All entries

Reasoning Consensus: Structural Ensembling of LLM Reasoning via Weighted DAG Aggregation

arXiv:2607.27783v1 Announce Type: new Abstract: Large Language Models (LLMs) explore problems through chain-of-thought, but this exploration is buried in unstructured prose. On high-stakes tasks, user

researcharxiv-cs-cl

Recall Before You Rank: Similarity-Guided Top-K Reuse for Efficient Long-Context Attention

DGX agent

arXiv:2607.27692v1 Announce Type: new Abstract: Top-K sparse attention reduces the cost of Softmax and value aggregation by attending to only a small subset of key--value (KV) entries. However, identi

researcharxiv-cs-cl

31 Jul 2026

Recognition and Label-Free Adaptation Across Recording Sessions in Surface-EMG Gesture Decoding

arXiv:2607.27568v1 Announce Type: new Abstract: Recognition accuracy obtained during a recording session does not persist when a user puts on the electrodes again after the electrodes had previously b

Recursive transformers for semiconductor thermo-mechanical reliability

arXiv:2607.27251v1 Announce Type: new Abstract: Transformer-based surrogate models are increasingly used to replace expensive first-principles simulation in engineering design. But conventional transf

RedFlow: Redirect Failure into Action-Level Corrections for Flow-matching VLA Policy

model-releasesarxiv-cs-ro

arXiv:2607.27782v1 Announce Type: new Abstract: Flow-matching Vision-Language-Action (VLA) policies have shown strong potential for robotic manipulation but often suffer from compounding errors caused

ReDiff: Reliability-Guided Diffusion for Trustworthy Ultra-Low-Field to High-Field MRI Synthesis

arXiv:2603.11325v2 Announce Type: replace Abstract: Low-field to high-field MRI synthesis has emerged as a promising strategy to improve image quality when access to high-field scanners is limited. Ho

tutorialsarxiv-cs-cv

ReDiPPO: Reference-Guided Value Calibration and Discrepancy-Aware Token Reweighting for Mathematical Reasoning

arXiv:2607.27631v1 Announce Type: cross Abstract: Reinforcement learning has emerged as an effective paradigm for enhancing the mathematical reasoning capabilities of large language models. Among exis

safetyarxiv-cs-cl

RefCaptioner: Multi-Reference Image-Grounded Video Captioning

model-releasesarxiv-cs-cv

arXiv:2607.28509v1 Announce Type: new Abstract: Existing video captioning models generate natural descriptions of video content but cannot explicitly ground local visual elements to multiple reference

REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning

arXiv:2603.13707v3 Announce Type: replace-cross Abstract: Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-

Agents

RefineSVG: Visual Feedback-Driven Reinforcement Learning for Image-to-SVG Generation

arXiv:2607.27699v1 Announce Type: new Abstract: We propose RefineSVG, a single-step closed-loop visual feedback framework that enables multimodal large language models (MLLMs) to perform high-fidelity

agentsarxiv-cs-cv

Reflected diffusion, no-flux continuity equations and confined Lagrangian flows in bounded domains

arXiv:2607.28344v1 Announce Type: cross Abstract: Motivated by marginal distribution flows of reflected diffusions in bounded domains, we investigate when a density/flux pair solving a no-flux continu

Hardware

ReGenVC: End-to-End Real-Time Generative Video Coding at Ultra-Low Bitrate

arXiv:2607.28144v1 Announce Type: cross Abstract: We present ReGenVC, an end-to-end generative video codec that compresses talking-head video to an ultra-low bitrate and decodes it in real time. The e

hardwarearxiv-cs-cv

Region-adaptable retrieval of coastal biogeochemical parameters from near-surface hyperspectral remote sensing reflectance using physics-aware meta-learning

arXiv:2605.05623v2 Announce Type: replace Abstract: Hyperspectral in situ sensing has shown promise in retrieving aquatic biogeochemical (BGC) parameters, such as total suspended solids, dissolved org

Regularizing modality contribution drift in multimodal continual learning

arXiv:2607.27260v1 Announce Type: new Abstract: Multimodal continual learning (MMCL) aims to learn emerging knowledge from multimodal data while preserving knowledge. To mitigate forgetting, current M

Relational Scene Graphs for Object Grounding of Natural Language Commands

arXiv:2602.04635v2 Announce Type: replace Abstract: Robots are finding wider adoption in human environments, increasing the need for natural human-robot interaction. However, understanding a natural l

tutorialsarxiv-cs-ro

RepBench: Compiling Benchmarks into Capability Representations for Large Language Models

arXiv:2607.28008v1 Announce Type: new Abstract: Representation engineering reads and steers capability directions in large language models, yet methods are typically evaluated on paper-specific synthe

