small language model

ARQM-LITE: A Small Tool for Requirements Quality Analysis

The featuretree IR: an Open Feature-Tree Format, Emitted Natively into Several CAD Systems and Checked by Re-Reading

When a program or a language-model agent writes a part, a person still has to be able to open it, see how it was built, and change one dimension without starting again. That needs a shared, small description of the feature tree (the "timeline" every history-based CAD system shows) that an agent can emit, each CAD syste...

Toolkit for Confidence-Corpus Consistency, Corpus Absorption and Rule Learning via Fine-Tuning on a Fabricated Corpus

An open toolkit that fine-tunes a small causal language model on a corpus asserting one fabricated arithmetic answer for each of the 81 single-digit addition pairs, and compares the model's post-fine-tuning confidence in each fabricated answer against its own pre-fine-tuning confidence in the corresponding true answer,...

IWAC Sentiment Analysis Visualization

Interactive SvelteKit dashboard for exploring large language model sentiment annotations of the Islam West Africa Collection (IWAC), a corpus of 12,349 francophone West African press articles from 57 newspapers, published between 1961 and 2025 in Benin, Burkina Faso, Côte d'Ivoire, Niger and Togo. Each article is annot...

Little Brains, Big Feats: Exploring Compact Language Models

NRITYAM: Language Models Meet Art and Heritage of Dance

Deploying LLM Inference on a Repurposed UMA APU: Transferable Lessons from a Vulkan-Only, 16 GB Edge Platform

Cost-driven interest in running large language models (LLMs) on non-mainstream silicon outpaces the maturity of the surrounding software stacks. This paper uses one such platform, the AMD BC-250 (a repurposed cryptocurrency-mining board with a GFX1013 "Cyan Skillfish" accelerated processing unit (APU), 16 GB of unified...

Verifier-Grounded Interference Search: Exact State Merging, Learned Inhibition, and Parallel Execution for Language-Model Reasoning

Beyond Plausibility: Verifiable Fine-Grained Image Editing on Structured Assets

Fine-grained image editing requires more than producing a visually plausible result: an editor must execute the requested attribute change precisely while leaving everything else intact. However, existing benchmarks leave a critical gap between realism and verifiability: benchmarks built on realistic images typically r...

Efficient Test-time Adaptation through Candidate Verification and Divergence Shifts

Vision-language models (VLMs) achieve strong zero-shot transferability but remain vulnerable to target-domain shifts at inference time. Test-time adaptation (TTA) offers a practical remedy, yet most existing VLM-TTA methods follow a prediction-side adaptation paradigm. They use test samples to adjust logits, prototypes...

Noise Out, Bias In: Targeted Bias Injection in Diffusion Language Models via Closed-Loop Activation Steering

Masked diffusion language models (dLLMs) generate text by iteratively denoising masked positions, re-predicting each token multiple times before it is committed. An autoregressive decoder exposes an answer's distribution once, at the step that commits it; a dLLM exposes it at every denoising step before commitment, and...

More Than Words: Compositional Tokenization for Efficient Language Models

Language models process and generate text sequentially in token units, and the tokenizer determines how much text each inference step covers. Under standard tokenization, a short English phrase such as"On the table."is usually produced as four separate predictions for the preposition (On), article (the), noun (table),...