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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
CL)
Recent studies have identified an ``Artificial Hivemind'' effect in Large Language Models (LLMs) causing models to converge on a narrow, homogenized consensus even for open questions
https://arxiv.org/html/2608.02616v1
com
Abstract
We present the first independent, systematic evaluation of OpenAI’s Privacy Filter (OPF), a 1
https://arxiv.org/html/2510.03215v2
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arXiv is now an independent nonprofit
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2608.03063v1
Beyond payment behavior, SeqLLM achieves state-of-the-art results against strong public baselines on open recommendation benchmarks
https://arxiv.org/html/2509.22186v2
com/opendatalab/MinerU \metadata[Model:]https://huggingface
https://arxiv.org/html/2407.02518v2
com
Abstract
Large language models (LLMs) for code are typically trained to align with natural language instructions to closely follow their intentions and requirements
https://arxiv.org/html/2608.02613v1
ae
Abstract
Edge-deployed personal memory assistants must handle private interpersonal conversations on-device with open-weight models
https://arxiv.org/html/2607.26245v1
Empirical work now documents tick-level Polymarket dynamics, decentralized prediction-market (DePM) design trade-offs, and combinatorial arbitrage [7, 23, 22]
https://arxiv.org/html/2506.01844v1
In particular, transparent, reproducible open-source models and training recipes are crucial for accelerating progress and fostering broader participation within the robotics research community
https://arxiv.org/list/cs.CL/new
We present TabletCraft, the first open-source system that enables bidirectional interaction with Mesopotamian writing
https://arxiv.org/html/2407.04503v4
For instance, we find that more open-ended instructions lead to stronger attraction effects compared to more constrained tasks
https://arxiv.org/html/2410.17799v2
Audio samples of dialogues generated by OmniFlatten can be found at this web site 111https://omniflatten
https://arxiv.org/html/2608.02975v1
com
Abstract
Large language models (LLMs) have demonstrated impressive performance in MQM-based translation quality (TQ) evaluation, and recent advances in large reasoning models (LRMs) promise even greater improvements
https://arxiv.org/html/2608.03494v1
com
1 Introduction
Modern large language models (LLMs) process text using subword tokenizers such as Byte-Pair Encoding (BPE) Sennrich et al
https://arxiv.org/html/2508.02739v1
Our pre-trained model is publicly available at https://github
https://arxiv.org/html/2608.02673v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/abs/1209.0146
We accomplish this by measuring the azimuths of stone arrangements described in site cards from the NSW Aboriginal Heritage Information Management System
https://arxiv.org/abs/1209.0146?context=physics
We accomplish this by measuring the azimuths of stone arrangements described in site cards from the NSW Aboriginal Heritage Information Management System
https://arxiv.org/abs/2607.25373
The second, defrel/bank, memoizes a relation against canonical fresh variables so that a single pruned answer stream is built bottom-up and replayed at every call site
https://arxiv.org/abs/2406.05970
This would dramatically lower tritium startup inventory requirements and reduce the amount of on-site tritium
https://arxiv.org/abs/2410.17799v2
Audio samples of dialogues generated by OmniFlatten can be found at this web site (this https URL)
https://arxiv.org/pdf/2608.02122
) /Title (DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/pdf/2608.01785
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https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/abs/1910.03771v5
03771v5] HuggingFace's Transformers: State-of-the-art Natural Language Processing
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https://arxiv.org/abs/1910.03771
03771] HuggingFace's Transformers: State-of-the-art Natural Language Processing
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https://arxiv.org/abs/2305.08291
08291] Large Language Model Guided Tree-of-Thought
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https://arxiv.org/abs/2403.08299
08299] AutoDev: Automated AI-Driven Development
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https://arxiv.org/abs/2407.17789
17789] Very Large-Scale Multi-Agent Simulation in AgentScope
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