CoDA: Collaborative Codebook Development for Text Annotation
An open-source assistant that pairs human experts with language models to build annotation guidelines for unstructured text.
Research Digest
Preprints and papers, rewritten in plain English.
External Memory Proposed to Ease Reasoning Models' Context Burden
A new arXiv paper highlights how reasoning models depend on in-context memory and suggests external memory as a way to manage growing context demands.
LLMs Are 'Lazy Poets': Study Finds Creative Mode Collapse at Scale
A new arXiv paper argues that aligned large language models produce highly self-similar creative outputs, a problem the authors call creative mode collapse.
Study: Encoder-Free Multimodal LLMs Still Learn Implicit Encoders
A new analysis of Gemma-4 12B and Inkling-Small shows that multimodal models without explicit encoders can still develop implicit encoder-like representations.
NVFP4 Training: Comparative Study of Low-Precision Recipes
A new arXiv study examines which design choices are proven for training LLMs directly in 4-bit floating-point formats.
Pixel-Based Language Models: Promise and Pitfalls for Noisy Text
A new arXiv paper outlines why pixel-based language models may help with noisy and multilingual inputs, but their practical reliability remains an open question.
Interruption Budgets Could Replace Question Counts for LLM Agents
A new arXiv paper argues that measuring user burden by the number of clarifying questions misses the difference between necessary and avoidable interruptions.
FlashMap: A New Speculative Decoding Method for Faster LLM Inference
A new arXiv paper proposes FlashMap, a flow-map-based approach to speculative decoding that targets the bottleneck of autoregressive drafting.
Distilling a Universal Local Feature Extractor for Speech Models
A new arXiv paper proposes distillation to create a universal local speech feature extractor, targeting the computational cost of CNN layers in self-supervised models.
New Bengali Dataset Targets Anti-Male Hate Speech Detection
A Bengali dataset and late-fusion transformer framework aim to close a blind spot in hate-speech moderation by focusing on misandry.
Legal Retrieval: Reranker Training Beats Bigger Candidate Pools
A new arXiv paper argues that once embedding recall saturates, industrial legal retrieval systems should focus on scaling hard-negative reranker training.
Converter Generates TypeScript from .NET Computational Models
A proposed architecture aims to let .NET computational models run locally in web applications by generating TypeScript implementations.
Scaling Concept Vectors from Stories for AI Interpretability
A new arXiv paper proposes scaling concept vectors—directions in a model's activation space tied to human concepts like honesty—to improve AI alignment and interpretability.
SOM-based model simulates bilingual naming with limited training data
A new computational model aims to explain individual-level bilingual naming using laterally-connected self-organizing maps that avoid traditional machine learning's data hunger.
LLMs Fail at Multi-Author Style Detection in New arXiv Study
The study reports that off-the-shelf LLMs and simple fine-tuning produce ineffective features for identifying author changes in documents.
Safety Kernel Aims to Close Cross-Lingual Gaps in LLM Guardrails
A new arXiv paper proposes a mechanistic alignment method to defend against cross-lingual attacks that exploit uneven safety in LLMs.
Cipher Training May Boost Rare-Term Learning in Translation Models
A new arXiv paper asks whether in-context fine-tuning truly teaches rare terms and proposes a cipher-based method to improve it.
Which Long Documents Matter for Context Extension? A Controlled Test
A new controlled study compares long-document data-selection pipelines for context extension under matched training budgets.