Skip to content
Log in
Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Category

large language models

2,592 papers

#large language models Open access Sep 2026

An Achromat Meets at Two Wavelengths Only ── A single lens shifts focus by 1.558361 mm with colour (1.5584 per cent of its focal length) ── Two glasses cut that to 0.044604 mm, a factor of 34.9374, but not to zero ── The cause of the residue can be named by one number: the difference in partial dispersion ── [Paper 377]

An achromatic lens is called a lens with the chromatic aberration removed. This paper asks how far it is removed──the answer is at two wavelengths only, with a third colour remaining. 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 c...

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

Consolidation Without Weights: What the Complementary Learning Systems Analogy Licenses in LLM Agent Memory, and Why the Systems That Borrow Its Name Do Not Inherit Its Guarantee

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Memory systems for language-model agents almost all contain a step called consolidation, and almost all of them cite, or gesture at, the complementary learning systems account of hippocampus and neocortex when they name it. In that account consolidation is a specif...

Pranay M. Mahendrakar · 0 citations
#large language models Open access Sep 2026

In a Gravity Assist the Probe Speeds Up, and in the Planet's Frame Its Speed Never Changed ── Against Jupiter's orbital speed of 13.07 km/s, the gain seen from the Sun is at most 26.14 km/s ── Jupiter thereby loses 2.4310x10^-20 m/s of orbital speed ── The probe's change in speed is 1.0753x10^24 times Jupiter's ── [Paper 381]

A gravity assist is described as accelerating on a planet’s gravity. This paper asks in which frame the acceleration happened──the answer is that from the Sun it accelerated and from the planet its speed never changed. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathe...

Yuuki Yamagishi · 0 citations
#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

From tech blogs

See all →

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.