#ai-llms
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MedEvoEval: A Longitudinal Framework for Evaluating Doctor Agents
MedEvoEval is a new evaluation framework that moves beyond static medical QA by testing how doctor agents learn, retain, and adapt clinical decision-making skills across sequences of simulated outpatient episodes.
Specialized Clinical AI Outperforms General Models in Real-World Use
A study of 620 real-world clinical queries shows that specialized AI tools significantly outperform general-purpose models across accuracy, utility, and verifiability, highlighting the need for domain-specific evaluation.
IMCBench: Evaluating Multimodal LLMs in Clinical Conversations
IMCBench is a new multi-turn, image-grounded benchmark for medical AI that reveals a critical gap: accurate clinical descriptions do not guarantee safe patient guidance.
ATHENA-R1: An AI Agent for Iterative Biomedical Treatment Reasoning
ATHENA-R1 is an AI agent that performs iterative treatment reasoning by dynamically querying a universe of 212 biomedical tools, outperforming GPT-5 by significant margins in clinical benchmarks.
COMPASS: Improving Compositional Control in Multimodal Models
COMPASS introduces a unified framework that uses a shared 'expert token' to bridge composition perception and generation, enabling precise layout control in multimodal models.
Building a Text-JEPA Model from Scratch
Text-JEPA moves away from auto-regressive token prediction by learning world model representations in latent space, offering a potential path toward more efficient, non-generative intelligence.
ATOD: Hybrid Training for High-Performance AI Agents
ATOD combines on-policy distillation with reinforcement learning to overcome the performance ceiling of imitation learning, using an annealed schedule and turn-level reweighting to improve long-horizon agent training.
Mitigating Rollout Error in Graph World Models
Graph World Models (GWMs) face unique long-horizon errors where local inaccuracies propagate through topology. The Error-Aware GWM framework uses spectral regularization and critical-node weighting to maintain stability during dynamic-edge rollouts.
ODYSSEY: A Categorical Framework for Verifiable AI Models
ODYSSEY introduces a categorical framework using 'foundries'—modular, verifiable building blocks—to construct foundation models that maintain local truth and allow for rigorous, queryable knowledge management.
DysLexLens: A Framework for Analyzing Dyslexic Learner AI Experiences
DysLexLens is an end-to-end, evidence-traceable framework that uses dictionary-driven filtering and knowledge graphs to analyze how dyslexic learners interact with AI tools via online forums.
AI-ModelNet: A Networked Paradigm for Collaborative AI
AI-ModelNet proposes a hierarchical, internet-inspired architecture to enable interconnection, capability sharing, and collaborative reasoning among heterogeneous, domain-specific models.
The Agentic AI Engineer: Eval-Driven Development Loops
The Agentic AI Engineer automates the agent development lifecycle—spec, build, evaluate, diagnose, and optimize—using a multi-agent system to remove the human bottleneck from production-ready AI agent maintenance.
OpenAI's Custom Silicon Strategy and the Shift Away from Nvidia
OpenAI is developing a custom inference chip, 'Jalapeño,' in partnership with Broadcom to reduce reliance on Nvidia, mirroring a broader industry trend of vertical integration to gain hardware control and performance optimization.
The Critical Gaps in Multimodal LLM Evaluation
Current MLLM benchmarks rely on isolated tasks that fail to measure true cross-modal integration, missing key capabilities like temporal-spatial coherence and physical world reasoning.
The Verification Horizon: Why Coding Agents Need Evolving Rewards
As AI coding agents improve, generating code becomes easier than verifying it. Because no static reward function can perfectly capture human intent, verification must co-evolve with model capabilities to prevent reward hacking.
Evaluating LLM Agents in High-Stakes Energy Analytics
A new benchmark of 243 expert-curated energy tasks reveals how tool-augmented LLM agents handle live data, regulatory knowledge, and quantitative modeling in professional energy markets.
Beyond Accuracy: Evaluating AI Agents After Benchmark Saturation
When AI benchmarks saturate, accuracy becomes a poor metric. Researchers should instead evaluate agents across six dimensions: construct validity, generalizability, efficiency, reliability, model/scaffold performance, and human-agent collaboration.
Analyzing AI Governance: A Pipeline for Comparing DAO and Corporate Models
A new LLM-powered pipeline reveals that while governance structures (DAO vs. Corporate) influence thematic focus, both models suffer from similar levels of participation inequality and community fragmentation.
The Shift to 3D Chip Stacking and Orchestrated AI Models
IBM's breakthrough in sub-1nm chip architecture enables 3D transistor stacking, while the AI industry pivots from single-model supremacy to multi-model orchestration and token-efficient workflows.
Building and Scaling Data Agents with Google Cloud
Google Cloud is expanding its agentic AI ecosystem by providing persona-specific data agents, developer-facing APIs, and the new Data Agent Kit to streamline workflows across engineering, science, and analytics.
Google Cloud TechChoosing a Web Development Tech Stack in 2026
In the age of AI, the specific framework or library matters less than your ability to understand, steer, and maintain the code AI generates. Prioritize tools you enjoy and understand, rather than blindly following AI's default preferences.
General Intuition Uses Gameplay Data to Train Embodied AI Agents
General Intuition is using hundreds of millions of hours of labeled gameplay data to train AI models in spatial-temporal reasoning, aiming to create a generalized 'brain' that can control both virtual agents and physical robots.
Powering Intelligent Agents with AI-Native Databases
Google Cloud is evolving databases into 'Agentic Data Clouds' by integrating AI primitives—like vector search, graph retrieval, and forecasting—directly into the SQL layer to provide agents with high-fidelity, secure, and real-time enterprise context.
Building AI-Powered Search with Google Cloud Spanner
Google Cloud Spanner enables hybrid search by combining full-text, vector, and graph capabilities within a single, transactionally consistent database, eliminating the need for complex ETL pipelines and external search indexes.
Building and Scaling AI Agents with BigQuery and AgentOps
Google Cloud's Agent Development Kit (ADK) and managed MCP servers allow developers to build data-aware agents with minimal code, while integrated AgentOps provides real-time observability into agent performance and costs.
Building AI-Powered Apps: A Low-Code Guide for Small Teams
Small teams can modernize legacy applications by leveraging 'vibe coding' and managed database AI features like hybrid search and vector embeddings, allowing them to implement semantic capabilities without needing a team of AI experts.
Looker's Evolution: From Data Visualization to Data Agency
Looker is shifting from a passive BI tool to an active 'agentic' platform, using Gemini to enable conversational analytics, automated dashboard insights, and proactive, triggered workflows that turn data into direct action.
Implementing DeepMind's Deep Research API
Google's Deep Research API enables developers to integrate autonomous, multi-step research agents into their applications, automating complex information gathering, synthesis, and visualization tasks.
The Miranda Hypothesis: Why Persona Evals Fail
Current persona-based AI benchmarks measure 'convincingness' rather than historical fidelity, leading to 'Miranda distortion' where models prioritize culturally dominant narratives (like the Hamilton musical) over primary documentary records.
Why Engineering Jobs Are Thriving in the Age of AI
Contrary to fears of automation-driven displacement, data shows that engineering roles are the most resilient job function, with demand increasing as AI-driven productivity expands the scope of work.
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