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arXiv:2505.15738v3 Announce Type: replace-cross Abstract: Large language models are increasingly deployed in user-facing products that handle sensitive personal data while interacting with untrusted e

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Byzantine-Resilient Federated Large Language Model via Hypergraph Message Passing

DGX agent

arXiv:2610.12711v1 Announce Type: new Abstract: Federated Large Language Models (FedLLMs) are promising to deliver privacy-preserving, collaborative intelligence. The decentralized training process is

model-releasesarxiv-cs-lg

12 Oct 2026

Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks

arXiv:2610.13693v1 Announce Type: cross Abstract: Neural networks (NNs), while highly flexible and versatile, are often poorly calibrated. Post-hoc calibration methods adjust the outputs of an already

Can Failed Trajectories Reveal Where Agents Go Wrong? Agentic Failure Attribution Without Step-Level Labels

arXiv:2610.13105v1 Announce Type: new Abstract: Failure attribution for LLM agents aims to identify decisive steps responsible for task failure, which is critical for debugging and improving agentic s

agentsarxiv-cs-lg

Can Language Models Play the Wikipedia Game?

model-releasesarxiv-cs-ai

arXiv:2610.13948v1 Announce Type: new Abstract: Language models increasingly support or automate information retrieval from document collections too large to index. This task is difficult when it requ

CASTER: Formalizing Zero-Shot Manipulation from Synthetic Videos as Constrained Optimization

arXiv:2610.12567v1 Announce Type: new Abstract: Prior research on learning from humans often requires real human demonstrations. But advances in video generation models offer a new paradigm: we can no

safetyarxiv-cs-ro

Causal Effect Identifiability in Causal Abstractions: A Strict Hierarchy of Criteria

arXiv:2610.13520v1 Announce Type: new Abstract: Identifying causal effects from observational data typically requires a fully specified causal graph, an assumption that is often unrealistic in complex

Tutorials

COBALT: Contextual Bandit Learning with Offline Trajectories for Multi-Turn Code Generation

arXiv:2602.03806v2 Announce Type: replace-cross Abstract: Training large language models (LLMs) for multi-turn code generation typically relies on online reinforcement learning (RL), in which models l

tutorialsarxiv-cs-ai

Controlled-Minimax: Stabilizing Adversarial Optimization via Control Theory

arXiv:2610.12915v1 Announce Type: new Abstract: Minimax optimization is fundamental to adversarial learning yet suffers from severe stability challenges, often manifesting as persistent limit cycles o

CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning

model-releasesarxiv-cs-ai

arXiv:2603.05911v2 Announce Type: replace-cross Abstract: Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis. Although

Credit Attribution in Timestamped Soccer Networks Using Multivariate Hawkes Processes

arXiv:2610.12775v1 Announce Type: cross Abstract: The two main traditional player statistics used in soccer are goals scored and assists, with an assist credited when a player's pass immediately leads

Deploying RDMA for Large-Scale Inference and Training Workloads

arXiv:2610.12815v1 Announce Type: new Abstract: RDMA can reduce communication overhead in distributed machine learning systems, but realizing these benefits in production requires more than replacing

Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion

arXiv:2610.13808v1 Announce Type: cross Abstract: Variational quantum circuits (VQCs) are highly sensitive to circuit architecture, yet most quantum machine learning pipelines select a single best des

Does Knowledge About Perceptual Uncertainty Help an Agent in Automated Driving?

arXiv:2502.11864v2 Announce Type: replace-cross Abstract: Agents in real-world scenarios like automated driving deal with uncertainty in their environment, in particular due to perceptual uncertainty.

agentsarxiv-cs-ro

Does Learning Recurrent Networks Matter in Partially Observable RL?

model-releasesarxiv-cs-ai

arXiv:2610.13197v1 Announce Type: cross Abstract: Reinforcement learning (RL) under partial observability typically re- lies on recurrent networks to encode temporal information, with the recurrent mo

