Natapong Nitarach (Schwyter)

[Tom Yeh @ProfTomYeh]

Softmax vs Sigmoid ✍️ Interact 👉 https://t.co/cYcveibilc

= Softmax =

Softmax is how deep networks turn raw scores into a probability distribution — the final layer of every classifier, and the core of every attention head in a transformer. To see what it does, picture five boba tea shops on the same block, all competing for your dollar. Five candidates: a, b, c, d, e — different chains, different brewing styles, different pearls. A boba reviewer hands you a 𝘤𝘩𝘦𝘸𝘪𝘯𝘦𝘴𝘴 𝘴𝘤𝘰𝘳𝘦 for each — higher means perfectly chewy "QQ" pearls with the right bite (ask a Taiwanese friend to find out what QQ means). Negative scores are real: mushy bobas, overcooked pearls, a batch left sitting too long.

How do you turn five chewiness scores into an allocation that adds to a whole dollar? You could spend everything at the chewiest shop, but that ignores how good the runners-up are. Softmax is the smooth alternative.

Read the diagram left to right. First, raise each score to e^{x} — this does two things: it turns negative chewiness into small positives, and it stretches the gaps between scores exponentially. Then sum all five into a single total Z. Finally, divide each e^{x} by Z to get a probability. The five probabilities add up to one, so you can read them as percentages of your dollar. The chewiest shop gets the biggest slice — but never the whole dollar. That's the point of softmax: it ranks confidently while still leaving room for the others.

= Sigmoid =

Sigmoid squashes any real number into a probability between 0 and 1 — the classic activation for binary classification, and still the gating function inside LSTMs and GRUs. Same boba block as the previous Softmax example, narrowed to just two contenders — a hot new shop `a` with chewiness score x, and your usual go-to `b` whose score is pinned at zero (the neutral baseline you've come to expect).

Sigmoid is just softmax with two players, one of them pinned to zero.

Read the diagram left to right. First, raise each score to e^{x} — for the usual shop `b` whose score is zero, this is just e^0 = 1 (the constant baseline). Then sum the two into a total Z. Finally, divide each e^{x} by Z to get a probability. The two probabilities add up to one — the new shop wins more of your dollar when its pearls get chewier, and your usual keeps the rest. That's the point of sigmoid: it turns a single chewiness score into a clean 0-to-1 chance you'll try the new place over your usual.

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AI Math, Algorithms, Architectures by hand ✍️
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[JP Aumasson @veorq]

seeing how many people believed this without checking the Pyhton script is a reminder that a well-orchestrated fake Q-Day announcement/rumor could absolutely crater crypto markets

[Anthropic @AnthropicAI]

Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software.

It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans.
https://t.co/NQ7IfEtYk7

[Swissblock @swissblock__]

Negative momentum is currently extreme as the Bear Market persists following the October 10 flash crash. However, we have recently seen the first failed attempt by buyers to establish a regime change.

Historically, momentum readings of +0.5 and -0.5 are key levels that determine substantial shifts in the structural regime. In mid-January, there was an attempt to trigger a bullish structural change, but sellers successfully defended their position, maintaining the bearish structure and returning to an environment of extreme negative momentum.

The behavior of momentum at these inflection points is key to determining powerful structural changes in the medium and long term.

[Turing Post @TheTuringPost]

What we learned about memory in 2025

8 comprehensive resources:

▪️ Memory in the Age of AI Agents
▪️ When Will We Give AI True Memory? (interview with @EdoLiberty, founder & CEO @pinecone)
▪️ Why AI Intelligence is Nothing Without Visual Memory (interview with
@shawnshenjx, co-founder @memories_ai)
▪️ From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs
▪️ Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions
▪️ Cognitive Memory in LLMs
▪️ MemOS: A Memory OS for AI System
▪️ MemEvolve: Meta-Evolution of Agent Memory Systems

Save the list and check this out for the links: https://t.co/sdlilMlYPo

[Jürgen Schmidhuber @SchmidhuberAI]

Who Invented Transformer Neural Networks (the T in ChatGPT)? Timeline of Transformer evolution https://t.co/7EJPAnRKvI

