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Revisiting the syntax of imperatives in Yemeni Arabic: An Agree across phases approach

arXiv:2605.08447v1 Announce Type: new Abstract: This article revisits the syntax of imperatives in Yemeni Arabic proposing an Agree acros phases (AAP) approach. I argue that the AAP approach successfu

researcharxiv-cs-cl

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Revitalizing the Beginning: Avoiding Storage Dependency for Model Merging in Continual Learning

DGX agent

arXiv:2605.08311v1 Announce Type: cross Abstract: Model merging provides a compelling paradigm for integrating specialized expertise into a unified multi-task model, a goal that aligns naturally with

safetyarxiv-cs-cv

12 May 2026

Reward Auditor: Inference on Reward Modeling Suitability in Real-World Perturbed Scenarios

arXiv:2512.00920v4 Announce Type: replace Abstract: Reliable reward models (RMs) are critical for ensuring the safe alignment of large language models (LLMs). However, current RM evaluation methods fo

Reward-Conditioned Reinforcement Learning

arXiv:2603.05066v2 Announce Type: replace Abstract: Single-task RL agents are typically trained under a fixed reward function, which limits their robustness to reward misspecification and their abilit

safetyarxiv-cs-lg

RewardHarness: Self-Evolving Agentic Post-Training

model-releasesarxiv-cs-ai

arXiv:2605.08703v1 Announce Type: new Abstract: Evaluating instruction-guided image edits requires rewards that reflect subtle human preferences, yet current reward models typically depend on large-sc

RigidFormer: Learning Rigid Dynamics using Transformers

arXiv:2605.09196v1 Announce Type: cross Abstract: Learning-based simulation of multi-object rigid-body dynamics remains difficult because contact is discontinuous and errors compound over long horizon

RIR-Former: Coordinate-Guided Transformer for Continuous Reconstruction of Room Impulse Responses

arXiv:2602.01861v3 Announce Type: replace-cross Abstract: Room impulse responses (RIRs) are essential for many acoustic signal processing tasks, yet measuring them densely across space is often imprac

applicationsarxiv-cs-lg

Rivian’s AI-powered voice assistant is ready to roll

Rivian's AI-powered voice assistant is rolling out today to the company's vehicle fleet. The assistant will be available through a software update to all compatible Rivian Gen 1 and Gen 2 vehicle owne

RL Fine-Tuning Heals OOD Forgetting in SFT

arXiv:2509.12235v3 Announce Type: replace-cross Abstract: Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) is a standard post-training recipe for improving Large Language Models (L

Rly looking forward to this Deep Agents workshop at @LangChain Interrupt Learning everything I can about Context Management and Deep Agents …

Rly looking forward to this Deep Agents workshop at @LangChain Interrupt Learning everything I can about Context Management and Deep Agents are the best examples of OSS best practices Harness to Fleet

agentsharrison-chase--x

RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark

model-releasesarxiv-cs-ro

arXiv:2605.10921v1 Announce Type: new Abstract: Memory is a critical component of robotic intelligence, as robots must rely on past observations and actions to accomplish long-horizon tasks in partial

Robust Building Damage Detection in Cross-Disaster Settings Using Domain Adaptation

arXiv:2603.14694v2 Announce Type: replace-cross Abstract: Rapid structural damage assessment from remote sensing imagery is essential for timely disaster response. Within human-machine systems (HMS) f

Robust Multi-Agent LLMs under Byzantine Faults

arXiv:2605.09076v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly collaborate over peer-to-peer networks to improve their reliability. However, these same interactions c

agentsarxiv-cs-ai

Robust Probabilistic Shielding for Safe Offline Reinforcement Learning

arXiv:2605.10293v1 Announce Type: cross Abstract: In offline reinforcement learning (RL), we learn policies from fixed datasets without environment interaction. The major challenges are to provide gua

Robust Remote Reinforcement Learning over Unreliable Communication Channels using Homomorphic State Encoding

arXiv:2508.07722v2 Announce Type: replace Abstract: Traditional Reinforcement Learning (RL) frameworks generally assume that the agent perceives the state of the underlying Markov process instantaneou

agentsarxiv-cs-lg

Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning

arXiv:2605.08616v1 Announce Type: new Abstract: Collaborative machine learning (CML) enables multiple clients to train a global model jointly in a data-distributed setting. To address data privacy and

