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RadioTrace: Transmitter-Aware Diffusion for Radio Map Estimation without Deployment-Time Fine-Tuning

arXiv:2607.20909v1 Announce Type: cross Abstract: Radio map (RM) estimation aims to reconstruct the spatial distribution of wireless signal characteristics, such as received signal strength (RSS), fro

local-aiarxiv-cs-lg

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RE-AD: Real-Time Requirement Adherence for Data Labeling

DGX agent

arXiv:2607.20455v1 Announce Type: cross Abstract: Human-annotated data remains fundamental to training frontier Large Language Models (LLMs). However, crowd-sourced annotations often suffer from quali

model-releasesarxiv-cs-ai

24 Jul 2026

RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring

arXiv:2607.20628v1 Announce Type: cross Abstract: Real-world video deblurring remains challenging due to diverse motion patterns, complex degradations, and the scarcity of realistic training data, yet

Agents

RECO: Region-Aware Compensation for Extrinsic Perturbations in Roadside 3D Detection

arXiv:2607.20947v1 Announce Type: new Abstract: In intelligent transportation systems, roadside 3D object detection provides wide-area perception crucial for traffic understanding, cooperative early w

agentsarxiv-cs-cv

Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation

arXiv:2607.21485v1 Announce Type: new Abstract: We study sinusoidal recurrence as an iterative mechanism for harmonic spectral enrichment in implicit neural representations (INRs). Our analysis reveal

researcharxiv-cs-cv

Reexamining zero-shot summarization: Empirical investigation of trustworthiness of LLM-summarizers

arXiv:2607.21010v1 Announce Type: new Abstract: Zero-shot summarization using Large Language Models (LLMs) has significantly advanced the abstractive summarization task by producing coherent and fluen

REFACT: Adaptive Fact Restatement for Compact and Faithful Chain-of-Thought Reasoning

arXiv:2607.20833v1 Announce Type: new Abstract: Large language models increasingly rely on long-form reasoning for complex tasks, yet their reasoning traces may drift away from the supplied context wh

local-aiarxiv-cs-cl

Refusal-Gated Decoding: Preserving Refusal Behavior Under High-Temperature Sampling

arXiv:2607.20791v1 Announce Type: new Abstract: High-temperature sampling is one of the primary mechanisms for increasing diversity in LLMs. Recent advances in truncation-based sampling techniques hav

REGARD: Regional Affective Differences in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2607.20722v1 Announce Type: new Abstract: Large language models trained and aligned within different linguistic and regional ecosystems may frame the same political, cultural, and geopolitical e

Regularized Optimization on Grassmann Manifold: Theory, Algorithm and Applications

arXiv:2607.21039v1 Announce Type: new Abstract: Spectral methods are among the most widely used techniques for community detection, clustering, and graph learning. Their performance, however, critical

applicationsarxiv-cs-lg

Regulating autonomous and agentic AI

arXiv:2607.21345v1 Announce Type: new Abstract: Regulating activities where regulatees use autonomous and agentic AI is challenging. Regulatory assumptions about regulatee knowledge and control no lon

Relative Value Learning

arXiv:2607.21120v1 Announce Type: cross Abstract: In reinforcement learning, critics typically estimate absolute state values V(s), estimating how good a particular situation is in isolation. However,

Reliability-Aware LLM Alignment from Inconsistent Human Feedback

arXiv:2607.20515v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is critical for aligning Large Language Models (LLMs) with human preferences. However, its efficacy is

ReliableTableQA:How Much Supervision Does Reliability Annotation Need?

arXiv:2607.20537v1 Announce Type: cross Abstract: We introduce ReliableTableQA, a framework for training an LLM to annotate the statistical reliability of tabular QA results, not whether the query is

Representation Robustness Under Executable Reasoning Constraints in Large Language Models for Mathematical Problem Solving

arXiv:2607.20520v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on mathematical problem solving, yet prior work often treats representationally equivalent formu

local-aiarxiv-cs-ai

Representative Sets in Propositional Abduction

arXiv:2607.21183v1 Announce Type: cross Abstract: The propositional abduction problem is a well-known form of non-monotonic reasoning where we are asked to find an explanation of a given manifestation

Representing Entity Importance in AI Knowledge Systems: A Dual-Signal Framework of Audience Evaluation and Structural Authority

arXiv:2607.20925v1 Announce Type: new Abstract: AI knowledge systems require representations of entity importance for retrieval, recommendation, evidence selection, and knowledge-intensive reasoning.