★ 1991. Original tech report on what's now called the unnormalized linear Transformer (ULTRA)[FWP0][ULTRA]. KEY/VALUE was called FROM/TO. ULTRA uses outer product rules to associate its self-invented KEYs/VALUEs through fast weights [FAST][FWP], and applies the resulting context-dependent attention mappings to incoming queries. ULTRA's computational costs scale linearly in input size, that is, for 1,000 times more text we need 1,000 times more compute, which is acceptable. Like modern quadratic Transformers (see below), the 1991 ULTRA is highly parallelizable. It was a by-product of more general research on neural networks (NNs) that learn to program fast weight changes of other NNs [FWP,FWP0-9,FWPMETA1-10], back then called fast weight controllers [FWP0] or fast weight programmers (FWPs) [FWP]. ULTRA was presented as an alternative to recurrent NNs [FWP0]. The 1991 experiments were similar to today's: predict some effect, given a sequence of inputs [FWP0].

★ 1992. Journal publication on ULTRA [FWP1], based on the 1991 tech report. Note that the terminology was different back then.

★ 1993. Recurrent ULTRA extension [FWP2] introducing the terminology of learning "internal spotlights of attention."

★ 2014. End-to-end sequence-to-sequence models [S2Sa,b,c,d] became popular for Natural Language Processing. They were not based on the 1991 unnormalized linear Transformer [ULTRA] above, but on the Long Short-Term Memory (LSTM) recurrent NN from the same lab. In 2014, this approach was combined with an attention mechanism [ATT14] that isn't linearized like the 1991-93 attention [FWP0-2] but includes a nonlinear softmax operation. The first Large Language Models (LLMs) were based on such LSTM-attention systems. See additional work on attention from 2016-17 [ATT16a-17b].

★ 2017. Modern quadratic Transformer ("attention is all you need"), scaling quadratically in input size [TR1], that is, for 1,000 times more text we need 1,000,000 times more compute. Note that in 1991 [ULTRA], no journal would have accepted an NN that scales quadratically, but by 2017, compute was cheap enough to apply the quadratic Transformer (a kind of fast weight programmer [FWP]) to large amounts of data on massively parallel computers. The quadratic Transformer combines the 1991 additive outer product fast weight principle [FWP0-2] and softmax (see 2014 above): attention (query, KEY, VALUE) ~ softmax (query KEY) VALUE.

★ 2020. New paper [TR5] using the terminology "linear Transformer" for a more efficient Transformer variant that scales linearly, leveraging linearized attention [TR5a].

★ 2021. Paper [FWP6] pointing out that the unnormalised linear Transformer [TR5-6] is actually MATHEMATICALLY EQUIVALENT to the 1991 fast weight controller [FWP0][ULTRA] published when compute was a million times more expensive than in 2021. Overview of ULTRA and FWPs (2021) [FWP].

★ 2021-25. Work on extensions of ULTRAs and other FWPs (such as the DeltaNet [FWP6]) has become mainstream research, aiming to develop sequence models that are both efficient and powerful [TR6,TR6a][LT23-25][FWP23-25b].

Of course, plain outer products in NNs go back at least to Konorski's informal 1948 rule [HEB48] (later sometimes called the "Hebb rule" [HEB49]) and concrete formal implementations through Steinbuch's Learning Matrix around 1960 [ST61-63][AMH1-2][KOH72][LIT74][PAL80]. See also bidirectional associative memories (1988) [KOS88]. However, these authors described pre-wired rules to associate user-given patterns with each other. Unlike ULTRA and other Transformers since 1991 [ULTRA][TR1], their NNs did not learn to use such rules for associating self-invented KEY/VALUE patterns, by backpropagating errors [BP4] THROUGH the rules, to generate appropriate KEYs/VALUEs at the right times and create useful changes of fast weights. (Neither did early NNs with fast weights by Malsburg (1981) and others [FAST][FASTa,b][DLP].)

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SELECTED REFERENCES (remaining references in: Who Invented Transformer Neural Networks? Technical Note IDSIA-11-25, Nov 2025 - see link above)

[ATT] Juergen's AI Blog (2020, updated 2025): 30-year anniversary of end-to-end differentiable sequential neural attention. Plus goal-conditional reinforcement learning. There was both hard attention for foveas (1990) and soft attention in form of Transformers with linearized self-attention (1991-93) [ULTRA]. Today, both types are very popular.

