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1,836 papers
It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.
It is found that roles of MVPs in startups were not fully aware by entrepreneurs, and entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP).
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
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Gender issues in software engineering education are gaining research attention due to the desire to promote female participation in the field. The objective of this work is to enhance the understanding of female students' participation in software engineering projects to support gender-aware course optimization. Since...
The first published version of StartCards is presented, which is considered useful for early-stage startups and can also be used as a pedagogical tool in startup education.
An LLM-based multi-agent system is indicated that an LLM-based multi-agent system is a capable solution to update components of a legacy application autonomously.
An LLM-based multi-agent system that autonomously upgrades legacy web applications to the latest versions and maintains context across tasks and agents, improving solution quality over the base model in some cases is proposed.
An experience report from a small full-stack team that applied contextual prompting and explicit architectural constraints to build a multi-project agent learning platform designed for sustained, production-oriented use and an academic retrieval-augmented generation system is presented.
Five domain-specific RAG applications developed for real-world scenarios across governance, cybersecurity, agriculture, industrial research, and medical diagnostics are presented, highlighting technical, operational, and ethical challenges affecting the reliability and usability of RAG systems in practice.
A generalized Multimodal Subspace Support Vector Data Description model with graph-embedded regularization is proposed, illustrating how relational and structural information can be systematically embedded into one-class models, enabling robust learning under complex, high-dimensional, and multimodal conditions.
Securing AI systems is not a task any single discipline can accomplish alone; it requires sustained collaboration between machine learning researchers, cybersecurity professionals, and policymakers if AI technologies are to remain reliable, trustworthy, and resilient in adversarial environments.
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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