Grounding
Grounding anchors a model's responses in verifiable external data, forcing it to retrieve facts from specific sources rather than relying solely on its internal training parameters.
What it is
Grounding typically happens by retrieving relevant documents or database entries via a semantic search index before inserting that data into the model's prompt. The model processes this context alongside the user query to synthesize an answer. This pattern often integrates with vector databases, where you fetch the top k results, usually between 3 to 10 chunks of text, to ensure the model has sufficient signal for accurate output.
Why it matters
Without grounding, models are prone to hallucination, as they prioritize plausible-sounding patterns over factual accuracy. Implementing grounding allows you to build features that provide citations or specific data point references, which is critical for compliance and trust. Ignoring this risks deploying systems that confidently state false information, leading to degraded user experience and potential liability.
In practice
You implement this by constructing a retrieval pipeline that executes a search query before your LLM call. In production, you monitor the relevance of retrieved context by tracking the distance metrics from your vector database or by evaluating the final response against the source material using an automated check.
The tradeoff
Grounding increases latency due to the extra retrieval step and requires maintaining a separate document index, but it significantly reduces the frequency of model hallucinations.
Where it appears
Research summaries that use Grounding, each linked to its source paper.
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Improving Real Time Streaming Video Agents
StreamArena: Toward Continuous, Interactive, and Long-Horizon Agentic Streaming Video Understanding
The paper introduces a benchmark and a two-tier architecture to enable agents to handle long-horizon streaming video tasks with improved latency and reasoning.
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Scaling Local Computer Use AI Agents
Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs
The paper investigates whether running extra computation during execution improves resource constrained local computer use agents and identifies the specific failure modes limiting their performance.