Search: “framework”

Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation

arXiv:2606.03866v1 Announce Type: cross Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry. However, aligning the LLM's semantic space

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Local Ai

Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference

DGX agent

arXiv:2606.03332v1 Announce Type: new Abstract: Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation.

local-aiarxiv-cs-lg

3 Jun 2026

Testing Most Influential Sets

arXiv:2510.20372v4 Announce Type: replace-cross Abstract: Small influential data subsets can dramatically impact model conclusions, with a few data points overturning key findings. While recent work i

The Agent's First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios

arXiv:2601.08173v2 Announce Type: replace Abstract: The rapid evolution of Multi-modal Large Language Models (MLLMs) has advanced workflow automation; however, existing research mainly targets perform

agentsarxiv-cs-ai

The Deliberative Illusion: Diagnosing Factual Attrition and Stance Homogenization in Multi-Agent LLM Deliberation

arXiv:2606.03032v1 Announce Type: new Abstract: Multi-agent LLM systems often treat consensus as evidence of successful interaction. For deliberative problems, however, reliability depends on whether

agentsarxiv-cs-cl

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit

arXiv:2501.02173v2 Announce Type: replace-cross Abstract: The deployment of Large Language Models (LLMs) in recommender systems for predicting Click-Through Rates (CTR) necessitates a delicate balance

The Impact of Temporal Granularity on Socio-Demographic Inference from Household Load Profiles

arXiv:2606.03358v1 Announce Type: new Abstract: Smart meter data can reveal sensitive socio-demographic characteristics of households, raising privacy concerns. While this risk has been demonstrated a

The Shape of Addition: Geometric Structures of Arithmetic in Large Language Models

arXiv:2606.03645v1 Announce Type: cross Abstract: Large Language Models exhibit paradoxical fragility in fundamental arithmetic, implying a disconnect between internal computation and discrete output.

ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning

arXiv:2606.03503v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have achieved remarkable progress thanks to Reinforcement Learning with Verifiable Rewards (RLVR) on Chain-of-Thoughts (Co

TimeOmni-VL: Unified Models for Time Series Understanding and Generation

arXiv:2602.17149v2 Announce Type: replace-cross Abstract: Recent time series modeling faces a sharp divide between numerical generation and semantic understanding, with research showing that generatio

TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching

arXiv:2606.03121v1 Announce Type: new Abstract: Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitori

applicationsarxiv-cs-lg

Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning

arXiv:2606.03762v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) equips large language models (LLMs) with tool-use capabilities that substantially improve reasoning on complex tas

Topology-Aware Gaussian Graph Repair for Robust Graph Neural Networks

model-releasesarxiv-cs-lg

arXiv:2606.03462v1 Announce Type: new Abstract: Graph neural networks have achieved strong performance on graph-structured data, but their effectiveness depends heavily on the quality of the observed

Local Ai

Toward a Modular Architecture for Embedded AI Agent Systems at the Edge

arXiv:2606.02862v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive

local-aiarxiv-cs-ai

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias

model-releasesarxiv-cs-lg

arXiv:2510.23469v2 Announce Type: replace Abstract: Self-supervised pre-training on unlabeled graph data has become a common paradigm for Graph Neural Networks (GNNs). However, an objective gap often

Tracking Urban Atmospheric Pollutants using Sentinel-5P Satellite Data

arXiv:2606.02592v1 Announce Type: cross Abstract: Urban nitrogen dioxide (NO_2) is a key indicator of combustion-related air pollution and exhibits strong spatial and temporal variability in cities. T

