AI

Microsoft secures 900 MW AI capacity at Crusoe Texas campus with mid 2027 target

Rogue AI is already here

Nonagon Capital and Startale Group Partner to Advance JPYSC Agentic Payment Use Cases

Federal Court Halts Trump Administration’s Anthropic AI Ban Over First Amendment Concerns

Anthropic wins court pause on Pentagon Claude ban

Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach

Researchers have developed the first formal mathematical framework for verifying AI agent protocols, specifically comparing Schema-Guided Dialogue (SGD) and Model Context Protocol (MCP). They proved these systems are structurally similar but identified critical gaps in MCP's capabilities, proposing MCP+ extensions to achieve full equivalence with SGD.

Supervising Ralph Wiggum: Exploring a Metacognitive Co-Regulation Agentic AI Loop for Engineering Design

Researchers developed a novel Co-Regulation Design Agentic Loop (CRDAL) system that uses metacognitive agents to improve AI-driven engineering design by reducing design fixation. The system showed better performance than traditional approaches in battery pack design tasks without significantly increasing computational costs.

ReLope: KL-Regularized LoRA Probes for Multimodal LLM Routing

Researchers introduce ReLope, a new routing method for multimodal large language models that uses KL-regularized LoRA probes and attention mechanisms to improve cost-performance balance. The method addresses the challenge of degraded probe performance when visual inputs are added to text-only LLMs.

FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol

Graph-of-Mark: Promote Spatial Reasoning in Multimodal Language Models with Graph-Based Visual Prompting

Researchers introduced Graph-of-Mark (GoM), a new visual prompting technique that overlays scene graphs onto images to improve spatial reasoning in multimodal language models. Testing across 3 open-source MLMs and 4 datasets showed GoM improved zero-shot visual question answering and localization accuracy by up to 11 percentage points compared to existing methods like Set-of-Mark.

See and Fix the Flaws: Enabling VLMs and Diffusion Models to Comprehend Visual Artifacts via Agentic Data Synthesis

Researchers introduce ArtiAgent, an automated system that creates pairs of real and artifact-injected images to help AI models better detect and fix visual artifacts in generated content. The system uses three specialized agents to synthesize 100K annotated images, addressing the costly and scaling challenges of human-labeled artifact datasets.

TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts

Researchers propose TAG-MoE, a new framework that improves unified image generation and editing models by making AI routing decisions task-aware rather than task-agnostic. The system uses hierarchical task semantic annotation and predictive alignment regularization to reduce task interference and improve model performance.

TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs

Researchers introduce TimeLens, a family of multimodal large language models optimized for video temporal grounding that outperforms existing open-source models and even surpasses proprietary models like GPT-5 and Gemini-2.5-Flash. The work addresses critical data quality issues in existing benchmarks and introduces improved training datasets and algorithmic design principles.

Instruction Following by Principled Boosting Attention of Large Language Models

Researchers developed InstABoost, a new method to improve instruction following in large language models by boosting attention to instruction tokens without retraining. The technique addresses reliability issues where LLMs violate constraints under long contexts or conflicting user inputs, achieving better performance than existing methods across 15 tasks.

CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers

CodeRefine is a new AI framework that automatically converts research paper methodologies into functional code using Large Language Models. The system creates knowledge graphs from papers and uses retrieval-augmented generation to produce more accurate code implementations than traditional zero-shot prompting methods.

A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis

Researchers developed UF-FGTG, a framework that automatically converts novice user prompts into model-preferred prompts for text-to-image AI systems. The system uses a novel Coarse-Fine Granularity Prompts dataset and achieved 5% improvement across quality metrics compared to existing methods.

DeepFAN, a transformer-based deep learning model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multi-reader, multi-case trial

DeepFAN, a transformer-based AI model, achieved 93.9% diagnostic accuracy for lung nodule classification and significantly improved junior radiologists' performance by 10.9% in clinical trials. The model was trained on over 10,000 pathology-confirmed nodules and validated across 400 cases at three medical institutions.

Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verification

A benchmarking study reveals demographic bias in multimodal large language models used for face verification, testing nine models across different ethnicity and gender groups. The research found that face-specialized models outperform general-purpose MLLMs, but accuracy doesn't correlate with fairness, and bias patterns differ from traditional face recognition systems.

TAAC: A gate into Trustable Audio Affective Computing

Researchers have developed TAAC, a framework for trustable audio-based depression diagnosis that protects user identity information while maintaining diagnostic accuracy. The system uses adversarial loss-based subspace decomposition to separate depression features from sensitive identity data, enabling secure AI-powered mental health screening.

Mapping the Course for Prompt-based Structured Prediction

Researchers propose combining large language models (LLMs) with combinatorial inference to address hallucinations and improve structured prediction accuracy. The study finds that incorporating symbolic inference yields more consistent predictions than prompting alone, with calibration and fine-tuning further enhancing performance on complex tasks.

Do Language Models Follow Occam's Razor? An Evaluation of Parsimony in Inductive and Abductive Reasoning

Researchers evaluated whether large language models follow Occam's Razor principle when performing inductive and abductive reasoning, finding that while LLMs can handle simple scenarios, they struggle with complex world models and producing high-quality, simplified hypotheses. The study introduces a new framework for generating reasoning questions and an automated metric to assess hypothesis quality based on correctness and simplicity.

Lightweight GenAI for Network Traffic Synthesis: Fidelity, Augmentation, and Classification

Researchers developed lightweight generative AI models for creating synthetic network traffic data to address privacy concerns and data scarcity in network traffic classification. The models achieved up to 87% F1-score when classifiers were trained solely on synthetic data, with transformer-based approaches providing the best balance of accuracy and computational efficiency.

The Information Dynamics of Generative Diffusion

Researchers present a unified theoretical framework for understanding generative diffusion models by connecting information theory, dynamics, and thermodynamics. The study reveals that diffusion generation operates as controlled noise-induced symmetry breaking, where the score function regulates information flow from noise to structured data.

MolQuest: A Benchmark for Agentic Evaluation of Abductive Reasoning in Chemical Structure Elucidation

Researchers introduce MolQuest, a new benchmark for evaluating AI models' ability to perform complex chemical structure elucidation through multi-step reasoning. Even state-of-the-art AI models achieve only 50% accuracy on this real-world scientific task, revealing significant limitations in current AI systems' strategic reasoning capabilities.

Probing the Lack of Stable Internal Beliefs in LLMs

Research reveals that large language models (LLMs) struggle to maintain consistent internal beliefs or goals across multi-turn conversations, failing to preserve implicit consistency when not explicitly provided context. This limitation poses significant challenges for developing persona-driven AI systems that require stable personality traits and behavioral patterns.