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Reversible Unlearnable Examples: Towards the Copyright Protection in Deep Learning Era

arXiv:2608.06211v1 Announce Type: cross Abstract: Significant advancements in deep learning have been made possible by the utilization of large datasets, underscoring the critical importance of copyri

tutorialsarxiv-cs-cv

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Revisiting Black-Box Model Ownership Verification through Information Theory

DGX agent

arXiv:2409.06130v2 Announce Type: replace-cross Abstract: Modern machine learning models require substantial computational resources and data to train, making them valuable intellectual property. Mode

applicationsarxiv-cs-ai

7 Aug 2026

RIG-RoPE: Relation- and Instance-Gated Rotary Positional Encoding with Duration-Aware Temporal Coordinates

arXiv:2608.05154v1 Announce Type: new Abstract: Rotary positional encoding (RoPE) is a core component of modern language models and has been extended to multimodal LLMs through multidimensional varian

researcharxiv-cs-cl

Robot Learning from Human Demonstrations: Handwritten Alphabet Trajectories and Human-Likeness Evaluation

arXiv:2608.06221v1 Announce Type: cross Abstract: Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dy

Robust Context-Aware Detection of Malicious Instructions in Text

arXiv:2608.05430v1 Announce Type: cross Abstract: The remarkable instruction-following ability of modern LLMs has enabled their practical use as the minds of agents that can autonomously complete incr

agentsarxiv-cs-lg

Robust Native Language Identification through Agentic Decomposition

arXiv:2509.16666v2 Announce Type: replace Abstract: Large language models (LLMs) often achieve high performance in native language identification (NLI) benchmarks by leveraging superficial contextual

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models

arXiv:2608.05903v1 Announce Type: new Abstract: Mainstream World-Action Models (WAMs) adapt pretrained video generation models (VGMs) for robot control, transferring their learned dynamics prior for a

safetyarxiv-cs-cv

Routing Is Least Learnable Where It Is Most Valuable: Bounds on Representation Routing for Web Agents

arXiv:2608.06171v1 Announce Type: new Abstract: Web agents observe a browser through text, pixels, or both, and the choice is usually fixed once for all tasks. We measure six observation modes across

agentsarxiv-cs-cl

RP-OPSD: Reasoning-Pivot-Guided On-Policy Self-Distillation for Multilingual Reasoning Transfer

arXiv:2608.06347v1 Announce Type: new Abstract: Multilingual reasoning transfer is crucial for extending reasoning capabilities of large language models (LLMs) beyond high-resource languages. On-polic

safetyarxiv-cs-cl

RRC: Unlocking Generative Reward Models in LLM Reinforcement Learning via Ranking-Based Reward Construction

arXiv:2608.06310v1 Announce Type: cross Abstract: Recent advances in reward modeling show a paradigm shift from discriminative reward models to generative reward models. However, despite their strong

researcharxiv-cs-cl

Runtime Observability for Heterogeneous Attention Memory

arXiv:2608.05863v1 Announce Type: new Abstract: Modern models no longer keep a plain KV cache: latent caches, learned sparse selectors and recurrent states each carry the model's memory in a different

RxnCLF: Contrastive Transformation-Aware Reaction Foundation Model for Improved Reactivity Prediction

arXiv:2608.06259v1 Announce Type: new Abstract: Reaction yield prediction remains challenging because labeled data are scarce and reaction space is both combinatorially large and sparsely populated, l

tutorialsarxiv-cs-lg

Safe Evolution with Circuit Anchors

arXiv:2608.05158v1 Announce Type: new Abstract: In biological evolution, unconstrained mutation can lead to catastrophic outcomes: organisms may evolve enhanced capabilities while losing essential fun

safetyarxiv-cs-cl

SafeDivertor: Faithful Divertor Heat Flux Reconstruction from Macroscopic Plasma State Signals via Time-Frequency Prior Exploitation

arXiv:2608.05669v1 Announce Type: cross Abstract: Divertor heat-flux analysis is essential for understanding plasma-wall interactions and protecting plasma-facing components in magnetic-confinement fu

SAGA: Score-Weighted Adaptive Generation Alignment for Low-Resource Nordic Language Models

arXiv:2608.06179v1 Announce Type: new Abstract: Preference optimisation has proven effective for improving large language models but typically relies on costly human preference annotations. Extending

safetyarxiv-cs-lg

Sample-Adaptive Latent Rewards for Uncertainty-Guided Diffusion Post-Training

arXiv:2608.06125v1 Announce Type: new Abstract: Latent reward models can supervise visual diffusion models without decoding intermediate states into pixel space. This makes alignment with human prefer

safetyarxiv-cs-cv

Scaffold-Mediated Post-Training: Co-Evolving Model Parameters and Procedural Scaffold Graphs

arXiv:2608.05156v1 Announce Type: new Abstract: Post-training of large language models optimizes only parameters, while inference-time procedural scaffolds are typically designed independently of para

