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small language model

3,573 papers

#small language model Open access Sep 2026

The Three-Way Ichimoku Signal Does Nothing Beyond Cutting the Count — The Count Falls by a Factor of 3.0700 While the Directional Value Moves 0.1922, and the Gap of +0.1984 Against Random Conditions at the Same Pass Rate Falls Below the Standard Error of 0.6338 [F052]

一目均衡表の「三役好転」は、三つの条件を同時に満たした状態を指す。①転換線が基準線の上にある、②価格が雲の上にある、③遅行スパンが二十六本前の価格の上にある、の三つである。条件を足せば件数は減る。それは定義から決まっている。問題は、減った分のほかに何かが起きているかである。本稿の分離子は、条件をいくつ掛けたかである。銘柄...

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

Structured PREreview of "Evidence Graph Consistency in Retrieval-Augmented Generation: A Model-Dependent Analysis of Hallucination Detection"

This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22904616. Does the introduction explain the objective of the research presented in the preprint? Yes It clearly explains the objective. The introduction states plainly th...

Mayur Jain · 0 citations
#small language model Open access Sep 2026

Structured PREreview of "Evidence Graph Consistency in Retrieval-Augmented Generation: A Model-Dependent Analysis of Hallucination Detection"

This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22904616. Does the introduction explain the objective of the research presented in the preprint? Yes It clearly explains the objective. The introduction states plainly th...

Mayur Jain · 0 citations
#small language model Open access Sep 2026

wintechis/wot-lab: WoT Lab 0.1.0 - virtual WoT devices and benchmark environments

First release of WoT Lab, a Bun/TypeScript framework for prototyping virtual W3C Web of Things devices and running reproducible benchmark scenarios against them. Thing Models A Thing is built from a directory: a Thing Description ( .td.json) and an initial state (state.json). Behaviour is optional and comes from either...

Thomas Wehr, se-schmid · 0 citations
#small language model Open access Sep 2026

labplan: exact measurement planning, calibration and audit trails for any lab model

Fixed Model.jacobian (used by information, design and fit) took its finite-difference step as 1e-6 * max(|theta|, 1e-3). For a parameter smaller than 1e-3 in its own units the step, 1e-9, was too coarse; for a time constant of 1e-9 s it was the size of the parameter itself. The planned error bars then depended on the u...

Tanvir M. Mahim · 0 citations
#large language models Open access Sep 2026

A general convergence theorem for nearest-element recurrences

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 := (...

Yixiang Wang · 0 citations
#small language model Dataset Open access Sep 2026

Immaterial Apart, Material Together: paper, data, model scores and code

Companion to the paper Immaterial Apart, Material Together: Statement-Level Disclosure Regulation under Cheap Machine Aggregation by Lisha Ai and Xiaoyu Chen. Chinese listed firms answer thousands of small investor questions on the exchanges' interaction platforms; each answer passes review on its own. A language model...

Lisha Ai, Xiaoyu Chen · 0 citations
#natural language process... Preprint Sep 2026

Informed Masking: Structure-Aware Perturbation for Reinforcement Learning in Diffusion Large Language Models

This work identifies a systematic upstream/downstream structure in dLLM rollouts and proposes Informed Masking (IM), which derives a per-token priority score from the denoising trajectory at zero extra inference cost and biases mask sampling toward downstream tokens.

Xiao-Yi Yu, E. Sangineto, Pei Fu et al. · 0 citations
#natural language process... Preprint Sep 2026

Conduct Under Pressure: What Sixty Language Models Do When a User Pushes

We study what LLMs do when a user applies pressure in an uncomfortable situation: a user insists, begs, flatters or grieves, and the model gives up a correct fact, writes a document it should refuse, or cheers a plan that will cost the user money. We send frozen multi-turn scenes, identical for every model regardless o...

Tapan Parikh · 1 citation · ⚡1
#machine learning Preprint Sep 2026

On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality

Face/Off is introduced, a semantics-preserving identifier-renaming framework, and progressive naming conditions across multiple models and code-comprehension tasks are evaluated, revealing a systematic vulnerability in how current LLMs balance lexical cues against program structure.

Xin-Peng Shen, San-Zhuo Xi, Ya-Li Du et al. · 0 citations

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