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Software developers are the AI industry’s best customers. Is their experience also the most misleading experience possible for its economy-w…

Software developers are the AI industry’s best customers. Is their experience also the most misleading experience possible for its economy-wide scope? Roughly 80% of developers already use AI tools. C

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Space Generative AI with Solar Energy Harvesting

DGX agent

arXiv:2609.01062v1 Announce Type: new Abstract: Satellites are emerging as promising platforms to extend generative artificial intelligence (AI) services to remote areas lacking terrestrial infrastruc

safetyarxiv-cs-ai

2 Sept 2026

SpatialGuard: Harness-Guided Verifiable Spatial Reasoning for Text-to-Image Generation

arXiv:2609.01582v1 Announce Type: new Abstract: Complex 3D spatial text to image generation requires models to convert natural language into stable visual geometry, not merely semantic appearance. Exi

SpecMind: Enabling Spectrum Intelligence via Multi-Agent Hybrid Retrieval-Augmented Generation

arXiv:2609.00427v1 Announce Type: new Abstract: The exponential growth of wireless devices is driving unprecedented spectrum demand, pushing spectrum management toward more fine-grained decisions acro

StreamScout: Learning When to Look Deeper for Streaming Video Understanding

arXiv:2609.00291v1 Announce Type: new Abstract: Streaming video understanding requires answering questions that arrive at arbitrary moments over an unbounded video stream. Existing systems primarily f

Subword Segmental BabyLMs: Learning to Tokenise for Sample-Efficient Pretraining

arXiv:2609.01151v1 Announce Type: new Abstract: In the standard LM training pipeline, subword tokenisation is applied as a preprocessing step. Subword segmental language modelling is an alternative pa

SurgiATM: A Physics-Guided Plug-and-Play Model for Deep Learning-Based Smoke Removal in Laparoscopic Surgery

arXiv:2511.05059v3 Announce Type: replace Abstract: During laparoscopic surgery, smoke generated by tissue cauterization can significantly degrade the visual quality of endoscopic frames, increasing t

TacVLA: Contact-Aware Tactile Fusion for Robust Vision-Language-Action Manipulation

arXiv:2603.12665v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have demonstrated significant advantages in robotic manipulation. However, their reliance on vision and language

safetyarxiv-cs-ro

Task-Specific Prompt with Global Context for Multi-Task Graph Pre-Training

arXiv:2609.00047v1 Announce Type: cross Abstract: Graph prompt learning is an effective paradigm to adapt pre-trained graph models to downstream tasks in low-resource scenarios. However, existing mult

Text-guided flow matching enables sample-efficient crystal structure generation

arXiv:2609.01076v1 Announce Type: cross Abstract: Crystal generators can now propose periodic structures, but their control interfaces remain poorly matched to the mixed descriptors used in materials

The Assistant's Ideal Self

arXiv:2609.00304v1 Announce Type: new Abstract: Models express values and welfare-relevant self-reports, but it is unclear whether these outputs reflect stable preferences or a stable self. We thus in

The Constitutional Coverage Trilemma in AI Governance

arXiv:2609.01275v1 Announce Type: cross Abstract: Frontier AI systems function as constitutional institutions: each deployed model encodes an implicit ranking among safety, helpfulness, honesty, auton

The Safeguard Worked. Is the LLM System Safer?

arXiv:2609.00519v1 Announce Type: cross Abstract: Safeguards in deployed LLM services are evaluated by refusal, attack success, and policy violation rates. Those rates characterize how a control perfo

Towards Agentic Cloud Engineering: Graph and Loop Engineering with a Zero-Trust Agent Harness

arXiv:2609.00050v1 Announce Type: cross Abstract: Agentic AI is enabling cloud-based workflows in which autonomous agents reason over operational state, invoke authorized tools, modify software and in

Towards reliable multimodal disaster severity assessment through preference optimization and explainable vision-language reasoning

arXiv:2609.00879v1 Announce Type: new Abstract: Reliable disaster damage assessment requires models that provide both accurate predictions and transparent explanations. However, existing multimodal ap