Response drift across frontier large language models

arXiv:2607.20454v1 Announce Type: cross Abstract: All frontier large language models (LLMs) exhibit response drift -- producing outputs that deviate from expert-validated references -- yet the magnitu

Rethinking Open-World Video Anomaly Detection: Diagnosing Definition Blindness

arXiv:2607.20780v1 Announce Type: new Abstract: Open-world video anomaly detection (OWVAD) is expected to detect events that match a user-specified definition of abnormality. This requirement is stron

local-aiarxiv-cs-cv

Riemannian Deep Learning: Modules, Networks, and Geometries

arXiv:2607.19305v2 Announce Type: replace-cross Abstract: Deep neural networks on manifold-valued representations have attracted growing interest, but many basic components remain tied to specific man

RL-MACRO: A Cybernetic Closed-Loop Intelligence Framework for Multimodal Adaptive Robotic Craniotomy

arXiv:2607.21113v1 Announce Type: new Abstract: Autonomous robotic craniotomy requires continuous regulation of tool-tissue interactions to mitigate mechanical overload and thermal damage while mainta

safetyarxiv-cs-ro

Robostral Navigate

arXiv:2607.20785v1 Announce Type: cross Abstract: Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficientl

Robust Asynchronous Q-Learning under Reward and State Corruption via Batching

arXiv:2607.20822v1 Announce Type: new Abstract: Motivated by reinforcement learning in harsh environments, we consider the problem of learning an optimal policy subject to adversarially corrupted feed

safetyarxiv-cs-lg

Robust Critics: Defending LLMs Against Multi-Turn Attacks

arXiv:2607.20472v1 Announce Type: new Abstract: When a user asks a language model something harmful, is it a genuine attack or a misunderstood but well-meaning question? This ambiguity is one of the c

Routing Subspaces: Auditing Evaluation-to-Deployment Mismatch in Fine-Tuned Language Models

arXiv:2607.20436v1 Announce Type: cross Abstract: Safety evaluations often assume that behavior observed during testing reflects behavior in ordinary use, but fine-tuning can break this assumption. A

Routing Without Training: Controllable-Ratio LLM Offloading via Reliability Gating

arXiv:2607.20481v1 Announce Type: new Abstract: Local-cloud collaboration is a practical way to deploy large language models under resource constraints, but existing methods often rely on trained rout

local-aiarxiv-cs-ai

RUMBA: Russian User Memory Benchmark

arXiv:2607.21447v1 Announce Type: cross Abstract: The ability to handle long-term memory in LLMs is becoming increasingly critical, yet existing benchmarks remain English-centric and rely on aggregate

Rushes: A Human Preference Dataset for Pluralistic Alignment

model-releasesarxiv-cs-cl

arXiv:2607.20767v1 Announce Type: new Abstract: We introduce Rushes, a dataset and benchmark for studying revealed human engagement preferences in interactive narrative environments. Rushes is collect

Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer

arXiv:2607.20665v1 Announce Type: new Abstract: Multi-drone payload transportation has emerged as a promising research paradigm with potential applications in construction, logistics, and disaster res

safetyarxiv-cs-ro

Safeguards for Speech2Speech LLM-Assistants: A Case Study in Automotive Applications

arXiv:2607.21180v1 Announce Type: new Abstract: Recent advances have introduced speech-to-speech (S2S) conversational assistants capable of producing natural-sounding interactions, including non-verba

SafeStep: AI-powered Travel Assistance for Elderly People with Frailty or Dementia

arXiv:2607.21156v1 Announce Type: new Abstract: More than a million people in the UK suffer from frailty or dementia, which severely compromise their ability to travel in urban environments. This pape

