Multimodal agents are increasingly evaluated on their ability to search the web, invoke tools, and solve complex tasks autonomously. Yet no existing benchmark systematically evaluates the capability of autonomously searching, parsing, and extracting precise answers from multimodal raw media files as a unified, end-to-end agentic task. We introduce RDQA - Raw Data Question Answering, a benchmark of 1,350 questions across three source modalities (document, video, audio), and 20 real-world task scenarios, where every answer is designed to require source access rather than model knowledge alone. RDQA provides capability-stratified evaluation through 9 capability families organized by modality and cognitive complexity, and introduces the Parametric Leakage Rate (PLR), a process-aware metric that flags correct answers produced without genuine evidence retrieval. We evaluate 12 agent systems spanning 6 backbone models and 2 scaffoldings. Our results show that the the best generic agent reaches only 24.0% adjusted accuracy, while a process-aware baseline (RDQA-Skill) reaches 41.1%. PLR reaches as high as 96.0%, indicating that many ostensibly correct answers are not grounded in retrieved evidence. We release the full dataset, evaluation framework, and a baseline to support future research.

Large Language Models (LLMs) excel at code-related tasks but often struggle in realistic software repositories, where project-specific APIs and cross-file dependencies are crucial. Retrieval-augmented methods mitigate this by injecting repository context at inference time. Low inference time latency budget either affects retrieval quality or the added latency impacts user experience adversely. We address this limitation with SpecAgent, an agent that enhances both latency and code-generation quality by proactively exploring repository files during indexing and constructing speculative context that anticipates future edits in each file. This indexing-time asynchrony allows thorough context computation masking latency and the speculative nature of the context improves code-generation quality. Additionally, we identify the problem of future context leakage in existing benchmarks, which can inflate reported performance. To address this, we construct a synthetic, leakage-free benchmark that enables a more realistic evaluation of our agent against baselines. Experiments show that SpecAgent consistently achieves absolute gains of 9–11% (48–58% relative) compared to the best-performing baselines, while significantly reducing inference latency.