AI News Archive: May 12, 2026 — Part 15
Sourced from 500+ daily AI sources, scored by relevance.
- Sam Altman To Testify At California Tech Titan Trial
Sam Altman To Testify At California Tech Titan Trial Barron's
- Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory
Training Neural Networks (NNs) without overfitting is difficult; detecting that overfitting is difficult as well. We present a novel Random Matrix Theory method that detects the onset of overfitting in deep learning models without access to train or test data. For each model layer, we randomize each...
- SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation
Segmenting small and sparse structures in large-scale images is fundamentally constrained by voxel-level, lattice-bound computation and extreme class imbalance -- dense, full-resolution inference scales poorly and forces most pipelines to rely on fixed regionization or downsampling, coupling computa...
- Scalable Token-Level Hallucination Detection in Large Language Models
Large language models (LLMs) have demonstrated remarkable capabilities, but they still frequently produce hallucinations. These hallucinations are difficult to detect in reasoning-intensive tasks, where the content appears coherent but contains errors like logical flaws and unreliable intermediate r...
- Trust the Batch, On- or Off-Policy: Adaptive Policy Optimization for RL Post-Training
Reinforcement learning is structurally harder than supervised learning because the policy changes the data distribution it learns from. The resulting fragility is especially visible in large-model training, where the training and rollout systems differ in numerical precision, sampling, and other imp...
- Discrete Flow Matching for Offline-to-Online Reinforcement Learning
Many reinforcement learning (RL) tasks have discrete action spaces, but most generative policy methods based on diffusion and flow matching are designed for continuous control. Meanwhile, generative policies usually rely heavily on offline datasets and offline-to-online RL is itself challenging, as ...
- ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows
Table processing-including cleaning, transformation, augmentation, and matching-is a foundational yet error-prone stage in real-world data pipelines. While recent LLM-based approaches show promise for automating such tasks, they often struggle in practice due to ambiguous instructions, complex task ...
- Vexilo
Claude Code planner w/ 31 agents, 92 commands, + 121 skills
- Devkat
shareaura/strava overlays for ai coding sessions
- MY AI Agent
AI assembles a 3-10 agent team from one sentence
- Handinger
Put agents to work and automate repetitive tasks
- DocsAI
Enterprise document automation workflows
- PhotoVibe
Upload a photo. Discover music that matches your vibe.
- AgentFirst
Unlimited AI development on a subscription
- ScreenMask
Auto-redact sensitive data on screen while presenting
- KText
The intent-driven AI writing assistant for every app
- ContentPilots
Turn any video into endless Shorts & Reels with AI
- zubhai
LeetCode for AI Skills
- AlphaGRPO: Unlocking Self-Reflective Multimodal Generation in UMMs via Decompositional Verifiable Reward
In this paper, we propose AlphaGRPO, a novel framework that applies Group Relative Policy Optimization (GRPO) to AR-Diffusion Unified Multimodal Models (UMMs) to enhance multimodal generation capabilities without an additional cold-start stage. Our approach unlocks the model's intrinsic potential to...
- Learning, Fast and Slow: Towards LLMs That Adapt Continually
Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating parameters forces them to absorb task-specific information, which can result in catastrophic forgetting and loss of plasticity. In contrast, in-context learning with fixed LLM...
- OmniNFT: Modality-wise Omni Diffusion Reinforcement for Joint Audio-Video Generation
Recent advances in joint audio-video generation have been remarkable, yet real-world applications demand strong per-modality fidelity, cross-modal alignment, and fine-grained synchronization. Reinforcement Learning (RL) offers a promising paradigm, but its extension to multi-objective and multi-moda...
- Reward Hacking in Rubric-Based Reinforcement Learning
Reinforcement learning with verifiable rewards has enabled strong post-training gains in domains such as math and coding, though many open-ended settings rely on rubric-based rewards. We study reward hacking in rubric-based RL, where a policy is optimized against a training verifier but evaluated ag...
- The Algorithmic Caricature: Auditing LLM-Generated Political Discourse Across Crisis Events
Large Language Models (LLMs) can generate fluent political text at scale, raising concerns about synthetic discourse during crises and social conflict. Existing AI-text detection often focuses on sentence-level cues such as perplexity, burstiness, or token irregularities, but these signals may weake...
- A Causal Language Modeling Detour Improves Encoder Continued Pretraining
When adapting an encoder to a new domain, the standard approach is to continue training with Masked Language Modeling (MLM). We show that temporarily switching to Causal Language Modeling (CLM) followed by a short MLM decay improves downstream performance. On biomedical texts with ModernBERT, this C...
- CAAFC: Chronological Actionable Automated Fact-Checker for misinformation / non-factual hallucination detection and correction
With the vast amount of content uploaded every hour, along with the AI generated content that can include hallucinations, Automated Fact-Checking (AFC) has become increasingly vital, as it is infeasible for human fact-checkers to manually verify the sheer volume of information generated online. Prof...
- Formalize, Don't Optimize: The Heuristic Trap in LLM-Generated Combinatorial Solvers
Large Language Models (LLMs) struggle to solve complex combinatorial problems through direct reasoning, so recent neuro-symbolic systems increasingly use them to synthesize executable solvers. A central design question is how the LLM should represent the solver, and whether it should also attempt to...
- Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space
Large Language Models (LLMs) update their behavior in context, which can be viewed as a form of Bayesian inference. However, the structure of the latent hypothesis space over which this inference operates remains unclear. In this work, we propose that LLMs assign beliefs over a low-dimensional geome...
- Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
AI agents negotiate and transact in natural language with unfamiliar counterparts: a buyer bot facing an unknown seller, or a procurement assistant negotiating with a supplier. In such interactions, the counterpart's LLM, prompts, control logic, and rule-based fallbacks are hidden, while each decisi...
- Semantic Reward Collapse and the Preservation of Epistemic Integrity in Adaptive AI Systems
Recent advances in reinforcement learning from human feedback (RLHF) and preference optimization have substantially improved the usability, coherence, and safety of large language models. However, recurring behaviors such as performative certainty, hallucinated continuity, calibration drift, sycopha...
- EmailTemple
The AI studio for creating high conversion, on-brand emails
- OGLS-SD: On-Policy Self-Distillation with Outcome-Guided Logit Steering for LLM Reasoning
We study {on-policy self-distillation} (OPSD), where a language model improves its reasoning ability by distilling privileged teacher distributions along its own on-policy trajectories. Despite the performance gains of OPSD, we identify a common but often overlooked mismatch between teacher and stud...
- FastAI Health Coach
The AI fasting coach that actually remembers you.
- Vennio
programmable scheduling for developers and AI agents
- MeetMorph
Turn meetings into actionable prototypes with AI
- 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