Scalable estimation of VARMA models

arXiv:2608.06340v1 Announce Type: cross Abstract: Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, th

safetyarxiv-cs-lg

Schema-Guided Hierarchical Information Extraction and Semantic Evaluation Using Generative AI

arXiv:2608.06167v1 Announce Type: new Abstract: We present a schema-based framework for extracting complex, structured information from unstructured text documents using generative AI, followed by aut

SCI-CLIP: Segment-Centric Inference with Reference Memory for Training-Free Open-Vocabulary Segmentation

arXiv:2608.05627v1 Announce Type: new Abstract: Training-free open-vocabulary segmentation remains limited by a missing inference abstraction. Frozen vision-language features are produced at patch lev

safetyarxiv-cs-cv

Scientific Machine Learning of Chaotic Systems Learns Reduced-Order Equations for Neural Populations

arXiv:2507.03631v4 Announce Type: replace Abstract: Extracting interpretable mathematical models from complex dynamical systems is difficult, especially for chaotic dynamics observed with noisy experi

SciQNet: Two-Stage Multimodal Adaptation for Scientific Image Quality Assessment

arXiv:2608.05691v1 Announce Type: new Abstract: Scientific images are essential for communicating experimental observations, quantitative evidence and conceptual knowledge. Unlike natural images, thei

researcharxiv-cs-cv

SCP-NL2TL: Selective Conformal Prediction with Semantic Verification for Natural Language to Temporal Logic Specifications

arXiv:2608.05439v1 Announce Type: new Abstract: Translating natural language instructions into machine-interpretable formal specifications enables robots and autonomous systems to plan, reason, and fo

safetyarxiv-cs-ai

SEAM: Global consistency beyond local accuracy in scientific machine learning

arXiv:2608.05702v1 Announce Type: new Abstract: Scientific machine learning commonly validates models at the level of a subdomain, a benchmark split, or an explanation for one prediction. Yet such loc

Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations

arXiv:2608.05588v1 Announce Type: cross Abstract: Lifelong Multi-Agent Path Finding (LMAPF) requires repeatedly planning collision-free paths for agents that continuously receive new goals upon reachi

safetyarxiv-cs-ai

Search2Skill: Skill Distillation Beyond Knowledge Boundaries Via Rubric-Based Reinforcement Learning

arXiv:2608.05245v1 Announce Type: new Abstract: Reusable skills, which encapsulate the procedural knowledge required to solve real-world professional tasks, offer LLM-based agents a path toward self-e

agentsarxiv-cs-ai

SearchAuditor: Auditing and Attributing Failures in Long-Horizon Search Agents

arXiv:2608.05212v1 Announce Type: new Abstract: Deep search agents tackle challenging questions through long-horizon web interactions, a process that is both complex and fragile: small reasoning error

Seeing Is Not Deciding: Can Multimodal LLMs Act as Effective CEOs?

arXiv:2608.05864v1 Announce Type: new Abstract: Large language models are increasingly applied as autonomous decision-making agents. However, in executive business decisions, existing benchmarks are l

SemiAdapt-Instruct: Extensible Instruction Tuning via Latent Domain-Specialised Adapters

arXiv:2608.05161v1 Announce Type: new Abstract: Instruction-tuned LLMs are deployed into environments where domains evolve, yet extending a fine-tuned model's capabilities without full retraining rema

Shape-Aware Oriented Bounding Box (OBB) to Horizontal Bounding Box (HBB) Conversion

arXiv:2608.05858v1 Announce Type: new Abstract: Accurate object detection in aerial and satellite imagery is dependent upon the bounding box representation. This is especially true for spatially orien

researcharxiv-cs-cv

Shapes from Examples: Foundations of Shape Learning in Recursive SHACL

arXiv:2607.27934v2 Announce Type: replace Abstract: SHACL shapes enable data graph validation, making automatic shape learning essential for knowledge graph applications. We investigate the well-known

researcharxiv-cs-ai

Shaping Human-AI Interactions to Provide Improvement Pathways and Balance Competing Objectives

arXiv:2608.05710v1 Announce Type: new Abstract: When an AI system is deployed, the individuals who use and or are evaluated by it form beliefs about how the system operates and use those beliefs to st

tutorialsarxiv-cs-ai

Shrinking the Generation-Verification Gap with Weak Verifiers

arXiv:2506.18203v3 Announce Type: replace Abstract: Verifiers can improve language model capabilities by scoring and ranking responses from generated candidates. Currently, high-quality verifiers are

