Search: “safety”

Q-VGM: Q-Guided Value-Gradient Matching for Flow-Matching VLA Policies

arXiv:2606.08015v1 Announce Type: new Abstract: We propose Q-Guided Value-Gradient Matching (Q-VGM), an off-policy reinforcement learning (RL) method that tackles a long-standing challenge in fine-tun

QnRL: Quantum-Native Reinforcement Learning

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DGX agent

arXiv:2606.08276v1 Announce Type: cross Abstract: Quantum reinforcement learning (QRL) is a promising approach to learn effective decision strategies across several applications with stochastic enviro

safetyarxiv-cs-lg

9 Jun 2026

Quantifying Uncertainty in Space Debris Capture with Active Tether-Net Systems Caused by Noisy Observations

arXiv:2606.07580v1 Announce Type: cross Abstract: As Low Earth Orbit has grown more crowded with space debris, the need for reliable and efficient debris removal solutions becomes more urgent. An acti

Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents

arXiv:2606.08552v1 Announce Type: new Abstract: I discuss some quantitative representations of Promise Theory for processes involving autonomous agents. Agent models are common in software systems, ma

ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies

arXiv:2606.09630v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies provide strong priors for language-conditioned manipulation, but remain brittle in off-nominal states requiring

ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstration

arXiv:2606.09381v1 Announce Type: new Abstract: Learning robot manipulation policies with deep neural networks from a single demonstration remains highly challenging, as even small deviations from the

Region-Wise Correspondence Prediction between Manga Line Art Images

arXiv:2509.09501v4 Announce Type: replace Abstract: Understanding region-wise correspondences between manga line art images is fundamental for high-level manga processing, supporting downstream tasks

Reinforcement Learning for Flow-Matching Policies with Density Transport

arXiv:2606.08602v1 Announce Type: cross Abstract: We present an online reinforcement learning (RL) algorithm for fine-tuning flow-matching policies in continuous-control problems. Our key insight is t

Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations

arXiv:2606.08104v1 Announce Type: new Abstract: Soft-bodied organisms such as octopuses and elephant trunks exhibit remarkable morphological adaptability, dynamically reconfiguring body shape and stif

Reinforcing Temporal Answer Grounding in Instructional Video via Candidate-Aware Causal Reasoning

arXiv:2606.08436v1 Announce Type: new Abstract: The task of temporal answer grounding in instructional video (TAGV), which aims to locate precise video segments that respond to natural language querie

Repair Before Veto, When Repair Is Hidden: Quantum-Accessible Features for Repair-Augmented Constraint Learning

arXiv:2606.08020v1 Announce Type: cross Abstract: Hard-constraint decision systems usually veto infeasible candidates. This is too rigid when the system can act: if a known affordable repair would mak

Rethinking the Divergence Regularization in LLM RL

arXiv:2606.09821v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a key component of post-training large language models (LLMs). In practice, LLM RL is often off-policy because of

Revisiting Articulated Parts Perception in Robot Manipulation

arXiv:2606.08103v1 Announce Type: cross Abstract: We are surrounded by various objects with movable, articulated parts, e.g., box, handle, door. An accurate and generalizable perception of articulated

Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective

arXiv:2602.02572v2 Announce Type: replace-cross Abstract: Existing alignment methods directly use the reward model learned from user preference data to optimize an LLM policy, subject to KL regulariza

ridiculous oversimplification, from a prominent OpenAI employee no less. does this capture your impression of how the two companies have beh…

ridiculous oversimplification, from a prominent OpenAI employee no less. does this capture your impression of how the two companies have behaved? The OAI / Anthropic values difference is deeply misund

safetygary-marcus--x

RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments

arXiv:2511.07317v2 Announce Type: replace-cross Abstract: We introduce Reinforcement Learning (RL) with Adaptive Verifiable Environments (RLVE), an approach using verifiable environments that procedur

Robot-DIFT: Correspondence-Sensitive Diffusion Features for Contact-Rich Robot Manipulation

arXiv:2602.11934v2 Announce Type: replace Abstract: Robot manipulation often fails in the final millimeters: a policy may recognize the right object yet miss the pose offsets, boundaries, or pre-conta