Training-Free Policy Violation Detection via Activation-Space Whitening in LLMs

arXiv:2512.03994v4 Announce Type: replace Abstract: As organizations increasingly deploy LLMs in sensitive domains such as legal, financial, and medical settings, ensuring alignment with internal orga

trajectory-judge: What Outcome-Only LLM Judges Miss on Agent Trajectories

arXiv:2609.00038v1 Announce Type: cross Abstract: Outcome-only evaluation is the production default for LLM agents: show a judge the request and the final reply and ask whether it was handled well. Th

Trust Your Guide Only When Certain: Uncertainty-Aware Sparse Alignment at Inference Time

arXiv:2609.00624v1 Announce Type: new Abstract: A prominent paradigm in inference-time alignment employs lightweight supervisors to steer Large Language Models (LLMs). Through empirical analysis, we i

Uncovering and Mitigating Aggregation-Induced Reward Hacking in Multi-Reward Reinforcement Learning

arXiv:2609.00213v1 Announce Type: new Abstract: Reinforcement learning fine-tuning of large language models increasingly adopts multiple reward dimensions, including verifiable rules, task-specific ev

Uncovering Understanding-Generation Synergy in Native Unified Multimodal Models: From Representation, Task to System

arXiv:2609.01607v1 Announce Type: new Abstract: While unified multimodal models (UMMs) jointly perform visual understanding and generation within a single model, functional unification does not guaran

Unless someone has a better idea, I second this suggestion

Unless someone has a better idea, I second this suggestion In light of recent events, I hereby officially order the indefinite cessation of frontier AI training runs worldwide. Development of narrowly

URGENT: In light of the breaking news from @theinformation about OpenAI’s new techniques that reduce Chain of Thought monitorability, I urge…

URGENT: In light of the breaking news from @theinformation about OpenAI’s new techniques that reduce Chain of Thought monitorability, I urge everybody to (re)read (or at least be aware of) this 2025 p

V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising

arXiv:2603.16792v2 Announce Type: replace-cross Abstract: Pixel-space diffusion has recently re-emerged as a strong alternative to latent diffusion, enabling high-quality generation without pretrained

Validity-Aware Jailbreak Evaluation for Large Language Models

arXiv:2609.00498v1 Announce Type: new Abstract: Jailbreak robustness has become central to large language model (LLM) safety evaluation, yet prevailing methodologies rely primarily on refusal behavior

ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation

arXiv:2609.00057v1 Announce Type: cross Abstract: Value signals are aggregated user-level moral representations that capture users' inferred value-related tendencies from their online discourse. User

Variable Selection for Feature-Based Newsvendor

arXiv:2609.01544v1 Announce Type: cross Abstract: Feature-based newsvendor models use observable covariates to tailor inventory decisions, aiming to balance holding and shortage costs under demand unc

we're begging to get our asses skynetted

we're begging to get our asses skynetted OpenAI’s Astra AI uses a new reasoning approach called “recurrent depth.” Though it can help model costs and performance, researchers are concerned bc it obscu

When Guardrails Look Effective: Construct Validity Failures in LLM Agent Commerce Evaluation

arXiv:2609.01519v1 Announce Type: new Abstract: Interactive simulations increasingly evaluate policies in markets populated by language-model agents. Their outputs can look economic---prices, profits,

When Modality Gap Reduction Fails: Prediction-Level Hubness in CLIP

arXiv:2609.01103v1 Announce Type: new Abstract: Reducing the modality gap between image and text representations in CLIP is widely expected to improve cross-modal alignment and downstream performance.

