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3,573 papers
手法の成績に t 値が添えられていることがある。その多くは、標準偏差を件数の平方根で割ったものを分母にした素朴な t である。だが同じ日の観測は独立でない。同じ相場の同じ動きを、何度も数えているからである。一日あたり m 件、日の中の相関を ρ とすると、標準誤差の比は √(1 + (m − 1)ρ) になる。ρ が 0 なら膨らまず、1 なら √m 倍にな...
「損小利大にする」「建値に逃がす」「トレールで伸ばす」。どれもいつ切るかの規則であって、どちらへ建てるかの規則ではない。だから方向の情報は原理的に増えない。では停止則を変えると何が動くのか。本稿の分離子はいつ切るかであり、入口・データ・期間・銘柄をすべて固定したまま、停止則だけを二十通りに変える。方向とそれ以外を分ける...
上位の足の向きにそろえてから入る、三本の移動平均が順に並ぶのを待つ、という作法がある。条件を足すほど精度が上がると言われる。だが条件を足せば件数は必ず減る。これは市場について何も言っておらず、掛け算の性質である。そして件数が減れば勝率は動きやすくなる。問うべきは、増えるのは情報なのか、減るのは件数だけなのか、である。...
Let b(n) be a strictly increasing sequence of positive integers satisfyinglim_{n→∞} b(n+1)/b(n) = 1. Define a recurrence by taking a_n to be the element of b nearest to a_{n-1} + a_{n-2}, with ties broken by taking the smaller element. We prove that for any initial values a_1, a_2 in b, lim_{n→∞} a_n / a_{n-1} = φ := (...
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Modern language model training applies a uniform gradient update to every token, regardless of its information content. We introduce NS-weighted training, which filters training examples by their Shannon information. On a 50MB sample of the C4 web corpus, unigram NS-data filtering matches a 1000-step standard training...
Generative visual AI has advanced by scaling data and compute, but its next bottleneck is oversight: the expert signals that evaluate, reward, and teach models what ``good'' looks like. Providing such oversight is increasingly difficult because foundation vision-language models now match or surpass most humans at the s...
Most language models deployed outside the United States and China are fine-tuned descendants of a small number of pretrained families. We argue that fine-tuning leaves the decisive properties of a model inherited from its base: vocabulary, knowledge, cultural alignment, memorized data and license. We contribute (i) an...
This dataset contains 3,326 recent arXiv computer science papers (first versions dated 16 March to 17 July 2026). For each paper, a large language model read the title and abstract and wrote one sentence each on the paper's main limitation, an open research gap for future work, and why that gap matters. It was built to...
This work was produced through an AI-assisted workflow directed and reviewed by the author. This study separates identifying a model run from checking whether its numeric answer is correct. It evaluates a membership-only source check and top-20 truncated token entropy on a small water-table task, then examines extracti...
The findings to date suggest that significant abrupt changes in climate-change stance do occur in this simple model, and the need to prevent LLM biases from interfering with the conversational dynamics and, more generally, to maintain agent personality and episodic memories of interactions in the face of such biases is...
The density-matrix renormalization group (DMRG) is a one-dimensional tensor-network technique, but it is not limited to one-dimensional systems: it can be applied to periodic 2D and 3D clusters and molecules, provided their sites are first enumerated along a line; a step one may call"lattice compilation". This paper di...
A recent and effective recipe adapts a large pretrained image-editing model to restoration using a small low-rank adapter with a text prompt to an instruction derived from the degraded image itself that outperforms text conditioning under a matched comparison.
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