Sample-Efficient Post-Training for LEGO Spatial-Physics Reasoning

arXiv:2606.07602v1 Announce Type: cross Abstract: LLM-based LEGO assembly generation requires both semantic grounding and physical feasibility. We identify a data-induced failure mode, PhysHack, in wh

SAW: Stage-Aware Dynamic Weighting for Multi-Objective Reinforcement Learning in Large Language Models

arXiv:2606.07705v1 Announce Type: cross Abstract: Although multi-objective reinforcement learning (MORL) is central to aligning large language models with complex human preferences, the prevailing pra

SecureClaw: Clawing Back Control of LLM Agents

arXiv:2606.09549v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents face two distinct security failures: unauthorized external actions and exposure of sensitive plaintext in

See More, Match Better: Multi-Source Feature Fusion for Two-View Correspondence Learning

arXiv:2606.09262v1 Announce Type: new Abstract: Two-view correspondence learning aims to distinguish true correspondences (inliers) from false ones (outliers) in image pairs by leveraging their underl

SEF-CLGC at SemEval-2026 Task 11: Logical Notation Impact on Language Model Performance

arXiv:2606.09157v1 Announce Type: cross Abstract: This paper revisits our pipeline called Syllogistic Evaluation Framework-Common Logic Grammar Construction (SEF-CLGC). We combine formal logical notat

Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control

arXiv:2606.08405v1 Announce Type: new Abstract: While data-intensive deep reinforcement learning can optimize complex control policies, scientific discovery in physical systems fundamentally requires

Self-Supervised Learning with a Multi-Task Latent Space Objective

arXiv:2602.05845v2 Announce Type: replace Abstract: We propose a multi-task formulation of self-predictive Siamese SSL in which each spatial transformation defines a distinct latent-space alignment ta

SemDINO: A DINOv3-Driven Network for Cross-Temporal Semantic Alignment in Change Detection

arXiv:2606.09772v1 Announce Type: new Abstract: Semantic change detection (SCD) aims to simultaneously locate land-cover changes and identify semantic categories before and after transition. However,

Sequential statistical inference for Large Language Models: Representation, validity, and monitoring

arXiv:2606.07624v1 Announce Type: new Abstract: This discussion argues that sequential statistical inference can naturally contribute to LLM trustworthiness. In deployment, LLM systems are queried rep

SG-OPD: Sign-Gated On-Policy Distillation via Sign-Consistency Gating and Phased Teacher Sampling

arXiv:2606.09304v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student on its own trajectories with dense per-token supervision from a stronger teacher, and often outperforms

sGPO: Trading Inference FLOPs for Training Efficiency in RLVR

arXiv:2606.08854v1 Announce Type: cross Abstract: Standard Reinforcement Learning with Verifiable Rewards (RLVR) training allocates a fixed rollout budget to every query, without regard for what each

SMI: Efficient Self-Supervised Learning via Mutual-Information-Inspired Dependency Optimization

arXiv:2606.08332v1 Announce Type: new Abstract: Self-supervised learning (SSL) has achieved remarkable representation learning performance, but many existing methods rely on large batch sizes, memory

'So There's a Catch-22 Here': How Early Adopters Who Build Multi-Agent LLM Systems Conceptualize Transparency

arXiv:2606.08323v1 Announce Type: cross Abstract: Multi-agent large language model (LLM) systems are rapidly emerging, yet transparency, a cornerstone of responsible AI, remains under-defined in these

some news: it turns out 34,000 Instagram accounts got hit by a Meta AI exploit the other day, per internal company docs hackers also used a …

some news: it turns out 34,000 Instagram accounts got hit by a Meta AI exploit the other day, per internal company docs hackers also used a senior 'Space Force' official's account to post anti-Iran wa

safetygary-marcus--x

SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning

arXiv:2606.08992v1 Announce Type: cross Abstract: Vision-and-Language Navigation in continuous environments requires agents to understand the spatial structure of previously unseen environments in ord

Sparrow: Sparse Rollout for Stable and Efficient Long-context RL of Large Language Models

arXiv:2606.08446v1 Announce Type: cross Abstract: Despite being powerful, reinforcement learning with verifiable rewards (RLVR) induces extremely long COT, making it computationally expensive. Since R