Agents

Representation and Invariance in Reinforcement Learning

arXiv:2112.07752v4 Announce Type: replace-cross Abstract: Researchers have formalized reinforcement learning (RL) in different ways. If an agent in one RL framework is to run within another RL framewo

agentsarxiv-cs-lg

Rethinking EEG-Based Disease Diagnosis: Decoupling Instance Representation Learning from Subject-Level Supervision

arXiv:2607.27274v1 Announce Type: new Abstract: EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject label

Rethinking LLM-Judged Helpfulness as a Pedagogy Signal: A Pre-Registered Audit Across Tutor Models

arXiv:2607.28128v1 Announce Type: new Abstract: LLM tutoring poses a measurement problem: can a general-purpose helpfulness rubric distinguish direct answer-giving from pedagogical guidance? We audit

Hardware

ReToken: One Token to Improve Vision-Language Models for Visual Retrieval

arXiv:2607.28627v1 Announce Type: new Abstract: Long visual context poses a challenge for vision-language models: performance degrades as the number of distractors grows, and processing all tokens at

hardwarearxiv-cs-cv

Reviewer Scores Are Not Comparable Across Research Areas in ML Peer Review

arXiv:2607.27209v1 Announce Type: cross Abstract: Peer review at ML conferences increasingly relies on reviewer scores as the primary decision instrument. As submissions have scaled from thousands to

Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches

arXiv:2607.27797v1 Announce Type: new Abstract: Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity

RiO-DETR: DETR for Real-time Oriented Object Detection

arXiv:2603.09411v2 Announce Type: replace Abstract: We present RiO-DETR: DETR for Real-time Oriented Object Detection, the first real-time oriented detection transformer to the best of our knowledge.

RIPPLE: Generating Multi-Channel Phase, Not Recovering It

arXiv:2607.27775v1 Announce Type: new Abstract: Generative models synthesize magnitude spectra with high fidelity, while phase is delegated to a recovery module---Griffin--Lim, a vocoder, or a latent

RLPF: Reinforcement Learning from Performance Feedback for Code Generation

arXiv:2607.27271v1 Announce Type: new Abstract: Code models are increasingly trained with execution feedback, but most training signals still stop at correctness. This leaves an important gap for syst

tutorialsarxiv-cs-lg

RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design

model-releasesarxiv-cs-ro

arXiv:2603.01229v3 Announce Type: replace Abstract: Robotic manipulation policies have made rapid progress in recent years, yet most existing approaches give limited consideration to memory capabiliti

ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation

arXiv:2607.28581v1 Announce Type: new Abstract: High-fidelity 3D generation predominantly relies on scaling model capacity and data, which incurs prohibitive computational costs. This paradigm typical

safetyarxiv-cs-cv

Agents

RoboBRIDGE: A Modular Framework for Bridging Policies to Robust Real-World Robotic Agents

arXiv:2607.27881v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have attracted growing interest as a scalable approach to robotic manipulation. While these models are effective act

agentsarxiv-cs-ro

Local Ai

Robotics and edge AI put new pressure on computing infrastructure

Physical AI is forcing the technology industry to rethink the entire computing stack. Robots, autonomous systems and intelligent devices need economical inference, secure data access and infrastructur

local-aisiliconangle

Robust Estimation of Sparse Numerical Vectors under Local Differential Privacy

arXiv:2607.27815v1 Announce Type: cross Abstract: Local differential privacy (LDP) protocols are vulnerable to poisoning attacks. Existing research have proposed efficient defense strategies for singl

Robust Residual Finite Scalar Quantization for Neural Compression

arXiv:2508.15860v4 Announce Type: replace-cross Abstract: Finite Scalar Quantization (FSQ) offers simplified training but suffers from residual magnitude decay in multi-stage settings, where subsequen

Robust Wavelength Selection for Partial Least Squares Sugar Content Estimation Using Combinatorial Bayesian Optimization

arXiv:2607.27645v1 Announce Type: cross Abstract: Wavelength selection is one of the important preprocessing methods in near-infrared spectroscopy to improve prediction accuracy and interpretability o