Train Once, Reuse Everywhere: Generalizable Implicit In-Context Learning by Routing Attention

arXiv:2509.22854v2 Announce Type: replace Abstract: Implicit in-context learning (ICL) has newly emerged as a promising paradigm that simulates ICL behaviors in the representation space of large langu

safetyarxiv-cs-cl

Training-Free Multi-Concept LoRA Composition with Prompt-Aware Weighting

arXiv:2606.03792v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) successfully enables personalization in text-to-image generation by adapting pre-trained diffusion models to specific visual

applicationsarxiv-cs-cv

Transformer-Guided Content-Adaptive Graph Learning for Hyperspectral Unmixing

arXiv:2509.03376v2 Announce Type: replace Abstract: Hyperspectral unmixing (HU) targets to decompose each mixed pixel in remote sensing images into a set of endmembers and their corresponding abundanc

tutorialsarxiv-cs-cv

TTT-VLA: Test-Time Latent Prompt Optimization for Vision-Language-Action Models

arXiv:2606.03127v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models trained on large-scale data have made remarkable progress, but they remain vulnerable to distribution shifts at depl

safetyarxiv-cs-ro

UnsOcc: 3D Semantic Occupancy Prediction in Unstructured Scene via Rendering Fusion

arXiv:2606.03581v1 Announce Type: new Abstract: Unstructured scenes present unique challenges for autonomous driving, as irregular obstacles and sparse scene layouts undermine the effectiveness of tra

safetyarxiv-cs-cv

Validation-Gated Multi-Agent Governance for Online Adaptation of Thermal-Hydraulic Surrogate Models under Operating-Regime Shift

arXiv:2606.03321v1 Announce Type: new Abstract: Artificial-intelligence surrogates can support second-by-second thermal-hydraulic forecasting, but models selected and frozen offline may become conditi

safetyarxiv-cs-lg

VLESA: Vision-Language Embodied Safety Agent for Human Activity Monitoring

model-releasesarxiv-cs-cv

arXiv:2606.03954v1 Announce Type: new Abstract: As AI systems increasingly assist humans in physical tasks, ensuring safety becomes paramount -- physical actions carry immediate and irreversible conse

Wavelet Fourier Diffuser: Frequency-Aware Diffusion Model for Reinforcement Learning

arXiv:2509.19305v2 Announce Type: replace-cross Abstract: Diffusion probability models have shown significant promise in offline reinforcement learning by directly modeling trajectory sequences. Howev

What Do Students Learn? A Feature-Level Analysis of Dark Knowledge

arXiv:2606.03052v1 Announce Type: new Abstract: Knowledge Distillation (KD) is a powerful tool for model compression, yet the precise mechanisms by which student models acquire feature representations

tutorialsarxiv-cs-lg

Wheel-Mounted/GNSS Fusion with AI-Aided Position Updates

arXiv:2606.03265v1 Announce Type: new Abstract: Accurate and robust localization remains a fundamental challenge for autonomous ground vehicles. In this work, we propose a hybrid neural inertial navig

agentsarxiv-cs-ro

When Attention Collapses: Stage-Aware Visual Token Pruning from Structure to Semantics

arXiv:2606.03569v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities but suffer from significant computational overhead during inference. While vis

When Model Merging Breaks Routing: Training-Free Calibration for MoE

arXiv:2606.03391v1 Announce Type: cross Abstract: Model merging has emerged as a cost-effective approach for consolidating the capabilities of multiple LLMs without retraining. However, existing mergi

Who Deserves the Reward? SHARP: Shapley Credit-based Optimization for Multi-Agent System

arXiv:2602.08335v2 Announce Type: replace Abstract: Integrating Large Language Models (LLMs) with external tools via multi-agent systems offers a promising new paradigm for decomposing and solving com

Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions

arXiv:2602.14279v2 Announce Type: replace-cross Abstract: Eliciting information to reduce uncertainty about latent group-level properties from surveys and other collective assessments requires allocat

agentsarxiv-cs-ai

X-RAY: Mapping LLM Reasoning Capability via Formalized and Calibrated Probes

arXiv:2603.05290v2 Announce Type: replace Abstract: Large language models (LLMs) achieve promising performance, yet their ability to reason remains poorly understood. Existing evaluations largely emph

