safety

BaLEEN: Biasing with Latent Encoded Entities for Context-Aware ASR

arXiv:2609.36913v1 Announce Type: cross Abstract: Transcribing domain-specific entities and rare proper nouns remains a major challenge in automatic speech recognition (ASR). In this paper, we propose

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Bathtubs, Boundaries, and Sandboxes: AI Regulatory Learning under Legal Uncertainty

DGX agent

arXiv:2601.04094v4 Announce Type: cross Abstract: Effective regulation of AI is a defining policy challenge, driven by their integration into all aspects of society. To remain responsive to their rapi

safetyarxiv-cs-ai

30 Sept 2026

Battery-Aware Reinforcement Learning for Aggressive Quadrotor Flight

arXiv:2609.37316v1 Announce Type: new Abstract: Agile flight tasks such as drone racing and pursuit-evasion require strong acceleration and precise turns, but the available thrust changes as the batte

BCNav: Bearing-Conditioned Depth Policies for Sound Source Navigation

arXiv:2609.37084v1 Announce Type: new Abstract: The ability to navigate toward sound sources extends a robot's reach beyond its visual field, enabling response to auditory events in unknown environmen

Beyond Keywords: Leveraging Generative LLMs and Label Aggregation to Classify Economic Policy Uncertainty in News Articles

arXiv:2609.35856v1 Announce Type: cross Abstract: This research describes the adaptation of Large Language Models (LLMs) for economic monitoring in the public sector to automatically determine whether

Beyond Semantics: How Temporal Biases Shape Retrieval in Transformer and State-Space Models

arXiv:2510.22752v2 Announce Type: replace-cross Abstract: In-context learning is governed by both temporal and semantic relationships, shaping how Large Language Models (LLMs) retrieve contextual info

Bias-variance decompositions: the exclusive privilege of Bregman divergences

arXiv:2501.18581v5 Announce Type: replace Abstract: Bias-variance decompositions are widely used to understand the generalization performance of machine learning models. While the squared error loss p

safetyarxiv-cs-lg

Binarization Flattens the Score Space

arXiv:2609.35797v1 Announce Type: cross Abstract: Large language model (LLM) judges are often used as rewards to train policies on objectives that deterministic verifiers cannot capture. However, thes

BlenDAgger: Blended Shared Control for Interactive Imitation Learning

arXiv:2609.37599v1 Announce Type: new Abstract: Robot policies are frequently trained from human corrections, yet teleoperating a robot to provide corrections is burdensome, and human demonstrators ar

Boids of a Feather Flock Together - Evolving Prey Behaviours Under Different Predator Attack Strategies

arXiv:2609.37885v1 Announce Type: cross Abstract: Flocking and schooling are thought to have evolved partly as defences against predation, but how prey should balance social and escape tendencies may

Boundary-State Control for Tool-Using Language-Model Agents: Commit-Time Consistency under State Drift

arXiv:2609.37475v1 Announce Type: new Abstract: Tool-using language-model agents can decide that an action is permissible and execute it only after security-relevant state has changed. We study this p

Brain-SAD: A Brain-Inspired Safe Autonomous Driving Control Framework with Dynamic Fear-Oriented Constraint on Dual-Policy

arXiv:2609.38016v1 Announce Type: new Abstract: Constrained Reinforcement Learning has recently gained increasing attention in the field of Safe Autonomous Driving, where the general mechanism is to m

Breakthroughs on every frontier. I can't wait to share more on what Praxis-1 will unlock across industries.

safetycristobal-valenzuela--x

Breakthroughs on every frontier. I can't wait to share more on what Praxis-1 will unlock across industries. Media Introducing Praxis-1, an open-weight World Action Model that turns our video pre-train

BRIDGE: Bilevel Retrieval-Credit-Aware Agentic Reinforcement Learning

arXiv:2609.36505v1 Announce Type: new Abstract: Agentic reinforcement learning (ARL) with verifiable rewards improves the ability of large language models (LLMs) to tackle knowledge-intensive tasks by

Bridging Semantic Gaps in RAG through Generated Context Knowledge Fusion

arXiv:2609.37171v1 Announce Type: new Abstract: Retrieval-Augmented Generation has established itself as a fundamental framework in natural language processing, seamlessly integrating information retr

