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Earlier sources: Language models
Language models learn patterns in text and other representations to predict or generate sequences. Follow model releases, training methods, reasoning, evaluation, and deployment research; a benchmark result is specific to its test conditions.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗449 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- arXiv · Published · First archived
PFArena: Benchmarking Language Models for Protein Modification ↗
Protein modification requires navigating an immense sequence space, yet wet-lab validation remains low-throughput and costly. Although computational paradigms including protein language models (PLMs), large language models (LLMs), and LLM-based agents have shown promise in protein modification, their relative efficacy across realistic experimental decision-making settings remains unclear. To bridge this gap, we introduce PFArena, a benchmark comprising four controlled task interfaces that cover single-mutant generation and multi-mutant ranking. By providing varying levels of mutation fitness d…
- OpenAlex · Published · First archived
A Tool-Augmented Agentic AI Pipeline for Reliable Circuit-Analysis Tutoring with Local Language Models ↗
LLM-based systems used for tutoring can produce fluent and apparently plausible explanations, but they do not guarantee Technical Correctness, which is paramount in STEM fields such as Electrical Circuit Analysis. This study investigates how three LLM-based response systems—parametric model knowledge (Pure LLM), Retrieval-Augmented Generation (RAG) and Agentic AI—affect response quality in the field of Electrical Circuit Analysis. It also introduces the HALO Agentic AI Pipeline, a local neuro-symbolic architecture that integrates LLMs, retrieval from curated pedagogical resources and determini…
- OpenAlex · Published · First archived
Text-to-SQL for Low-Resource Languages: A Unified Large Language Model Framework Evaluated on Arabic, Japanese, and Vietnamese ↗
Text-to-SQL research has concentrated on English, while prior work on Arabic, Japanese, and Vietnamese has largely relied on grammar-constrained parsers or zero-shot prompting, with recent work beginning to explore trained LLM-based alternatives. We investigate whether a unified fine-tuned open-source pipeline can serve typologically distant low-resource languages and quantify the same-pipeline English-to-target gap on parallel Spider instances. We fine-tune Qwen2.5-Coder-7B-Instruct with QLoRA using shared schema grounding, value grounding, an English translation hint, and execution-based sel…
- OpenAlex · Published · First archived
Graded identification of air leakage in coal-mine gas drainage boreholes using a physics-augmented transformer–LSTM framework ↗
Air leakage in gas drainage boreholes in coal mines reduces methane concentration and extraction efficiency, yet reliable grading remains difficult because weak leakage signatures can be obscured by noise and changes in operating conditions. This study proposes a physics-augmented Global–Local Transformer–LSTM (GL-TransLSTM) framework for classifying normal, slight, moderate, and severe leakage. The four grades were defined in controlled underground tests by setting the valve opening of a drainage branch exposed to ambient mine air to 0%, 10%, 50%, and 100%, respectively. Complete ensemble emp…
- OpenAlex · Published · First archived
SentiMatrix: Parameter-Efficient Fine-Tuning of Encoder-Based Transformers for Multidimensional Sentiment Analysis ↗
Abstract Transformer-based architectures have established state-of-the-art benchmarks across Natural Language Processing (NLP) tasks; however, the computational overhead of full fine-tuning remains a significant barrier to scalable deployment. This paper presents SentiMatrix, a systematic evaluation of Low-Rank Adaptation (LoRA)-based Parameter-Efficient Fine-Tuning (PEFT) across four sentiment analysis paradigms: Intent-based, Aspect-based, Fine-grained, and Emotion detection, spanning seven benchmark datasets under a consistent three-stage protocol: inference using task-specific pretrained b…
- OpenAlex · Published · First archived
Investigation of Word Recognition Methods for Japanese Fingerspelling Using Large Language Models ↗
The development of automatic fingerspelling recognition systems is critical for facilitating seamless communication for the deaf and hard-of-hearing community. Historically, recognition technologies relied on wearable sensors, such as data gloves, inertial measurement units (IMUs), and surface electromyography (sEMG), which provided stable kinematic data but suffered from high hardware costs and user discomfort. Non-invasive vision-based sensing has emerged to eliminate wearable overhead but is subject to severe physical bottlenecks: continuous high-speed movements inevitably introduce motion…
- OpenAlex · Published · First archived
Representation Control for Large Language Models: Survey and Research Challenges ↗
Large language models (LLMs) are capable of completing a variety of tasks, but remain unpredictable and intractable. Representation Control (RepControl) seeks to resolve this problem through targeted interventions that modify high-level representations of concepts such as honesty, harmfulness or power-seeking. We formalize the goals and methods of RepControl to present a cohesive picture of work in this emerging field, focusing on techniques that steer model behavior by manipulating internal activations at inference time. We compare these control methods with alternative approaches, such as pr…
- OpenAlex · Published · First archived
Automated Brief Hospital Course Summarization in Cardiac Surgery Using a Lightweight Large Language Model–Based Framework: Development and Evaluation Study on the Medical Information Mart for Intensive Care-IV ↗
Background Physician documentation requirements are a known contributor to clinician burnout, with the manual creation of brief hospital course (BHC) summaries being particularly time-consuming. Automating BHC summarization may mitigate this workload and reduce documentation errors. However, current natural language processing (NLP) methods are often limited to single-document inputs, and large language models (LLMs) face privacy and deployment challenges. Furthermore, existing methods often require manual information extraction and struggle to maintain temporal accuracy. Objective We develope…
- OpenAlex · Published · First archived
From Raw Signals to Understood States: A Perspective on Neuro-Symbolic and LLM-Driven Intelligent Sensing of Human Affective and Cognitive States ↗
Intelligent sensor technologies now provide unprecedented multimodal access to human affective and cognitive processes, spanning physiological, ocular, facial, kinematic, ambient, and behavioral streams. Yet, despite dramatic advances in acquisition and deep representation learning, the pipeline from raw signals to psychologically meaningful, actionable understanding remains fragile. Deep-only architectures excel at pattern extraction but struggle with contextual reasoning, uncertainty communication, and human-facing explanation; symbolic-only frameworks resist noisy, high-dimensional streams.…
- OpenAlex · Published · First archived
Document-Level Claim Extraction and Decontextualization Method Based on Large Language Models ↗
Claim extraction and decontextualization are fundamental steps in automated fact-checking tasks. Claim extraction aims to identify core check-worthy claims from unverified text, while decontextualization seeks to eliminate the contextual dependencies of core claims so that they can be understood independently of the original text. In practical applications, information to be verified is usually presented in the form of documents, and existing document-level claim extraction and decontextualization methods often rely on complex multi-model architectures. This paper proposes LLM-CED, a document-…
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How to assess evidence about Language models
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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