[ATT14] D. Bahdanau, K. Cho, Y. Bengio. Neural Machine Translation by Jointly Learning to Align and Translate. 2014-16. Preprint arXiv/1409.0473, 2014-16.

[FAST] C. v.d. Malsburg. Tech Report 81-2, Abteilung f. Neurobiologie, Max-Planck Institut f. Biophysik und Chemie, Goettingen, 1981. First paper on fast weights or dynamic links.

[FWP] 26 March 1991: Neural nets learn to program neural nets with fast weights—like Transformer variants. 2021: New stuff! AI Blog, 26 March 2021, updated 2025.

[FWP0] J. Schmidhuber. Learning to control fast-weight memories: An alternative to recurrent nets. Technical Report FKI-147-91, TU Munich, 26 March 1991. First paper on neural fast weight programmers (FWPs) that separate storage and control: a slow net learns by gradient descent to compute weight changes of a fast net. The outer product-based version (Eq. 5) is now known as the unnormalized linear Transformer or the "Transformer with linearized self-attention" [ULTRA][FWP].

[FWP1] J. Schmidhuber. Learning to control fast-weight memories: An alternative to recurrent nets. Neural Computation, 4(1):131-139, 1992. Based on [FWP0].

[FWP2] J. Schmidhuber. Reducing the ratio between learning complexity and number of time-varying variables in fully recurrent nets. In Proceedings of the International Conference on Artificial Neural Networks, Amsterdam, pages 460-463. Springer, 1993. A recurrent extension of the 1991 unnormalized linear Transformer [ULTRA], introducing the terminology of learning "internal spotlights of attention." First recurrent NN-based fast weight programmer using outer products to program weight matrix changes.

[FWP6] I. Schlag, K. Irie, J. Schmidhuber. Linear Transformers Are Secretly Fast Weight Programmers. ICML 2021. Preprint: arXiv:2102.11174. Shows that the unnormalised linear Transformer is actually MATHEMATICALLY EQUIVALENT to the 1991 system [FWP0][ULTRA] published when compute was a million times more expensive than in 2021.

[FWP7] K. Irie, I. Schlag, R. Csordas, J. Schmidhuber. Going Beyond Linear Transformers with Recurrent Fast Weight Programmers. NeurIPS 2021. Preprint: arXiv:2106.06295

[HEB48] J. Konorski (1948). Conditioned reflexes and neuron organization. Translation from the Polish manuscript under the author's supervision. Cambridge University Press, 1948. Konorski published the so-called "Hebb rule" before Hebb [HEB49].

[HEB49] D. O. Hebb. The Organization of Behavior. Wiley, New York, 1949. Konorski [HEB48] published the so-called "Hebb rule" before Hebb.

[KOS88] B. Kosko. Bidirectional associative memories. IEEE Transactions on Systems, Man, and Cybernetics, 18(1):49-60, 1988.

[LT20] A. Katharopoulos, A. Vyas, N. Pappas, F. Fleuret. Transformers are RNNs: Fast autoregressive Transformers with linear attention. In Proc. Int. Conf. on Machine Learning (ICML), July 2020.

[LT21] I. Bello. LambdaNetworks: Modeling Long-Range Interactions Without Attention. Preprint arXiv:2102.08602. A linear transformer variant.

[LT23] K. Irie, R. Csordas, J. Schmidhuber. Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions. EMNLP 2023.

[LT24] S. Yang, B. Wang, Y. Zhang, Y. Shen, Y. Kim. Parallelizing Linear Transformers with the Delta Rule over Sequence Length. NeurIPS 2024.

[LT25] S. Yang, J. Kautz, A. Hatamizadeh. Gated Delta Networks: Improving Mamba2 with Delta Rule. ICLR 2025. "Mamba2" is essentially the 1991 ULTRA with a scalar time-decay factor on the fast weight matrix.

[LT25b] R. Grazzi, J. Siems, A. Zela, J. K.H. Franke, F. Hutter, M. Pontil. Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues. ICLR 2025. Shows that the delta-rule extension [FWP6][LT23] is more expressive than the quadratic Transformer and other naive linear Transformers (e.g., it can do parity and modular arithmetics).