Speaker-Invariant Representation Learning for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck

arXiv:2606.08678v1 Announce Type: cross Abstract: Sophisticated generative speech technology can undermined the reliability of voice biometrics. While spoofing detection systems excel when assessed un

SPIN: Decentralized Swarm Control via Tensorized Policy Coordination

arXiv:2606.07557v1 Announce Type: new Abstract: Decentralized multi-agent swarm coordination on resource-constrained edge platforms remains fundamentally bottlenecked by the exponential scaling of joi

Stage-1 Controls the Entropy Regime, Not the Outcome

arXiv:2606.09059v1 Announce Type: cross Abstract: Two-stage post-training -- a Stage-1 warm-start (supervised fine-tuning, SFT, or on-policy distillation, OPD) followed by Stage-2 reinforcement learni

STARIXNet: Multivariate and Multi-attribute Deep Learning Approach to Real-Time Resource Allocation in Cloud Platforms

arXiv:2606.07565v1 Announce Type: new Abstract: Intelligent scaling of microservices in cloud platforms is crucial for mitigating escalating compute costs while avoiding service disruptions. Current s

Steganography Without Modification: Hidden Communication via LLM Seeds

arXiv:2606.09135v1 Announce Type: cross Abstract: We demonstrate that widely deployed Large Language Model (LLM) inference stacks harbor a steganographic channel that requires no modification to model

STELLAR: Spatio-Temporal Environmental Learning with Latent Alignment and Refinement for Long-Tailed Species Distribution Modeling

arXiv:2606.08484v1 Announce Type: cross Abstract: Joint Species Distribution Modeling (JSDM) is a key enabler for biodiversity monitoring and conservation planning. However, accurate JSDM faces two co

Structure-Conditioned Actor-Critic Branches for Quality-Diversity Reinforcement Learning

arXiv:2606.08735v1 Announce Type: new Abstract: Quality-diversity reinforcement learning (QD-RL) aims to construct policy repertoires that contain both high-performing and behaviorally diverse policie

Summarization is Not Dead Yet

arXiv:2606.08000v1 Announce Type: cross Abstract: The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising

Symbolic Reasoning Frameworks Modulate LLM Risk Aversion in Multi-Agent Strategic Settings

arXiv:2606.07552v1 Announce Type: cross Abstract: Large language models exhibit innate behavioral tendencies when deployed as strategic agents -- notably a risk-averse 'turtle' bias toward defensive p

SynthICL: Scalable In-context Imitation Learning with Synthetic Data

arXiv:2606.08154v1 Announce Type: new Abstract: In-context imitation learning (ICIL) enables robots to learn new tasks from a small number of demonstrations by conditioning a pre-trained policy on tas

Systems-Level Planning and Coordination of Truck-Drone Collaborative Delivery Networks

arXiv:2606.08738v1 Announce Type: cross Abstract: Urban last-mile parcel delivery increasingly relies on heterogeneous fleets whose performance depends on timely coordination, reliable communication,

Targeting World Models to Compromise Robot Learning Pipelines

arXiv:2606.09499v1 Announce Type: cross Abstract: World models have recently seen a rapid growth in both their popularity and capability as more data efficient tools for generating robot training data

Testing the Black Box: Structural Barriers to Independent Evaluation of Consumer-Facing Health LLMs

arXiv:2606.08483v1 Announce Type: new Abstract: Background: Consumer-facing large language models are now a common source of health information, and they interpret and personalize responses rather tha

The ACUTE Protocol: Operationalizing Language Model Activations for Better Calibration, Utility, and Trust

arXiv:2606.07822v1 Announce Type: cross Abstract: As language models improve and become increasingly deployed to solve a variety of tasks, trustworthiness becomes essential. Calibration is a good prox

The Cross-Architecture Substrate: A Domain-Transcendent, Calibration-Surviving Geometric Invariant of Modern Vision Encoders

arXiv:2606.07882v1 Announce Type: cross Abstract: Different vision neural networks -- trained to classify, contrast, reconstruct, or match images to text -- should have correspondingly different inter