Search: “safety”

Cyclops: LiDAR as a Camera That Dreams in Color

arXiv:2608.16264v1 Announce Type: new Abstract: Conventionally, robotic perception relies heavily on cameras due to the rich semantic texture they provide. However, their performance degrades signific

D^{2}R^{2}: Discrete Diffusion with Regulation Reinforcement for Single-Cell Perturbation Prediction

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arXiv:2608.15288v1 Announce Type: new Abstract: Predicting single-cell transcriptomic responses to genetic perturbations is central to functional genomics and virtual-cell modeling. Existing approache

safetyarxiv-cs-ai

18 Aug 2026

Deep Thought Alignment: Trajectory-Level Latent Distillation for Video Reasoning

arXiv:2608.16316v1 Announce Type: cross Abstract: Large Multimodal Models (LMMs) for video reasoning have long been hindered by the high computational cost of processing vast amounts of visual informa

Dense Expands, Sparse Anchors: Channel-Asymmetric Query Expansion for Hybrid Retrieval

arXiv:2608.15851v1 Announce Type: cross Abstract: LLM-based query expansion improves retrieval by generating document-like passages. In hybrid retrieval, however, most evaluations fuse fixed top-L den

Density-Reweighted Entropic Optimal Transport: Decoupling Geometry from Sampling Density

arXiv:2608.16506v1 Announce Type: cross Abstract: Dataset alignment is a central step in data analysis across science and engineering, where the goal is to match observations between datasets. Entropi

Discovering High-Quality Chess Puzzles with Offline Reinforcement Learning

arXiv:2608.14851v1 Announce Type: new Abstract: Learning and skill mastery require extensive and deliberate practice. In many learning settings, producing high-quality pedagogical materials can requir

DR.GAP: Mitigating Bias in Large Language Models using Gender-Aware Prompting with Decoupled Reasoning

arXiv:2502.11603v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit strong natural language understanding capabilities but also inherit and amplify societal biases, particul

Dynamic Pricing and Advertising with Demand Learning

arXiv:2304.14385v4 Announce Type: replace-cross Abstract: We consider a novel pricing and advertising framework in which a seller not only sets the product price but also designs flexible advertising

EchoMask: Speech-Queried Attention-based Mask Modeling for Holistic Co-Speech Motion Generation

arXiv:2504.09209v3 Announce Type: replace-cross Abstract: Masked modeling has shown promise in co-speech gesture generation. However, it struggles to identify semantically significant frames for effec

Eigenanalysis framework for autoregressive neural emulators of multi-scale chaotic dynamics

arXiv:2608.16084v1 Announce Type: new Abstract: Neural autoregressive models have rapidly emerged as powerful emulators of high-dimensional chaotic systems, yet their long-term instability and error g

Every Coin Has Two Sides: On the Dual Nature of Generalization in On-Policy Distillation of Large Language Models

arXiv:2608.16647v1 Announce Type: new Abstract: On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's own policy, yet its generalization be

FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting

arXiv:2608.15310v1 Announce Type: cross Abstract: Multimodal data collected by heterogeneous devices are used for collaborative training, where federated learning (FL) serves as a key paradigm for eff

FMReward: Aligning and Evaluating Audio-Driven 3D Facial Animation with Human Preferences

arXiv:2608.15296v1 Announce Type: new Abstract: Audio-driven 3D facial animation is essential for advancing immersion and interactivity in virtual experiences. Although recent advances have shown prom

Free-Flow Class-Incremental Learning: Towards Robust CIL under Variable Class Arrivals

arXiv:2604.02765v2 Announce Type: replace Abstract: Class-incremental learning (CIL) is commonly evaluated under predefined schedules with fixed or nearly equal class increments, leaving irregular cla

From Positionwise Confidence to Prefix Scheduling: Verifier Skipping in Speculative Decoding

arXiv:2608.14787v1 Announce Type: cross Abstract: Speculative decoding is a leading technique to reduce the cost of autoregressive generation by using a small drafter to propose several tokens, which

From 'What-If' to 'What-Is': Counterfactual Thinking-Inspired Semantic Alignment for Visual Brain Decoding

arXiv:2608.15163v1 Announce Type: new Abstract: Visual brain decoding reconstructs visual content perceived by a person from neural measurements such as fMRI, providing a computational approach to stu

