AI News Archive: May 12, 2026 — Part 16
Sourced from 500+ daily AI sources, scored by relevance.
- Exam Prep
AI-powered prep for cloud certification exams
- Screener AI (Linkedin real-time data)
300 Linkedin profiles screened in 2m, no cookies - real time
- AI MVP Cost Calculator
AI-Powered MVP Cost Estimation Before You Build
- Rewright.ai
AI autocomplete for people who still want to write.
- AI Tutorium
Master AI for Everyday Success
- Kenoki
Relationship intelligence (GraphRAG) for your Network
- AiQ: Learn AI Prompting
Duolingo for AI prompting. AI teaching you to be good at AI.
- Becoming
your journal that writes back
- Rekall MCP Tools for Unreal Engine
The AI-native MCP server for Unreal Engine.
- Stingwave
AI voice keyboard for field sales reps — talk and it writes
- PromptMate
Save prompts once. Use on ChatGPT and Claude.
- Krew AI
Hire an AI krew. Get work done. Pay less than a chai.
- X Filter Pro
Clean your X feed: hide ads, focus mode, AI summaries
- Maisonnez
AI real estate search in plain French — DVF, DPE
- Swalex
Screen stocks by asking, not filtering
- RepoToPitch
Auto-generates pitch decks from GitHub repos
- Vulcanizare Mobilă
First AI-native roadside assistance API, open SDK, MCP ready
- Polymind
Ask multiple AIs. Get one synthesized answer.
- Xaloia AI
Privacy-first AI chat with live avatar conversations
- Agent Stack
5 tiny npm libs to stop AI agents misbehaving in prd
- MacMind
Run private AI on your Mac
- ResumeForge
AI resume optimizer that gets you more interviews
- Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models
Visual latent reasoning lets a multimodal large language model (MLLM) create intermediate visual evidence as continuous tokens, avoiding external tools or image generators. However, existing methods usually follow an output-as-input latent paradigm and yield unstable gains. We identify evidence for ...
- QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning
Qubit routing is a fundamental problem in quantum compilation, known to be NP-hard. Its dynamic nature makes local routing decisions propagate and compound over time, making global efficient solutions challenging. Existing heuristic methods rely on local rules with limited lookahead, while recent le...
- A Family of Quaternion-Valued Differential Evolution Algorithms for Numerical Function Optimization
The numerical optimization of continuous functions is a fundamental task in many scientific and engineering domains, ranging from mechanical design to training of artificial intelligence models. Among the most effective and widely used algorithms for this purpose is Differential Evolution (DE), know...
- MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering
Evaluating large language models (LLMs) in the biomedical domain requires benchmarks that can distinguish reasoning from pattern matching and remain discriminative as model capabilities improve. Existing biomedical question answering (QA) benchmarks are limited in this respect. Multiple-choice forma...
- BSO: Safety Alignment Is Density Ratio Matching
Aligning language models for both helpfulness and safety typically requires complex pipelines-separate reward and cost models, online reinforcement learning, and primal-dual updates. Recent direct preference optimization approaches simplify training but incorporate safety through ad-hoc modification...
- EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records
Electronic Health Records (EHR) contain rich longitudinal patient information and are widely used in predictive modeling applications. However, effectively leveraging historical data remains challenging due to long trajectories, heterogeneous events, temporal irregularity, and the varying relevance ...
- Reinforcing VLAs in Task-Agnostic World Models
Post-training Vision-Language-Action (VLA) models via reinforcement learning (RL) in learned world models has emerged as an effective strategy to adapt to new tasks without costly real-world interactions. However, while using imagined trajectories reduces the sample complexity of policy training, ex...
- Towards Automated Air Traffic Safety Assessment Around Non-Towered Airports Using Large Language Models
We investigate frameworks for post-flight safety analysis at non-towered airports using large language models (LLMs). Non-towered airports rely on the Common Traffic Advisory Frequency (CTAF) for air traffic coordination and experience frequent near mid-air collisions due to the pilot self-announcem...
- LISA: Cognitive Arbitration for Signal-Free Autonomous Intersection Management
Large language models (LLMs) show strong potential for Intelligent Transportation Systems (ITS), particularly in tasks requiring situational reasoning and multi-agent coordination. These capabilities make them well suited for cooperative driving, where rule-based approaches struggle in complex and d...
- Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling
Random delays weaken the temporal correspondence between actions and subsequent state feedback, making it difficult for agents to identify the true propagation process of action effects. In cross-task scenarios, changes in task objectives and reward formulations further reduce the reusability of pre...
- Executable Agentic Memory for GUI Agent
Modern GUI agents typically rely on a model-centric and step-wise interaction paradigm, where LLMs must re-interpret the UI and re-decide actions at every screen, which is fragile in long-horizon tasks. In this paper, we propose Executable Agentic Memory (EAM), a structured Knowledge Graph (KG) that...
