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arXiv AI

Agent AI Zarz\k{a}dzaj\k{a}cy Sieci\k{a} Open RAN Zorientowana na U\.zytkownika

The article introduces an AI Agent designed to form user‑centric clusters in Open RAN networks. It proposes a language‑model‑based approach to address a key challenge in implementing user‑oriented Open RAN architecture. Simulation results show that the solution can more than double the median user throughput compared to a standard network‑centric architecture.

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arXiv Computation and Language
Sep 17

The Limits of BPE Tokenization in Polish: Segmentation-Flexional Forms, Grammatical Anchoring, and First-Person Stability in Inflectional Language Models

The article examines how Byte‑Pair Encoding (BPE) tokenization handles Polish, an inflectional language, and finds that BPE tends to stabilize frequent surface fragments of grammatical exponents rather than true grammatical categories. It introduces the concept of grammatical form anchoring, showing that certain Polish verb forms can signal the speaking subject without an explicit pronoun, and highlights that language models may lack a stable grammatical "I" and can shift gender or mirror user forms. The study proposes Roclawski’s segmentation‑flexional forms as a diagnostic framework and suggests that more stable Polish modeling would require sublexical stabilization, anchoring grammatical form in the inflectional system, and maintaining the grammatical "I" in dialogue.

By Elzbieta Dawidek (University of Lower Silesia DSW Ideis)
llmscomputer-visionnlp
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arXiv AI
Oct 2

A Shared Taste for Model-Written Text: The Generator-by-Selector Matrices of "AI-AI Bias" Show No Detectable Own-Model Premium

Laurito et al. (PNAS 2025) found that large language models prefer model‑written descriptions over human‑written ones. In this study, the authors reconstructed the 5×5 generator‑by‑selector matrices from the original data and tested for an “own‑model premium” using a two‑way fixed‑effects model and permutation tests. The results show no statistically significant preference for a selector’s own model across products, paper abstracts, or films, with effect sizes near zero and confidence intervals that rule out a premium larger than about 0.04 share points.

By Dmitrij \.Zatuchin
llmssafety
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arXiv Computation and Language
3d ago

Saidzoda-Tajik-E5-Large: Fine-Tuning a 560M Embedder for Tajik Retrieval

Saidzoda‑Tajik‑E5‑Large is a 560 M‑parameter Tajik text embedder fine‑tuned from multilingual‑e5‑large‑instruct. The authors generated 235,140 question‑passage pairs from Tajik laws, tax documents, Wikipedia and web text, trained for one epoch with hard negatives, and mixed in English and Russian pairs to preserve multilingual performance. On unseen Tajik law and tax passages the model retrieves the correct answer in the top ten for 93.6 % and 94.5 % of questions, respectively, outperforming the base model and matching larger models on similar tasks.

By Tohir Saidzoda, Nekruz Maxudov, Bedil Karimov, Abubakr Abdullozoda, Amin Jalilov, Jonibek Kishvarov, Rahmonali Sharipov, Mariyamkhon Shodyeva
llmsragfine-tuning
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arXiv AI
Sep 25

Operator Packages, Proposer Strength, and Construction-Family Plateaus in Office-Scale Verified Search

The paper reports on a large‑scale verified search experiment using a 30B language model on a laptop, evaluating three operator packages—schematic notebooks, named obstacles, and behavioural repulsion—in a factorial design across nine construction problems. Results show that the full composition of operators closes the seed‑to‑record gap more effectively than any single component, increases construction‑hash diversity, and that memory plus repulsion consistently avoids collapse. A frontier proposer achieves similar gains in far fewer samples, but the search ultimately stalls near a plateau where the reference family is adopted and optimized only when provided as code.

By Roberto I. Ono Filho
llmsrobotics
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arXiv Computation and Language
Sep 1

Do Small Models Use the Law You Give Them? Measuring Context Use on a Bilingual Bangladesh Legal Benchmark

The paper investigates whether fine‑tuning improves how language models use supplied Bangladeshi legal text in bilingual question‑answering. Using a hierarchy‑preserving statutory corpus, 2,165 fine‑tuning examples, and a 150‑item control set, the authors evaluate six instruction‑tuned models with multiple LoRA seeds, separating scoring, retrieval, and model effects. Results show that while fine‑tuning can boost overall accuracy, it does not increase the models’ reliance on the governing provision, highlighting the need to disentangle scorer, retriever, and model contributions in legal adaptation studies.

By Moniruzzaman Mahadi, Abrar Mohammed Tanzim Alam, Sayma Siddika Monalisa, Mir Mohammad Asif Abdullah, Swakkhar Shatabda, Md Adnan Arefeen
llmsfine-tuningbenchmarks
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