Robust Spectral Watermark for Synthetic Tabular Data

arXiv:2511.21600v2 Announce Type: replace-cross Abstract: The rise of generative AI has enabled the production of high-fidelity synthetic tabular data across fields such as healthcare, finance, and pu

ROM: Real-time Overthinking Mitigation via Streaming Detection and Intervention

model-releasesarxiv-cs-ai

arXiv:2603.22016v2 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) often reach a correct solution before their long Chain-of-Thought trace ends, yet continue with redundant verifi

Root Cause Analysis of Measurement and Mechanistic Anomalies

arXiv:2601.23026v2 Announce Type: replace Abstract: Root cause analysis of anomalies aims to identify how and why a sample deviates from the normal process. Existing methods primarily focus on telling

applicationsarxiv-cs-lg

Route Before Retrieve: Activating Latent Routing Abilities of LLMs for RAG vs. Long-Context Selection

model-releasesarxiv-cs-cl

arXiv:2605.10235v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have expanded the context window to beyond 128K tokens, enabling long-document understanding and multi-s

Route by State, Recover from Trace: STAR with Failure-Aware Markov Routing for Multi-Agent Spatiotemporal Reasoning

arXiv:2605.10057v1 Announce Type: new Abstract: Compositional spatiotemporal reasoning often requires a system to invoke multiple heterogeneous specialists, such as geometric, temporal, topological, a

RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems

arXiv:2605.10862v1 Announce Type: new Abstract: This paper demonstrates RUBEN, an interactive tool for discovering minimal rules to explain the outputs of retrieval-augmented large language models (LL

RubiConv -- Efficient Boundary-Respecting Convolutions

arXiv:2605.08451v1 Announce Type: new Abstract: Convolutional architectures have emerged as powerful alternatives to Transformers for sequence modeling. The primary advantage is that they offer improv

applicationsarxiv-cs-lg

RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards

arXiv:2605.10899v1 Announce Type: new Abstract: Training deep research agents, namely systems that plan, search, evaluate evidence, and synthesize long-form reports, pushes reinforcement learning beyo

RubricRefine: Improving Tool-Use Agent Reliability with Training-Free Pre-Execution Refinement

arXiv:2605.09730v1 Announce Type: new Abstract: Iterative self-refinement is a popular inference-time reliability technique, but its effectiveness in code-mode tool use depends heavily on the structur

RuPLaR : Efficient Latent Compression of LLM Reasoning Chains with Rule-Based Priors From Multi-Step to One-Step

arXiv:2605.09346v1 Announce Type: cross Abstract: The Chain-of-Thought (CoT) paradigm, while enhancing the interpretability of Large Language Models (LLMs), is constrained by the inefficiencies and ex

RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild

model-releasesarxiv-cs-ai

arXiv:2605.10357v1 Announce Type: cross Abstract: Multimodal misinformation increasingly leverages visual persuasion, where repurposed or manipulated images strengthen misleading text. We introduce ex

S2FT: Parameter-Efficient Fine-Tuning in Sparse Spectrum Domain

model-releasesarxiv-cs-cv

arXiv:2605.08589v1 Announce Type: new Abstract: Parameter Efficient Fine-Tuning (PEFT) is a key technique for adapting a large pretrained model to downstream tasks by fine-tuning only a small number o

S2P-Net: A Spectral-Spatial Polar Network for Rotation-Invariant Object Recognition in Low-Data Regimes

arXiv:2605.09667v1 Announce Type: cross Abstract: We present S2P-Net (Spectral-Spatial Polar Network), a compact deep learning architecture that achieves mathematically guaranteed rotation invariance

SABER: A Scalable Action-Based Embodied Dataset for Real-World VLA Adaptation

arXiv:2605.09613v1 Announce Type: cross Abstract: Robotic deployment in real-world environments depends on rich, domain-specific action data as much as on strong model architecture. General-purpose ro

applicationsarxiv-cs-cv

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning

arXiv:2605.08391v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning agents that act on partial local observations face a fundamental information bottleneck: the knowledge ne

SAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Network

arXiv:2510.03648v2 Announce Type: replace Abstract: Continuous learning of novel classes is crucial for edge devices to preserve data privacy and maintain reliable performance in dynamic environments.

