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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
from the top result below
https://arxiv.org/list/cs.AI/new
Biological systems, in contrast, exhibit adaptive plasticity, maintaining function through dynamic re-organisation around damage
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
https://arxiv.org/html/2501.13956v1
com
Abstract
We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark
https://arxiv.org/html/2608.03048v1
com/JetAstra/PI-Mem
Introduction
The ability to reason over long contexts is increasingly important for large language models (LLMs), supporting applications such as long-document question answering (Gemini Team 2025; Yang et al
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
https://arxiv.org/html/2608.00648v1
Distributed Multi-Agent Systems
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https://arxiv.org/pdf/2101.00190
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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
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
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https://arxiv.org/html/2608.03494v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2402.03300v3
2 ArXiv Papers Seem Ineffective in Improving Mathematical Reasoning
5
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
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
https://arxiv.org/list/cs.IR/recent
IR)
Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
https://arxiv.org/list/cs.IR/new
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https://arxiv.org/html/2407.05132v1
1 Introduction
1.1 Background and Motivation
1.2 Objectives
2 Literature Review
2.1 Related Works on Karma
2.2 Value proposition of Karma
3 Materials and Methods
3.1 Game Theoretic Formalism
3.1.1 Notation
3.1.2 Karma as a game
3.1.3