs1, a simple yet effective method that improves the factuality of reasoning traces by collecting them from large reasoning models and

Local Ai

Forest Before Trees: Latent Superposition for Efficient Visual Reasoning

arXiv:2601.06803v2 Announce Type: replace Abstract: While Chain-of-Thought empowers Large Vision-Language Models with multi-step reasoning, explicit textual rationales suffer from an information bandw

local-aiarxiv-cs-cl

Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens

arXiv:2604.17785v1 Announce Type: new Abstract: Unlearning in large language models (LLMs) has emerged as a promising safeguard against adversarial behaviors. When the forgetting loss is applied unifo

FrameVGGT: Geometry-Aligned Frame-Level Memory for Bounded Streaming VGGT

arXiv:2603.07690v2 Announce Type: replace Abstract: Streaming Visual Geometry Transformers such as StreamVGGT enable strong online 3D perception, but their KV-cache grows unbounded over long streams,

From Attribution to Abstention: Training-Free Attention-Based Auditing for Clinical Summarization

arXiv:2601.16397v2 Announce Type: replace Abstract: Deploying multimodal large language models (MLLMs) for clinical summarization demands not only fluent generation but also transparency about where e

From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning

arXiv:2601.04278v2 Announce Type: replace Abstract: Although machine unlearning is essential for removing private, harmful, or copyrighted content from LLMs, current benchmarks often fail to faithfull

From User Recognition to Activity Counting: An Identity-Agnostic Approach to Multi-User WiFi Sensing

arXiv:2604.16572v1 Announce Type: new Abstract: Wi-Fi Channel State Information (CSI) enables device-free human activity recognition, but existing multi-user approaches assume a fixed set of known use

Local Ai

Geometry-Aware CLIP Retrieval via Local Cross-Modal Alignment and Steering

arXiv:2604.16487v1 Announce Type: new Abstract: CLIP retrieval is typically framed as a pointwise similarity problem in a shared embedding space. While CLIP achieves strong global cross-modal alignmen

local-aiarxiv-cs-cv

Geometry-Guided 3D Visual Token Pruning for Video-Language Models

arXiv:2604.18260v1 Announce Type: new Abstract: Multimodal large language models have demonstrated remarkable capabilities in 2D vision, motivating their extension to 3D scene understanding. Recent st

Applications

Hierarchical Retrieval with Out-Of-Vocabulary Queries: A Case Study on SNOMED CT

arXiv:2511.16698v2 Announce Type: replace Abstract: SNOMED CT is a biomedical ontology with a hierarchical representation, modelling terminological concepts at a large scale. Knowledge retrieval in SN

applicationsarxiv-cs-cl

HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models

arXiv:2508.00553v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) encode images and videos into abundant tokens, which contain substantial redundancy and computation cost. While visual

Applications

HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification

arXiv:2604.17271v1 Announce Type: new Abstract: Node classification on text-attributed graphs (TAGs) is a fundamental task with broad applications in citation analysis, social networks, and recommenda

applicationsarxiv-cs-cl

Applications

Horizon-Aware Forecasting of Passenger Assistance Demand for Rail Station Workforce Planning

arXiv:2604.16464v1 Announce Type: cross Abstract: Passenger assistance services are essential for accessible rail travel, yet demand varies substantially across stations and over time, creating challe

applicationsarxiv-cs-lg

Agents

Human Cognition in Machines: A Unified Perspective of World Models

arXiv:2604.16592v1 Announce Type: cross Abstract: This comprehensive report distinguishes prior works by the cognitive functions they innovate. Many works claim an almost 'human-like' cognitive capabi

agentsarxiv-cs-cv

Hardware

I tested @huggingface ml-intern, given the prompt 'Fine-tune a Segment Anything Model (SAM) on a useful medical dataset. Train the model, an…

I tested @huggingface ml-intern, given the prompt 'Fine-tune a Segment Anything Model (SAM) on a useful medical dataset. Train the model, and provide a comprehensive tutorial in a Jupyter Notebook fil

hardwareclem-delangue--x

Information Representation Fairness in Long-Document Embeddings: The Peculiar Interaction of Positional and Language Bias

arXiv:2601.16934v2 Announce Type: replace Abstract: To be discoverable in an embedding-based search process, each part of a document should be reflected in its embedding representation. To quantify an

safetyarxiv-cs-cl