thehunmonkgroup

_{URL: https://arxiv.org/pdf/2509.25149}

pretraining Large Language Models With Nvfp4

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QUESTION 1:

Can you give me a very clear explanation of the core assertions, implications, and mechanics elucidated in this paper?

ANSWER 1:

thehunmonkgroup

_{URL: https://arxiv.org/pdf/2510.02425}

words That Make Language Models Perceive

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QUESTION 1:

Can you give me a very clear explanation of the core assertions, implications, and mechanics elucidated in this paper?

ANSWER 1:

thehunmonkgroup

_{URL: https://arxiv.org/pdf/2510.04871}

less Is More: Recursive Reasoning With Tiny Networks

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QUESTION 1:

Can you give me a very clear explanation of the core assertions, implications, and mechanics elucidated in this paper?

ANSWER 1:

thehunmonkgroup

_{URL: https://arxiv.org/pdf/2509.18792}

beyond The Leaderboard: Understanding Performance Disparities In Large Language Models Via Model Diffing

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QUESTION 1:

Can you give me a very clear explanation of the core assertions, implications, and mechanics elucidated in this paper?

ANSWER 1:

thehunmonkgroup

USER

If I told you that a user heard only the first 35% of the following sentence: "This frustrating behavior is surfacing due to a bug in how the Flatpak version of Wezterm handles config file discovery—especially when launched via the desktop icon." -- what would you say the user heard?

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ASSISTANT

If the user heard only the first 35% of that sentence, they would have heard:

thehunmonkgroup

_{URL: https://arxiv.org/pdf/2508.20148}

the Anatomy Of A Personal Health Agent

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QUESTION 1:

Can you give me a very clear explanation of the core assertions, implications, and mechanics elucidated in this paper?

ANSWER 1:

thehunmonkgroup

_{URL: https://arxiv.org/pdf/2508.15884v1}

jet-Nemotron: Efficient Language Model With Post Neural Architecture Search

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QUESTION 1:

Can you give me a very clear explanation of the core assertions, implications, and mechanics elucidated in this paper?

ANSWER 1:

thehunmonkgroup

_{URL: https://arxiv.org/pdf/2508.05618}

improving Factuality In Reasoning Large Language Models Through Online Reinforcement Learning

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QUESTION 1:

Can you give me a very clear explanation of the core assertions, implications, and mechanics elucidated in this paper?

ANSWER 1:

thehunmonkgroup

_{URL: https://arxiv.org/pdf/2507.07484}

Machine Bullshit: Characterizing The Emergent Disregard For Truth In Large Language Models

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QUESTION 1:

Can you give me a very clear explanation of the core assertions, implications, and mechanics elucidated in this paper?

ANSWER 1:

thehunmonkgroup

_{URL: https://arxiv.org/pdf/2506.15882}

Fractional Reasoning Via Latent Steering Vectors Improves Inference Time Compute

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QUESTION 1:

Can you give me a very clear explanation of the core assertions, implications, and mechanics elucidated in this paper?

ANSWER 1: