LLM Digest

Story

arxiv_cs_ai · Nov 12, 2026 · paper

Source brief

Reducing Batch Makespan for LLM Agent Workloads through Inference-Environment Coordination

arxiv.orgNov 12, 2026
original source linked

In brief

Efficient batch execution of agent tasks is critical to LLM training and development workflows, including benchmark evaluation, synthetic-data generation, and RL rollouts. Since these tasks alternate between LLM infer...

Feed lens

agentevaluation

Read the original at arxiv.org →Open in live feed Read that day’s brief

Reducing Batch Makespan for LLM Agent Workloads through Inference-Environment Coordination

Earlier in this thread 4 items

Joi-Bench-Task: Benchmarking Task Coordination in Full-duplex Voice Agents through Interactive Simulation

Unprompted coordination through shared memory in LLM agents: a locked two-implementation replication and extension

CrowdDirector: Runtime Orchestration of Crowd Simulation through Natural-Language Directives

CausalXRL: Explainable Reinforcement Learning through Causal Graph Reasoning