Arxiv Papers podcast

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[QA] The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation 7:35

منذ 8 hours7:35

7:35

This paper reviews Large Language Models' memorization, exploring its causes, detection methods, implications, and mitigation strategies, while highlighting challenges in balancing memorization minimization with model utility. https://arxiv.org/abs//2507.05578 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation 23:36

منذ 8 hours23:36

23:36

This paper reviews Large Language Models' memorization, exploring its causes, detection methods, implications, and mitigation strategies, while highlighting challenges in balancing memorization minimization with model utility. https://arxiv.org/abs//2507.05578 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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[QA] Differential Mamba 7:09

منذ 8 hours7:09

7:09

This paper introduces a novel differential mechanism for Mamba architecture, enhancing retrieval capabilities and performance while addressing attention overallocation issues found in sequence models like Transformers and RNNs. https://arxiv.org/abs//2507.06204 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Differential Mamba 18:31

منذ 8 hours18:31

18:31

This paper introduces a novel differential mechanism for Mamba architecture, enhancing retrieval capabilities and performance while addressing attention overallocation issues found in sequence models like Transformers and RNNs. https://arxiv.org/abs//2507.06204 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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[QA] Cascade: Token-Sharded Private LLM Inference 7:04

منذ 1 day7:04

7:04

The paper presents Cascade, a multi-party inference protocol that enhances performance and scalability while maintaining privacy for large language models, outperforming existing secure schemes. https://arxiv.org/abs//2507.05228 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Cascade: Token-Sharded Private LLM Inference 35:03

منذ 1 day35:03

35:03

The paper presents Cascade, a multi-party inference protocol that enhances performance and scalability while maintaining privacy for large language models, outperforming existing secure schemes. https://arxiv.org/abs//2507.05228 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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[QA] Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data 7:28

منذ 1 day7:28

7:28

Real-TabPFN enhances tabular data performance by continued pre-training on curated real-world datasets, outperforming models trained on broader datasets, achieving significant gains on 29 OpenML AutoML Benchmark datasets. https://arxiv.org/abs//2507.03971 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data 10:15

منذ 1 day10:15

10:15

Real-TabPFN enhances tabular data performance by continued pre-training on curated real-world datasets, outperforming models trained on broader datasets, achieving significant gains on 29 OpenML AutoML Benchmark datasets. https://arxiv.org/abs//2507.03971 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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[QA] Strategic Intelligence in Large Language Models Evidence from evolutionary Game Theory. 7:21

منذ يومين7:21

7:21

This study explores Large Language Models' strategic intelligence in competitive settings, revealing their reasoning abilities and distinct strategies in evolutionary Iterated Prisoner's Dilemma tournaments against traditional strategies. https://arxiv.org/abs//2507.02618 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Strategic Intelligence in Large Language Models Evidence from evolutionary Game Theory. 34:06

منذ يومين34:06

34:06

This study explores Large Language Models' strategic intelligence in competitive settings, revealing their reasoning abilities and distinct strategies in evolutionary Iterated Prisoner's Dilemma tournaments against traditional strategies. https://arxiv.org/abs//2507.02618 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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[QA] Fast and Simplex: 2-Simplicial Attention in Triton 7:28

منذ يومين7:28

7:28

This paper explores the 2-simplicial Transformer, which enhances token efficiency over standard Transformers, improving performance on mathematics, coding, reasoning, and logic tasks within fixed token budgets. https://arxiv.org/abs//2507.02754 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Fast and Simplex: 2-Simplicial Attention in Triton 17:55

منذ يومين17:55

17:55

This paper explores the 2-simplicial Transformer, which enhances token efficiency over standard Transformers, improving performance on mathematics, coding, reasoning, and logic tasks within fixed token budgets. https://arxiv.org/abs//2507.02754 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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[QA] Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning 7:21

منذ 8 أيام7:21

7:21

https://arxiv.org/abs//2507.00432 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

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Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning 15:33

منذ 8 أيام15:33

15:33

https://arxiv.org/abs//2507.00432 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

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[QA] DABstep: Data Agent Benchmark for Multi-step Reasoning 7:54

منذ 8 أيام7:54

7:54

DABstep is a benchmark for evaluating AI agents on multi-step data analysis tasks, featuring 450 real-world challenges that test data processing and contextual reasoning capabilities. https://arxiv.org/abs//2506.23719 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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[QA] Where to find Grokking in LLM Pretraining? Monitor Memorization-to-Generalization without Test 7:49