Signal or Spurious Cue? A Randomized Audit of Survey-Country Metadata in LLM Social Inference

arXiv:2608.06085v1 Announce Type: new Abstract: Survey-country metadata can improve an LLM's forecast of an individual response when informative, yet the same cue may redirect the forecast when assign

researcharxiv-cs-ai

Simulator-Grounded Large Language Models for Industrial Causal Reasoning: Tool-Use, Structured Injection, and Plant-Portable Retrieval for Wastewater Treatment Decision Support

arXiv:2608.05151v1 Announce Type: cross Abstract: Wastewater operators need answers grounded in how their plant's variables interact and how fast effects propagate, not in generic pretraining text, wh

SkillHEX: Improving Agent Skills via Hypothesis-Driven Autonomous Exploration and Exploitation

arXiv:2608.05628v1 Announce Type: new Abstract: Although agent skills equip LLMs with reusable procedural knowledge, manual maintenance suffers from high costs, unscalability, and misalignment. Real-w

SkillMemo: Expert-guided Skill Memory Framework for Compositional Embodied Manipulation

arXiv:2608.05970v1 Announce Type: cross Abstract: Embodied visuomotor models, including Diffusion Policy (DP) and Vision-Language-Action (VLA) models, have demonstrated promising performance on roboti

SkillTFM: Gated Skill Evolution for Training-Free Adaptation of Tabular Foundation Models

arXiv:2608.06137v1 Announce Type: new Abstract: Tabular data are ubiquitous in real-world applications and are crucial for data-driven prediction and decision-making across science, industry, finance,

SkillTrace: Multi-Trace Provenance Auditing for LLM-Agent Skill Reuse

arXiv:2608.05204v1 Announce Type: new Abstract: LLM-agent ecosystems are rapidly growing around reusable skills: mixed-modality packages of metadata, natural-language instructions, code, tools, refere

agentsarxiv-cs-ai

SkillTV-Bench: Benchmarking How Well Judges Perform on Skill-Augmented Agentic Execution

arXiv:2608.05573v1 Announce Type: new Abstract: LLM agents increasingly execute long-horizon tasks through tool use and environment interaction, shifting evaluation from final-response scoring to veri

SkillZip: Contract-Preserving Graph Compression for Scalable Agent Skill Libraries

arXiv:2608.05604v1 Announce Type: cross Abstract: Large Language Models (LLMs) increasingly act as agents whose procedural knowledge is stored in reusable skill packages and loaded at inference time.

agentsarxiv-cs-ai

Sliding Sensors: Configurable Confidence in State Estimation for Continuum Robots

arXiv:2608.05410v1 Announce Type: new Abstract: Continuum robots often operate in uncertain environments, where accurate state estimation is essential for safe interactions. Estimate uncertainty is in

researcharxiv-cs-ro

Small Foundation Models of Human Cognition and Behaviour

arXiv:2608.05224v1 Announce Type: new Abstract: Large language models fine-tuned on human behavioural data have emerged as general-purpose cognitive proxies, but the scale this requires, and whether t

researcharxiv-cs-ai

Applications

Sparse Mixture-of-Experts for Non-Uniform Noise Reduction in MRI Images

arXiv:2501.14198v3 Announce Type: replace-cross Abstract: Magnetic Resonance Imaging (MRI) is an essential diagnostic tool in clinical settings, but its utility is often hindered by noise artifacts in

applicationsarxiv-cs-cv

Sparse Mutual Information Graph Averaging for Improving Random Indexing Embeddings

arXiv:2608.05724v1 Announce Type: new Abstract: Sparse word embedding pipelines can avoid dense co-occurrence matrix materialization, dense factorization, and gradient training while still relying on

researcharxiv-cs-cl

Spectral Aliasing Pretext: A novel task for Self-Supervised fault diagnosis in rotating machinery

arXiv:2608.05705v1 Announce Type: cross Abstract: Deep learning is a new way for machinery fault diagnosis but requires extensive labeled data, a scarce resource in industrial settings. We propose Spe

tutorialsarxiv-cs-ai

Spectral Distillation: From Nonlinear Dynamics to Linear State-Space Models

arXiv:2608.05416v1 Announce Type: new Abstract: Can nonlinear dynamical systems be learned through a compact linear state-space representation, without directly solving a non-convex system-identificat

tutorialsarxiv-cs-lg

SR-JEPA: Learning Predictive Latent State in 3D Scenes

arXiv:2608.05774v1 Announce Type: new Abstract: Joint-embedding predictive architectures learn by predicting latent representations of missing observations, yet many masked JEPAs are evaluated primari

tutorialsarxiv-cs-cv