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@askalphaxiv

Think-Anywhere: LLMs Reasoning at Any Token Position During Coding

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@askalphaxiv

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03 April 2026 23h12

Happy to see Think-Anywhere hitting 🔥 Top-2 Hot Paper on alphaXiv and 🚀 Trending Paper on Hugging Face! LLMs think before coding, but humans don't just plan upfront. We pause and reason during implementation whenever things get tricky. Therefore, we propose Think-Anywhere: letting LLMs invoke reasoning at any token position during code generation, on demand. The model discovers on its own where thinking is needed. 📄 Paper: arxiv.org/abs/2603.29957 💻 Code: github.com/jiangxxxue/Think-… PS: We are currently seeking Research Assistants. Outstanding performers will be eligible for direct PhD admission or receive recommendations for both academic and industry positions. If you are interested, please send your CV to yihong.dong001@gmail.com. Let's work together to conduct impactful and meaningful research!

→ View original post on X — @askalphaxiv, 2026-04-03 21:12 UTC

3 April 2026
Read more on alphaxiv.org/abs/2604.01178

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03 April 2026 20h23

read more:
alphaxiv.org/abs/2604.01178 [Translated from EN to English]

→ View original post on X — @askalphaxiv, 2026-04-03 18:23 UTC

3 April 2026
Multiscreen: Efficient Attention Through Independent Key Screening

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03 April 2026 20h23

“Screening Is Enough” As Transformers struggle with long context because softmax attention only gives relative relevance, irrelevant tokens would still get weight and useful ones get diluted as context grows. The paper proposes Multiscreen where it judges each key independently, drop the irrelevant ones, and aggregate only what actually matters. This gives the model a better relevance knowledge, including the ability to know "nothing here is useful", which standard attention can’t do. Empirically, this gives roughly 40% fewer parameters for similar loss, much stronger long-context retrieval, and 2.3–3.2x faster 100K-context inference.

→ View original post on X — @askalphaxiv, 2026-04-03 18:23 UTC

3 April 2026
MLPerf Power Selected for IEEE MICRO Top Picks 2025

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02 April 2026 19h00

Super excited to share that MLPerf Power (HPCA 2025) was selected for IEEE MICRO Top Picks 2025, 1 of the 12 most impactful computer architecture & systems papers of the year! Power consumption is the defining constraint for modern ML systems. Microsoft, Google, Amazon, Meta, and OpenAI have all announced plans for gigawatt-scale datacenters (for context, 5 GW = 5 nuclear reactors = Miami's power footprint). On the other end of the spectrum, we're anticipating billions of AI-enabled devices at the edge. We created MLPerf Power to be the industry-standard to measure, understand, and compare energy use across all deployment scales. We're excited to see that it's already impacting individual companies' strategies and has been incorporated into the IEEE semiconductor roadmap. We @MLCommons also collect and open source over 1,800 reproducible measurements from 60 diverse systems. These reveal several important insights that shed light on the nonlinear scaling of energy efficiency in modern systems and can enable many new data-driven optimization approaches. Just as @MLPerf aligned industry towards shared performance goals, we are hopeful that MLPerf Power will do the same for power and energy efficiency!

→ View original post on X — @askalphaxiv, 2026-04-02 17:00 UTC

2 April 2026
Adaptive Block-Scaled Data Types for Efficient 4-bit LLM Quantization

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02 April 2026 8h54

“Adaptive Block-Scaled Data Types” A lot of 4-bit LLM quantization assumes every block should use the same number format, this paper argues that’s the wrong abstraction. So they let each 16-value block choose between FP4 and scaled INT4, depending on which gives lower error. This format, IF4 (Int/Float 4), reuses the unused sign bit of the shared FP8 scale factor to store the choice, so it gets adaptivity with no extra storage overhead. This is because at 4 bits, precision is so scarce that matching the format to the local value distribution really matters. The result is lower quantization error and better performance than existing 4-bit block-scaled formats in both training and PTQ.

→ View original post on X — @askalphaxiv, 2026-04-02 06:54 UTC

2 April 2026
Discover the full article on alphaxiv.org

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@askalphaxiv

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02 April 2026 8h54

read more:
alphaxiv.org/abs/2603.28765 [Translated from EN to English]

→ View original post on X — @askalphaxiv, 2026-04-02 06:54 UTC

2 April 2026
New Scientific Publication Available on AlphaXIV

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@askalphaxiv

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01 April 2026 19h53

read more:
https://www.alphaxiv.org/abs/2603.18297 [Translated from EN to English]

→ View original post on X — @askalphaxiv, 2026-04-01 17:53 UTC

1 April 2026
Check out the full list on alphaxiv.org

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31 March 2026 21h31

check out the full list: alphaxiv.org/shared/folder/0… [Translated from EN to English]

→ View original post on X — @askalphaxiv, 2026-03-31 19:31 UTC

31 March 2026
World Models Research Reading List: JEPA and Recent Advances

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@askalphaxiv

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31 March 2026 21h31

As Yann LeCun and his team has brought a new wave of World Models research into 2026, we have compiled a reading list to recap some of the must-read papers to get you up-to-date about JEPA & World Modeling!

→ View original post on X — @askalphaxiv, 2026-03-31 19:31 UTC

31 March 2026
Discover the full article on alphaxiv.org

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@askalphaxiv

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31 March 2026 21h29

read more: alphaxiv.org/abs/2603.27646 [Translated from EN to English]

→ View original post on X — @askalphaxiv, 2026-03-31 19:29 UTC

31 March 2026