منذ 13 يومًا7:49

7:49

This study explores grokking in large language models during pretraining, revealing how training pathways evolve from random to structured, enhancing generalization despite converged loss. https://arxiv.org/abs//2506.21551 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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Where to find Grokking in LLM Pretraining? Monitor Memorization-to-Generalization without Test 18:35

منذ 13 يومًا18:35

18:35

This study explores grokking in large language models during pretraining, revealing how training pathways evolve from random to structured, enhancing generalization despite converged loss. https://arxiv.org/abs//2506.21551 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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[QA] MMSearch-R1: Incentivizing LMMs to Search 8:11

منذ 13 يومًا8:11

8:11

MMSearch-R1 is a reinforcement learning framework for large multimodal models, enabling efficient, on-demand multi-turn search in real-world environments, outperforming existing methods while reducing search calls by over 30%. https://arxiv.org/abs//2506.20670 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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MMSearch-R1: Incentivizing LMMs to Search 18:50

منذ 13 يومًا18:50

18:50

MMSearch-R1 is a reinforcement learning framework for large multimodal models, enabling efficient, on-demand multi-turn search in real-world environments, outperforming existing methods while reducing search calls by over 30%. https://arxiv.org/abs//2506.20670 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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[QA] Thought Anchors: Which LLM Reasoning Steps Matter? 7:51

منذ 14 يومًا7:51

7:51

The paper explores sentence-level analysis of reasoning in large language models, presenting three methods to identify influential "thought anchors" that shape multi-step reasoning processes. An open-source tool is provided. https://arxiv.org/abs//2506.19143 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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Thought Anchors: Which LLM Reasoning Steps Matter? 15:41

منذ 14 يومًا15:41

15:41

The paper explores sentence-level analysis of reasoning in large language models, presenting three methods to identify influential "thought anchors" that shape multi-step reasoning processes. An open-source tool is provided. https://arxiv.org/abs//2506.19143 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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[QA] Scaling Speculative Decoding with LOOKAHEAD REASONING 8:06

منذ 14 يومًا8:06

8:06

LOOKAHEAD REASONING enhances token-level speculative decoding by introducing step-level parallelism, improving speedup from 1.4x to 2.1x while maintaining answer quality across various benchmarks. https://arxiv.org/abs//2506.19830 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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Scaling Speculative Decoding with LOOKAHEAD REASONING 22:49

منذ 14 يومًا22:49

22:49

LOOKAHEAD REASONING enhances token-level speculative decoding by introducing step-level parallelism, improving speedup from 1.4x to 2.1x while maintaining answer quality across various benchmarks. https://arxiv.org/abs//2506.19830 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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[QA] Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations 7:55

منذ 16 يومًا7:55

7:55

This paper introduces Tar, a multimodal framework integrating visual understanding and generation through a shared semantic representation, enhancing efficiency and performance in cross-modal tasks. https://arxiv.org/abs//2506.18898 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations 16:59

منذ 16 يومًا16:59

16:59

This paper introduces Tar, a multimodal framework integrating visual understanding and generation through a shared semantic representation, enhancing efficiency and performance in cross-modal tasks. https://arxiv.org/abs//2506.18898 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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[QA] Watermarking Autoregressive Image Generation 7:39

منذ 17 يومًا7:39

7:39

https://arxiv.org/abs//2506.16349 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

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Arxiv Papers

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Watermarking Autoregressive Image Generation 27:33

منذ 17 يومًا27:33

27:33

https://arxiv.org/abs//2506.16349 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

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Arxiv Papers

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[QA] Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights 6:43

منذ 17 يومًا6:43

6:43

DnD introduces a prompt-conditioned parameter generator for LLMs, enabling rapid task-specific customization without separate training, achieving significant performance gains and lower overhead compared to traditional methods. https://arxiv.org/abs//2506.16406 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights 11:26

منذ 17 يومًا11:26

11:26

DnD introduces a prompt-conditioned parameter generator for LLMs, enabling rapid task-specific customization without separate training, achieving significant performance gains and lower overhead compared to traditional methods. https://arxiv.org/abs//2506.16406 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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[QA] Flat Channels to Infinity in Neural Loss Landscapes 7:16

منذ 18 يومًا7:16

7:16

The paper characterizes special channels in neural network loss landscapes where slow loss decrease occurs, leading to gated linear units, enhancing understanding of gradient dynamics and optimization methods. https://arxiv.org/abs//2506.14951 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers…

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Arxiv Papers

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