[LT25c] J. Siems, T. Carstensen, A. Zela, F. Hutter, M. Pontil, R. Grazzi. DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products ICLR 2025 Workshop FM-Wild. Extending the DeltaNet [FWP6][LT23] through additional "micro-steps."

[S2Sa] M.L. Forcada and R.P. Ñeco. Recursive hetero-associative memories for translation. International Work-Conference on Artificial Neural Networks, 1997.

[S2Sb] T. Mikolov and G. Zweig, G. December. Context dependent recurrent neural network language model. IEEE Spoken Language Technology Workshop (SLT), 2012.

[S2Sc] A. Graves. Sequence transduction with recurrent neural networks. Representation Learning Workshop, Int. Conf. on Machine Learning (ICML), 2012

[S2Sd] I. Sutskever, O. Vinyals, Quoc V. Le. Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (NIPS), 2014, 3104-3112.

[ST61] K. Steinbuch. Die Lernmatrix. Kybernetik, 1(1):36-45, 1961.

[TR1] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin (2017). Attention is all you need. NIPS 2017, pp. 5998-6008.

[TR2] J. Devlin, M. W. Chang, K. Lee, K. Toutanova (2018). Bert: Pre-training of deep bidirectional Transformers for language understanding. Preprint arXiv:1810.04805.

[TR3] K. Tran, A. Bisazza, C. Monz. The Importance of Being Recurrent for Modeling Hierarchical Structure. EMNLP 2018, p 4731-4736. ArXiv preprint 1803.03585.

[TR4] M. Hahn. Theoretical Limitations of Self-Attention in Neural Sequence Models. Transactions of the Association for Computational Linguistics, Volume 8, p.156-171, 2020.

[TR5] A. Katharopoulos, A. Vyas, N. Pappas, F. Fleuret. Transformers are RNNs: Fast autoregressive Transformers with linear attention. In Proc. Int. Conf. on Machine Learning (ICML), July 2020.

[TR5a] Z. Shen, M. Zhang, H. Zhao, S. Yi, H. Li. Efficient Attention: Attention with Linear Complexities. WACV 2021.

[TR6] K. Choromanski, V. Likhosherstov, D. Dohan, X. Song, A. Gane, T. Sarlos, P. Hawkins, J. Davis, A. Mohiuddin, L. Kaiser, et al. Rethinking attention with Performers. In Int. Conf. on Learning Representations (ICLR), 2021.

[TR6a] H. Peng, N. Pappas, D. Yogatama, R. Schwartz, N. A. Smith, L. Kong. Random Feature Attention. ICLR 2021.

[TR7] S. Bhattamishra, K. Ahuja, N. Goyal. On the Ability and Limitations of Transformers to Recognize Formal Languages. EMNLP 2020.

[ULTRA] References on the 1991 unnormalized linear Transformer (ULTRA): original tech report (March 1991) [FWP0]. Journal publication (1992) [FWP1]. Recurrent ULTRA extension (1993) introducing the terminology of learning "internal spotlights of attention” [FWP2]. Modern "quadratic" Transformer (2017: "attention is all you need") scaling quadratically in input size [TR1]. 2020 paper [TR5] using the terminology "linear Transformer" for a more efficient Transformer variant that scales linearly, leveraging linearized attention [TR5a]. 2021 paper [FWP6] pointing out that ULTRA dates back to 1991 [FWP0] when compute was a million times more expensive. Overview of ULTRA and other Fast Weight Programmers (2021) [FWP]. See the T in ChatGPT.

[Aston Martin Aramco F1 Team @AstonMartinF1]

The Energy Recovery System collects energy during braking and from the turbocharger, storing it for later deployment.

See the Science. ⬇️

[Riccardo De Santi @r_de_santi]

Over the last years we understood how to control generative models via RL/control to maximize expected rewards under KL regularization. But for many applications, that’s not enough. Hence the question: can we control them further?

Excited to present our ✨Oral✨ paper “Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning” this Friday at the Generative AI and Biology Workshop at ICML 2025!

We propose a theoretically grounded method to fine-tune a pre-trained diffusion or flow model for arbitrary objective functions — even beyond expected reward. For instance, this is crucial in scientific discovery, where the goal is often to maximize the quality of the single best sample rather than the average sample quality. We achieve this by bridging probability-space optimization and flow modeling enabling general optimization processes over generative models via sequential fine-tuning - with guarantees.

