All entries

ReSAE: Residualized Sparse Autoencoders for Multi-Layer Transformer Interventions

model-releasesarxiv-cs-ai

arXiv:2605.27819v1 Announce Type: cross Abstract: Sparse autoencoders are usually trained one layer at a time, even though transformer residual stream activations are strongly coupled across depth. Th

Researchers develop a new process to get lithium out of rocks

DGX agent

MIT researchers developed a low-temperature process for extracting battery-grade lithium from the common mineral spodumene. The process uses a liquid reagent to dissolve the rock into useful forms inc

industryars-technica

28 May 2026

ResearchLoop: An Evidence-Gated Control Plane for AI-Assisted Research

arXiv:2605.28282v1 Announce Type: new Abstract: AI-assisted research compresses ideation, implementation, evaluation, and manuscript writing into a single interactive loop. This compression is useful,

applicationsarxiv-cs-ai

ResearchMath-14K: Scaling Research-Level Mathematics via Agents

arXiv:2605.28003v1 Announce Type: new Abstract: The frontier of mathematics is defined by problems whose solutions are not yet known, yet it remains unclear whether language models can meaningfully en

agentsarxiv-cs-cl

Residualized Temporal Sparse Autoencoders for Interpreting Diffusion Models

arXiv:2605.27813v1 Announce Type: cross Abstract: Text-to-image diffusion models generate images through an iterative denoising process, so internal neural layers produce trajectories of activations r

Resolution-free neural surrogates for geometric parameterization and mapping with spatially varying fields

model-releasesarxiv-cs-cv

arXiv:2605.28551v1 Announce Type: new Abstract: Many imaging problems require computing spatial transformations induced by spatially varying intensity, feature, or density fields. Canonical examples i

Resource-Constrained Affect Modelling via Variance Regularisation Pruning

model-releasesarxiv-cs-ai

arXiv:2605.27479v1 Announce Type: cross Abstract: Affective computing systems are increasingly embedded in pervasive and interactive environments, such as adaptive games, assistive technologies, and r

Restoring the Sweet Spot: Pass-Rate Weighted Self-Distillation for LLM Reasoning

arXiv:2605.27765v1 Announce Type: cross Abstract: Self-Distillation Policy Optimization (SDPO) provides dense token-level credit assignment for reinforcement learning with large language models by lev

safetyarxiv-cs-ai

Rethinking Calibration for Early-Exit Neural Networks

arXiv:2508.21495v3 Announce Type: replace Abstract: Early-exit neural networks (EENNs) accelerate inference by allowing intermediate classifiers to stop computation once predictions are confident enou

researcharxiv-cs-lg

Rethinking Memory as Continuously Evolving Connectivity

arXiv:2605.28773v1 Announce Type: cross Abstract: Existing memory-augmented LLM agents often treat memory as a static repository with pre-defined representations and fixed retrieval pipelines, which i

agentsarxiv-cs-ai

Rethinking Video-Language Model from the Language Input Perspective

arXiv:2605.27920v1 Announce Type: new Abstract: Driven by the wave of large language models, Video-Language Models (VLMs) have become a significant yet challenging technology to bridge the gap between

applicationsarxiv-cs-cv

Rethinking Visual Neglect: Steering via Context-Preference for MLLM Hallucination Mitigation

arXiv:2605.27993v1 Announce Type: new Abstract: Object hallucination remains a primary obstacle to the reliable deployment of Multimodal Large Language Models (MLLMs). Current inference-time mitigatio

researcharxiv-cs-cl

Retrieval, Reward, and Training Protocols: What Matters in Training Search Agents?

arXiv:2605.27881v1 Announce Type: new Abstract: Search agents powered by large language models can autonomously decompose queries, retrieve information, and synthesize answers through multi-step reaso

safetyarxiv-cs-cl

REVEAL: Reference-Grounded Reasoning for Multimodal Manipulation Detection

arXiv:2605.28459v1 Announce Type: new Abstract: Multimodal manipulation detection aims to simultaneously identify forged image--text pairs and localize tampered regions, yet existing methods typically

researcharxiv-cs-cv

Tutorials

Revealing Algorithmic Deductive Circuits for Logical Reasoning

arXiv:2605.27824v1 Announce Type: new Abstract: Recent studies have shown that Large Language Models (LLMs) can achieve strong reasoning performance by incorporating functional symbolic representation

tutorialsarxiv-cs-ai

Local Ai

Reverse Probing: Supervised Token-level Uncertainty Quantification for Large Language Models in Clinical Text

