Best Video Encoding Podcasts (2025)

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[QA] AGENTSNET: Coordination and Collaborative Reasoning in Multi-Agent LLMs 7:37

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13h ago7:37

7:37

AGENTSNET is a new benchmark for evaluating multi-agent systems' collaborative problem-solving, self-organization, and communication, revealing performance limitations as network size increases among large-language models. https://arxiv.org/abs//2507.08616 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Ap…

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AGENTSNET: Coordination and Collaborative Reasoning in Multi-Agent LLMs 19:47

13h ago19:47

19:47

AGENTSNET is a new benchmark for evaluating multi-agent systems' collaborative problem-solving, self-organization, and communication, revealing performance limitations as network size increases among large-language models. https://arxiv.org/abs//2507.08616 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Ap…

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[QA] One Token to Fool LLM-as-a-Judge 7:56

13h ago7:56

7:56

Generative reward models using LLMs for evaluating answer quality are vulnerable to superficial manipulations, prompting the need for improved evaluation methods and a robust new model to enhance reliability. https://arxiv.org/abs//2507.08794 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: …

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One Token to Fool LLM-as-a-Judge 17:55

13h ago17:55

17:55

Generative reward models using LLMs for evaluating answer quality are vulnerable to superficial manipulations, prompting the need for improved evaluation methods and a robust new model to enhance reliability. https://arxiv.org/abs//2507.08794 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: …

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[QA] Should We Still Pretrain Encoders with Masked Language Modeling? 8:09

1d ago8:09

8:09

This paper compares Masked Language Modeling and Causal Language Modeling for text representation, finding MLM generally performs better, but CLM offers data efficiency and stability, suggesting a biphasic training strategy. https://arxiv.org/abs//2507.00994 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers …

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Should We Still Pretrain Encoders with Masked Language Modeling? 16:52

1d ago16:52

16:52

This paper compares Masked Language Modeling and Causal Language Modeling for text representation, finding MLM generally performs better, but CLM offers data efficiency and stability, suggesting a biphasic training strategy. https://arxiv.org/abs//2507.00994 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers …

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[QA] Token Bottleneck: One Token to Remember Dynamics 7:30

1d ago7:30

7:30

The paper presents Token Bottleneck (ToBo), a self-supervised learning method for compact visual representations, enhancing sequential scene understanding and demonstrating effectiveness in various tasks and real-world applications. https://arxiv.org/abs//2507.06543 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv…

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Token Bottleneck: One Token to Remember Dynamics 16:06

1d ago16:06

16:06

The paper presents Token Bottleneck (ToBo), a self-supervised learning method for compact visual representations, enhancing sequential scene understanding and demonstrating effectiveness in various tasks and real-world applications. https://arxiv.org/abs//2507.06543 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv…

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[QA] A Systematic Analysis of Hybrid Linear Attention 7:55

3d ago7:55

7:55

https://arxiv.org/abs//2507.06457 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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A Systematic Analysis of Hybrid Linear Attention 15:40

3d ago15:40

15:40

https://arxiv.org/abs//2507.06457 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] First Return, Entropy-Eliciting Explore 7:43

3d ago7:43

7:43

https://arxiv.org/abs//2507.07017 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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First Return, Entropy-Eliciting Explore 21:32

3d ago21:32

21:32

https://arxiv.org/abs//2507.07017 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] Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs 8:31

3d ago8:31

8:31

Pretrained neural networks can adapt their architecture dynamically for different inputs, improving efficiency and performance by customizing layer usage without finetuning, as shown through Monte Carlo Tree Search optimization. https://arxiv.org/abs//2507.07996 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pap…

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Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs 15:32

3d ago15:32

15:32

Pretrained neural networks can adapt their architecture dynamically for different inputs, improving efficiency and performance by customizing layer usage without finetuning, as shown through Monte Carlo Tree Search optimization. https://arxiv.org/abs//2507.07996 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pap…

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[QA] Scaling RL to Long Videos 8:19

3d ago8:19

8:19

https://arxiv.org/abs//2507.07966 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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Scaling RL to Long Videos 15:24

3d ago15:24

15:24

https://arxiv.org/abs//2507.07966 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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From VHS Tapes to Huddle: The Evolution of Sports Video Analysis 54:06

4d ago54:06

54:06

Casey Bateman, Principal Engineer at Huddle, reveals how their video platform revolutionized sports analysis by replacing the old system of coaches exchanging physical tapes with instant digital access. Founded in 2006 at the University of Nebraska, Huddle now serves 97% of US high school football programs and has expanded globally to 40+ sports. •…

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[QA] Towards Solving More Challenging IMO Problems via Decoupled Reasoning and Proving 8:09

5d ago8:09

8:09

The paper proposes a decoupled framework for Automated Theorem Proving, enhancing reasoning and proving performance by using specialized models, achieving success on challenging mathematical problems. https://arxiv.org/abs//2507.06804 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://…

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Towards Solving More Challenging IMO Problems via Decoupled Reasoning and Proving 21:33

5d ago21:33

21:33

The paper proposes a decoupled framework for Automated Theorem Proving, enhancing reasoning and proving performance by using specialized models, achieving success on challenging mathematical problems. https://arxiv.org/abs//2507.06804 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://…

