small language model

Fine-grained Computation-Communication Overlap via Tile-level Signaling and Scheduling for Mixture-of-Experts

This work presents a fine-grained approach that overlaps expert compute with the second all-to-all via tile-level signaling and scheduling, making it practical for improving distributed MoE execution efficiency on multi-GPU systems.

Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems

An Evaluation Agent, middleware that combines Natural Language Inference factual verification, a five-signal poison detector with relevance-weighted aggregation, and a Trust Index is proposed, which reliably blocks instruction injection of unsafe advice while contradiction and subtle semantic weakening remain hard.

People behind the ideas

All information has an origin story and it is not the person, it is the data. This is my origin story for my project. These are from files a year and some change ago. When I started to gather the pieces together of my year and a half of researching. It is nice that everyone appreciates a final product because that mean...

afzalahmed786/Adaptive-Split-Computing-for-Text-to-Image-Diffusion: v1.0.0

First public release. Reinforcement-learning controller that adaptively partitions Stable Diffusion inference between an edge client and a server, selecting split point, quantization, decoder, and privacy settings per request based on live device and network conditions. The raw prompt and final image stay on-device; on...

Zero-shot Cross-lingual Transfer Performance of Intermediate-Task Trained mT5 Models Across Model Sizes on XTREME-R

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on...

Zero-shot Cross-lingual Transfer Performance of Intermediate-Task Trained mT5 Models Across Model Sizes on XTREME-R

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on...

Attention as Race-Architecture: attention as the landscape-governed initiation of races

Selective attention, read at the level of the substrate, is the landscape-governed initiation of races: a bounded predictive system runs competing prediction-error resolutions ("races"), and what determines which races start is the system's installed landscape — in humans the four fields of Behavioural Friction Theory...

Multi-Peptide Prompting Enables In-Context Learning in Protein Language Models

It is shown that single-sequence PLMs can perform in-context peptide learning without gradient updates, task-specific retraining, or architectural modification, and MPEP conditioning is established as a lightweight strategy for low-data peptide classification.

Populism, radicalism, and protest mobilization by parties in Europe

As European democracies struggle with a ‘crisis of representation’, populist parties appear to be instrumental in channeling popular discontent with governments across the continent, including through protests. While contemporary theories propose a strong connection between populism and protest mobilization, this has...

Re-instrumenting quality function deployment for generative engineering design: a small language model-based adaptive QFD framework

Confidence-aware pseudo-label selection and verifier training for semi-supervised LLM reasoning with minimal labels

An adaptive threshold selection policy that chooses thresholds on validation data using pseudo-label precision and sample count is introduced and is combined with confidence-aware verifier training to support confidence-based selection of pseudo-labeled subsets.

GS-Chaff: Multi-Agent Prompt-Level Semantic Chaffing for Privacy-Preserving LLM Inference

Generative semantic chaffing (GS-Chaff), a training-free multi-agent framework for privacy-preserving LLM inference over natural-language text queries that hides the user’s true intent among semantically plausible chaff queries, is proposed.