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small language model

3,445 papers

#machine learning Preprint Sep 2026

Why Adaptive Optimizers Underestimate Rare Tokens

In the softmax output layer, a rare token receives a small positive logit gradient on most steps and a much larger negative gradient on the few steps when it is the target. SGD simply adds these contributions. Coordinate-wise adaptive methods such as Adam, RMSProp, and sign descent instead divide each update by a runni...

Sangsidhya Kar · 0 citations
#natural language process... Preprint Sep 2026

MERGE: Multi-LLM Ensemble for Retrieval via Generative Enrichment

Large Language Models (LLMs) are increasingly used to enrich user queries in information retrieval (IR) so that a standard retriever such as BM25 can bridge vocabulary gaps with the target corpus. Any single LLM, however, is limited by its training data and architectural biases, and its enrichment behavior depends on h...

Tzu-I Ho, Yung-Yu Shih, Shang-Yu Su et al. · 0 citations
#small language model Preprint Sep 2026

DROM: A Language-Guided Diffusion Framework for Multi-Skill Robotic Manipulation

Learning robust manipulation policies for diverse, long-horizon tasks from limited demonstrations remains a fundamental challenge in robotics. We present DROM, a language-guided diffusion framework that enables robots to learn, represent, and compose multiple manipulation skills within a single generative policy. DROM...

Vincenzo Pomponi, Rocco Felici, Paolo Franceschi et al. · 0 citations
#natural language process... Preprint Sep 2026

Authority Bias in Language Models: Source Deference and User Agreement Are Not Interchangeable

Language models tend to agree with whatever a user asserts, and post-training increasingly targets this sycophancy so that models evaluate claims on their merits rather than deferring to the user. Yet the same models are far more compliant when a wrong answer is attributed to a verified source, which is how retrieval r...

Abhinav Kumar, Paras Chopra · 2 citations
#natural language process... Preprint Sep 2026

Pretraining Latent Information Feedback Transformers with Teacher Supervision

Transformer language models (LMs) are feed-forward: deep-layer representations are never fed back to shallower layers, and the only pathway for information to flow downward across generation steps is the decoded token. This narrow channel forces models to recompute intermediate results and to discard alternative contin...

Dor Tirosh, Ido Amos, Mor Geva · 0 citations
#small language model Review Sep 2026

Decoding the Giants: a systematic taxonomy and empirical analysis of large language model architectures, training, and applications

This comprehensive survey provides an in-depth analysis of modern LLMs, with a systematic comparison of cutting-edge proprietary and open-source architectures including DeepSeek, GPT-4, Gemini, LLaMA, and Claude.

Mohamed Nazih Omri, B. Louhichi · 0 citations
#machine learning Conference Jan 2024

An Analysis of Object Detection in Bad Weather Conditions using Deep Learning Models

Object detection, a task, in the field of computer vision faces obstacles when dealing with weather conditions such as fog, rain, snow, and low light situations. This paper provides an overview of advancements in the realm of object detection under challenging weather conditions. It delves into groundbreaking research...

Janvi Verma, Harsh Verma, Supriya Raheja · 1 citation
#machine learning Conference Jun 2024

Exploring the Landscape of Cloud Robotics: A Comprehensive Review

Cloud robotics is an innovative field that leverages cloud technologies-including cloud computing (CC), cloud storage, deep learning, big data, and the Internet of Things to augment the capabilities of robotics. This integration facilitates the execution of robotic functions through a converged infrastructure and share...

Shahnawaz Ahmad, Shahadat Hussain, Khalid Anwar et al. · 2 citations
#data science Open access Apr 2024

Autonomous Multi-Agent Systems for Enterprise Decision-Making

An integrated conceptual framework is presented which maps layers of the MAS architecture to decision postures in the enterprise, a cross domain performance synthesis, and a research agenda for the next generation of enterprise-scale autonomous agent systems are presented.

Harsh Verma · 0 citations
#artificial intelligence Open access Nov 2024

AI Agentic Architectures for Autonomous Data Engineering Pipelines

This study delves into the notion of AI agentic architectures for autonomous data engineering pipelines and investigates the potential benefits of intelligent agents in enhancing automation, resilience, and decision-making processes in contemporary data ecosystems.

Harsh Verma · 0 citations
#artificial intelligence Open access Jun 2025

Economic impact and productivity modeling of AI agents

The paper provides a comprehensive analytical tool to make sense of micro and macro evidence, unpacks scenarios when AI agents will drive inclusive productivity growth, and outlines a policy roadmap focused on complementary investments, incentives for task-redesign, and workforce transition support measures.

Harsh Verma · 1 citation

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