Feel free to reach out if you’re interested — and check out https://t.co/d8yUOosuTF for updates!

📄 Paper: https://t.co/kWsPrEVIw7
⏳Friday at 2:35pm (Vancouver time), BioGen Workshop at ICML 2025

Work done with amazing collaborators @vlastelicap, @yapinghsieh, @ZebangShen, Niao He, and @arkrause

[THE STANDARD WEALTH @Standard_Wealth]

SCB 10X ส่ง 5 งานวิจัยร่วมด้าน AI สู่เวทีประชุมวิชาการนานาชา���ิ
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‘กลุ่มเอสซีบีเอกซ์’ หวังพัฒนาและผลักดันงานวิจัยสู่ระดับโลก โดยเน้นเป้าหมายสู่��ารสร้าง ‘A-Star (A*) Research’ หรือผลงานวิจัยที่ได้รับการยอมรับในงานประชุมวิชาการระดับสูงสุดของโลกที่จัดอันดับโดยองค์กร CORE (Computing Research and Education Association of Australasia) ซึ่งถือเป็นกลไกสำคัญในการยกระดับศักยภาพนักวิจัยไทยและสร้างอิทธิพลทางความรู้ในระดับนานาชาติ
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ล่าสุด ทีมวิจัย Typhoon จาก SCB 10X บริษัทด้านการลงทุนในเทคโนโลยีปฏิวัติวงการ (Disruptive Technology) ภายใต้กลุ่มเอสซีบีเอกซ์ส่ง 5 ผลงานวิจัยร่วมด้านเทคโ��โลยีปัญญาประดิษฐ์ (AI) เข้าสู่การประชุม Association for Computational Linguistics (ACL) 2025 เวทีประชุมวิชาการระดับโลกด้านการประมวลผลภาษาธรรมชาติและภาษาศาสตร์คอมพิวเตอร์ที่ทรงเกียรติที่สุดแห่งหนึ่งของโลก
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ความสำเร็จครั้งนี้เป็นผลจากความร่วม��ือในการทำวิจัยร่วมกับสถาบันชั้นนำ 4 แห่ง ได้แก่ สถาบันวิทยสิริเมธี (VISTEC) มหาวิทยาลัยสแตนฟอร์ด มหาวิทยาลัยเคมบริดจ์ และเครือข่ายวิจัย SEACrowd โดยแบ่งเป็น 3 งานใน Main Conference, 1 งานใน Findings และ 1 งานใน LLM Security Workshop
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กวีวุฒิ เต็มภูวภัทร ประธานเจ้าหน้าที่บริหาร SCB 10X กล่าวว่า “เรารู้สึกเป็นเกียรติอย่างยิ่งที่ผลงานวิจัยทั้ง 5 ชิ้นที่ทีม Typhoon มีส่วนร่วมได้รับการตอบรับให้เผยแพร่ในงานประชุม ACL 2025 ��ึ่งเป็นงานประชุมวิชาการระดับนานาชาติด้าน AI โดยเฉพาะอย่างยิ่งการประมวลผลภาษาธรรมชาติ (NLP) และภาษาศาสตร์คอมพิวเตอร์ที่ทรงเกียรติที่สุดงานหนึ่งของโลก มีระบบ peer review ที่เข้มข้นและได้รับความสนใจสูงจากชุมชนวิจัยนานาชาติ”
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5 ผลงานวิจัยร่วมของทีม Typhoon ในงานประชุม ACL 2025 ได้แก่
ผลงานใน Main Conference 3 เรื่อง ได้แก่
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SkillAggregation: Reference-free LLM-Dependent Aggregation ผลงานวิจัยร่วมกับมหาวิทยาลัยเคมบริดจ์ และมหาวิทยาลัยสแตนฟอร์ด - การรวม��ลจากโมเดล LLM หลายภาษาโดยไม่ต้องใช้ข้อมูลคำตอบจริง (reference labels) โดยใช้วิธีเรียนรู้ความแม่นยำของแต่ละโมเดลจากบริบท ช่วยให้การตัดสินใจแม่นยำและปรับตามสถานการณ์ได้ดีขึ้น พร้อมผลลัพธ์ที่เหนือกว่าวิธีเดิมในการประเมินคุณภาพของ LLM