Hardware

ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation

arXiv:2607.27744v1 Announce Type: new Abstract: Modern recommendation models gain prediction quality by scaling feature-interaction and sequence modules, but production cost constraints cap how far sy

hardwarearxiv-cs-lg

RRM: Experience-Driven Reflective Retrieval Memory for Long-Horizon Multimodal Reasoning

arXiv:2607.28156v1 Announce Type: new Abstract: Existing multimodal long-term memory agents use external memory to overcome the limited context available for long videos. However, most methods emphasi

researcharxiv-cs-cl

S-Avatar: Diffusion-Guided Gaussian Head Avatars from a Single Image

arXiv:2607.28164v1 Announce Type: new Abstract: We propose S-Avatar, a novel method for generating photorealistic 3D head avatars from a single image using a diffusion-guided 3D model generation modul

S-CEReBrO: Breaking the Memory Barrier in Continuous EEG Monitoring

arXiv:2607.27913v1 Announce Type: new Abstract: Foundation models offer a promising paradigm for Electroencephalography (EEG) analysis, leveraging generalizable representations from vast unlabeled dat

Safety-Gated Agentic Supervisory Control on a Coupled Distillation Benchmark: Regime Map, Auditable Gate, and Co-Design Findings

arXiv:2607.27849v1 Announce Type: cross Abstract: An open-weight LLM can write composition setpoints every five minutes. What a plant still needs is a hard check: named constraints, logged margins, an

Safety Verification of Wait-Only Non-Blocking Broadcast Protocols

arXiv:2403.18591v3 Announce Type: replace-cross Abstract: Broadcast protocols are programs designed to be executed by networks of processes. Each process runs the same protocol, and communication betw

safetyarxiv-cs-cl

SAFViT: Spatial Attention Fusion Gating for Vision Transformer-Based Nucleus Segmentation and Classification

arXiv:2607.27835v1 Announce Type: new Abstract: Accurate cell segmentation and classification are foundational to digital pathology, enabling quantitative tissue analysis for diagnosis and treatment p

tutorialsarxiv-cs-cv

Same Branches, Different Trees: A Bifurcation Connectedness Metric for Coronary Artery Segmentation and FFR-CT Decision Agreement

arXiv:2607.28327v1 Announce Type: new Abstract: Fractional flow reserve derived from CT angiography (FFR-CT) simulates flow through a patient-specific vessel model, so its accuracy depends on the conn

Same Facts, Different Diagnosis: Measuring and Mitigating Narrative Anchoring in Clinical Language Models

arXiv:2607.27384v1 Announce Type: new Abstract: Large language models used for clinical diagnostic reasoning are sensitive to sociolinguistic register, not just clinical content. We term this failure

Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors

arXiv:2607.28525v1 Announce Type: new Abstract: Many real-world graphs support multiple predictive tasks over the same underlying structure, creating an opportunity to reuse supervision across node cl

tutorialsarxiv-cs-lg

Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B

arXiv:2607.28576v1 Announce Type: new Abstract: Methods that make a language model plan, criticise and rewrite its own answer, reflect on mistakes, pick the best of several attempts, or debate with co

researcharxiv-cs-cl

SARC-DQ: Runtime Data-Quality Gating for Agentic AI: Silent Evidence Defects, the Incompetence Shield, and Downstream-Only Remediation

model-releasesarxiv-cs-ai

arXiv:2607.26313v1 Announce Type: cross Abstract: Agentic systems act, so a defect in the evidence they retrieve becomes a wrong action with a currency cost. The most dangerous enterprise defects are

Local Ai

ScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs

arXiv:2607.28538v1 Announce Type: new Abstract: Classifying pathological scars from clinical photographs requires distinguishing keloids from hypertrophic scars despite limited expert-labeled data and

local-aiarxiv-cs-cv

Applications

Scalable Drift Monitoring in Medical Imaging AI

arXiv:2410.13174v3 Announce Type: replace-cross Abstract: The integration of artificial intelligence (AI) into medical imaging has advanced clinical diagnostics but poses challenges in managing model

applicationsarxiv-cs-cv

Scaling medical imaging report generation with multimodal reinforcement learning

arXiv:2601.17151v2 Announce Type: replace-cross Abstract: Frontier models have demonstrated remarkable capabilities in understanding and reasoning with natural-language text, but they still exhibit ma

Scaling Vision-Language Models Is Not Enough to Mitigate Bias

model-releasesarxiv-cs-cv

arXiv:2607.28211v1 Announce Type: new Abstract: Vision-Language Models (VLMs) such as CLIP are now foundational to multimodal systems, yet their robustness to spurious correlations remains poorly unde