Search: “inference”

arXiv:2607.26642v1 Announce Type: new Abstract: Automated alpha mining has increasingly adopted large language model (LLM) agents for factor generation and iterative discovery. However, existing LLM-b

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Model Releases

Back from the Future: Key-Value Cache Management by Counter-Causal Surprise

DGX agent

arXiv:2607.27600v1 Announce Type: new Abstract: Key-value (KV) cache management through compression and eviction strategies has emerged as an important research direction in recent years. Computationa

model-releasesarxiv-cs-lg

31 Jul 2026

CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance

arXiv:2607.28292v1 Announce Type: new Abstract: Large Language Models (LLMs) deployed in dynamic financial environments face a critical challenge: maintaining factual accuracy as market conditions, re

applicationsarxiv-cs-cl

Certifying when decision-time information justifies adaptive experimentation

arXiv:2607.27651v1 Announce Type: new Abstract: Adaptive laboratories choose measurements during experiments, yet most methods begin after adaptation is permitted. We introduce Opportunity-aware Polic

safetyarxiv-cs-lg

Deep learning-based hierarchical insect classification using camera trap imagery

arXiv:2607.28005v1 Announce Type: new Abstract: Declining insect populations make reliable biodiversity monitoring increasingly urgent, yet monitoring of insect biodiversity is hampered by a lack of s

Divergence Decoding: Training-Free Capability Fusion

arXiv:2607.27248v1 Announce Type: cross Abstract: While large language models excel in reasoning, these generalists often lack knowledge for specialized scientific domains. Conversely, domain models~(

Efficient LLMs with AMP: Attention Heads and MLP Pruning

arXiv:2504.21174v2 Announce Type: replace Abstract: Deep learning drives a new wave in computing systems and triggers the automation of increasingly complex problems. In particular, Large Language Mod

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata

arXiv:2607.28338v1 Announce Type: new Abstract: Clustered Federated Learning (CFL) addresses data heterogeneity in federated settings by grouping clients with similar data distributions to enable effe

researcharxiv-cs-lg

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models

arXiv:2603.05950v2 Announce Type: replace Abstract: Visual token reduction is critical for accelerating Vision-Language Models (VLMs), since visual inputs are represented as token sequences that intro

Hardware

FARI: Robust One-Step Inversion for Watermarking in Diffusion Models

arXiv:2607.26723v1 Announce Type: cross Abstract: Inversion-based watermarking is a promising approach to authenticate diffusion-generated images, yet practical use is bottlenecked by inversion that i

hardwarearxiv-cs-ai

Fully Inductive Cardinality Estimation

arXiv:2607.28311v1 Announce Type: cross Abstract: Query optimization of Basic Graph Patterns (BGP) SPARQL queries over Knowledge Graphs (KG) requires accurate cardinality estimation. Recently publishe

applicationsarxiv-cs-lg

Generalization Bounds on Optimal Control for Transformer Training and Wasserstein Distributional Robustness

arXiv:2607.27975v1 Announce Type: new Abstract: We derive finite-sample generalization bounds for Transformers trained with dynamic programming recursions. Building on the doubly lifted, measure-value

researcharxiv-cs-lg

GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning

arXiv:2607.26160v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather t

Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA

model-releasesarxiv-cs-cl

arXiv:2502.10497v2 Announce Type: replace Abstract: Recent advancements in Generative AI have significantly improved the efficiency and adaptability of natural language processing (NLP) systems, parti

Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancement

arXiv:2607.27659v1 Announce Type: new Abstract: Personalized image enhancement should reflect individual aesthetic taste, yet learning such preferences commonly depends on private photos and ratings t

LoMeVQA: A Comprehensive Benchmark for Longitudinal Medical VQA

arXiv:2607.27806v1 Announce Type: new Abstract: In clinical practice, patients often undergo multiple imaging examinations over successive visits, yielding longitudinal data. Modeling such temporal in

Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning

arXiv:2409.13688v2 Announce Type: replace Abstract: Plastic pollution presents an escalating global issue, impacting health and environmental systems, with micro- and nanoplastics found across mediums