arXiv:2605.28740v1 Announce Type: cross Abstract: As large language models are increasingly deployed for clinical text, ensuring they can reliably signal their own uncertainty becomes critical. Most e

local-aiarxiv-cs-ai

ReverseMath: Answer Inversion for Scalable and Verifiable Mathematical Problem Generation

arXiv:2605.27709v1 Announce Type: new Abstract: Mathematical reasoning benchmarks are vital for evaluating large language models (LLMs), but many are static and repeatedly exposed through public evalu

researcharxiv-cs-cl

Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification

model-releasesarxiv-cs-cv

arXiv:2512.12887v3 Announce Type: replace Abstract: 3D medical image classification is essential for modern clinical workflows. Medical foundation models (FMs) have emerged as a promising approach for

Revisiting Anthropomorphic Reflection Markers in Large Language Model Reasoning

arXiv:2605.28305v1 Announce Type: cross Abstract: Large Language Models (LLMs) often produce explicit reflective traces during complex reasoning, accompanied by anthropomorphic markers such as wait, h

Revisiting Change Detection Methods for their Application to Serac Fall Time-Lapse Monitoring

arXiv:2605.28100v1 Announce Type: cross Abstract: In an era where climate change aggravates environmental uncertainties, the identification and detection of event precursors are becoming crucial to mi

applicationsarxiv-cs-ai

Revisiting Graph Autoencoders as Implicit Contrastive Learners

arXiv:2410.10241v2 Announce Type: replace-cross Abstract: Graph autoencoders (GAEs) and graph contrastive learning (GCL) are two major paradigms for self-supervised representation learning on graphs,

Revisiting Metafeatures to Explain Model Differences on Tabular Data

arXiv:2605.28418v1 Announce Type: new Abstract: With the rise of tabular foundation models alongside traditional models still performing well on many tasks, choosing the right model for a tabular data

Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms

arXiv:2605.27782v1 Announce Type: new Abstract: We present the first theoretical convergence analysis of machine learning training under fully homomorphic encryption (FHE), combined with a differentia

researcharxiv-cs-lg

Reward Bias Substitution: Single-Axis Bias Mitigations Redirect Optimization Pressure

arXiv:2605.27996v1 Announce Type: new Abstract: Single-axis mitigations of reward-model biases (e.g., reducing proxy reliance on length, sycophancy, or style) can rotate optimization pressure onto cor

safetyarxiv-cs-ai

Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach

arXiv:2605.27834v1 Announce Type: new Abstract: We study the transfer of rewards learned using inverse reinforcement learning from expert demonstrations in one environment to reinforcement learning in

safetyarxiv-cs-lg

RGC: a radio AGN classifier based on deep learning. I. A semi-supervised multiclass model for VLA images

arXiv:2510.22190v2 Announce Type: replace-cross Abstract: Bent radio active galactic nuclei (RAGNs) -- wide-angle tails (WATs) and narrow-angle tails (NATs) -- trace dense environments in galaxy group

Right now, 20 British MPs are deciding which bills they'll introduce to Parliament. Sir Stephen Fry just asked them to bring forward our bil…

Right now, 20 British MPs are deciding which bills they'll introduce to Parliament. Sir Stephen Fry just asked them to bring forward our bill to ban superintelligence! Grateful to receive this strong

safetyconnor-leahy--x

Rising AI spend turns FinOps into a boardroom strategist

Artificial intelligence has turned technology spending into a strategic boardroom priority across the entire enterprise. Companies are now using FinOps data to decide where capital goes and the value

applicationssiliconangle

Risk-aware Selective Prompting for Hallucination Mitigation in Large Vision-Language Models

arXiv:2605.28123v1 Announce Type: new Abstract: Prompt-based verification is widely used to mitigate hallucinations in large vision-language models (LVLMs), yet when it helps remains poorly understood

researcharxiv-cs-cl

Risk-Controlled Lean-as-Judge for Natural-Language Mathematical Reasoning

arXiv:2605.28365v1 Announce Type: new Abstract: Lean is increasingly used to judge natural-language mathematical answers, but its signal is partial: many answers never formalize, and a failed proof ma