Safe Aerial 3D Path Planning for Autonomous UAVs using Magnetic Potential Fields

arXiv:2605.10880v1 Announce Type: new Abstract: Safe autonomous Uncrewed Aerial Vehicle (UAV) navigation in urban environments requires real-time path planning that avoids obstacles. MaxConvNet is a p

agentsarxiv-cs-ro

Safe and Real-Time Consistent Planning for Autonomous Vehicles in Partially Observed Environments via Parallel Consensus Optimization

arXiv:2409.10310v3 Announce Type: replace Abstract: Ensuring safety and driving consistency is a significant challenge for autonomous vehicles operating in partially observed environments. This work i

safetyarxiv-cs-ro

Safe Exploration for Nonlinear Processes Using Online Gaussian Process Learning

arXiv:2605.09772v1 Announce Type: cross Abstract: This paper proposes a safe data-driven control framework for nonlinear systems with partially known dynamics. The method ensures stability and constra

safetyarxiv-cs-ro

Safety-Critical LiDAR-Inertial Odometry with On-Manifold Deterministic Protection Level

arXiv:2605.09383v1 Announce Type: new Abstract: In safety-critical scenarios, the protection level of the autonomous navigation system is crucial for enabling mobile robots to perform safe tasks. Howe

safetyarxiv-cs-ro

SAFformer:Improving Spiking Transformer via Active Predictive Filtering

arXiv:2605.08270v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) offer notable advantages in biological plausibility and energy efficiency, making them promising candidates for buildin

SAGE: Agentic Framework for Interpretable and Clinically Translatable Computational Pathology Biomarker Discovery

arXiv:2602.00953v2 Announce Type: replace Abstract: Engineered image-based biomarkers offer a clinically interpretable alternative to black-box AI in computational pathology, yet their discovery remai

agentsarxiv-cs-lg

SAID: Safety-Aware Intent Defense via Prefix Probing for Large Language Models

arXiv:2510.20129v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) remain vulnerable to jailbreak attacks, where adversarially crafted prompts induce policy-violating responses des

SalesSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators

arXiv:2605.08334v1 Announce Type: new Abstract: We present SalesSim, a framework and testbed for evaluating the ability of Multimodal Large Language Models (MLLMs) to simulate realistic, persona-drive

Sam Altman says Elon Musk’s mind games were damaging OpenAI

OpenAI CEO Sam Altman says Elon Musk did 'huge damage' to the culture of the AI startup. During testimony as part of Musk's lawsuit against OpenAI, Altman said Musk required OpenAI president Greg Broc

Sam Altman swearing to tell the whole truth, and then failing to do so. May 2023.

Sam Altman made statements under oath in May 2023 regarding AI safety and OpenAI's practices, but Gary Marcus critiqued these statements as incomplete or misleading, suggesting Altman failed to fully

safetygary-marcus--x

Sam Altman takes the stand in trial against Elon Musk

OpenAI CEO Sam Altman has begun his testimony against Elon Musk in a high-profile jury trial in a California federal courtroom. Altman, alongside OpenAI president Greg Brockman, is a primary defendant

Sam Altman was winning on the stand, but it might not be enough

After two weeks of hearing from assorted witnesses that he was a lying snake, the jury finally heard from the lying snake himself: Sam Altman. At the end of the testimony, his lawyer William Savitt as

SAMOFT: Robust Multi-Object Tracking via Region and Flow

arXiv:2605.09417v1 Announce Type: new Abstract: Multi-object tracking (MOT) is a fundamental task in computer vision that requires continuously tracking multiple targets while maintaining consistent i

researcharxiv-cs-cv

Sample-Mean Anchored Thompson Sampling for Offline-to-Online Learning with Distribution Shift

arXiv:2605.10289v1 Announce Type: new Abstract: Offline-to-online learning aims to improve online decision-making by leveraging offline logged data. A central challenge in this setting is the distribu

safetyarxiv-cs-lg

Samsung and its South Korean labor union fail to reach a pay deal; the union has said workers will strike for 18 days from May 21 if its demands are not met (Reuters)

Reuters: Samsung and its South Korean labor union fail to reach a pay deal; the union has said workers will strike for 18 days from May 21 if its demands are not met — Samsung Electronics (005930.KS)

industrytechmeme

Sanity Checks for Long-Form Hallucination Detection

arXiv:2605.08346v1 Announce Type: cross Abstract: Hallucination detection methods for large language models increasingly operate on chain-of-thought reasoning traces, yet it remains unclear whether th