CAMEO: A Class-Activation-Mapped Equitable Overlay Framework for Fair and Robust Deep Learning-based Skin Condition Diagnosis

arXiv:2609.36400v1 Announce Type: cross Abstract: Deep learning classifiers for dermoscopic skin lesions often reach high in-distribution accuracy while quietly relying on spurious background cues suc

Can Representation Learning Decouple from Loss Minimization? Polar Updates Have an Answer

arXiv:2609.36240v1 Announce Type: new Abstract: Does representation learning stop when the training loss stops improving? We study this question for matrix Muon, whose polar-normalised updates have a

safetyarxiv-cs-lg

Can Vision-Language Models Stay Helpful When Facing Implicit Risks? Intent-Privilege OPSD for Efficient Safety-Helpfulness Alignment

arXiv:2609.37837v1 Announce Type: new Abstract: Vision-Language Models (VLMs) remain vulnerable to cross-modal implicit risks: visual and textual inputs that appear benign in isolation can jointly eli

CEO George Kurian outlines NetApp’s data strategy for production AI

Intelligent data infrastructure is becoming critical as enterprises move artificial intelligence from curated pilots into production. NetApp Inc. is positioning its Novus architecture for that transit

safetysiliconangle

CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference

arXiv:2609.36689v1 Announce Type: cross Abstract: Large language models have achieved significant progress in event forecasting, yet their probability outputs exhibit systematic calibration bias that

CHOQOLATE: Organizing Concept Bottleneck Latent Spaces with Choquet Integrals

arXiv:2609.37786v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) built on vision-language models such as CLIP represent a latent space as human-understandable concepts. These represent

ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learning

arXiv:2609.36238v1 Announce Type: new Abstract: A goal that is close in space can be far away in time. Obstacles, terrain, and the agent's own capabilities determine how long it takes to get there. Ye

CLeaR: A Unified Framework for Resolving the Leakage-Degradation Dilemma in Style Transfer

arXiv:2609.38136v1 Announce Type: new Abstract: Style transfer aims to render target content in the style of a reference image, but existing methods often suffer from content leakage, where objects, l

Codebook-Guided Cross-Modal Knowledge Distillation for Structurally Heterogeneous Features

arXiv:2609.37243v1 Announce Type: cross Abstract: Cross-modal knowledge distillation transfers knowledge from a teacher modality to a student modality. Existing feature-level alignment methods typical

CoEM: Empowering Long-Context Reasoning with Commit-on-Evidence Memory

arXiv:2609.36935v1 Announce Type: new Abstract: Long-context reasoning is essential for complex and long-horizon tasks, yet the performance of large language models (LLMs) degrades as context length i

Cognitive Expert Language Models Better Align with the Corresponding Brain Systems

arXiv:2609.36239v1 Announce Type: new Abstract: Large language models (LLMs) can predict human brain activity across a variety of brain regions during natural language comprehension. Typically, howeve

COMiT: Learning Structured Visual Tokens through Sequential Communication

arXiv:2602.20731v2 Announce Type: replace-cross Abstract: Discrete image tokenizers provide a sequential interface for vision and multimodal models, but are typically optimized for reconstruction or c

Commitment Hierarchies under Intent Revision: A Belief-Revision Account of Salvage in Tool-Use Agents

arXiv:2609.37453v1 Announce Type: new Abstract: When a user changes their mind partway through a task, an agent that has already split the task into sub-goals and paid for tool calls must decide, per

Compositional Safety Failures in Harness Evolution: Identification and Runtime Monitoring

arXiv:2609.33123v2 Announce Type: replace Abstract: Self-evolving agent harnesses continually update persistent components such as memory, prompts, skills, and tools. We call this process harness evol

Compress to Remember: Learning Compact Memory via On-Policy Distillation for Long Video Generation

arXiv:2609.36364v1 Announce Type: cross Abstract: Standard video generators do not natively compact historical context into reusable memory tokens. As generation continues, the growing history makes i

Constitutional adapters: Inference-time interventions for misalignment and misuse

arXiv:2609.36657v1 Announce Type: cross Abstract: Training models to act in accordance with an explicitly defined set of principles, or 'constitution,' has shown promise as a robust and transparent me

