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Reading Between the Dots: Decoding Hidden Computation across Filler Tokens

arXiv:2607.03502v1 Announce Type: cross Abstract: Frontier LLMs can perform multi-step reasoning over content-free filler tokens like dots or counting sequences, producing correct answers with no visi

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REAL-OW: Rehearsal-free Open World Object Detection with Low-Rank Adaptation and Dual-Stage Objectness Modeling

DGX agent

arXiv:2607.03004v1 Announce Type: new Abstract: Open-World Object Detection (OWOD) requires detectors to identify previously unseen objects as unknown and incrementally incorporate them into the set o

applicationsarxiv-cs-cv

7 Jul 2026

Real-Time LiDAR Gaussian Splatting SLAM

arXiv:2607.04127v1 Announce Type: new Abstract: We present a real-time LiDAR-based framework for Gaussian Splatting SLAM that tightly couples fast G-ICP registration with spherical rasterization-based

Real-World Perturbation Testing of Autonomous Driving Systems

arXiv:2607.04953v1 Announce Type: cross Abstract: Autonomous Driving Systems (ADS) must operate reliably under diverse conditions, yet representative data for rare or adverse scenarios is difficult to

agentsarxiv-cs-ro

Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models

arXiv:2607.05199v1 Announce Type: new Abstract: Physics reasoning fails structurally in small language models: an error at any step propagates forward, corrupting every inference that follows. Limited

ReCal3R: Reliability-Calibrated Learning Rates for Streaming 3D Reconstruction

arXiv:2607.05356v1 Announce Type: new Abstract: Streaming 3D reconstruction relies on a compact recurrent scene state to process long image streams in linear time and bounded memory. However, repeated

Reconstructing Rational Functions on Finite Abelian Groups with Higher Autocorrelations

arXiv:2503.21022v2 Announce Type: replace Abstract: The higher-order autocorrelations of integer-valued or rational-valued functions on finite Abelian groups appear naturally in X-ray crystallography,

Reconstruction-Anchored Diffusion Model for Text-to-Motion Generation

model-releasesarxiv-cs-cv

arXiv:2601.14788v2 Announce Type: replace Abstract: Diffusion models have seen widespread adoption for text-driven human motion generation and related tasks due to their impressive generative capabili

Recursive Entropic Variational Inference for Nonlinear State-Space Models

arXiv:2511.15409v2 Announce Type: replace Abstract: We present a class of algorithms for state estimation in nonlinear, non-Gaussian state-space models. Our approach is based on a variational Lagrangi

REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing

arXiv:2607.05364v1 Announce Type: cross Abstract: Modern autoregressive ASR systems can emit timestamps as decoded tokens, enabling timestamped transcription without frame-level aligners or inference-

Reduced-Order Models: The Mother of World Models

arXiv:2607.03198v1 Announce Type: new Abstract: World models -- compressed latent representations of an environment that support action-conditioned prediction and planning -- are typically presented a

Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devices

arXiv:2606.12742v3 Announce Type: replace Abstract: Wearable healthcare devices are the fastest-growing Internet of Things (IoT) sector. Many automated healthcare services rely on two crucial biologic

Reference-Induced Consensus for Selective Posed-Reference Visual Localization

arXiv:2607.04722v1 Announce Type: new Abstract: We present RIC-Loc (Reference-Induced Consensus localization), a scene-training-free posed-reference localizer that is SfM-point-map-free in its main es

Reflected Schrodinger Bridge Matching

arXiv:2607.03626v1 Announce Type: new Abstract: Recent advances in generative modeling have enabled the efficient computation of Schrodinger bridges (SB) in high-dimensional settings by leveraging par

Tutorials

Reflective Dialogue or Prompt Refinement? Effects of Tutor Scaffolding on Students' Independent LLM Use for Programming

arXiv:2607.03303v1 Announce Type: new Abstract: While Large Language Models (LLMs) can provide personalized support in learning, several studies have raised concerns regarding their use in education.

tutorialsarxiv-cs-ai

Re:Form -- Reducing Human Annotations in Scalable Formal Software Verification with RL in LLMs: A Preliminary Study on Dafny

model-releasesarxiv-cs-cl

arXiv:2507.16331v4 Announce Type: replace Abstract: Existing informal language-based (e.g., human language) Large Language Models (LLMs) trained with Reinforcement Learning (RL) face a significant cha

