Showing 433-444 of 462
AgentArk distills multi-agent debate into a single LLM via three hierarchical distillation strategies, shifting computation to training to cut inference cost while preserving reasoning.
Analysis of OMG-Agent (arXiv:2602.04144): a three-stage planner->retriever->executor that separates semantic planning from detail synthesis to curb hallucination and guide adoption.
Bloomberg/The Verge say Apple may let ChatGPT, Claude, Gemini and other voice chat apps run inside CarPlay — but Siri's button and wake word stay, so manual app launch is required.
Describes Empirical-MCTS: a dual-loop MCTS that evolves meta-prompts (PE-EMP) and uses a Memory Optimization Agent to distill and reuse reasoning traces across complex problems.
InterPReT lets lay users restructure a policy via instructions and continue training from demonstrations. In a 34-user racing game study it improved robustness without hurting usability.
ORBIT trains LLMs via cross-episode meta-RL so models learn from interaction traces at inference; authors report Qwen3-14B matches GPT-5.2 on unseen environments after meta-training.
Step-by-step tutorial to prototype an Interfaze-style stack: multimodal perception modules, context-construction pipeline, and action layer with a thin controller and benchmark targets.
Agentic workflows and prompt coercion are the new attack surface. This tutorial shows a concrete, deployable boundary strategy (policy engine + sandbox + attested channels) to reduce agentic compromise risk — with configs, code, metrics and a founder cost/risk frame.
Guide to building Scalable Interactive Oversight: decompose intent into a decision tree, collect low‑burden node feedback, aggregate signals, and gain 54% better alignment.
Examines a state-level selective verification pipeline—feasibility gating, learned scoring and ranking, and adaptive verifier allocation—that trims verifier calls by 44% on MATH.
TMK prompts (Task / Method / Knowledge) raised LLM planning accuracy on PlanBench Blocksworld from 31.5% to 97.3%. Practical steps and reproduction tips for builders.
A builder-focused breakdown of Agentic RL for GPT-OSS: what changed, what to implement first, and how founders can decide if the economics work.