Riverbed accelerates enterprise adoption of autonomous IT operations

With artificial intelligence tools proliferating across the enterprise world, observability is a more critical sphere than ever before. Riverbed Technology LLC, which specializes in unified observabil

agentssiliconangle

Industry

Rivian’s software chief thinks you don’t need CarPlay or buttons

Today, I’m talking with Wassym Bensaid, the chief software officer at Rivian, and the co-CEO of Rivian’s platform joint venture with Volkswagen, which everyone just calls RV Tech. That joint venture k

industrythe-verge-ai

RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs

arXiv:2509.21128v2 Announce Type: replace Abstract: Large language models (LLMs) are typically trained by reinforcement learning (RL) with verifiable rewards (RLVR) and supervised fine-tuning (SFT) on

RMPL: Relation-aware Multi-task Progressive Learning with Stage-wise Training for Multimedia Event Extraction

model-releasesarxiv-cs-cl

arXiv:2602.13748v2 Announce Type: replace Abstract: Multimedia Event Extraction (MEE) aims to identify events and their arguments from documents that contain both text and images. It requires groundin

Robo-Blocks: Generative Scaffolding in End-User Design and Programming of Social Robots

arXiv:2605.28154v1 Announce Type: cross Abstract: Programming social robots is challenging for novice robot programmers due to required expertise in planning, interaction design, and programming. Whil

researcharxiv-cs-ro

Local Ai

Robust Contrastive Graph Clustering with Adaptive Local-Global Integration

arXiv:2605.28209v1 Announce Type: new Abstract: Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contr

local-aiarxiv-cs-lg

Robust Moment-Based Estimation via Spectral Gradient Reweighting

arXiv:2605.27718v1 Announce Type: cross Abstract: Moment-based estimation is a theoretically attractive approach to parametric inference, especially when likelihood-based estimation is unavailable, mi

Roles with Rails: Contract-Preserving Role Evolution in Multi-Agent Structured Reasoning

arXiv:2605.28433v1 Announce Type: new Abstract: Role-based LLM multi-agent systems need adaptive role pools, yet adapting such systems is not merely a matter of prompt optimization: roles often carry

agentsarxiv-cs-cl

ROOM: A Physics-Based Continuum Robot Simulator for Photorealistic Medical Datasets Generation

arXiv:2509.13177v2 Announce Type: replace Abstract: Continuum robots are advancing bronchoscopy procedures by accessing complex lung airways and enabling targeted interventions. However, their develop

safetyarxiv-cs-ro

ROSD: Reflective On-Policy Self-Distillation for Language Model Reasoning across Domains

arXiv:2605.28014v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves the reasoning performance of large language models (LLMs) by providing dense token-level supervision for on-

safetyarxiv-cs-cl

Routing-Aligned Fine-Tuning for Multilingual Downstream Tasks in Mixture-of-Experts Models

arXiv:2605.28306v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models have emerged as a dominant paradigm for efficient LLM scaling, yet adapting them to non-English downstream tasks remai

safetyarxiv-cs-ai

Local Ai

ROVER: Routing Object-Centric Visual Evidence for Grounded Multi-Image Reasoning

arXiv:2605.27959v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have increasingly localized and interleaved visual evidence for deliberative reasoning. Grounding-based appro

local-aiarxiv-cs-ai

RULER: Representation-Level Verification of Machine Unlearning

arXiv:2605.27569v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific training records from a deployed model without retraining from scratch. Current protocols ve

RW-TTT: Batched Serving for Request-Owned Test-Time Training State

arXiv:2605.28053v1 Announce Type: new Abstract: Test-time training (TTT) adapts an LLM during generation by reading and updating request-owned state, such as fast weights, low-rank deltas, or streamin

S-Cheetah: A Novel Quadrupedal Robot with a 3-DOF Active Spine Learning Agile Locomotion

arXiv:2605.27909v1 Announce Type: new Abstract: The biological spine of quadrupeds enables sagittal flexion/extension, lateral bending, and axial rotation, playing a crucial role in highly agile and d

researcharxiv-cs-ro

SA4Depth: Consistent Pose-Depth Scale Alignment for Self-Supervised Monocular Depth Estimation

arXiv:2605.28477v1 Announce Type: new Abstract: Self-supervised depth estimation from monocular sequences relies on the joint learning of a depth and a pose network. Despite abundant research done to

safetyarxiv-cs-cv

SAFE-Diff: Scale-Aware Attention and Feature-Dispersive Diffusion with Uncertainty Estimation for Contrast-Enhanced Breast MRI Synthesis

arXiv:2605.25767v2 Announce Type: replace Abstract: Synthesizing high fidelity contrast enhanced MRI is clinically valuable for safer and more efficient breast cancer screening, yet remains challengin

researcharxiv-cs-cv

Safe In-Context Reinforcement Learning

arXiv:2509.25582v3 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks w