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SAPO: Single-Rollout Autoregressive Policy Optimization for Agentic Reinforcement Learning
A new reinforcement learning method called Single-rollout Autoregressive Policy Optimization (SAPO) is proposed to address the memory overhead, computation efficiency, and performance issues of existing methods. SAPO…
Specification-delta-driven data governance: an empirical study of the «spec-delta» as the unit of change in lakehouse data platforms
The paper discusses the concept of Spec-Delta, which is the idea that changes in data platforms should be reviewed as units of change in requirements rather than just code changes. The paper proposes a formalized…
The 2026 AI Index Report
Stanford HAI's 2026 AI Index organizes measurement across research and development, technical performance, responsible AI, the economy, science, medicine, education, policy, and public opinion.
Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection
The paper presents Task-CoEvolve, a new approach to optimize language model agent harnesses efficiently by adaptively selecting validation tasks. It addresses the challenge of using fixed validation sets by co-evolving…
Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection
Researchers have developed a method called Task-CoEvolve to improve the efficiency of harness optimization for large language models (LLMs). This method co-evolves validation tasks with the harness by focusing on tasks…
The Third Restructuring of Software Form: From the Three-Tier Architecture to Storage, Models, and Agents
The paper suggests a third shift in software development, called Software 3.0, which focuses on context and reasoning determining behavior. The proposed terminal form includes a generalized database, a large model, and…
The Third Restructuring of Software Form: From the Three-Tier Architecture to Storage, Models, and Agents
The paper suggests a third paradigm shift in software form, moving from data determining behavior (Software 2.0) to context and reasoning determining behavior (Software 3.0). This new form is proposed to converge into…
Write Once, Run Everywhere: The Axon DSL for Shape-Safe and Framework-Agnostic LLM Architectures
Researchers introduce Axon, a strongly typed domain-specific language with Haskell-like syntax that aims to enable a write-once, run everywhere paradigm for Language Model architectures. This allows for efficient model…
A Theory of Post-hoc Debate Judgement
In this research paper published in 2026, Xiang Yin et al. propose a theory of post-hoc debate judgement for agentic AI. The authors discuss the use of debates to improve AI performance and user engagement, and how the…
A Theory of Post-hoc Debate Judgement
In this research, Xiang Yin and colleagues propose a theory of post-hoc judgement for debates used by agentic AI. The goal is to improve the performance, explainability, and user engagement of AI agents through debate…
Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering
Researchers have developed an adaptive memory and reflection multi-agent system for medical question answering. This system, which uses specialized agents with dedicated memory and feedback, aims to improve the…
Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering
Researchers introduce a new multi-agent system called Adaptive Memory and Reflection (AMR) for medical question answering. This system aims to address the limitations of existing systems by using specialized agents…
ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning
The paper introduces ADEPT, a reinforcement learning framework for training multi-fingered robots to perform complex tasks directly from raw visual and tactile perception. The framework involves pre-training a…
Beyond Teacher Likelihood: Group-Calibrated On-Policy Distillation for Long-Context Reasoning
Researchers introduce Group-Calibrated On-Policy Distillation (GC-OPD), a method for long-context reasoning tasks that aims to address the mismatch between on-policy distillation and task-specific verifiers. The new…
Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication
Researchers have developed Verifiable Latent Alignments (VLA), a framework for monitoring and controlling communication between language-model agents that occurs through hidden states not visible in public transcripts…
Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication
Researchers have developed Verifiable Latent Alignments (VLA), a framework to monitor and steer private communication channels used by language model agents in multi-agent systems. The framework links the private…
Breaking the weakest link to evade vision language models
The authors investigate the vulnerability of Vision Language Models (VLMs) to adversarial attacks, which can disrupt or force the models' interpretation of visual inputs. They propose a gradient-based attack method…
Decomposing Wrong-Consensus Agreement in LLM Self-Consistency: A GPT-4.1 Case Study
The study presents an analysis of the inconsistency in the answers provided by Large Language Models (LLMs) when using majority voting to improve accuracy. The authors propose a quantitative measure called Gamma, which…
DeepWeaver: Bridging the Evidence Synthesis Gap in Open-Ended Question Answering
The paper introduces DeepWeaver, a framework designed to address the gap between evidence retrieval and generation in open-ended question answering systems. DeepWeaver organizes and synthesizes noisy and fragmented…
DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning
Researchers have developed DentAgent, an evidence-centric multi-agent framework to address the limitations of existing dental AI systems. This system integrates various modalities like domain knowledge, radiographs…
DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning
A new system called DentAgent is introduced that aims to improve the accuracy and reliability of dental assessments by integrating various forms of evidence from different modalities. The system uses an…
Do Large Language Models Hallucinate Electric Fata Morganas?
The paper explores the issue of AI hallucinations (outputs that are made up or contradict source material) and their implications for machine consciousness. The author investigates causes of hallucinations in large…
Epistemic Subordination: Generative AI and the Infrastructure of Knowledge
The paper discusses how Generative AI encodes the dominant culture's way of knowing into its architecture, creating an epistemic condition known as 'epistemic subordination'. This affects minority epistemologies and…
Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery
The paper presents Eureka, a Meta-Agent architecture that can complete long-horizon tasks by forming specialized Macro-Agents for specific tasks. The architecture allows for cost-benefit-gated evolution to update the…
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