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Normality-Preserving Continual Industrial Anomaly Detection via Orthogonal LoRA Banks

arXiv:2606.02042v1 Announce Type: new Abstract: Continual industrial anomaly detection with diffusion models suffers from historical normality prior drift and catastrophic forgetting. Existing continu

researcharxiv-cs-cv

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Research

Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures

DGX agent

arXiv:2606.00557v1 Announce Type: new Abstract: To understand how a neural network (NN) functions and makes predictions, it has become increasingly clear that analyzing only the input domain is insuff

researcharxiv-cs-lg

2 Jun 2026

NormEval: A Unified Multi-Metric Framework for Evaluating Semantic Fidelity in Text Normalization

arXiv:2511.20409v2 Announce Type: replace Abstract: Text normalization methods such as stemming and lemmatization are fundamental components of NLP pipelines. As new normalization tools are developed

safetyarxiv-cs-cl

NOS-Gate: Queue-Aware Streaming IDS for Consumer Gateways under Timing-Controlled Evasion

model-releasesarxiv-cs-lg

arXiv:2601.00389v2 Announce Type: replace-cross Abstract: Timing and burst patterns can leak through encryption, and an adaptive adversary can exploit them. This undermines metadata-only detection in

Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference

arXiv:2606.02430v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into high-performance computing (HPC) workflows, accelerating scientific discovery through di

researcharxiv-cs-ai

Not All Explanations Simulate Equally: Comparing Verbalized Feature Attributions and Self-Generated Rationales

arXiv:2606.01148v1 Announce Type: new Abstract: Natural-language explanations are often treated as a unified interface for understanding model behavior, but different explanation sources may support s

Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate

arXiv:2606.00820v1 Announce Type: new Abstract: Multi-agent debate (MAD) is a promising strategy for improving LLM reasoning, but when agents converge on a shared answer, it is unclear whether that co

agentsarxiv-cs-cl

Tutorials

Not All Points Are Equal: Uncertainty-Aware 4D LiDAR Scene Synthesis

arXiv:2606.02510v1 Announce Type: new Abstract: Constructing faithful 4D worlds from LiDAR-acquired sequences is crucial for embodied AI, yet current generative frameworks apply uniform modeling capac

tutorialsarxiv-cs-cv

Not What, But How: A Communicative Audit of LLM Response Framing

arXiv:2606.02493v1 Announce Type: new Abstract: Large language models (LLMs) are being increasingly used to answer subjective, information-seeking questions, where users are sensitive to how responses

OARelatedWork: A Large-Scale Dataset of Related Work Sections with Full-texts from Open Access Sources

arXiv:2405.01930v2 Announce Type: replace Abstract: This paper introduces OARelatedWork: a dataset for related work generation from open-access sources. It is the first large-scale multi-document summ

ObjEmbed: Towards Universal Multimodal Object Embeddings

arXiv:2602.01753v3 Announce Type: replace Abstract: Aligning objects with corresponding textual descriptions is a fundamental challenge and a realistic requirement in vision-language understanding. Wh

safetyarxiv-cs-cv

Hardware

Observation, Not Prediction: Conversation-Level Disaggregated Scheduling for Agentic Serving

arXiv:2606.01839v1 Announce Type: cross Abstract: LLM-based agents resolve a user task through many turns of dependent inference and tool calls, producing a workload whose total cost is unknown when t

hardwarearxiv-cs-lg

OCC-RAG: Optimal Cognitive Core for Faithful Question Answering

arXiv:2606.00683v1 Announce Type: new Abstract: Recent progress in the development of language models has been defined by scale, with each generation absorbing more of the world's knowledge into its w

