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...
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
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
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
Reach audiences
Advertise in front of researchers, engineers, and readers.
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...
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.
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...
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.· International Conference on...· 2 citations
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· International Journal of Eng...· 0 citations
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· International journal of res...· 0 citations
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· World Journal of Advanced Re...· 1 citation
It is concluded that future research should prioritize interdisciplinary collaboration, robust regulatory frameworks, and continuous monitoring to promote the ethical use of AI.
Harsh Verma· World Journal of Advanced Re...· 0 citations