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American billionaire Mark Cuban sees ‘deep trouble’ for Sam Altman’s OpenAI
Shark Tank investor Mark Cuban warns OpenAI's massive fundraising may lead to 'deep trouble' if economics don't deliver, questioning AI infrastructure spending returns. He also expressed uncertainty about the LLM market's future and criticized AI firms for hyping risks. Separately, Cuban revealed selling most Bitcoin, finding it an unreliable hedge against global turmoil.
Do You Actually Need to Pay for Transcription Software?
I'm constantly seeing ads for Wispr Flow, an AI-powered transcription tool. The pitch—that you'll be able to write faster by talking out loud instead of typing—is compelling, especially if you're a slow typist. The marketing promises you'll be able to "write at the speed of thought, 4x faster than your keyboard."
FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences
arXiv:2606.03330v1 Announce Type: new Abstract: Literature reveals that a Large Language Model's (LLM) behavior is not only conditioned by its original weights but also its instance-level parameters, such as instructional prompt, sampling configuration or quantization. A model that generates safe outputs under one configuration may produce toxic content under another.
Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions
arXiv:2604.08304v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external knowledge, but this access path also introduces security risks that existing work often conflates with inherent LLM flaws. We frame secure RAG as securing external knowledge access and organize the literature with SLOT, a taxonomy along four axes: the attack Surface (S) where an adversary acts, the defense Layer (L) that controls the same point, the...
COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation
Announce Type: new Abstract: LLM agents are increasingly expected not only to complete isolated tasks, but also to carry bounded representations of human expertise, judgment, and interaction style. Building such person-grounded agents remains difficult because actionable knowledge associated with a person or role is usually embedded in heterogeneous traces rather than written as clean instructions. Existing memory and persona systems capture fragments of this evidence, while skill frameworks...
Show HN: Formally verified polygon intersection – Opus 4.8 oneshots, prev failed
To my knowledge, this is the first formally verified implementation of an intersection algorithm for polygons. The experience of working with AI agents on this project changed a lot with recent model releases, as I describe in the readme. Opus 4.8 is able to provide algorithm implementation with formal proof in one shot, whereas previous models required me to provide proof strategies in multiple steps.
PerceptTwin: Semantic Scene Reconstruction for Iterative LLM Planning and Verification
arXiv:2606.04226v1 Announce Type: new Abstract: Simulation environments are useful for both robot policy learning and planning verification and validation. Traditionally, the process of creating a simulation was onerous. Creating a bespoke simulation environment for each individual environment that a robot would operate in was simply infeasible.
FOXGLOVE: Understanding Goal-Oriented and Anchored Writing Feedback from Experts and LLMs on Argumentative Essays
arXiv:2606.06271v1 Announce Type: new Abstract: While large language models (LLMs) are increasingly used to generate writing feedback, there remains no systematic comparison of LLM and expert feedback on the dimensions that writing research identifies as central to revision: goal-orientation, anchoring to specific sentences, and prioritization. We introduce FOXGLOVE, a dataset of 696 feedback comments written by trained writing instructors on 69 twelfth-grade argumentative essays, paired...
Fast and Expressive Multi-Byte Prediction with Probabilistic Circuits
arXiv:2511.11346v2 Announce Type: replace Abstract: Multi-token prediction (MTP) is a prominent strategy to significantly speed up generation in large language models (LLMs), especially in byte-level LLMs, which are tokeniser-free but prohibitively slow. However, many existing MTP methods either assume independence between future tokens, sacrificing expressiveness, or generate tokens one at a time within the window, increasing latency. In this work, we investigate the trade-off between...
Do Matching Mechanisms Work with LLM Agents?
Announce Type: new Abstract: This study examines whether standard matching mechanisms function as intended in LLM-agent markets, where LLM agents make allocation-related decisions as delegated decision-makers. We compare decentralized free-negotiation markets with centralized mechanism-based markets including several representative mechanisms. Across controlled one-to-one matching environments, mechanism-based markets generally outperform free negotiation in terms of stability and efficiency.