惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

U
Unit 42
V
V2EX
Martin Fowler
Martin Fowler
博客园 - Franky
P
Proofpoint News Feed
P
Palo Alto Networks Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
B
Blog
The Register - Security
The Register - Security
Latest news
Latest news
S
Security @ Cisco Blogs
Simon Willison's Weblog
Simon Willison's Weblog
Recorded Future
Recorded Future
大猫的无限游戏
大猫的无限游戏
M
Microsoft Research Blog - Microsoft Research
Scott Helme
Scott Helme
T
Tailwind CSS Blog
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Application and Cybersecurity Blog
Application and Cybersecurity Blog
T
True Tiger Recordings
有赞技术团队
有赞技术团队
I
Intezer
Cisco Talos Blog
Cisco Talos Blog
Hacker News - Newest:
Hacker News - Newest: "LLM"
The GitHub Blog
The GitHub Blog
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
T
Tenable Blog
博客园 - 叶小钗
Hugging Face - Blog
Hugging Face - Blog
Hacker News: Ask HN
Hacker News: Ask HN
S
Security Archives - TechRepublic
F
Future of Privacy Forum
爱范儿
爱范儿
PCI Perspectives
PCI Perspectives
H
Help Net Security
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
T
The Blog of Author Tim Ferriss
MyScale Blog
MyScale Blog
N
Netflix TechBlog - Medium
罗磊的独立博客
Apple Machine Learning Research
Apple Machine Learning Research
MongoDB | Blog
MongoDB | Blog
Security Latest
Security Latest
美团技术团队
博客园 - 三生石上(FineUI控件)
S
Schneier on Security
量子位
C
CERT Recently Published Vulnerability Notes
SecWiki News
SecWiki News