OctoT2I: A Self-Evolving Agentic Text-to-Image Router

arXiv:2606.01803v1 Announce Type: new Abstract: The explosive growth of Text-to-Image (T2I) models, from large-scale versions to lightweight, real-time ones, now faces diminishing marginal returns fro

agentsarxiv-cs-ai

ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning

arXiv:2606.02433v1 Announce Type: cross Abstract: The rapid development of LLMs has significantly advanced tabular question answering, but most systems cannot perform future-oriented numerical predict

agentsarxiv-cs-ai

Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation

arXiv:2509.03456v2 Announce Type: replace-cross Abstract: Off-policy evaluation (OPE) and off-policy learning (OPL) are foundational for decision-making in offline contextual bandits. Recent advances

safetyarxiv-cs-lg

Off-the-Shelf LLMs as Process Scorers: Training-Free Alternative to PRMs for Mathematical Reasoning

arXiv:2606.01682v1 Announce Type: cross Abstract: Selecting the best response from multiple small-model samples using a stronger scorer is a simple inference-time strategy, but fails when the small mo

OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models

model-releasesarxiv-cs-lg

arXiv:2606.00815v1 Announce Type: new Abstract: Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions. E

OmniOPD: Logit-Free On-Policy Distillation via Speculative Verification

arXiv:2606.01476v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) trains a student model on its own generative trajectories under dense token-level feedback from a stronger teacher, mitig

On Effectiveness and Efficiency of Agentic Tool-calling and RL Training

arXiv:2606.00135v1 Announce Type: cross Abstract: Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge. This pa

Applications

On Imbalanced Regression with Hoeffding Trees

arXiv:2602.22101v3 Announce Type: replace-cross Abstract: Many real-world applications generate continuous data streams for regression. Hoeffding trees and their variants have a long-standing traditio

applicationsarxiv-cs-ai

On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents

arXiv:2603.12109v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a de facto paradigm for building LLM-based agents that act, interact, and reason over extended task horizons.

agentsarxiv-cs-ai

On Median of Incomplete U-Statistics

arXiv:2606.00661v1 Announce Type: cross Abstract: We establish the finite-sample concentration rate for the Median-of-Incomplete-U-Statistics (MIU), an efficient robust estimator for the expectation o

On the Collapse of Generative Paths: A Criterion and Correction for Diffusion Steering

arXiv:2512.10339v2 Announce Type: replace Abstract: Inference-time steering adapts pretrained diffusion and flow models to new tasks without retraining, often utilizing ratio-of-densities construction

researcharxiv-cs-ai

Tutorials

On the Difficulty of Learning a Meta-network for Training Data Selection

arXiv:2606.00571v1 Announce Type: cross Abstract: Synthetic data are increasingly used to train neural networks, yet distributional mismatch with real data limits their effectiveness when used indiscr

tutorialsarxiv-cs-ai

On the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection

arXiv:2606.01442v1 Announce Type: cross Abstract: Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are compu

On the evolution of the concept of probability as a mirror of the evolution of reason

arXiv:2606.00102v1 Announce Type: new Abstract: Over the centuries, probability theory has grown from the calculus of games of chance into a central framework for reasoning under uncertainty. This art

researcharxiv-cs-ai

On the Generalization Gap in Self-Evolving Language Model Reasoning

arXiv:2606.01075v1 Announce Type: new Abstract: Recent work suggests that large language models (LLMs) can improve through self-evolution (SE), using supervision signals generated by the model itself.

On the Generalization in Topology Optimization via Sensitivity-Conditioned Bernoulli Flow Matching

arXiv:2606.02179v1 Announce Type: cross Abstract: Surrogate models for topology optimization (TO) exhibit highly variable out-of-distribution (OOD) generalization under distribution shifts such as cha

On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance

arXiv:2606.00467v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-intern

On the Limits of Token Reduction for Efficient Unified Vision Language Training

arXiv:2606.01503v1 Announce Type: cross Abstract: Unified vision-language models (VLMs) integrate visual understanding and visual generation within a single autoregressive backbone, but their joint tr

On the Recoverability of Causal Relations from Bulk Gene Expression Data

arXiv:2606.00568v1 Announce Type: new Abstract: Bulk gene expression profiling, which aggregates pooled RNA across cells within a biological sample, remains important in the single-cell era because it

On the Salience of Low-Probability Tokens for AI-Generated Text Detection: A Multiscale Uncertainty Perspective

arXiv:2606.02158v1 Announce Type: new Abstract: AI-generated text increasingly blends with human writing, raising practical risks such as misinformation, academic misuse, and corpora contamination. Wh

