Automatic prompt optimization is an uncertain sequential decision problem: an editing operator’s quality gain and context-dependent token cost are unknown before execution. We introduce BRAVE (Budget-Aware Reflective AutoPrompting for Efficient Evolutionary Prompt Optimization), a population-based optimizer that treats this operational uncertainty as a control signal. Its EVC Controller maintains per-operator Bayesian models of quality gain, token cost, and improvement probability, then selects among eleven generation and mutation operators by uncertainty-penalized predicted utility per token under a predicted-cost feasibility guard. An EMA return-on-investment mechanism revises this decision process from realized outcomes, while diversity management preserves alternative prompt hypotheses. On seven datasets, BRAVE outperforms EvoPrompt, PromptBreeder, and GEPA on 7/7 tasks; its advantage over the strongest external baseline ranges from 0.002 to 0.150 absolute score points. Under 20k, 40k, and 80k nominal budgets, BRAVE respectively achieves average quality 0.779, 0.807, and 0.812 versus GEPA’s 0.771, 0.775, and 0.781, while using 21.4k, 41.0k, and 72.4k tokens versus 46.1k, 65.2k, and 96.5k. BRAVE therefore formulates reliable prompt optimization as uncertainty-aware resource allocation: uncertainty about an edit’s value and cost directly changes the action selected.

Deep research systems iteratively plan, gather evidence, synthesize, and revise, making end-to-end latency a key bottleneck for interactive use. Almost all production systems are built on autoregressive (AR) language models and inherit left-to-right decoding in planning and long-form writing. We present DiffResearch, an open multi-agent deep research framework that places a diffusion language model (dLLM) at the core of every stage, and measure how a diffusion backbone changes latency and report quality. Holding the agentic scaffold, retrieval code, prompts, and evaluation fixed, we swap the backbone between an open diffusion model and a comparable AR model. Across 100 Deep Research Bench tasks, the diffusion backbone is roughly 6× faster end-to-end, driven by order-of-magnitude reductions in planning and writing. We further introduce dFast, a decomposition mode that reads intermediate denoising states and extracts subqueries before generation finishes, providing a 1.99× median speedup for decomposition in isolation with no loss of report quality. On the benchmark’s RACE rubric, dFast obtains the best overall score among the three configurations. Finally, in a public system-level leaderboard snapshot, an instance built on a stronger diffusion model ranks above several commercial systems. We release DiffResearch under an Apache-2.0 license.