Fully distributed and resilient source seeking for robot swarms

arXiv:2410.15921v3 Announce Type: replace Abstract: Existing source-seeking algorithms for robot swarms typically require either direct gradient measurements or rigid geometric formations, limiting th

FusionBERT: Multi-View Image--3D Retrieval via Cross-Attention Visual Fusion and Normal-Aware 3D Encoder

arXiv:2604.02583v2 Announce Type: replace Abstract: We propose FusionBERT, a novel multi-view visual fusion framework for image--3D multimodal retrieval. Existing image--3D representation learning met

GenRouter: Unified Workflow Routing for Agentic Image Generation

arXiv:2608.16721v1 Announce Type: new Abstract: The rapid evolution of text-to-image (T2I) generation models has effectively solved the foundational challenge of raw pixel synthesis, shifting the comm

Geometric Self-Supervised Pre-training for Neural Combinatorial Optimization

arXiv:2608.00270v2 Announce Type: replace Abstract: Neural Combinatorial Optimization (NCO) techniques have emerged as a highly efficient alternative to traditional exact algorithms for solving routin

Geometry Is Not Robustness: A Trajectory-Level Study of PGD Evaluation

arXiv:2608.14594v1 Announce Type: new Abstract: Projected Gradient Descent (PGD) is widely used to evaluate adversarial robustness, typically via final adversarial accuracy, which does not capture mod

GRIP: Grounded Reasoning via Information-Restricted Premises

arXiv:2608.16776v1 Announce Type: new Abstract: High-capacity encoders in retrieval-augmented generation (RAG) can let the query dominate the latent state, leaving retrieved evidence functionally irre

Grounding LTL Tasks in Sub-Symbolic RL Environments for Zero-Shot Generalization

arXiv:2602.09761v2 Announce Type: replace-cross Abstract: In this work we address the problem of training a Reinforcement Learning agent to follow multiple temporally-extended instructions expressed i

Guaranteed Adaptive Modality Acquisition: When the Policy Chooses Its Own Calibration Group

arXiv:2608.15520v1 Announce Type: new Abstract: A multimodal system may begin inference holding only some of its inputs and may acquire the rest at a cost. With adaptive acquisition, the policy determ

Hallucination Span Detection with Input-Side Evidence Alignment

arXiv:2608.15804v1 Announce Type: new Abstract: Hallucinations remain a major obstacle to the reliable use of large language models (LLMs) in conditional text generation. Existing methods primarily as

Model Releases

HarmProfile: Characterizing Harmful Distributions in Frontier LLMs

model-releasesarxiv-cs-ai

arXiv:2608.14577v1 Announce Type: cross Abstract: Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analys

HarmTrace: Anchor-Calibrated Decoupled Optimization for Fine-Grained Target Identification in Harmful Memes

arXiv:2608.16622v1 Announce Type: cross Abstract: Multimodal harmful meme detection is typically formulated as image--text harmfulness classification. A model may correctly predict harmfulness while m

I had a great time discussing with @SciencePetr about the growing risks of AI and how these often stem from current AI training techniques. …

I had a great time discussing with @SciencePetr about the growing risks of AI and how these often stem from current AI training techniques. Thanks for coming to visit @LawZero_ and for covering these

safetyyoshua-bengio--x

I-Perceive: A Foundation Model for Active Perception with Language Instructions

arXiv:2603.00600v2 Announce Type: replace Abstract: Active perception - the ability of a robot to proactively select viewpoints to acquire task-relevant information - is essential for robust operation

Identifying Confusion Trends in Concept-based XAI for Multi-Label Classification

arXiv:2608.15731v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) deployed in high-risk domains, such as healthcare and autonomous driving, must be not only accurate but also understandabl

Imagining Recovery: Inference-Time Counterfactual Realignment for Vision-Language-Action Models

arXiv:2608.14822v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have improved the flexibility and generality of robotic manipulation, yet they remain fragile to online disruption

Inferential Evaluation of Surrogate-Derived Models under Covariate Shift

arXiv:2608.15783v1 Announce Type: cross Abstract: In transfer-learning settings, a model derived from abundant surrogate labels may be deployed in a target population where gold-standard outcomes are

