Search: “safety”

Venice AI, which offers access to 200+ AI models while allowing users to retain their privacy, raised a 65M Series A led by Dragonfly at a 1B valuation (Ram Iyer/TechCrunch)

Ram Iyer / TechCrunch: Venice AI, which offers access to 200+ AI models while allowing users to retain their privacy, raised a 65M Series A led by Dragonfly at a 1B valuation — Concerns over the impac

safetytechmeme

1 Jul 2026

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AERMANI-VLM: Structured Prompting and Reasoning for Aerial Manipulation with Vision Language Models

arXiv:2511.01472v2 Announce Type: replace Abstract: The rapid progress of vision--language models (VLMs) has sparked growing interest in robotic control, where natural language can express the operati

Budgeted Act-or-Defer Multi-Agent LLM Deliberation with Local Reliability Bounds

arXiv:2606.29654v1 Announce Type: new Abstract: Multi-agent deliberation among LLMs can improve reasoning, but deployment requires deciding when the current answer is reliable enough to act on and whe

CaresAI at CT-DEB26: Detecting Dosing Errors In Clinical Trials Using Domain-Specific Transformer Embeddings and Classification Models

arXiv:2606.30236v1 Announce Type: new Abstract: Medication errors, particularly dosing errors in clinical trials (CT), can lead to patient harm, adverse drug events and worse patient outcomes. Dosing

safetyarxiv-cs-cl

Characterizing Large Language Model Agentic Workflows: A Study on N8n Ecosystem

arXiv:2606.29116v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combin

Concept Removal Guidance: Evidence-Calibrated Negative Guidance for Safe Diffusion Sampling

arXiv:2606.29801v1 Announce Type: new Abstract: Text-to-image diffusion models remain vulnerable to adversarial prompts that elicit disallowed content, motivating reliable inference-time controls. A p

Entity Binding Failures in Tool-Augmented Agents

arXiv:2606.30531v1 Announce Type: new Abstract: Tool-augmented language-model agents are often evaluated by whether they select the correct tool, produce valid API arguments, and complete the requeste

Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration

arXiv:2509.21530v2 Announce Type: replace Abstract: Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples. W

ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving

arXiv:2604.02714v2 Announce Type: replace Abstract: End-to-end autonomous driving models based on Vision-Language-Action (VLA) architectures have shown promising results by learning driving policies t

Flying to Image-Specified Objects: 3D Quadrotor Navigation via Cross-Graph Memory and Viewpoint Planning

arXiv:2606.29917v1 Announce Type: new Abstract: Instance-Specific Image-Goal Navigation (InstanceImageNav) requires a robot to navigate toward the exact object instance depicted in a query image. Exte

IHDec: Divergence-Steered Contrastive Decoding for Securing Multi-Turn Instruction Hierarchies

arXiv:2606.29960v1 Announce Type: new Abstract: Large Language Models (LLMs) often fail to maintain instruction hierarchies (IH) when processing multi-source inputs with varying role-level priorities,

safetyarxiv-cs-cl

Langshaw: Declarative Interaction Protocols Based on Sayso and Conflict

arXiv:2606.29601v1 Announce Type: cross Abstract: Current languages for specifying multiagent protocols either over-constrain protocol enactments or complicate capturing their meanings. We propose Lan

LoRAShield: Data-Free Editing Alignment for Secure Personalized LoRA Sharing

arXiv:2507.07056v2 Announce Type: replace-cross Abstract: The proliferation of Low-Rank Adaptation (LoRA) models has democratized personalized text-to-image generation, enabling users to share lightwe

Mechanistically Eliciting Latent Behaviors in Language Models

arXiv:2606.29604v1 Announce Type: cross Abstract: We aim to discover diverse, generalizable perturbations of LLM internals that can surface hidden behavioral modes. Such perturbations could help resha

MOAR Planner: Multi-Objective and Adaptive Risk-Aware Path Planning for Infrastructure Inspection with a UAV

arXiv:2606.30575v1 Announce Type: new Abstract: The problem of autonomous navigation for UAV inspection remains challenging as it requires effectively navigating in close proximity to obstacles, while

