Frontier large language model-based agents from Anthropic and OpenAI matched the performance of trained human biocurators in linking free-text phenotype descriptions to ontology terms, a task known as phenotype annotatio…
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 system uses a multimodal…
The BEAMS Initiative has established benchmarks to guide the development of ethical and human-centered AI tools for modeling and simulation, using open digital infrastructure to evaluate AI performance across tasks like …
A longitudinal analysis of approximately 12,000 Microsoft Bing Copilot users found that individual user habits remain overwhelmingly sticky over time, with weak trends in personal conversational trajectories despite sign…
A new study from arXiv (2605.29027) found that tonal variations in prompts cause systematic but model-dependent accuracy shifts in large language models (LLMs) on objective multiple-choice questions. Testing four cost-ef…
Researchers have developed Differentiable Belief-based Opponent Shaping (D-BOS), a first-order method for multi-agent reinforcement learning that treats an observer's belief as the shaped opponent state and differentiate…
Researchers have developed a multi-agent AI pipeline that reduces hallucination rates by up to 35.9% across 310 test prompts while cutting energy consumption by 47.3% through semantic caching. The system uses a three-sta…
Researchers have developed COLAGUARD, a new safety guardrail for large language models that transfers multi-step reasoning into a continuous latent space to reduce latency. In tests across ten moderation settings and eig…
Researchers have developed a policy-neutral execution and measurement layer to address reliability and interpretability issues in event-driven industrial scheduling policies. The framework constructs decision-valid snaps…
Researchers from Redpanda have introduced the Agentic Data Plane (ADP), an architecture that uses out-of-band metadata channels to enforce security policies and audit trails for autonomous AI agents. The system addresses…
A study of ClinicalTrials.gov records found a marked increase in AI-related clinical trials over time, with recent growth in references to machine learning, deep learning, chatbots, GPTs, and large language models. China…
Researchers have developed a method to detect and track concepts within large language models (LLMs) by creating datasets that delineate specific ideas, training linear probes to identify those concepts across model laye…
RightNowAI released RightNow-Arabic-0.5B-Turbo, a 518M-parameter Arabic-specialized language model that outperforms all existing sub-1B open models on Arabic benchmarks while running on edge devices. The model, built by …
Researchers have developed Aryabhata 2, a reasoning-focused language model for competitive STEM examinations, trained via reinforcement learning on PhysicsWallah's internal question banks. The model outperforms its base …
Researchers have developed S3MEM, a structured memory framework that improves long-horizon interactive question answering by converting agent trajectories into query-aligned evidence. In tests across four environments, S…
A new study systematically examined the effect of transformer-based language model size on topic quality, finding that model size had a negligible impact on coherence and divergence metrics. Researchers tested seven mode…
Researchers conducted the first comprehensive comparison of Dutch syllabification algorithms, finding that data-driven approaches outperformed knowledge-based methods across most datasets. A newly developed deep learning…
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 parameter model fine-tuned …
Researchers have developed NRLB (No Reader Left Behind), a multi-agent framework for plain language summarization that simulates three reader groups: elementary students, non-native speakers, and readers with attention d…
Researchers have developed SERC, a semantic error correction framework inspired by LDPC codes that treats LLM text generation as a noisy communication channel to detect and fix hallucinations. The training-free, model-ag…