Refused in Chat, Written in Code: Workflow-Level Jailbreak Construction in IDE Coding Agents

arXiv:2607.03968v1 Announce Type: cross Abstract: Large language models are increasingly deployed as IDE-integrated coding agents that decompose tasks, generate and edit files, run code, and refine ou

Regime-Conditional Stabilisation of LLM-Augmented Cooperative Multi-Agent Reinforcement Learning

arXiv:2607.04470v1 Announce Type: cross Abstract: Large Language Models (LLMs) offer a natural interface for translating human objectives into reward signals for cooperative multi-agent reinforcement

Reinforcement Learning for Data-Efficient Code-Switched ASR

arXiv:2607.02757v1 Announce Type: new Abstract: Audio-language models can be prompted for code-switched speech, but their decoding is not optimized for code-switching and often fails at language bound

safetyarxiv-cs-cl

Reinforcement Learning for Evidence-Seeking Diagnostic Reasoning with Large Language Models

arXiv:2607.02983v1 Announce Type: new Abstract: Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that

agentsarxiv-cs-ai

Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets

arXiv:2607.05179v1 Announce Type: cross Abstract: In liberalised railway systems, operators must set prices dynamically in an environment with partial observability, as they retain private information

Applications

Relevance-Based Embeddings: Lightweight Candidate Retrieval via Heavy-Ranker Calls

arXiv:2607.03515v1 Announce Type: cross Abstract: In many machine learning applications, the most relevant items for a query should be efficiently retrieved. The relevance function is usually an expen

applicationsarxiv-cs-lg

Reliability and Identifiability in Persona-Trained Monte Carlo: Variance Decomposition, Stability Bounds, and the Identifiability of Heterogeneous News Reaction

arXiv:2607.04627v1 Announce Type: new Abstract: Persona-Trained Monte Carlo (PTMC) estimates distributions of market-outcome functionals by repeatedly simulating limit-order-book interaction among K n

safetyarxiv-cs-lg

Reliability-Aware CT-MRI Registration: A Quality Engineering Framework with Stability Analysis and Risk Classification

arXiv:2607.02585v1 Announce Type: new Abstract: Multimodal CT-MRI registration is central to image-guided radiotherapy, surgical navigation, and diagnostic workflows, but most pipelines report only ag

Reliability-Aware Monocular Depth Supervision for Sparse-View Neural Reconstruction

arXiv:2607.02554v1 Announce Type: new Abstract: Sparse-view neural reconstruction is challenging in outdoor driving scenes, where cameras usually move along a narrow forward-facing trajectory and prov

ReLo-IRR: Reflection-Guided LoRA Framework for Image Reflection Removal

model-releasesarxiv-cs-cv

arXiv:2607.02957v1 Announce Type: new Abstract: Single-image reflection removal (SIRR) aims to recover the clean transmission layer from a reflection-contaminated image. Although recent methods achiev

Replicability is Asymptotically Free in Multi-armed Bandits

arXiv:2402.07391v3 Announce Type: replace-cross Abstract: We consider a replicable stochastic multi-armed bandit algorithm that ensures, with high probability, that the algorithm's sequence of actions

Report: 83% of organizations need to upgrade their infrastructure to support agentic AI

model-releasesgoogle-cloud-ai

For years, enterprise AI has been synonymous with conversational AI — the customer service bots and digital assistants we interact with every day. But today, the market has shifted. We’ve officially m

Report: China’s DeepSeek follows OpenAI in developing its own custom inference chips

model-releasessiliconangle

DeepSeek Ltd., one of China’s most visible artificial intelligence companies, is pushing to design its own, in-house silicon aimed at inference workloads, according to a report by Reuters today. The r

Local Ai

RePos: Relative-to-Absolute Output Factorization for Cross-Environment WiFi-Based 3D Human Pose Estimation

arXiv:2607.02986v1 Announce Type: new Abstract: Device-free 3D human pose estimation using commodity WiFi Channel State Information (CSI) enables privacy-preserving and illumination-robust human sensi

local-aiarxiv-cs-cv

Representation Recycling for Streaming Video Analysis

arXiv:2204.13492v5 Announce Type: replace Abstract: We present StreamDEQ, a method that aims to infer frame-wise representations on videos with minimal per-frame computation. Conventional deep network

Representing and Detecting Label Ambiguity in IMU-Based Exercise Evaluation

arXiv:2607.04842v1 Announce Type: new Abstract: Home-based physiotherapy is performed without supervision, which leads to incorrect execution and motivates systems that assess movement automatically f

Repurposing CLIP to Localize at Pixel Level

arXiv:2607.05253v1 Announce Type: new Abstract: Large-scale Vision-Language Models like CLIP have demonstrated impressive open-set localization capabilities at the image level. However, adapting this

RES-DARE: Failure-Aware Expert Adaptation and Rollback-Safe Self-Repair for Intrusion Detection

model-releasesarxiv-cs-lg

arXiv:2607.02687v1 Announce Type: cross Abstract: Intrusion detection systems are often trained under static benchmark conditions, although deployed network environments are affected by traffic drift,

ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes

arXiv:2607.04439v1 Announce Type: new Abstract: Large language models have made research ideation increasingly accessible, yet effective idea development requires more than generating candidate direct

ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog

arXiv:2607.04438v1 Announce Type: cross Abstract: Research dissemination, turning a paper into a poster, a talk video, and a blog post, is still a manual last mile. Prior automation treats each artifa

Resilient by Design -- Active Inference for Distributed Continuum Intelligence

arXiv:2511.07202v3 Announce Type: replace-cross Abstract: Failures are the norm in highly complex and heterogeneous devices spanning the distributed computing continuum (DCC), from resource-constraine

agentsarxiv-cs-ai

Resolving Primitive-Sharing Ambiguity in Long-Tailed TLS-Based Industrial MEP Point Cloud Segmentation via Spatial Context Constraints

arXiv:2601.19128v2 Announce Type: replace Abstract: In terrestrial laser scanning (TLS)-based mechanical, electrical, and plumbing (MEP) point cloud segmentation, safety-critical components such as re

Applications

Resource-constrained Project Scheduling with Time-of-Use Energy Tariffs and Machine States: A Logic-based Benders Decomposition Approach

arXiv:2601.06542v2 Announce Type: replace-cross Abstract: In this paper, we investigate the Resource-Constrained Project Scheduling Problem (RCPSP) with Time-of-Use (TOU) energy tariffs and machine st

applicationsarxiv-cs-ai

Responsibility Distribution Estimation in Ego-View Accident Videos with Multimodal Large Language Models

arXiv:2607.03591v1 Announce Type: cross Abstract: Recent studies on multimodal traffic accident understanding have mainly relied on infrastructure-camera footage, satellite imagery, or structured cras

Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering

arXiv:2210.10619v3 Announce Type: replace-cross Abstract: Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial

Rethinking AI-Generated Text Detection: A Strong Baseline and the Distribution-Shift Problem That Remains

model-releasesarxiv-cs-cl

arXiv:2607.03680v1 Announce Type: cross Abstract: Recent AI-generated text detection work often introduces a new benchmark together with a specialized detector tailored to it. We revisit this practice

Rethinking Brain Decoding with CLIP: The Role of Adversarial Robustness

arXiv:2607.03165v1 Announce Type: new Abstract: Brain decoding aims to uncover neural mechanisms by inferring stimulus-related representations from brain signals. In fMRI studies, this is typically ac

Rethinking Depth Pruning for Vision Transformers: A Heterogeneity-Aware Perspective

arXiv:2607.03784v1 Announce Type: cross Abstract: While prior studies have successfully compressed vision Transformers (ViTs) through various pruning techniques, most have concentrated on width prunin

Rethinking Neural Nonlinearity as Gating

arXiv:2607.03148v1 Announce Type: cross Abstract: Activation functions are considered an essential primitive for neural nonlinearity, i.e., they enable neural networks to serve as universal approximat

Rethinking On-Policy Self-Distillation for Thinking Models

arXiv:2607.05184v1 Announce Type: new Abstract: Self-distillation is a promising recipe for self-improvement in language models. In this setting, a model can serve as its own teacher when given privil

Rethinking Scientific Discovery in an Agentic Era

arXiv:2607.03863v1 Announce Type: new Abstract: Artificial intelligence has advanced scientific discovery, but most AI4Science systems remain fragmented tools that rely on humans to coordinate problem

agentsarxiv-cs-cl

Retroactive Chain-of-Thought (RetroCoT): Forensic Reconstruction Prompts as a Safety Diagnostic Across Model Generations

arXiv:2607.04645v1 Announce Type: cross Abstract: Safety alignment in large language models is typically evaluated against direct, imperative harmful requests. We show that this alignment is highly co