Your Autoregressive Model Already Reveals the Causal Graph

arXiv:2602.01135v3 Announce Type: replace Abstract: Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process. We

tutorialsarxiv-cs-lg

ZX-Calculus:Trace-Indexed Dependent Types and Epistemic Semantics

arXiv:2606.03063v1 Announce Type: cross Abstract: We propose ZX-Calculus (Knowledge Evolution Calculus), a conservative extension of Martin-Lof Dependent Type Theory (MLTT) integrating trace-indexed t

researcharxiv-cs-cl

21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables

arXiv:2606.00219v1 Announce Type: cross Abstract: We are witnessing a surge in observations of the cosmic dawn (CD) and epoch of reionisation (EoR), driving an increasing demand for fast and robust th

A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs

arXiv:2606.02267v1 Announce Type: cross Abstract: The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment. Existing techniques to enha

applicationsarxiv-cs-cv

A Communication-Centric 6G-LLM Architecture for Scalable Tactical Autonomous Defense Vehicle Networks

arXiv:2606.01312v1 Announce Type: cross Abstract: The integration of Artificial Intelligence (AI) and emerging 6G networks introduces new opportunities for scalable coordination in tactical autonomous

agentsarxiv-cs-ai

A Doeblin-Anchored Contrastive Chart for Learning Markov Transition Kernels

arXiv:2606.02232v1 Announce Type: new Abstract: Learning a Markov transition model is not merely conditional density estimation: the learned object must be a valid transition kernel before it is itera

A Four-Tier Communication Architecture and Sim-to-Real Validation of a Graphical Open-Source Platform for Robotic Engineering Education

arXiv:2606.00550v1 Announce Type: cross Abstract: The persistent challenge in scaling authentic manipulator education within university laboratories is a structural dichotomy: commercial digital twins

applicationsarxiv-cs-ro

A Kinetic Theory of Encounter-Based Information Propagation in Multi-Robot Systems

arXiv:2606.02296v1 Announce Type: new Abstract: Multi-robot systems cannot assume persistent network connectivity. We study this problem through target tracking, where performance depends on how quick

researcharxiv-cs-ro

A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis

arXiv:2507.12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient asses

A Pre-Training Analogue of Grokking in Language Models: Tracing Delayed Grammatical Generalization

arXiv:2606.00230v1 Announce Type: new Abstract: Grokking, the phenomenon in which neural networks generalize long after fitting their training data, has been studied in supervised settings on many epo

A Primer in Post-Training Reasoning Data: What We Know About How It Works

arXiv:2606.02113v1 Announce Type: cross Abstract: Post-training has become a primary driver of recent progress in large reasoning models, and reasoning data are often the key variable determining whet

A Registry-Bound LLM Pipeline for Evidence-Grounded Trait Extraction across Tropical Plants, Aquatic Species, and Exotic Pets

arXiv:2606.00994v1 Announce Type: new Abstract: We describe a registry-bound large-language-model extraction pipeline producing evidence-grounded structured trait records at scale, on cultivated tropi

researcharxiv-cs-cl

A Simulation Platform for Flapping-Wing Vehicles

arXiv:2606.02370v1 Announce Type: new Abstract: Flapping-wing aerial vehicles (FWAVs) demonstrate remarkable agility but face substantial autonomy challenges due to their high sensitivity to aerodynam

applicationsarxiv-cs-ro

AblationBench: Evaluating Automated Planning of Ablations in Empirical AI Research

arXiv:2507.08038v3 Announce Type: replace-cross Abstract: Language model agents are increasingly used to automate scientific research, yet evaluating their scientific contributions remains a challenge

Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture

model-releasesarxiv-cs-lg

arXiv:2606.01110v1 Announce Type: cross Abstract: Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informe

ACON: Optimizing Context Compression for Long-horizon LLM Agents

arXiv:2510.00615v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as agents in dynamic real-world environments, where success depends on maintaining precise re

AdaptiveK: Complexity-Driven Sparse Autoencoders for Interpretable Language Model Representations

arXiv:2508.17320v3 Announce Type: replace Abstract: Understanding the internal representations of large language models (LLMs) remains a central challenge for interpretability research. Sparse autoenc

tutorialsarxiv-cs-lg