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Top signals · Oct 27–28, 2026 · Updated Oct 28, 13:05 UTC
Where Should the Evaluation Budget Go in LLM-Driven Agent Scaffold Evolution?
Large language model (LLM)-driven agent scaffold evolution improves agent workflows through iterative candidate generation and evaluation. Evaluation both guides subsequent search and determines the final scaffold, bu... Context & related coverage →
Concordat: Typed Sessions for LLM Agents
Agents built on large language models are increasingly allowed to take consequential actions: cancelling bookings, issuing refunds, changing accounts. The rules that govern those actions, such as "obtain the customer'... Context & related coverage →
ETAMem: Decoupling and Aligning Heterogeneous Memory for Long-Term Conversational Agents
Long-term memory enables Large Language Models (LLMs) to maintain coherent and personalized interactions across extended conversations. However, existing memory systems face two key challenges: (1) Information Entangl... Context & related coverage →
CRISP: Co-designing Pruning and Scheduling for Efficient MoE Inference on Edge Servers
Mixture-of-Experts (MoE) inference on memory-constrained edge platforms is often dominated by expert-weight movement rather than computation. Although each token activates only a small subset of experts, routing is in... Context & related coverage →
Uber Moves beyond CPU Cores with Performance-Aware Scheduling for Stateful Workloads
Uber has introduced Normalized Compute Units (NCUs) to make CPU allocation more consistent across its highly heterogeneous server fleet. By Craig Risi Context & related coverage →
Legora is building the agentic operating system for legal work
See how Legora uses OpenAI models, ChatGPT, and Codex to accelerate legal workflows, expand contract analysis, and free lawyers to focus on higher-value work. Context & related coverage →
Retell uses OpenAI models to help power 100M monthly calls
GPT-6 Astra tops Retell’s voice agent benchmark, while Codex helps it reach a $1.6M annual revenue run rate per employee. Context & related coverage →