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[QA] Small Batch Size Training for Language Models: When Vanilla SGD Works, and Why Gradient Accumulation Is Wasteful 7:03

5d ago7:03

7:03

This paper challenges conventional wisdom on small batch sizes in language model training, demonstrating their stability, robustness, and efficiency, while providing guidelines for hyperparameter adjustments and batch size selection. https://arxiv.org/abs//2507.07101 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxi…

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Small Batch Size Training for Language Models: When Vanilla SGD Works, and Why Gradient Accumulation Is Wasteful 18:57

5d ago18:57

18:57

This paper challenges conventional wisdom on small batch sizes in language model training, demonstrating their stability, robustness, and efficiency, while providing guidelines for hyperparameter adjustments and batch size selection. https://arxiv.org/abs//2507.07101 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxi…

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

5d ago7:35

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_paper…

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

5d ago23:36

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_paper…

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[QA] Differential Mamba 7:09

5d ago7:09

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_pape…

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Differential Mamba 18:31

5d ago18:31

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_pape…

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

6d ago7:04

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://podcas…

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Cascade: Token-Sharded Private LLM Inference 35:03

6d ago35:03

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://podcas…

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

6d ago7:28

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 App…

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

6d ago10:15

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 App…

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

7d ago7:21

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/…

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

7d ago34:06

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/…

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

7d ago7:28

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…

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

7d ago17:55

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…

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Cores Galore: Video Processing Without the Computational Gymnastics 1:01:22

11d ago1:01:22

1:01:22

ARM architecture is revolutionizing video processing with power-efficient processors that deliver predictable performance without the computational gymnastics required by traditional x86 systems. • Ampere builds ARM-based processors with massive core counts (up to 192 cores) focused on sustainable computing • Traditional x86 architecture struggles …

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

13d ago7:21

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

13d ago15:33

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

13d ago7:54

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.co…

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DABstep: Data Agent Benchmark for Multi-step Reasoning 16:50

13d ago16:50

16:50

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.co…

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[QA] Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in Inference-time Scaling? 8:16

14d ago8:16

8:16

This paper explores the effectiveness of inference-time techniques in vision-language models, finding that generation-based methods enhance reasoning more than verification methods, while self-correction in RL models shows limited benefits. https://arxiv.org/abs//2506.17417 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.co…

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Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in Inference-time Scaling? 16:52

14d ago16:52

16:52

This paper explores the effectiveness of inference-time techniques in vision-language models, finding that generation-based methods enhance reasoning more than verification methods, while self-correction in RL models shows limited benefits. https://arxiv.org/abs//2506.17417 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.co…

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[QA] LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs 8:19

15d ago8:19

8:19

LLaVA-Scissor introduces a training-free token compression method for video multimodal models, utilizing Semantic Connected Components for effective, non-redundant semantic coverage, outperforming existing methods in various benchmarks. https://arxiv.org/abs//2506.21862 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@a…

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LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs 14:25

15d ago14:25

14:25

LLaVA-Scissor introduces a training-free token compression method for video multimodal models, utilizing Semantic Connected Components for effective, non-redundant semantic coverage, outperforming existing methods in various benchmarks. https://arxiv.org/abs//2506.21862 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@a…

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[QA] Performance Prediction for Large Systems via Text-to-Text Regression 8:40

15d ago8:40

8:40

https://arxiv.org/abs//2506.21718 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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Performance Prediction for Large Systems via Text-to-Text Regression 20:32

15d ago20:32

20:32

https://arxiv.org/abs//2506.21718 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] From Memories to Maps: Mechanisms of In-Context Reinforcement Learning in Transformers 7:47

15d ago7:47

7:47

This study explores how transformers can model rapid adaptation in learning, highlighting the role of episodic memory and caching in decision-making, paralleling cognitive processes in the brain. https://arxiv.org/abs//2506.19686 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podca…

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From Memories to Maps: Mechanisms of In-Context Reinforcement Learning in Transformers 20:44

15d ago20:44

20:44

This study explores how transformers can model rapid adaptation in learning, highlighting the role of episodic memory and caching in decision-making, paralleling cognitive processes in the brain. https://arxiv.org/abs//2506.19686 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podca…

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[QA] OmniGen2: Exploration to Advanced Multimodal Generation 7:44

15d ago7:44

7:44

OmniGen2 is an open-source generative model for diverse tasks like text-to-image and image editing, featuring distinct decoding pathways and achieving competitive results with modest parameters. https://arxiv.org/abs//2506.18871 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcas…

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OmniGen2: Exploration to Advanced Multimodal Generation 32:16

15d ago32:16

32:16

OmniGen2 is an open-source generative model for diverse tasks like text-to-image and image editing, featuring distinct decoding pathways and achieving competitive results with modest parameters. https://arxiv.org/abs//2506.18871 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcas…

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[QA] OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling 7:28

17d ago7:28

7:28

https://arxiv.org/abs//2506.20512 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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OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling 25:52

17d ago25:52

25:52

https://arxiv.org/abs//2506.20512 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