LaGSplat: Inferring Physics-Governed Interactive Simulation from Monocular Video Using Latent Lagrangian Gaussian Splatting

arXiv:2608.16324v1 Announce Type: new Abstract: We present LaGSplat (Latent Lagrangian Gaussian Splatting), a framework that infers interactive, physics-governed dynamics from one or a few monocular v

Le Critique: Privileged Value Functions for LLM Reinforcement Learning

arXiv:2608.16739v1 Announce Type: new Abstract: Reinforcement learning algorithms for Large Language Models (LLMs) are largely distinguished by their variance reduction strategy. Group-relative method

Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning

arXiv:2608.15770v1 Announce Type: new Abstract: Designing effective trading strategies using reinforcement learning remains challenging due to delayed and noisy rewards, poor exploration, and the diff

Learning to Unlearn: Machine Unlearning via Learning the Unlearning Behaviors

arXiv:2608.16700v1 Announce Type: cross Abstract: Various machine unlearning techniques have been developed in response to privacy legislation requirements, enabling individuals to exercise their lega

Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models

arXiv:2608.15129v1 Announce Type: cross Abstract: We systematically compare word order preferences in decoder-only language models across 192 artificial languages and typologically diverse natural lan

Leveraging Machine Unlearning for Cost-Efficient Preference Alignment

arXiv:2504.06659v2 Announce Type: replace-cross Abstract: Despite advances in Preference Alignment (PA) for Large Language Models (LLMs), mainstream methods like reinforcement learning with human feed

LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning

arXiv:2608.15041v1 Announce Type: new Abstract: Coordinating multiple interacting units in complex engineering systems is challenging when system interactions are difficult to model, operational infor

Making two action heads agree: coordination mechanisms and a runtime collapse certificate for flow-matching policies

arXiv:2608.15748v1 Announce Type: new Abstract: A dual-representation flow-matching policy decodes each predicted motion into joint and end-effector spaces, and the residual between the two kinematica

Marker-Constrained Pose-Graph Correction for Cross-Platform Georeferencing in GNSS-Denied Environments

arXiv:2608.16281v1 Announce Type: new Abstract: Autonomous operation in GNSS-denied environments requires heterogeneous mapping pipelines to maintain a consistent spatial reference. This paper present

Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans

arXiv:2608.16514v1 Announce Type: cross Abstract: Human visual search is serial: the fovea must land on a candidate to confirm it, and those landings form a scanpath. Whether multimodal large language

MatchingPolicy: Correspondence-Aware Policy Enables Cross-Object In-Context Learning

arXiv:2608.16715v1 Announce Type: new Abstract: In-context imitation learning enables few-shot policy generalization but struggles to maintain performance on unseen objects and novel scenarios. To add

Max-Q Selective Imitation for Human-in-the-Loop Online Robot Learning

arXiv:2608.15088v1 Announce Type: cross Abstract: Human-in-the-loop (HIL) online reinforcement learning for real robots must absorb human interventions quickly while continuing to improve beyond the h

MicroVerse: An Instrument for Measuring Self-Authored Identity Drift in Long-Horizon Multi-Agent Language-Model Simulations

arXiv:2608.15844v1 Announce Type: new Abstract: Long-horizon, multi-agent language model (LM) simulations are widely proposed for studying social behavior, yet instruments to measure whether persona-c

MINT: Min-Selection Preference Distillation for Balanced Multi-Objective Alignment

arXiv:2608.14828v1 Announce Type: new Abstract: Aligning a language agent to several objectives at once is a persistent failure mode of preference-based training: when objectives are combined additive

MLLM-Guided Semantic Correction for Text-to-Video Generation

arXiv:2608.16513v1 Announce Type: cross Abstract: Recent advances in diffusion models and Transformer architectures have led to significant progress in text-to-video generation. However, these models

Modality-Invariant Coarse-to-Fine Retinal Image Registration

arXiv:2608.14829v1 Announce Type: cross Abstract: Retinal image registration is essential for ophthalmic diagnosis, longitudinal disease monitoring, and multimodal retinal image analysis. Existing ret