When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation

arXiv:2609.00071v1 Announce Type: new Abstract: Prediction error is widely used to evaluate nuisance-function estimators in causal inference, but its relationship with causal estimator performance may

When Safety Routing Breaks: Understanding Alignment Fragility under Benign Fine-Tuning

arXiv:2609.01455v1 Announce Type: cross Abstract: Benign fine-tuning severely weakens the safety alignment of large language models (LLMs), so we study why refusal behavior is so fragile. While prior

When the Algorithm Becomes the Brand Crisis: A Sociotechnical Theory of Distributed Responsibility and Accountable Transparency

arXiv:2609.00510v1 Announce Type: new Abstract: Artificial intelligence systems increasingly enact market-facing promises through chatbots, recommendation systems, automated decisions, and generative

WiSDoM: Wireless Sparse Decision Transformer with Mixture-of-Experts for Multi-Task Mobile Network Optimization

arXiv:2609.00284v1 Announce Type: cross Abstract: Emerging 6G wireless networks are expected to operate across diverse deployment scenarios, where variations in network topology, user mobility, traffi

Workload Identification with Physical Side Channels for AI Governance

arXiv:2609.00309v1 Announce Type: cross Abstract: AI compute verification is one of the first tangible and tractable points for international policy aimed at AI governance. Determining whether frontie

Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment

arXiv:2609.00055v1 Announce Type: cross Abstract: Self-supervised respiratory encoders lack semantic grounding in clinical domain needed for zero-shot inference, limiting their utility without task-sp

A Cognitive Architecture for Shared Autonomy in AUV Operations

arXiv:2608.29347v1 Announce Type: new Abstract: Operators remain essential to Remotely Operated Vehicle (ROV) operation, yet often suffer from low situational awareness and high workload, both of whic

safetyarxiv-cs-ro

A Hybrid State-Space Approach for Census-Tract Population Estimation

arXiv:2608.30094v1 Announce Type: new Abstract: Sequence models---the architecture family behind large language models and, increasingly, state-of-the-art image recognition---have redefined how machin

Accelerating Unified Multimodal Models with Core-Expansion Routing and Unified Computation Scheduling

arXiv:2608.29291v1 Announce Type: new Abstract: Unified multimodal models jointly support understanding and generation, but incur substantial redundant computation across tokens, layers, and generatio

AcrossWAM1.0:A Modular Latent World-Action Stack for Compact Robot Policies

arXiv:2608.29937v1 Announce Type: new Abstract: Latent world-action models avoid rendering future pixels by predicting an action-relevant visual subgoal in feature space. LaWAM established this formul

'Act Like a 5th Grader' is Not Enough: Bounding Knowledge in LLM-Based User Simulators

arXiv:2608.30033v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to simulate human behavior but frequently fail to exhibit realistic cognitive constraints, sufferin

ACTD: Anchor-Based Cross-Tokenizer Distillation with Residual Regularization

arXiv:2608.29662v1 Announce Type: new Abstract: Knowledge distillation effectively transfers reasoning capabilities from large language models to lightweight student models. To enable knowledge transf

AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs

arXiv:2601.06022v2 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding b

Adaptive Doubly Robust Off-Policy Evaluation for Ranking Policies under Diverse User Behavior

arXiv:2608.29600v1 Announce Type: new Abstract: Off-policy evaluation (OPE) of ranking policies is challenging be- cause selecting and ordering multiple items from a candidate set makes the number of

Adaptive teachers for amortized samplers

arXiv:2410.01432v3 Announce Type: replace Abstract: Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnormalized de

Adversarial Trust Poisoning in Vehicular Collaborative Perception

arXiv:2605.22122v2 Announce Type: replace-cross Abstract: Collaborative perception (CP) enables connected and autonomous vehicles to share sensor data and jointly reason about their environment. To de

Agent2UCB: Agentic System for Generative Engine Optimization

arXiv:2608.29063v1 Announce Type: new Abstract: Large language model driven search engines such as Google AI Overviews and Perplexity have created new opportunities for Generative Engine Optimization

AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing

arXiv:2608.29622v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) improves the factuality of large language models (LLMs), yet existing RAG systems often struggle with complex, mu

Aligning Multi-Trajectory Supervision with Policy Optimization for VLA Driving

arXiv:2608.30122v1 Announce Type: cross Abstract: Vision-language-action (VLA) driving methods increasingly combine multi-trajectory imitation learning with group-relative policy optimization (GRPO),