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Mind the Gap! Static and Interactive Evaluations of Large Audio Models ผลงานวิจัยร่วมกับมหาวิทยาลัยสแตนฟอร์ด - วิธีการประเมินโมเดลเสียงขนาดใหญ่ (Large Audio Models) ด้านรูปแบบ (preferences) และความเข้าใจคำสั่งของผู้ใช้ (user needs) ผ่านการโต้ตอบโดยตรง
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Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia ผลงานวิจัยร่วมกับ SEACrowd - การสร้างชุดข้อมูลภาพ-ภาษาคุณภาพสูงที่สะท้อนวัฒนธรรมเอเชียตะวันออกเฉียงใต้ โดยประกอบไปด้วยรูป���าพที่เกี่ยวข้องจำนวน 1.28 ล้านรูป
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ผลงานใน Findings 1 เรื่อง คือ Towards Better Understanding of Program-of-Thought Reasoning in Cross-Lingual and Multilingual Environments ผลงานวิจัยร่วมกับสถาบันวิทยสิริเมธี (VISTEC) - งานวิจัยนี้ศึกษาวิธีปรับปรุงความสามารถในการให้เหตุผลของ AI ในสภาพแวดล้อมหลายภาษา โดยใช้เทคนิค Program-of-Thought (PoT) ซึ่งแยกขั้นตอนการคิดออกจากการคำนวณ เพื่อให้โมเดลเข้าใจและให้คำตอบได้แม่นยำยิ่งขึ้น
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ผลงานใน LLM Security Workshop 1 เรื่อง คือ Shortcut Learning in Safety: The Impact of Keyword Bias in Safeguards [ผลงานวิจัยร่วมกับสถาบันวิทยสิริเมธี (VISTEC)] - ศึกษาผลกระทบในการแยกแยะประเภทของคำสั่ง (prompt) จากพฤติกรรมจดจำคำสัญ (keyword bias) ในโมเดลประเภท safeguard
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ผลงานวิจัยเหล่านี้เป็นรากฐานสำคัญเบื้องหลังการพัฒนาโมเดลปัญญาประดิษฐ์ของ SCB 10X เช่น Typhoon T1, Typhoon2 R1 และ Typhoon2 Audio ที่ถูกนำเสนอในงานประชุมระดับโลกดังกล่าว พร้อมทั้งมีการเผยแพร่ technical reports ผ่านแพลตฟอร์ม https://t.co/Sqlzo4rWIm เพื่อเปิดโอกาสให้นักวิจัยทั่วโลกสามารถเข้าถึงองค์ความรู้และนำไปต่อยอดได้อย่างเสรี และแม้ว่าในแต่ละงานวิจัยจะมีประเด็นเฉพาะของตนเอง แต่ทั้งหมดล้วนสะท้อนจุดมุ่งหมายร่วมกัน คือ การพัฒนา AI ที่เข้าใจบริบท ครอบคลุมความหลากหลาย และนำไปใช้ได้จริง
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แม้จะยังไม่มีการจัดอันดับอย่างเป็นทางการในระดับประเทศเกี่ยวกับการมีส่วนร่วมใน A* Conference แ���่ประเทศไทยเริ่มเห็นสัญญาณของการเติบโตในสายนี้ โดยมีองค์กรที่ลงมือทำงานอย่างจริงจัง อาทิ กลุ่มเอสซีบีเอกซ์ VISTEC และจุฬาลงกรณ์มหาวิทยาลัย เป็นต้น ซึ่งองค์กรเหล่านี้กำลังสร้างฐานรากที่สำคัญในการผลักดันให้นักวิจัยไทยได้มีพื้นที่ในเวทีโลก
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#TheStandardWealth

[Natapong Nitarach (Schwyter) @nat_nitarach]

@saksorn_r 🔥🔥🔥🔥

[IACR @IACR_News]

#award The 2025 IACR Test-of-Time Award for Eurocrypt goes to: "On Ideal Lattices and Learning with Errors over Rings", by V. Lyubashevsky, C. Peikert & O. Regev. More info at: https://t.co/qI0SIT7bu3
Congratulations!

