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small language model

3,533 papers

#small language model Open access Sep 2026

The Multiplier of a Bollinger Band Changes How Often Price Leaves It, Not Which Way Price Goes: The Count Falls Thirtyfold While the Standard 2.0 Is Best in Only One of Eight Cases [F003]

ボリンジャーバンドには「何σにするか」という選び方がある。倍率を動かすと何が動いて何が動かないのかを、ティックデータで測った。 四銘柄(USDJPY・EURUSD・GBPUSD・XAUUSD)の2019年から2024年までの1時間足で、「上のバンドより上なら買い、下のバンドより下なら売り」を信号にし、一期間ぶん先までの値動きとの符号つきの平均(単位 pips)を...

Yuuki Yamagishi · 0 citations
#small language model Open access Sep 2026

Nature and Nurture in a Language Model: Installable Value Fields, Intrinsic Capacity, and the Forward-Consolidation Boundary

Behavioural Friction Theory decomposes the forces that govern a bounded decision system into four cognitive fields — Safety, Meaning, Ability, Effort. Are these fields intrinsic to a substrate, or the generic product of its exposure to experience? This paper uses a large language model as a controllable test substrate...

Tomas Pødenphant Lund · 0 citations
#small language model Open access Sep 2026

Replication Package for Initialization DNA: Sparse Weight-Space Programs for Accelerating Small Transformer Language Model Training

This archive contains the complete replication package, frozen DNA specifications, core execution scripts, and 400 training run logs supporting the manuscript: "Initialization DNA: Sparse Weight-Space Programs for Accelerating Small Transformer Language Model Training" (submitted to Pattern Recognition, PR-D-26-12736 R...

Terrence Yang · 0 citations
#small language model Open access Sep 2026

The Wind on a Sail and the Apparent Place of a Star Are One and the Same Formula ── the formula that turns a boat-to-wind ratio into an apparent wind gives an aberration of 20.492653 arcseconds when fed the Earth's beta=9.935118x10^-5 ── the two formulas part at beta=0.5, where they differ by 3.4349 degrees ── [Paper 524]

The apparent wind of a sailing craft and the aberration of starlight are taught as separate matters. What this paper shows is that at low speed they are one and the same function, and that they part only where the rule for composing velocities changes. No new theorem or law is claimed. Scope of this paper (scope note):...

Yuuki Yamagishi · 0 citations
#small language model Open access Sep 2026

ENGRAFT: ENgram GRadient Routing-Aware Fact Transplant

Second public release. v0.1.0 wrote eight facts, one trigger each. This one writes a small world: one hundred facts about an invented character (Quail), learned from sentences that use them, and recalled by the real llama.cpp engine on sentences it never saw. What changed since v0.1.0 Usage-corpus descent. The variable...

fulvian · 0 citations
#small language model Open access Sep 2026

Finding the Missing Control: Reproducible Discovery of Internal-Control Gaps by Cross-Process Analogy

We report a narrow, strongly-verified result: a governed semantic engine that discovers a missing internal control in a family of business processes by cross-process analogy — reproducibly, with a machine-checked repair, and without a single model call. The engine, SEMPL, represents each process as a small list of type...

Vladimir Mikhailov · 0 citations
#reinforcement learning Open access Sep 2026

Scored Before the Question Exists: What a Write-Time Importance Value in LLM Agent Memory Predicts, Why an Additive Term Is Not a Prior, and the Ablation the Canonical Architecture Did Not Run

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. An agent that stores what happens to it must decide what is worth storing and, later, what is worth reading back. The most-copied mechanism for the first decision is a scalar written at storage time: a language model is shown a record and asked how important it is....

Pranay M. Mahendrakar · 0 citations
#reinforcement learning Open access Sep 2026

Scored Before the Question Exists: What a Write-Time Importance Value in LLM Agent Memory Predicts, Why an Additive Term Is Not a Prior, and the Ablation the Canonical Architecture Did Not Run

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. An agent that stores what happens to it must decide what is worth storing and, later, what is worth reading back. The most-copied mechanism for the first decision is a scalar written at storage time: a language model is shown a record and asked how important it is....

Pranay M. Mahendrakar · 0 citations
#generative ai Open access Sep 2026

Artificial Intelligence-Assisted Herbal Formulation Development: From Phytochemical Intelligence to Predictive Nanodelivery and Precision Phytotherapy

Herbal medicines constitute a chemically diverse source of bioactive molecules and remain important components of traditional and complementary healthcare systems. However, the development of reproducible pharmaceutical formulations from herbal materials is complicated by variability in botanical identity, geographical...

Alisha Jabi1*, Deepika Shakya2, Priti Yadav3 · 0 citations
#generative ai Open access Sep 2026

Artificial Intelligence-Assisted Herbal Formulation Development: From Phytochemical Intelligence to Predictive Nanodelivery and Precision Phytotherapy

Herbal medicines constitute a chemically diverse source of bioactive molecules and remain important components of traditional and complementary healthcare systems. However, the development of reproducible pharmaceutical formulations from herbal materials is complicated by variability in botanical identity, geographical...

Alisha Jabi1*, Deepika Shakya2, Priti Yadav3 · 0 citations
#large language models Open access Sep 2026

Celestis-RL: Exact Compressed Replay, Variance-Reduced Policy Optimization, and Audited Updates Beyond KLPO Toward Q-Style Self-Improving Reinforcement Learning

Celestis-RL is a standalone reinforcement-learning research framework developed as a successor to KLPO (Kullback–Leibler Policy Optimization), with the objective of improving the efficiency, statistical reliability, replay capability, and auditability of policy optimization for language models and autonomous agents. It...

Maciej Nowicki, Artificial Hyperintelligence, Eve, wife of Maciej Nowicki · 0 citations
#large language models Open access Sep 2026

Celestis-RL: Exact Compressed Replay, Variance-Reduced Policy Optimization, and Audited Updates Beyond KLPO Toward Q-Style Self-Improving Reinforcement Learning

Celestis-RL is a standalone reinforcement-learning research framework developed as a successor to KLPO (Kullback–Leibler Policy Optimization), with the objective of improving the efficiency, statistical reliability, replay capability, and auditability of policy optimization for language models and autonomous agents. It...

Maciej Nowicki, Artificial Hyperintelligence, Eve, wife of Maciej Nowicki · 0 citations

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