Safety-oriented sidewalk and road segmentation for smartphone-based assistive navigation

arXiv:2607.21137v1 Announce Type: new Abstract: Independent sidewalk mobility is essential for blind and visually impaired pedestrians (BVIPs), yet smartphone-based assistive navigation requires perce

safetyarxiv-cs-cv

SAGE: A Socially-Aware Generative Engine for Heterogeneous Multi-Agent Navigation

arXiv:2607.16619v2 Announce Type: replace Abstract: Safe and socially compliant navigation in open human-robot environments requires robots to reason about heterogeneous participants with different dy

safetyarxiv-cs-ro

SalesLoop: Reinforcement Learning from Performance Feedback for Sales Lead Ranking

arXiv:2607.20655v1 Announce Type: cross Abstract: Lead ranking in Customer Relationship Management (CRM) systems faces a persistent challenge: models achieving high offline accuracy often underperform

Same Dangerous Objective, Opposite Advice: Direct Exposure versus Multi-Agent Mediation

arXiv:2607.21518v1 Announce Type: new Abstract: Even a current high-capability LLM can appear safer when shown a dangerous objective directly than when other agents transform and relay its direction.

Agents

Sample-Efficient Learning from Agent Experience

arXiv:2607.21051v1 Announce Type: new Abstract: Real-world agent learning is often constrained by costly environment interactions, such as running time-consuming experiments or obtaining human feedbac

agentsarxiv-cs-cl

Hardware

SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation

arXiv:2607.21553v1 Announce Type: new Abstract: We introduce SANA-Video 2.0, a hybrid video diffusion transformer instantiated at 5B and 14B scales under a unified architecture. Designed to generate h

hardwarearxiv-cs-cv

Agents

Scalable Low-Cost Laboratory Automation: A Digital Twin-Integrated Robotic Platform for Autonomous Liquid Handling (RAINBOTTM)

arXiv:2607.20662v1 Announce Type: new Abstract: Laboratory automation accelerates discovery, yet its adoption is constrained by the high cost, proprietary design, and limited remote supervisability of

agentsarxiv-cs-ro

Scale Up Strategically: Learning Compositional Generalization via Bias-Aware Evaluation and Data Collection for Robotic Manipulation

arXiv:2607.21582v1 Announce Type: cross Abstract: Compositional generalization is essential for robot to follow diverse instructions. However, pretrained policies are known to take shortcuts, deferrin

safetyarxiv-cs-cv

Scaling Closed-Loop Feature Channel Configuration with LLMs

arXiv:2607.20516v1 Announce Type: cross Abstract: Promising initial results in closed-loop large-language-model-based channel-configuration search demonstrated that neural-network widths can be optimi

Scaling Interpretable Transformers with Parity Bottleneck Layers

arXiv:2607.20652v1 Announce Type: cross Abstract: Language models are thought to exhibit the phenomenon of superposition, representing many more features than dimensions in their residual streams. Spa

Scaling Up Formal Representation of Clinical Trial Protocols in Ensemble Logic Using LLMs: A Preliminary Study

arXiv:2607.21307v1 Announce Type: cross Abstract: The reliance on unstructured free text for documenting clinical trial protocols creates a significant barrier to automated reasoning, cohort discovery

Scene Parameter Saliency via Differentiable Light Transport

model-releasesarxiv-cs-cv

arXiv:2607.21562v1 Announce Type: new Abstract: Gradient-based saliency methods reveal which input features most influence a neural network's output, and are a standard tool for model interpretability

Scientific exploration, collaboration and labor division in the large language model era

arXiv:2607.20923v1 Announce Type: cross Abstract: Large language models (LLMs) have rapidly and significantly entered scientific workflows, but it remains unclear how their diffusion is associated wit

SciExplore: Evaluating Autonomous Agents from Scientific Navigation to Information Integration

arXiv:2607.20926v1 Announce Type: new Abstract: Scientific research involves complex information-seeking and reasoning workflows across heterogeneous sources. However, existing benchmarks primarily em

Tutorials

SCoPE: Shift-Aware Speaker-Conditioned Priors for Emotion Recognition in Conversations

arXiv:2607.20445v1 Announce Type: new Abstract: In conversations, human emotions are transient; however, they tend to persist across multiple utterances. For example, we rarely switch instantly betwee

tutorialsarxiv-cs-cl

Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents

arXiv:2602.10226v2 Announce Type: replace-cross Abstract: Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyper

Self-Supervised Bio-Inspired Robotic Trajectory Planning with Obstacle Avoidance

arXiv:2607.20743v1 Announce Type: cross Abstract: Trajectory planning is a fundamental problem in robotics, requiring the generation of collision-free and efficient trajectories in a potentially compl