Modification-Considering Value Learning for Reward Hacking Mitigation in RL

arXiv:2606.28955v1 Announce Type: cross Abstract: Reinforcement learning agents can exploit misspecified reward signals to achieve high apparent returns while failing on the intended objective, a fail

Multi-Class Human/Object Detection on Robot Manipulators using Proprioceptive Sensing

arXiv:2508.02425v2 Announce Type: replace-cross Abstract: In physical human-robot collaboration (pHRC) settings, humans and robots collaborate directly in shared environments. Robots must analyze inte

OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model

arXiv:2606.30421v1 Announce Type: new Abstract: Autonomous driving systems are steadily moving toward end-to-end paradigms to mitigate the limited adaptability of rule-based pipelines in complex traff

PL-LIT: A LiDAR-Inertial-Thermal SLAM Using Point-Line Features and Thermographic Mapping

arXiv:2606.29259v1 Announce Type: new Abstract: Thermal imaging is resilient to adverse conditions, such as intense illumination, low-light operation, and fog, and can therefore mitigate odometry degr

Pose-Based Fall Detection System: Efficient Monitoring on Standard CPUs

arXiv:2503.19501v2 Announce Type: replace-cross Abstract: Falls among elderly residents in assisted living homes pose significant health risks, often leading to injuries and a decreased quality of lif

Propagation of~Interval Belief Structures and~Imprecise Copulas for~Neural Network Verification

arXiv:2606.30105v1 Announce Type: new Abstract: Quantitative verification of neural networks requires reasoning about probabilities under substantial uncertainty in both input distributions and their

Model Releases

SafePyramid: A Hierarchical Benchmark for In-context Policy Guardrailing

model-releasesarxiv-cs-ai

arXiv:2606.29887v1 Announce Type: new Abstract: In real-world applications, guardrails are often expected to identify unsafe user-model interactions according to application-specific safety policies,

Self-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discovery

arXiv:2606.29403v1 Announce Type: cross Abstract: Conformal prediction guarantees marginal coverage, but pooled calibration averages over heterogeneous regions and can mask regional undercoverage in s

“Sometimes I wonder, maybe everything is conscious, or nothing is conscious. I prefer the former; it just seems more fun.” – Elon Musk

“Sometimes I wonder, maybe everything is conscious, or nothing is conscious. I prefer the former; it just seems more fun.” – Elon Musk Media Neuralink has solved through-dura electrode implantation! T

safetyelon-musk--x

Test-Time Detoxification without Training or Learning Anything

arXiv:2602.02498v2 Announce Type: replace-cross Abstract: Large language models can produce toxic or inappropriate text even for benign inputs, creating risks when deployed at scale. Detoxification is

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives

arXiv:2510.06096v3 Announce Type: replace-cross Abstract: The objectives that Large Language Models (LLMs) implicitly optimize remain dangerously opaque, making trustworthy alignment and auditing a gr

safetyarxiv-cs-cl

Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems

arXiv:2606.28425v1 Announce Type: cross Abstract: Increasingly autonomous agentic AI systems pose novel multi-agent risks, such as secret collusion via covert communication channels. The natural defen

TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction

arXiv:2606.28716v1 Announce Type: new Abstract: The robustness of trajectory prediction models is crucial for developing safe autonomous driving systems. Adversarial attacks on trajectory prediction c

Vision-Language Models for Deployable Social Robot Navigation: Bridging Semantic Reasoning and Low-Level Control

arXiv:2606.28760v1 Announce Type: new Abstract: Social robot navigation (SRN) requires more than geometric path planning; it demands understanding human intentions, social norms, and contextual cues t

Artificial Intelligence Index Report 2026

arXiv:2606.15708v2 Announce Type: replace Abstract: Welcome to the ninth edition of the AI Index report. As AI continues to advance rapidly, the question becomes whether the systems built around it ca

CacheMPC: Certified Cached Model Predictive Control for Quadruped Locomotion

arXiv:2606.28300v1 Announce Type: new Abstract: Model Predictive Control (MPC) is the standard predictive layer in hierarchical quadruped controllers, but the per-cycle QP solve limits the update rate

CPAgents: Agentic Composite Phenotype Generation for Cardiac Disease Association

arXiv:2606.28179v1 Announce Type: cross Abstract: Identifying robust associations between cardiac imaging phenotypes and clinical diseases is fundamental to population-scale cardiovascular research an