[World Intellectual Property Organization (WIPO) @WIPO]

Investment in intangible asset surges, led by funding for software and databases amid AI boom, new WIPO report shows

Swipe through for key findings⬇️ and read more here: https://t.co/YgE5lMZWXj

#wipoGInvestment in intangible asset surges, led by funding for software and databases amid AI boom, new WIPO report shows

Swipe through for key findings⬇️ and read more here: https://t.co/YgE5lMZWXj

#wipoGInvestment in intangible asset surges, led by funding for software and databases amid AI boom, new WIPO report shows

Swipe through for key findings⬇️ and read more here: https://t.co/YgE5lMZWXj

#wipoGInvestment in intangible asset surges, led by funding for software and databases amid AI boom, new WIPO report shows

Swipe through for key findings⬇️ and read more here: https://t.co/YgE5lMZWXj

#wipoGInvestment in intangible asset surges, led by funding for software and databases amid AI boom, new WIPO report shows

Swipe through for key findings⬇️ and read more here: https://t.co/YgE5lMZWXj

#wipoGInvestment in intangible asset surges, led by funding for software and databases amid AI boom, new WIPO report shows

Swipe through for key findings⬇️ and read more here: https://t.co/YgE5lMZWXj

#wipoGA

[Rohan Paul @rohanpaul_ai]

LLMs struggle with precise, interpretable, and domain-aligned reasoning in finance.

FinCoT addresses this by embedding expert financial workflows as Mermaid diagrams into structured Chain-of-Thought prompts, greatly improving accuracy and interpretability without model fine-tuning.

Methods 🔧:

- FinCoT uses a system message to frame the financial task.

- It employs Guided Step-by-Step Execution with thinking and output tags for intermediate reasoning and the final answer.

- An Expert Reasoning Blueprint, a domain-specific Mermaid diagram, serves as a prompt hint, outlining recommended step-by-step problem-solving.

- A Re-Reading and Semi Self-Reflection step includes a brief self-check within the output to ensure consistency.

📌 FinCoT provides substantial accuracy gains; Qwen3-8B-Base improved 17.3 percentage points.

📌 The approach significantly reduces inference cost by decreasing generated tokens eight-fold.

📌 FinCoT delivers highly interpretable, expert-aligned reasoning traces for financial tasks.

----------------------------

Paper - arxiv. org/abs/2506.16123

Paper Title: "FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning"

[Saksorn Ruangtanusak @saksorn_r]

🚀: Our RAG from SCBX and SCB 10x just got published at #SIGIR LiveRAG!

Dynamic-of-Thought Aggregation RAG (DoTA-RAG): Fast & Accurate Retrieval-Augmented Generation at Scale

Tackling the challenges in large-scale, web-scale knowledge data.

🔗: https://t.co/YtboQv1r7i

[Synthetic_soul @Synthetic_Copy]

trying to make sense in real time

[el.cine @EHuanglu]

omg.. this cant be real

China’s 4DV AI just dropped 4D Gaussian Splatting, you can turn 2D video into 4D with sound..

imagine.. we will be able to change camera angle, zoom in/out while watching movies

5 examples:

[Jinay @jinaycodes]

Introducing soarXiv ✈️, the most beautiful way to explore human knowledge

Take any paper's URL and replace arxiv with soarxiv (show in video) to teleport to its place in the universe

I've embedded all 2.8M papers up until April 2025

Try it at: soarxiv dot org

[Boston Dynamics @BostonDynamics]

Atlas is demonstrating reinforcement learning policies developed using a motion capture suit. This demonstration was developed in partnership with Boston Dynamics and @rai_inst.

[Rohan Rao @simply_rohan_]

🙂👍

[SCB 10X @SCB10X_OFFICIAL]

🎓 Inspiring the Next Gen of AI Builders at KMITL Innovation Expo 2025!
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Our Research Scientist, Warit Sirichotedumrong, and AI Engineer, Natapong Nitarach had a blast sharing the Typhoon journey with passionate students. From the core concepts behind Typhoon open-source LLMs to deep dives into its audio and vision models.
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Breaking down practical strategies for students eager to start their model development journey! 💪

#AI #OpenSourceAI #ThaiTech #KMITLExpo #Typhoon #SCB10X