Local Ai

DP-MIND: Differentially private memory-guided Long context Inference

arXiv:2610.13132v1 Announce Type: new Abstract: LLM-as-a-Service has become a dominant paradigm for accessing large language models without requiring local deployment. However, the process raises seve

local-aiarxiv-cs-ai

Local Ai

Efficient Audio-Visual Speech Enhancement via Reinforcement Learning-Based Neural Architecture Search

arXiv:2610.12796v1 Announce Type: cross Abstract: Audio-visual speech enhancement (AVSE) improves speech intelligibility in noisy environments but often relies on computationally expensive models unsu

local-aiarxiv-cs-ai

EmoGavel: Grounding-Aware Reinforcement Learning with Verifiable Rewards for Multimodal Emotion Preference Learning

arXiv:2610.13353v1 Announce Type: cross Abstract: Multimodal emotion preference learning uses pairwise comparisons to identify which open-ended emotion descriptions align more closely with human judgm

Fast Gradient-Based Approximation of Continuous Dynamic Time Warping

arXiv:2610.13129v1 Announce Type: cross Abstract: Dynamic Time Warping (DTW) is a common choice for measuring the discrepancy between time series. It proceeds by finding an optimal warping between tim

hardwarearxiv-cs-lg

From Imbalance to Evidence: Deep Nonparametric Testing for Complex Data under Severe Sample Imbalance

arXiv:2610.12946v1 Announce Type: cross Abstract: Comparing distributions under severe sample imbalance requires effectively using abundant majority observations while retaining sensitivity to differe

Goal-Oriented Bundle Management and Flow Control for Deep-Space Communications via Reinforcement Learning

arXiv:2610.13923v1 Announce Type: cross Abstract: Deep-space communications operate under intermittent connectivity, long propagation delays, and relay storage constraints. In such networks, the Bundl

safetyarxiv-cs-lg

Grounded-Dreamer: Robot Learning from World Model Synthetic Data with Physical Grounding

arXiv:2610.13164v1 Announce Type: new Abstract: Scaling robot manipulation policies requires large amounts of diverse, action-labeled data, yet collecting such data in the real world remains expensive

safetyarxiv-cs-ro

Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning

arXiv:2505.11304v4 Announce Type: replace-cross Abstract: Federated learning (FL) commonly involves clients with diverse communication and computational capabilities. Such heterogeneity can significan

Hyper Algorithm Design Agent: Evolving Learnable Optimizer from Zero

arXiv:2609.35328v2 Announce Type: replace Abstract: Meta-Black-Box Optimization (MetaBBO) is one of the highlights in the recent AI for Optimization trend. This paradigm's bi-level workflow leverages

safetyarxiv-cs-ai

Instance-Level Difficulty: A Missing Perspective in Machine Unlearning

arXiv:2410.03043v3 Announce Type: replace Abstract: Current research on deep machine unlearning primarily focuses on improving or evaluating the overall effectiveness of unlearning methods while overl

Kernelized Parameter Sensitivity Atlases for Amortized Conditional Interpretability

arXiv:2610.12737v1 Announce Type: new Abstract: Gradient-based attribution provides input-specific information about model parameters, but computing it via backpropagation for many samples is expensiv

L_0 Feature Selection via Monte Carlo Tree Search in High-Dimensional Financial Regression

arXiv:2610.13574v1 Announce Type: new Abstract: Feature selection is a central problem in high-dimensional financial regression, where large numbers of engineered variables and strong correlations amo

safetyarxiv-cs-lg

Learning by Compression: Risk Estimates Attain the Law-of-Large-Numbers Rate

arXiv:2610.13442v1 Announce Type: cross Abstract: Compression schemes provide a general framework for deriving data-dependent risk bounds for learning algorithms. For learning rules based on preferent

Lightweight Time-Frequency Vibration Sensing for Consumer Device Condition Monitoring

arXiv:2610.13040v1 Announce Type: cross Abstract: Consumer rotating devices, including washing machines, air conditioners, electric fans, robotic vacuum cleaners, and home fitness equipment, increasin

Looking Ahead and Back: Anticipatory Generative Modeling for Road-Network Trajectories

arXiv:2610.13131v1 Announce Type: new Abstract: Trajectory generation underpins urban applications such as traffic management and urban planning. Within road networks, vehicle movement along road segm

Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices

arXiv:2610.12970v1 Announce Type: cross Abstract: The increasing demand for GPU-accelerated machine learning inference has exposed limitations in centralized GPU infrastructures, including high cost,

hardwarearxiv-cs-lg

MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing

arXiv:2610.12963v1 Announce Type: cross Abstract: Video editing models achieve strong performance in modifying appearance, backgrounds, styles, and motions, empowered by large-scale training data. Bey

safetyarxiv-cs-ai

Neuromorphic Satellite Intelligence: A Formal Framework for Event-Driven Architectures, Spike-Based Algorithms, and Quantitative Energy-Accuracy Trade-offs

arXiv:2610.12471v1 Announce Type: cross Abstract: Satellite onboard computing faces an irreducible tension: mission intelligence requirements grow monotonically while power, mass, and radiation budget

hardwarearxiv-cs-lg

Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States

arXiv:2610.13190v1 Announce Type: cross Abstract: Autonomous landing of unmanned aerial vehicle (UAV) on unmanned surface vehicle (USV) is difficult in high sea states. Existing hierarchical landing m

agentsarxiv-cs-lg

Local Ai

Probabilistic Ship Trajectory Forecasting using Transformer Models on AIS Data

arXiv:2610.13557v1 Announce Type: new Abstract: Anticipation of vessel trajectories is an essential task when navigating at sea. This applies to both human navigators and autonomous surface vessels. T

local-aiarxiv-cs-lg

RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance

arXiv:2602.00183v2 Announce Type: replace-cross Abstract: Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the perva

safetyarxiv-cs-lg

Sampling-based Kinodynamic Planning with World Models

arXiv:2610.12910v1 Announce Type: new Abstract: Visual world models predict the consequences of actions from images, but planning with them is typically done by optimizers such as CEM and MPPI. Howeve

hardwarearxiv-cs-ro

Self-Aware Knowledge Probing: Evaluating Language Models' Relational Knowledge through Confidence Calibration

arXiv:2601.18901v2 Announce Type: replace Abstract: Knowledge probing quantifies how much relational knowledge a language model (LM) has acquired during pre-training. Existing knowledge probes evaluat

researcharxiv-cs-cl

Applications

Simulating Training Spikes of Deep Neural Networks by Layer-wise Random Feature Models

arXiv:2610.13524v1 Announce Type: new Abstract: Intermittent ``spikes'' in loss, during the pre-training of Large Language Models (LLMs), remains a mysterious phenomenon both theoretically and empiric

applicationsarxiv-cs-lg

Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels

arXiv:2610.13221v1 Announce Type: new Abstract: Cancer diagnosis and treatment increasingly rely on multi-modal diagnostic technologies that generate high-dimensional datasets. Although these datasets

Test-Time Memory Construction

arXiv:2610.13348v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to answer questions over long interaction histories, where relevant evidence may be scattered across distant sessi

researcharxiv-cs-cl

Towards a Measure Theory of Semantic Information

arXiv:2508.00525v2 Announce Type: replace-cross Abstract: A classic account of the quantification of semantic information is that of Bar-Hiller and Carnap. Their account proposes an inverse relation b

Towards a Unified Taxonomy of Agricultural Harvesting Technologies: Linking Physical Interaction, Automation, and Selectivity

arXiv:2610.13688v1 Announce Type: new Abstract: Harvesting technologies have evolved from conventional mechanized systems based on bulk mechanical interaction toward increasingly automated and robotic

researcharxiv-cs-ro

Towards Defining, Measuring, and Leveraging Creativity in AI Applications

arXiv:2610.12845v1 Announce Type: new Abstract: Despite extensive research, computational creativity still lacks a conceptually grounded, practically relevant, and empirically applicable formal defini

Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities

arXiv:2610.13456v1 Announce Type: new Abstract: Cyber-Physical Systems are developed through multiple model based activities, comprising several stages of development. As the development progresses th

researcharxiv-cs-cl

Unreliable Narrator Framing as a Cost-effective Mechanism for Satire Explanation of Australian and American Satirical News

model-releasesarxiv-cs-cl

arXiv:2610.12863v1 Announce Type: new Abstract: Satirical news is a unique form of misinformation: it is intended to be humorous by the original publication source but can be misinterpreted. Explanati

Applications

Verifiable Retrieval for Legal Reasoning through Structure-driven Knowledge Graph

arXiv:2610.13695v1 Announce Type: new Abstract: Any automation within reliability-critical contexts such as medicine or law has to be transparent and verifiable. Despite their advantages, current LLM-

applicationsarxiv-cs-cl

When Faster Decisions Do Not Make Better Agents: An Empirical Study of Jev in Agent Workflows

arXiv:2610.13092v1 Announce Type: new Abstract: AI agents depend on repeated decisions to accomplish tasks, with large language models (LLMs) commonly serving as their core decision makers. The recent

agentsarxiv-cs-ai