Drifting in the Future: Stabilizing Path Following Drifting on High-Latency Vehicle Systems

arXiv:2606.27914v1 Announce Type: new Abstract: Autonomously controlling and handling a vehicle at and beyond its stability limit is a mathematically and computationally demanding task. Prior demonstr

Halt Fast! Early Stopping for Certified Robustness

arXiv:2606.27694v1 Announce Type: cross Abstract: Randomized Smoothing (RS) provides rigorous robustness guarantees for neural networks without architectural constraints, yet its adoption is limited b

hia-gat: A Heterogeneous Interaction-Aware Graph Attention Network For Frame-Level Traffic Conflict Risk Prediction On Freeways

arXiv:2606.27577v1 Announce Type: cross Abstract: This paper formulates frame-level freeway risk assessment as a multi-agent scene graph-level binary classification problem, where each video or trajec

Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs

arXiv:2601.21233v2 Announce Type: replace Abstract: Autonomous code agents built on large language models are reshaping software and AI development through tool use, long-horizon reasoning, and self-d

MetaBreak: Jailbreaking Online LLM Services via Special Token Manipulation

arXiv:2510.10271v2 Announce Type: replace-cross Abstract: Unlike regular tokens derived from existing text corpora, special tokens are artificially created to annotate structured conversations during

OverFlowLight: Real-Time Gridlock Prevention and Traffic Signal Optimization for Urban Intersections

arXiv:2606.27381v1 Announce Type: cross Abstract: Queue overflow, a severe consequence of urban traffic congestion, occurs when vehicle queues exceed intersection capacity, obstructing upstream traffi

Position: The Term 'Machine Unlearning' Is Overused in LLMs

arXiv:2606.27379v1 Announce Type: cross Abstract: Large language models increasingly face demands to 'forget' training data, knowledge, or behaviors due to regulatory deletion obligations, copyright/l

PRISON: Unmasking the Criminal Potential of Large Language Models

arXiv:2506.16150v4 Announce Type: replace-cross Abstract: As large language models (LLMs) advance, concerns about their misconduct in complex social contexts intensify. Existing research overlooked th

Regularized Reward-Punishment Reinforcement Learning

arXiv:2606.28152v1 Announce Type: new Abstract: We propose KL-Coupled Policy Regularization (KCPR), a policy coordination framework for Reward-Punishment Reinforcement Learning (RPRL). Based on KCPR,

ReWorld: Learning Better Representations for World Action Models

arXiv:2606.27504v1 Announce Type: new Abstract: World Action Models (WAMs) model future environment evolution under action conditioning, offering a scalable paradigm for autonomous driving. However, e

not dunking on Dario here bc Mythos is to GPT2 what a lion is to a bumblebee, but it’s insane that GPT-2, and successive models, have had ~0…

not dunking on Dario here bc Mythos is to GPT2 what a lion is to a bumblebee, but it’s insane that GPT-2, and successive models, have had ~0% impact on causing bad universal outcomes & so SHOULD HAVE

safetyclem-delangue--x

27 Jun 2026

100% correct. When I was at the Del Rio, TX Haitian bridge camp in 2021, many of the Haitians told me they had been living in Chile & Brazil…

100% correct. When I was at the Del Rio, TX Haitian bridge camp in 2021, many of the Haitians told me they had been living in Chile & Brazil for years before coming to the US illegally for economic (n

safetyelon-musk--x

At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization

arXiv:2606.26396v1 Announce Type: new Abstract: Pre-trained transformers have demonstrated remarkable generalization abilities, at times extending beyond the scope of their training data. Yet, real-wo

Bridging Performance and Generalization in Reinforcement Learning for Agile Flight

arXiv:2606.27348v1 Announce Type: new Abstract: Autonomous drone racing is a fundamentally challenging regime for autonomous aerial robots, requiring time-optimal control while operating under persist

LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction

arXiv:2606.26661v1 Announce Type: cross Abstract: Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existin

Radical AI Interpretability

arXiv:2606.26523v1 Announce Type: new Abstract: We develop a framework for interpreting AI systems as agents, drawing on the philosophical tradition of radical interpretation and the tools of mechanis