On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters

arXiv:2606.02437v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) is usually treated as a cheaper alternative to full fine-tuning. We study a broader role: small trainable adapt

On the Theoretical Limitations of Embedding-based Link Prediction

arXiv:2506.22271v3 Announce Type: replace Abstract: Neural networks often map low-dimensional embeddings to high-dimensional output spaces. Usually, the output layer is linear, which can create a 'ran

On the Uncertainty Quantification Ability of Tabular Foundation Models

arXiv:2606.01427v1 Announce Type: cross Abstract: Foundation models (FMs) have achieved substantial success in generalizing across tasks without problemspecific training or fine-tuning. However, many

On Wednesdays, We Ask Questions: Optimizing 'Active Listening' in Automated Legal Triage and Referral

arXiv:2606.00272v1 Announce Type: new Abstract: The FETCH classifier generates follow-up questions to help refine the best match for the applicant's legal problem, using a low-cost ensemble of LLMs. I

OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction

arXiv:2510.17532v2 Announce Type: replace Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinica

One Bias After Another: Mechanistic Reward Shaping and Persistent Biases in Language Reward Models

arXiv:2603.03291v2 Announce Type: replace-cross Abstract: Reward Models (RMs) are crucial for online alignment of language models (LMs) with human preferences. However, RM-based preference-tuning is v

Local Ai

One Channel to Rule Them All: Rethinking Input Representation for Visual Place Recognition

arXiv:2606.00936v1 Announce Type: new Abstract: Visual Place Recognition (VPR) is fundamental to long-term robot localization and SLAM, yet current systems overwhelmingly rely on RGB input, implicitly

local-aiarxiv-cs-cv

Local Ai

One-Shot Crowd Counting With Density Guidance For Scene Adaptation

arXiv:2602.07955v2 Announce Type: replace Abstract: Crowd scenes captured by cameras at different locations vary greatly, and existing crowd models have limited generalization for unseen surveillance

local-aiarxiv-cs-cv

OneVLA: A Unified Framework for Embodied Tasks

model-releasesarxiv-cs-ro

arXiv:2606.01241v1 Announce Type: new Abstract: Navigation and manipulation are fundamental capabilities of embodied intelligence, enabling robots to interpret natural language commands and interact p

Online Learning in MDPs with Partially Adversarial Transitions and Losses

arXiv:2602.09474v2 Announce Type: replace Abstract: We study reinforcement learning in MDPs whose transition function is stochastic at most steps but may behave adversarially at a fixed subset of Lamb

Online Packet Scheduling with Deadlines and Learning

arXiv:2606.00835v1 Announce Type: new Abstract: Network routers that enforce Quality-of-Service (QoS) guarantees must decide, at every clock cycle, which expiring packet of information to transmit, ev

Ontology-Guided Reasoning for Affordance-Based Explanations of Robot Navigation

model-releasesarxiv-cs-ro

arXiv:2606.00117v1 Announce Type: new Abstract: This paper proposes ontology-guided reasoning for affordance-based explanations of robot navigation. In human environments, it is not sufficient for a r

OP-LoRA: The Blessing of Dimensionality

arXiv:2412.10362v2 Announce Type: replace-cross Abstract: Low-rank adapters (LoRA) enable finetuning of large models with only a small number of parameters. However, they often suffer from an ill-cond

researcharxiv-cs-cv

OPD+: Rethinking the Advantage Design for On-Policy Distillation

arXiv:2606.01039v1 Announce Type: cross Abstract: On-policy distillation (OPD) is a widely used technique to transfer capabilities from capable teacher language models to the base student models, and

OpenDPR: Open-Vocabulary Change Detection via Vision-Centric Diffusion-Guided Prototype Retrieval for Remote Sensing Imagery

model-releasesarxiv-cs-cv

arXiv:2603.27645v2 Announce Type: replace Abstract: Open-vocabulary change detection (OVCD) seeks to recognize arbitrary changes of interest by enabling generalization beyond a fixed set of predefined