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This study synthesizes adjacent research and introduces Enactive AI as a conceptual framework for enterprise and industry reasoning, site-level decision support, and execution feedback
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Artificial Intelligence

https://arxiv.org/list/cs.AI/new
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Computation and Language

https://arxiv.org/list/cs.CL/new
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

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https://arxiv.org/pdf/2607.27188v1
) /Title (Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes) /Trapped /False /arXivID (https://arxiv

Machine Learning Dec 2024

https://arxiv.org/list/cs.LG/2024-12
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Report GitHub Issue

https://arxiv.org/html/2608.02613v1
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

1 Introduction

https://arxiv.org/html/2501.13956v1
2 Knowledge Graph Construction 2.1 Episodes 2.2 Semantic Entities and Facts 2.2.1 Entities 2.2.2 Facts 2.2.3 Temporal Extraction and Edge Invalidation 2.3 Communities 3 Memory Retrieval 3.1 Search 3.2 Reranker 4 Experiments 4.1 Choice

Report GitHub Issue

https://arxiv.org/html/2608.03048v1
× Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to Abstract Download PDF

Hardware Architecture

https://arxiv.org/list/cs.AR/new
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Electrical Engineering and Systems Science

https://arxiv.org/list/eess/new
Initially, with the aim of addressing bandwidth extension, this work proposes \(AEROMamba_{P}\), an efficient variant of the AERO super-resolution architecture where attention and LSTM layers are replaced by the Mamba state-space model, and which incorporates a newly developed differentiable perceptual loss derived

Machine Learning Sep 2023

https://arxiv.org/list/cs.LG/2023-09
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Computer Science

https://arxiv.org/list/cs/recent
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Machine Learning Nov 2025

https://arxiv.org/list/cs.LG/2025-11
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Image and Video Processing

https://arxiv.org/list/eess.IV/recent
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Condensed Matter

https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries

Homogeneous Distributed Observers for Quasilinear Systems

https://arxiv.org/html/2407.05763v1
fr    Siyuan Wang siyuanwang0603@buaa

[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking

Report GitHub Issue

https://arxiv.org/html/2608.02937v1
2 Bayesian Inference in Inverse Scattering 2

[2607.27188v1] Inverse Learning of Latent Risk-Neutral Densities from

https://arxiv.org/abs/2607.27188v1
Welsh View a PDF of the paper titled Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes, by Lennon J

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option

https://arxiv.org/html/2607.27188v1
Inverse learning of latent risk-neutral densities

Information Retrieval

https://arxiv.org/list/cs.IR/recent
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Information Retrieval

https://arxiv.org/list/cs.IR/new
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[2309.06553] Query-Dependent Prompt Evaluation and Optimization with

https://arxiv.org/abs/2309.06553
06553] Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL Skip to main content arXiv is now an independent nonprofit

Graphics

https://arxiv.org/list/cs.GR/new
We introduce RGBX-Next, a unified generative framework for forward and inverse rendering, which allows estimating G-buffers from images, videos, and streams, and rendering realistic images, videos, and streams from G-buffers

1 Introduction

https://arxiv.org/html/2407.16741v3
2 OpenHands Architecture 2.1 Agent Definition and Implementation 2.2 Agent Runtime: How Execution of Actions Results in Observations 2.3 Agent Skills: The Extensible Agent-Computer Interface 2.4 Agent Delegation: Cooperative Multi-agent

[1202.4083] Search for neutral Higgs bosons decaying to tau pairs in pp

https://arxiv.org/abs/1202.4083
6 inverse femtobarns recorded by the CMS experiment at the LHC

Computational Geometry

https://arxiv.org/list/cs.CG/new
Restricting each factor to an r-subset of a cone's extreme rays collapses the factorization constraint to entrywise nonnegativity of a single r x r witness matrix; witness feasibility is a certificate that an exact size-r NMF exists, decided in one matrix inverse

[1803.10826] Leptogenesis, dark matter and neutrino masses

https://arxiv.org/abs/1803.10826
We present two alternative scenarios for the implementation of the sterile fermion hypothesis: the $\nu$MSM and the Inverse Seesaw

Statistical Finance

https://arxiv.org/list/q-fin.ST/recent
LG) [pdf, html, other] Title: Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes Lennon J