Property-driven Causal Abstractions for Markov Decision Processes

arXiv:2607.26787v1 Announce Type: new Abstract: Markov Decision Processes (MDPs) are widely used as decision-making models, commonly specified over factored state spaces through state variables and th

researcharxiv-cs-ai

Tutorials

Relational Scene Graphs for Object Grounding of Natural Language Commands

arXiv:2602.04635v2 Announce Type: replace Abstract: Robots are finding wider adoption in human environments, increasing the need for natural human-robot interaction. However, understanding a natural l

tutorialsarxiv-cs-ro

ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation

arXiv:2607.28581v1 Announce Type: new Abstract: High-fidelity 3D generation predominantly relies on scaling model capacity and data, which incurs prohibitive computational costs. This paradigm typical

safetyarxiv-cs-cv

SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination

arXiv:2607.28488v1 Announce Type: cross Abstract: Can supply-chain AI move beyond isolated decision modules toward unified operational planning? A complete replenishment plan specifies which products

safetyarxiv-cs-lg

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection

model-releasesarxiv-cs-cl

arXiv:2607.27680v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has become the standard mechanism for fine-tuning large pretrained models, yet its statistical properties remain only parti

Towards Generalized Synapse Detection Across Invertebrate Species

arXiv:2509.17041v2 Announce Type: replace Abstract: Behavioural differences across organisms, whether healthy or pathological, are closely tied to the structure of their neural circuits. Yet, the fine

Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization

arXiv:2607.27767v1 Announce Type: new Abstract: Graph neural networks (GNNs) can operate on large graphs but become infrastructure-sensitive at the scale of millions of nodes and typically require sca

applicationsarxiv-cs-lg

When Robots Exchange Meaning: A Demo of Goal-Oriented Semantic Communications for Collaborative Robotics

arXiv:2607.28256v1 Announce Type: new Abstract: Collaborative robotics is a representative task-oriented 6G use-case, where communication quality should be reflected in mission execution, environment

researcharxiv-cs-ro

WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models

arXiv:2607.26621v2 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their adoption as backbones for foundation recommendation mod

DIRECT: Direct Decoding for Efficient and Aligned Sequence Labeling with Large Language Models

arXiv:2607.26891v1 Announce Type: new Abstract: Sequence labeling is a fine-grained information extraction task, yet existing large language model-based approaches suffer from insufficient domain alig

safetyarxiv-cs-cl

Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives

arXiv:2607.26735v1 Announce Type: new Abstract: Prompt inversion, as a typical reverse engineering technique, enables text-to-image (T2I) diffusion models to generate the desired target images without

safetyarxiv-cs-cv

FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows

arXiv:2607.26645v1 Announce Type: new Abstract: Existing point-based generative methods for outdoor scenes primarily focus on LiDAR-conditioned completion. During training, noisy point clouds are cons

safetyarxiv-cs-cv

From Found to Designed: Concepts as a Design Axis for Large Language Models

arXiv:2607.26825v1 Announce Type: new Abstract: Large language models (LLMs) encode rich concept-like information, but represent it implicitly through distributed statistical associations rather than

safetyarxiv-cs-cl

Learning Dynamic User Personas from Implicit Interaction Streams via Iterative Refinement

arXiv:2607.26473v1 Announce Type: cross Abstract: Personalizing large language models (LLMs) to individual users is essential for improving user experience, yet existing approaches typically rely on e

applicationsarxiv-cs-cl

Low-cost Embedded Breathing Rate Determination Using 802.15.4z IR-UWB Hardware for Remote Healthcare

arXiv:2504.03772v3 Announce Type: replace-cross Abstract: Respiratory diseases account for a significant portion of global mortality. Affordable and early detection is an effective way of addressing t

