Researchers introduced GradeSQL, a framework for training Outcome Reward Models (ORMs) to improve test-time verification in Text-to-SQL tasks. ORM-based selection outperformed execution-based Best-of-…
The EU Cyber Resilience Act (CRA) imposes new security requirements on AI products sold in the EU, including mandatory vulnerability reporting within 24 hours starting September 2026. Developers must …
Enterprises adopting multi-agent workflows are failing because they apply the human-focused Software Development Lifecycle (SDLC) to non-human AI agents, which have different failure modes. The propos…
Researchers introduced AlgoEvolve, an LLM-driven evolutionary framework that generates and iteratively improves algorithmic trading strategies expressed as Python code. The system exhibits emergent re…
Handshake is hiring students and graduates to remotely test Large Language Models for top AI labs, paying up to $30 per hour with flexible hours. The role requires no specific expertise, only attentio…
A systematic experimental analysis of eight state-of-the-art Diffusion Language Models (DLMs) across eight benchmarks reveals that generation-time design choices strongly influence performance and com…
A comprehensive review published on arXiv examines fine-tuning techniques for Large Language Models (LLMs), covering methodologies from supervised and unsupervised learning to parameter-efficient meth…
Researchers propose CogGuard, a proactive-warning framework for edge intelligent services that uses LLMs for offline profile construction and SLMs for online score prediction. The framework reduces pr…
Researchers propose UP-NRPA, an online framework using large language models for dynamic dialogue policy adaptation based on user portraits, achieving a 100% success rate in multiple dialogue tasks an…
Researchers have developed ReLoRA, a knowledge-reusing re-adaptation framework that enables fast recovery of task-specific Low-Rank Adaptation (LoRA) adapters after large language model (LLM) base-mod…
Researchers have developed GenesisFunc, an automated pipeline that uses a multi-agent framework to generate high-quality, diverse training data for large language model function-calling. An 8B paramet…
Researchers have developed VFEAgent, an end-to-end multi-agent system that automates finite element analysis (FEA) modeling and simulation directly from input images and problem descriptions. The syst…
Researchers have introduced Micro-Macro Retrieval (M2R), a new framework designed to reduce hallucination in large language models during long-form text generation. The system addresses the problem of…
Researchers have found that hallucinations in small language models (SLMs) can be useful for solving complex multi-step reasoning problems. A new study proposes a "answer first-reason later" framework…
Researchers have developed a new Large Language Model (LLM)-based architecture that detects and quantifies human values in text, addressing limitations of previous approaches tied to specific value th…
Researchers have developed a framework that uses large language models to deliver just-in-time adaptive feedback to students by grounding the AI with domain-specific expert knowledge. Deployed in a un…
Researchers have developed CoReVAD, a training-free video anomaly detection framework that uses a single frozen Vision-Language Model to generate both anomaly scores and temporal descriptions without …
Limitations of stateless AI systems, which require feeding entire conversation histories into each new request, and introduces the concept of stateful AI agents that maintain persistent memory and sel…
The article argues that Generative AI contributes to a "post-truth" society by presenting incorrect information with certainty, as illustrated by the author's experience where a GenAI search result fa…
While AI tools like LLMs are valuable for automating tasks and boosting productivity, they have limitations such as hallucination and bias that require careful human oversight. It concludes that the e…