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ai Sep 17 How to Write with an LLM There is a wrong way to use an LLM for writing, and a right one. The wrong way is to let the model generate your prose. The right way is to write everything yourself, then hand the draft to a model and let it find the problems you... ai Sep 17 Show HN: Die With Me – Claude and Codex rate limits as AIM away messages Die With Me Turns AI Token Limits Into a Social Experience Running out of tokens used to be a solitary frustration. You hit your Claude Code limit mid-refactor, stared at the error message, and waited. Now there is an app that tur... ai Sep 17 A Zeroth-Order Paradigm for LLM Preference Alignment The Likelihood Displacement Problem in Preference Alignment Training large language models to follow human preferences has become a core bottleneck in shipping reliable AI systems. Reinforcement learning from human feedback (RLHF)... ai Sep 17 Dreaming the Sound of Contact: Leveraging Video and Audio Generation for Zero-Shot Force-Aware Manipulation and Data Generation Why Generated Video Alone Fails at Contact-Rich Manipulation Video generation models have opened a new path for robot learning. Instead of teleoperating a robot hundreds of times to collect demonstrations, you can generate a video... ai Sep 16 Breaking the 1.58-bit Barrier for Ternary LLMs Ternary large language models pack every weight into one of three values: -1, 0, or +1. The information-theoretic floor for storing those choices is about 1.585 bits per weight, and the standard five-trit packing scheme used in pr... ai Sep 16 Agentic Societies Need a Social Harness When AI Agents Talk to Each Other, They Need More Than a Protocol Imagine two AI agents, each working on behalf of a different person, trying to schedule a meeting. One has access to its principal's calendar. The other has access... ai Sep 16 When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control LLMs Should Know When to Say "I Don't Know" Language models are fluent by default. Ask them a question they cannot answer, and they will produce something that sounds right, reads well, and might be completely wrong. In healthcare... ai Sep 16 Verifiable Social Reasoning for LLM Assistants Why Your Chatbot Can't Read Between the Lines When you tell your LLM assistant that a coworker seems a little too eager to help, you're giving it a subjective, filtered, emotionally loaded account of reality. The assistant never s... ai Sep 16 LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence A Tabular Foundation Model That Learns Joint Distributions, Not Just Predictions Tabular data is the unglamorous backbone of machine learning in practice. Healthcare records, financial transactions, sensor readings, scientific mea... ai Sep 16 JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management Fitting a 27B-Parameter LLM into 24 GiB of Laptop Memory The promise of local LLM inference is straightforward: run capable models on hardware you own, keep your data private, and avoid API costs. The obstacle is equally straightf...