Agents

SkillCAT: Contrastive, Assessment-Augmented and Topology-AwareSkill Self-Evolution for LLM Agents

arXiv:2606.13317v2 Announce Type: replace Abstract: Skill self-evolution methods for LLM agents aim to turn execution trajectories into reusable skill documents. However, current pipelines typically d

agentsarxiv-cs-cl

Tutorials

StatePlay: State-Aware Game World Models for Mechanics-Consistent Generation

arXiv:2607.26754v1 Announce Type: new Abstract: Recent game world models can generate visually realistic and interactive environments conditioned on player actions. However, games are not defined by p

tutorialsarxiv-cs-cv

Automated Numerical Stability Analysis of Deep Learning Operators

arXiv:2607.25494v1 Announce Type: cross Abstract: Finite-precision arithmetic unavoidably introduces numerical approximation errors. Numerical computations may use insufficient precision or an imprope

researcharxiv-cs-ai

Desktop-Delta Bench: Do Computer-Use Models Understand Desktop GUI Transitions?

arXiv:2607.26041v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly act through desktop GUIs to complete long-horizon tasks. Current benchmarks primarily measure end-task success o

Tutorials

Explicit Layer Modeling for Video Object Insertion and Layer Decomposition

arXiv:2607.25802v1 Announce Type: new Abstract: Most video editing systems still lack explicit layered video representations, limiting their ability to perform realistic compositing, object reuse, and

tutorialsarxiv-cs-cv

Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms

arXiv:2607.24960v1 Announce Type: cross Abstract: We present a neural network surrogate model that emulates the NRSur7dq4 gravitational waveform model for precessing binary black hole mergers. The sur

Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion

arXiv:2607.25563v1 Announce Type: new Abstract: Open-vocabulary segmentation labels arbitrary categories from a text query without per-class training, yet on remote sensing imagery it underperforms on

FORGE: Frame Orthogonality in Relevance Geometry for Long-Form Video Understanding

arXiv:2607.25266v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have enabled long-form video understanding at a scale that was not previously possible. However, the density of

Agents

HiSkill: Empowering LLM Agents with Hierarchical Skill Graphs

arXiv:2607.25853v1 Announce Type: new Abstract: Skills have become an important abstraction for enabling large language model (LLM) agents to reuse past experience in long-horizon interactive tasks. H

agentsarxiv-cs-ai

Laplace-PSN-IRT: Uncertainty Quantification for Neural Item Response Theory Models of LLM Benchmarks

arXiv:2607.25257v1 Announce Type: cross Abstract: Item Response Theory (IRT) has recently been proposed as a framework for evaluating large language model (LLM) benchmarks by separating a model's late

LLM Scheming Inversely Scales with Pretraining Language Coverage

arXiv:2607.24769v1 Announce Type: new Abstract: With the growing capabilities of frontier models, AI alignment becomes increasingly critical in high-risk deployment settings. While recent work has emp

safetyarxiv-cs-ai

Local Ai

Mind the Missing Split: Resolving Feature Heterogeneity in Swarm Learning with Random Forests

arXiv:2607.25538v1 Announce Type: new Abstract: Swarm Learning is a decentralized collaborative learning mechanism that allows multiple organizations to train a shared model without central coordinati

local-aiarxiv-cs-lg

Local Ai

OmniDelta: Skill-Driven Budget Allocation for Token Compression in OmniLLMs

arXiv:2607.25669v1 Announce Type: new Abstract: Emerging Omni-modal Large Language Models (OmniLLMs) enable unified understanding of text, audio, and video, but their long audio-video token sequences

local-aiarxiv-cs-ai

RDQ: Residual Distribution Quantization for Large Language Models

arXiv:2607.10137v2 Announce Type: replace Abstract: Post-training quantization (PTQ) of large language models degrades sharply below 4-bit precision. We identify the root cause as residual stream dist

SpecPrefetch: Parameter-Efficient Expert Prefetching for Sparse MoE Foundation Models

arXiv:2607.24787v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models expand foundation model capacity through conditional expert activation, but their full expert pools remain diffic

Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks

arXiv:2606.06313v2 Announce Type: replace-cross Abstract: Wall shear stress (WSS) governs near-wall transport dynamics and is a key hemodynamic indicator in cardiovascular flows, yet remains difficult