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https://arxiv.org/html/2407.17789v2
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https://arxiv.org/html/2510.03215v2
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https://arxiv.org/html/2608.03210v1
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arXiv:2608
https://arxiv.org/html/2608.03388v1
The Game Master hides a target Python function ff and interacts with the LLM through a black-box protocol
https://arxiv.org/list/cs.CV/new
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https://arxiv.org/html/2608.02625v1
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https://arxiv.org/list/cs.AI/new
Extending the pattern-generation paradigm of Neural Cellular Automata (NCA), it navigates the degenerate Boolean search space to satisfy a computational task, rather than regenerating a fixed target state
https://arxiv.org/html/2608.03573v1
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arXiv:2608
https://arxiv.org/html/2608.01366v2
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arXiv:2608
https://arxiv.org/html/2607.22511v2
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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/2608.02616v1
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We present the first independent, systematic evaluation of OpenAI’s Privacy Filter (OPF), a 1
https://arxiv.org/html/2608.02620v1
Finally, by combining existing human annotations with LLM-judge evaluations of a target model, JudgeArena can simulate LMArena Elo scores with high accuracy offering a practical, open, and low-cost alternative to large-scale human annotation campaigns
https://arxiv.org/html/2608.02613v1
Then it must make an even harder judgment: even if the answer is known, is it actually appropriate to tell Bob
https://arxiv.org/html/2407.01093v1
com/ and GPT-4 OpenAI (2023) could be used to simulate human intelligence
https://arxiv.org/html/2210.00131v4
GPT-3 Turbo, GPT-4 & GPT-4 Turbo Preview (OpenAI 2023a), 222GPT-3 Turbo, GPT-4 & GPT-4 Turbo Preview accessed 2024-02-17, at which time GPT-4 Turbo Preview is the latest available model
https://arxiv.org/html/2608.03532v1
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arXiv:2608
https://arxiv.org/html/2608.03154v1
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arXiv:2608
https://arxiv.org/html/2508.16279v1
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arXiv:2508
https://arxiv.org/html/2410.15608v2
When benchmarked against OpenAI’s Whisper tiny
https://arxiv.org/abs/2303.12712v5
The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data
https://arxiv.org/abs/2303.12712
12712] Sparks of Artificial General Intelligence: Early experiments with GPT-4
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https://arxiv.org/html/2608.03077v1
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https://arxiv.org/tb/2005.14165
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Some blogging software supports trackback autodiscovery -- in this case, your software will automatically send a trackback as soon as your create a link to our abstract page
https://arxiv.org/html/2609.02852v1
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https://arxiv.org/html/2608.02975v1
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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/2608.03275v1
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https://arxiv.org/html/2407.16741v3
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https://arxiv.org/html/2402.03300v3
2 ArXiv Papers Seem Ineffective in Improving Mathematical Reasoning
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