- PriorZero: Bridging Language Priors and World Models for Decision Making
Leveraging the rich world knowledge of Large Language Models (LLMs) to enhance Reinforcement Learning (RL) agents offers a promising path toward general intelligence. However, a fundamental prior-dynamics mismatch hinders existing approaches: static LLM knowledge cannot directly adapt to the complex...
- Iterative Audit Convergence in LLM-Managed Multi-Agent Systems: A Case Study in Prompt Engineering Quality Assurance
Prompt specifications for multi-agent large language model (LLM) systems carry data contracts and integration logic across many interdependent files but are rarely subjected to structured-inspection rigor. This paper reports a single-system empirical case study of iterative, agent-driven auditing ap...
- NARA: Anchor-Conditioned Relation-Aware Contextualization of Heterogeneous Geoentities
Geospatial foundation models have primarily focused on raster data such as satellite imagery, where self-supervised learning has been widely studied. Vector geospatial data instead represent the world as discrete geoentities with explicit geometry, semantics, and structured spatial relations, includ...
- How Useful Is Cross-Domain Generalization for Training LLM Monitors?
Using prompted language models as classifiers enables classification in domains with limited training data, but misses some of the robustness and performance benefits that fine-tuning can bring. We study whether training on multiple classification tasks, each with its own prompt, improves performanc...
- Long Horizon
Your coding agent writes the feature and runs the tests
- Reconnecting Fragmented Citation Networks with Semantic Augmentation
Citation graphs are fundamental tools for modeling scientific structure, but are often fragmented due to missing citations of scientifically connected articles. To address this issue, we propose a computationally efficient hybrid framework integrating citation topology with large language model (LLM...
- Beyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training
In settings where labeled verifiable training data is the binding constraint, each checked example should be allocated carefully. The standard practice is to use this data directly on the model that will be deployed, for example by running GRPO on the deployment student. We argue that this is often ...
- ToolCUA: Towards Optimal GUI-Tool Path Orchestration for Computer Use Agents
Computer Use Agents (CUAs) can act through both atomic GUI actions, such as click and type, and high-level tool calls, such as API-based file operations, but this hybrid action space often leaves them uncertain about when to continue with GUI actions or switch to tools, leading to suboptimal executi...
- KV-Fold: One-Step KV-Cache Recurrence for Long-Context Inference
We introduce KV-Fold, a simple, training-free long-context inference protocol that treats the key-value (KV) cache as the accumulator in a left fold over sequence chunks. At each step, the model processes the next chunk conditioned on the accumulated cache, appends the newly produced keys and values...
- Solve the Loop: Attractor Models for Language and Reasoning
Looped Transformers offer a promising alternative to purely feed-forward computation by iteratively refining latent representations, improving language modeling and reasoning. Yet recurrent architectures remain unstable to train, costly to optimize and deploy, and constrained to small, fixed recurre...
- Enabling AI-Native Mobility in 6G: A Real-World Dataset for Handover, Beam Management, and Timing Advance
To address the issues of high interruption time and measurement report overhead under user equipment (UE) mobility especially in high speed 5G use cases the use of AI/ML techniques (AI/ML beam management and mobility procedures) have been proposed. These techniques rely heavily on data that are most...
- Classifier Context Rot: Monitor Performance Degrades with Context Length
Monitoring coding agents for dangerous behavior using language models requires classifying transcripts that often exceed 500K tokens, but prior agent monitoring benchmarks rarely contain transcripts longer than 100K tokens. We show that when used as classifiers, current frontier models fail to notic...
- $δ$-mem: Efficient Online Memory for Large Language Models
Large language models increasingly need to accumulate and reuse historical information in long-term assistants and agent systems. Simply expanding the context window is costly and often fails to ensure effective context utilization. We propose $δ$-mem, a lightweight memory mechanism that augments a ...
- A New Technique for AI Explainability using Feature Association Map
Lack of transparency in AI systems poses challenges in critical real-life applications. It is important to be able to explain the decisions of an AI system to ensure trust on the system. Explainable AI (XAI) algorithms play a vital role in achieving this objective. In this paper, we are proposing a ...
- KAN-CL: Per-Knot Importance Regularization for Continual Learning with Kolmogorov-Arnold Networks
Catastrophic forgetting remains the central obstacle in continual learning (CL): parameters shared across tasks interfere with one another, and existing regularization methods such as EWC and SI apply uniform penalties without awareness of which input region a parameter serves. We propose KAN-CL, a ...
- TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching
Direct Preference Optimization (DPO) is a widely used RL-free method for aligning language models from pairwise preferences, but it models preferences over full sequences even though generation is driven by per-token decisions. Existing token-level extensions typically decompose a sequence-level Bra...
- Why Conclusions Diverge from the Same Observations: Formalizing World-Model Non-Identifiability via an Inference
When people share the same documents and observations yet reach different conclusions, the disagreement often shifts into a judgment that the other party is cognitively defective, irrational, or acting in bad faith. This paper argues that such divergence is better described as a form of non-identifi...