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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
, the meaning of a customized field) originates from users' domain knowledge and is rarely documented publicly
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/html/2608.02613v1
A million-token cloud context window does not help here, as it requires off-device upload, which privacy-sensitive personal memory management is meant to avoid
https://arxiv.org/list/cs.CL/new
Many practically important problems are naturally treated as black-box optimization (BBO) problems, in which only objective values are observable, and the functional form is unavailable
https://arxiv.org/html/2508.08980v3
A relation admits such a representation if and only if it is strongly acyclic and its transitive closure is separable, where separability means embeddability in a preorder possessing a countable separating stratification—a countable family of pairwise disjoint subsets, none containing a strictly ranked pair, that
https://arxiv.org/html/2608.03703v1
This project was begun on July 20, 2026; the present version is dated August 4, 2026
https://arxiv.org/list/math/new
It is natural to expect that if many pairs $(x_i,x_j)$ lie at distance close to $1$ from each other, then some clustering phenomenon must occur, implying that a significant number of these pairs are also very close to each other
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/list/cs.IR/new
IR)
Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed
https://arxiv.org/list/cs.AR/new
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https://arxiv.org/html/2608.02909v1
This approach is theoretically valid, easy to implement, and does not require new data
https://arxiv.org/html/2407.00575v3
However, if the game is uncontrolled, the NE does not optimize global objectives
https://arxiv.org/html/2607.28847v1
3 Research Question 1: Does Masked Inequality Exist
https://arxiv.org/html/2607.27099v1
4 Rainfall is rough at large time scales
1
https://arxiv.org/html/2608.02800v1
Dendorfer (2024) decomposes the two sides of platform integration: first-party selling raises welfare by mitigating double marginalization, while self-preferencing erodes part of that gain, so welfare is highest when the platform does not self-prefer
https://arxiv.org/html/2608.03573v1
Our results reveal a distinction: interference in SFT is norm-limited, scaling with the absolute gradient magnitude, whereas interference in RL is variance-limited, bounded by the gradient variance induced by advantage normalization and on-policy optimization
https://arxiv.org/html/2607.22511v2
A common approach is to close the research loop with a large language model (LLM) reviewer
https://arxiv.org/html/2608.02943v1
The central technical observation is that a fixed-horizon response does not require an unrestricted first-order approximation of the entire quantile process
https://arxiv.org/html/2604.05327v4
Any researcher or decision-maker who relies on a statistical model to learn about a parameter of interest must therefore contend with the possibility that the likelihood is misspecified
https://arxiv.org/html/2309.08431v3
Next, we derive a self-financing and closed-form optimal liquidity provision strategy where the width of the LP’s liquidity range is determined by the profitability of the pool (provision fees minus gas fees), the predictable losses (PL) of the LP’s position, and concentration risk
https://arxiv.org/html/2608.01085v1
Figure 1: A backdoor in one agent is activated by a local input trigger ([TRIGGER] denotes a predefined word or phrase)
https://arxiv.org/pdf/1811.12628
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https://arxiv.org/pdf/2006.02318
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www
https://arxiv.org/list/cs.CV/new
RO)
Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to parallelize
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/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/2608.03452v1
2 Membership is decodable beyond the surface forms
4
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/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/list/cs.IR/recent
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