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large language models

2,285 papers

#large language models Open access Sep 2026

What Chooses the First Principal Component Is Not the Data but the Units ── The same sample points at 55.6503 degrees in cm, 89.7345 in m, and 4.1934 in mm ── Not one value was changed; they were multiplied ── Standardising settles it at 45.0000 degrees, but standardising is itself a choice ── [Paper 375]

Principal component analysis is described as extracting the main direction in the data. This paper asks whether the data alone fix that direction──the answer is that they do not: the units do. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathematical theorem and no new...

Yuuki Yamagishi · 0 citations
#large language models Open access Sep 2026

"Moore's Law Is Ending" Has Been Said for Years, but What Ended Was the Other Law ── Under Dennard scaling, halving the dimensions leaves power per area at exactly 1.0000 ── Once the voltage stops falling it becomes 4.0000 ── The count still doubles every two years, reaching 1024 in twenty, and now the power does too ── [Paper 374]

“Moore’s law is ending” has been said for a long time. This paper asks what ended──the answer is that what ended was not the law of counting but the law of power. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathematical theorem and no new law is claimed──Moore’s law,...

Yuuki Yamagishi · 0 citations
#large language models Open access Sep 2026

Bell Algebra without Bell Violation: CHSH Operators for Ising Anyons in the Kitaev Honeycomb Model

We construct four spatially separated Majorana zero modes (MZMs) per party on a finite Kitaev honeycomb lattice in its non-Abelian phase, bound to well-separated Ising vortices, and use them to realize a local, noncommuting CHSH measurement algebra. Two static fusion-parity observables per party, Z_A=iγ_{A,1}γ_{A,2} an...

Berkay Yüksel Sayim · 0 citations
#large language models Open access Sep 2026

A Premium Is Not the Expected Loss ── And What Sets the Loading Is Not the Accuracy of the Probability but the Number of Policies ── With one policy, keeping the ruin probability at 1 per cent takes a loading of 23.1469 times the expected loss ── With a million policies it falls to 0.0231 ── The expected loss has not moved by a single unit ── [Paper 383]

It is tempting to think a premium is the probability of loss times its size. This paper asks whether that suffices──the answer is that it does not, and what sets the shortfall is the number of policies. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathematical theorem...

Yuuki Yamagishi · 0 citations
#large language models Open access Sep 2026

How Much You Remember Is Decided by How You Test ── One memory, one day later: 71.70 per cent by free recall and 91.07 per cent by savings in relearning ── The gap widens with time, reaching 3.2698 after a month ── And an exponential forgetting curve predicts 10^-13 at a month, breaking down ── [Paper 378]

A forgetting curve shows memory falling with time. This paper asks what quantity is falling──the answer is a different quantity for each way of testing, giving three answers one day later. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathematical theorem and no new law...

Yuuki Yamagishi · 0 citations
#large language models Open access Sep 2026

Bell Algebra without Bell Violation: CHSH Operators for Ising Anyons in the Kitaev Honeycomb Model

We construct four spatially separated Majorana zero modes (MZMs) per party on a finite Kitaev honeycomb lattice in its non-Abelian phase, bound to well-separated Ising vortices, and use them to realize a local, noncommuting CHSH measurement algebra. Two static fusion-parity observables per party, Z_A=iγ_{A,1}γ_{A,2} an...

Berkay Yüksel Sayim · 0 citations
#large language models Open access Sep 2026

Large language models as optimization controllers: Adaptive continuation for SIMP topology optimization

We present a framework in which a large language model (LLM) acts as a state-conditioned software control layer for solid isotropic material with penalization (SIMP) topology optimization, selecting continuation parameters from the current solver state rather than a fixed open-loop schedule. Every k th iteration, the L...

Shaoliang Yang, Jun Wang, Yunsheng Wang · 0 citations
#large language models Open access Sep 2026

The Brain Uses "2 Per Cent of the Mass and 20 Per Cent of the Energy" Because What It Is Compared With Is Resting ── The brain's specific power is 14.2857 W/kg against a whole-body average of 1.4286, a ratio of 10 ── But skeletal muscle in exercise runs at 100 W/kg, seven times the brain ── The factor of 10 comes from the smallness of the denominator, not the largeness of the brain ── [Paper 382]

The brain is said to use 2 per cent of the body’s mass and 20 per cent of its energy. This paper asks where that factor of 10 comes from──the answer is not that the brain is high but that the denominator is low. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathematical...

Yuuki Yamagishi · 0 citations
#large language models Open access Sep 2026

A Premium Is Not the Expected Loss ── And What Sets the Loading Is Not the Accuracy of the Probability but the Number of Policies ── With one policy, keeping the ruin probability at 1 per cent takes a loading of 23.1469 times the expected loss ── With a million policies it falls to 0.0231 ── The expected loss has not moved by a single unit ── [Paper 383]

It is tempting to think a premium is the probability of loss times its size. This paper asks whether that suffices──the answer is that it does not, and what sets the shortfall is the number of policies. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathematical theorem...

Yuuki Yamagishi · 0 citations
#large language models Open access Sep 2026

"Free-Space Loss" Is Not a Loss: The Vacuum Absorbs Nothing ── 92.4478 dB at 1 km and 1 GHz; doubling the distance and doubling the frequency both add the same 6.0206 dB ── the frequency term comes not from space but from the receiving antenna, whose effective area scales as lambda^2 ── at 10 GHz that area is exactly 1/100 ── [Paper 384]

Radio waves are said to suffer a “loss” as the distance grows. This paper asks what is being lost──and the answer is that nothing is lost; what looks like loss is a fact about the receiver. No new theorem or law is claimed. Scope of this paper (scope note): No new theorem or law is claimed──the Friis transmission formu...

Yuuki Yamagishi · 0 citations
#artificial intelligence Dataset Open access Sep 2026

Chinese AI, Humanoid Robotics & Neural Systems Open Intelligence Catalog

Official Scientific Intelligence Gateway: https://sinoailab.com This repository provides a comprehensive, structured open dataset indexing 928 authentic Chinese scientific research publications in Artificial Intelligence, Humanoid Robotics, Large Language Models & Neuromorphic Edge. Curated and verified by the SinoAILa...

SinoAILab Academic Editorial Office · 0 citations

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Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

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