cs.AI updates on arXiv.org

Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents AI-Enabled Serious Games: Integrating Intelligence and Adaptivity in Training Systems Trace2Skill: Verifier-Guided Skill Evolution for Long-Context EDA Agents Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost Unlocking Proactivity in Task-Oriented Dialogue What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct AOP-Wiki EMOD 3.0: Data Model Expansions and Content Evaluation Framework for Using Agentic AI to Improve Integration between AOPs and New Approach Methodologies (NAMs) Implicit Safety Alignment from Crowd Preferences Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability Evaluating Large Language Models as Live Strategic Agents: Provider Performance, Hybrid Decomposition, and Operational Gaps in Timed Risk Play IdleSpec: Exploiting Idle Time via Speculative Planning for LLM Agents ECPO: Evidence-Coupled Policy Optimization for Evidence-Certified Candidate Ranking S2ED: From Story to Executable Descriptions for Consistency-Aware Story Illustration Who Uses AI? Platforms, Workforce, and AI Exposure A Causal Argumentation Method for Explainability of Machine Learning Models The Impact of AI Usage and Informativeness on Skill Development in Logical Reasoning Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs Cross-domain benchmarks reveal when coordinated AI agents improve scientific inference from partial evidence Active Evidence-Seeking and Diagnostic Reasoning in Large Language Models for Clinical Decision Support Knowledge Graph Re-engineering Along the Ontological Continuum (extended version) Meta-Soft: Leveraging Composable Meta-Tokens for Context-Preserving KV Cache Compression Towards a compositional semantics for quantitative confidence assessment in assurance arguments ExComm: Exploration-Stage Communication for Error-Resilient Agentic Test-Time Scaling Skill Weaving: Efficient LLM Improvement via Modular Skillpacks MPDocBench-Parse: Benchmarking Practical Multi-page Document Parsing Latent-space Attacks for Refusal Evasion in Language Models SGR-Bench: Benchmarking Search Agents on State-Gated Retrieval SciCore-Mol: Augmenting Large Language Models with Pluggable Molecular Cognition Modules LLM-Metrics: Measuring Research Impact Through Large Language Model Memory AttuneBench: A Conversation-Based Benchmark for LLM Emotional Intelligence The Shape of Testimony: A Scalable Framework for Oral History Archive Comparison TO-Agents: A Multi-Agent AI Pipeline for Preference-Guided Topology Optimization MindLoom: Composing Thought Modes for Frontier-Level Reasoning Data Synthesis FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation SMDD-Bench: Can LLMs Solve Real-World Small Molecule Drug Design Tasks? A Camera-Cooperative ISAC Framework for Multimodal Non-Cooperative UAVs Sensing The Log is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Tasks CLORE: Content-Level Optimization for Reasoning Efficiency Format-Constraint Coupling in Knowledge Graph Construction from Statistical Tables KAPPS: A knowledge-based CPPS Architecture for the Circular Factory Investigating Concept Alignment Using Implausible Category Members Evaluation of Pipelines for Data Integration into Knowledge Graphs ArborKV: Structure-Aware KV Cache Management for Scaling Tree-based LLM Reasoning Perception or Prejudice: Can MLLMs Go Beyond First Impressions of Personality? Ratchet: A Minimal Hygiene Recipe for Self-Evolving LLM Agents Efficient Agentic Reasoning Through Self-Regulated Simulative Planning LACO: Adaptive Latent Communication for Collaborative Driving Echo: Learning from Experience Data via User-Driven Refinement Planning in the LLM Era: Building for Reliability and Efficiency ST-SimDiff: Balancing Spatiotemporal Similarity and Difference for Efficient Video Understanding with MLLMs Enhancing Visual Token Representations for Video Large Language Models via Training-Free Spatial-Temporal Pooling and Gridding Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings The Devil is in the Condition Numbers: Why is GLU Better than non-GLU Structure? Agent JIT Compilation for Latency-Optimizing Web Agent Planning and Scheduling PACD-Net: Pseudo-Augmented Contrastive Distillation for Glycemic Control Estimation from SMBG Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search APEX: Autonomous Policy Exploration for Self-Evolving LLM Agents Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction The Hidden Signal of Verifier Strictness: Controlling and Improving Step-Wise Verification via Selective Latent Steering Self-Training Doesn't Flatten Language -- It Restructures It: Surface Markers Amplify While Deep Syntax Dies Sutra: Tensor-Op RNNs as a Compilation Target for Vector Symbolic Architectures DeCoR: Design and Control Co-Optimization for Urban Streets Using Reinforcement Learning Distribution-Aware Reward: Reinforcement Learning over Predictive Distributions for LLM Regression JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX Runtime-Certified Bounded-Error Quantized Attention Winfree Oscillatory Neural Network PREFINE: Preference-Based Implicit Reward and Cost Fine-Tuning for Safety Alignment DASH: Fast Differentiable Architecture Search for Hybrid Attention in Minutes on a Single GPU SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation Variance Reduction for Expectations with Diffusion Teachers \textit{Stochastic} MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent On the Regularity and Generalization of One-Step Wasserstein-guided Generative Models for PDE-Induced Measures Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Pseudo-Siamese Network for Planning in Target-Oriented Proactive Dialogues Data-Efficient Neural Operator Training via Physics-Based Active Learning Code Generation by Differential Test Time Scaling Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards NeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning STELLAR: Scaling 3D Perception Large Models for Autonomous Driving CAdam: Context-Adaptive Moment Estimation for 3D Gaussian Densification in Generative Distillation Multi-agent Collaboration with State Management \ECUAS{n}: A family of metrics for principled evaluation of uncertainty-augmented systems From Circuit Evidence to Mechanistic Theory: An Inductive Logic Approach Behavior-Consistent Deep Reinforcement Learning torchtune: PyTorch native post-training library TimeSRL: Generalizable Time-Series Behavioral Modeling via Semantic RL-Tuned LLMs -- A Case Study in Mental Health OCTOPUS: Optimized KV Cache for Transformers via Octahedral Parametrization Under optimal Squared error quantization How Much Online RL is Enough? Informative Rollouts for Offline Preference Optimization in RLVR Data Scaling as Progressive Coverage of a Predictive Contribution Spectrum SDM: A Powerful Tool for Evaluating Model Robustness Improving Quantized Model Performance in Qualitative Analysis with Multi-Pass Prompt Verification Approximation Theory for Neural Networks: Old and New AgentAtlas: Beyond Outcome Leaderboards for LLM Agents DEL: Digit Entropy Loss for Numerical Learning of Large Language Models Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning HiRes: Inspectable Precedent Memory for Reaction Condition Recommendation
Scaling Observation-aware Planning in Uncertain Domains
Adrian Zvizd · 2026-05-23 · via cs.AI updates on arXiv.org

View PDF

Abstract:Deciding which sensing capabilities to deploy on an agent in uncertain domains is a fundamental engineering challenge, in which one balances task achievability against the high costs of hardware and processing. This problem has previously been formalized as the Optimal Observability Problem (OOP), based on the well-known Partially Observable Markov Decision Process (POMDP) model for decision-making. This work studies (sub-)symbolic techniques to scale solving of decidable fragments of the OOP, namely the Sensor Selection Problem (SSP) and the Positional Observability Problem (POP). Besides improving the original approach based on parameter synthesis, we develop a new solving method that identifies sensible observation functions via decomposition of POMDPs, improving performance by 3 and 5 orders of magnitude for instance size and runtime, respectively.
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.22364 [cs.AI]
  (or arXiv:2605.22364v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2605.22364

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Alberto Lluch Lafuente [view email]
[v1] Thu, 21 May 2026 11:58:01 UTC (99 KB)