Contact-Adaptive Robotic Ultrasound Probe Control for Tissue Exploration and Continuous Task-Relevant Visualization Using Robot-Free Image-Motion Demonstration

arXiv:2609.37456v1 Announce Type: new Abstract: Robotic ultrasound commonly targets standardized views, predefined scanning protocols, or expert-scan reproduction. We target a different role: while a

Controlled Decoding Attacks on Black-Box LLMs

arXiv:2609.36956v1 Announce Type: cross Abstract: Manipulating next-token probabilities during generation can bypass the safety alignment of large language models. Existing approaches, however, rely o

CoRe: Co-Evolving Reward Models for Mitigating Latent Reward Hacking in Video Diffusion Models

arXiv:2609.36245v1 Announce Type: new Abstract: Latent reward models (LRMs) enable efficient alignment of video diffusion models by scoring intermediate states directly in latent space. However, we fi

CoRe-VLA: Preserving Cross-View Coordination in VLAs under Camera Shifts

arXiv:2609.37150v1 Announce Type: new Abstract: VLAs combine pretrained vision-language representations with action generation to enable language-guided control across diverse tasks, becoming a mainst

CorrGRPO: Correlation-Normalized GRPO for Multi-Reward Learning

arXiv:2609.36820v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) is widely used to train reasoning language models, where it computes advantages by centering and normalizing

Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation

arXiv:2609.38172v1 Announce Type: cross Abstract: Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots. Howe

Credit-Guided Policy Improvement for Test-time Adaptive Vision-Language Navigation

arXiv:2609.37591v1 Announce Type: cross Abstract: Test-time adaptation for vision-language navigation (TTA-VLN) enables pretrained policies to adapt online to unseen environments using only test-time

Cross-Organizational SysML Model Integration: A Survey of Challenges and AI-Supported Tasks

arXiv:2609.37000v1 Announce Type: cross Abstract: Cross-organizational collaboration is widely regarded as a key promise of SysML-based Model-Based Systems Engineering (MBSE), yet practitioners still

CrossBFM: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments

arXiv:2609.38087v1 Announce Type: new Abstract: Behavior Foundation Models (BFMs) give humanoids a promptable policy over a latent behavior space, enabling one single vector to represent a motion to i

Diffusion Policy Improvement with Proposal-Conditioned Refinement Flows

arXiv:2609.36812v1 Announce Type: cross Abstract: Diffusion and flow policies can model complex behaviors in offline reinforcement learning (RL). However, penalizing their KL divergence from the behav

Direct Experience World-Model Optimization: Learning the World Beyond Action Imitation

arXiv:2609.37398v1 Announce Type: new Abstract: World-Action Models (WAMs) couple action generation with predictions of how physical interactions unfold. However, current post-deployment learning para

Disentangling Spurious Correlations in Vision-Language-Action Models via Predicting Domain-Invariant Latent Lookahead

arXiv:2609.37165v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models remain brittle under visual distribution shifts, often relying on spurious correlations tied to domain-specific fact

DispFlow-GS: Displacement Flow Supervision with Motion Disentangling for Monocular Deformable 3D Gaussian Splatting

arXiv:2609.36940v1 Announce Type: new Abstract: Accurate dynamic scene reconstruction is important for robotic perception, where temporally consistent representations of dynamic environments are essen

Distilled Reinforcement Learning for LLM Post-training

arXiv:2607.17247v2 Announce Type: replace-cross Abstract: Large language model (LLM) post-training is essential for improving reasoning, adaptation, and alignment. Existing methods mainly follow two p

Distilling Privileged Control Barrier Functions into RGB-Only Safety Filters for Dynamic Visual Navigation

arXiv:2609.36520v1 Announce Type: cross Abstract: RGB-only end-to-end visual navigation policies remain vulnerable to collisions in real-world dynamic environments, motivating a dedicated safety layer

Distilling What Matters: Confidence-Aware Selective Distillation for Large Language Models

arXiv:2609.36734v1 Announce Type: new Abstract: Knowledge Distillation (KD) trains a smaller-capacity student model to imitate a larger-capacity teacher model by matching output distributions, implici

Distinguish or Homogenize: Last-Chance Policy Identification and Risk-Budgeted Recovery under Irreversible Resource Depletion

arXiv:2609.36741v1 Announce Type: new Abstract: Under irreversible resource depletion, an agent can spend resources to distinguish among latent fault models, or to change the system state so that the