{
  "annotation_id": "fd0e0759-7040-4d73-930e-bc92a783eadc",
  "date_created": "2026-02-17T05:53:11.818000Z",
  "date_modified": "2026-02-17T05:53:11.818000Z",
  "file_hash": "fe4961172c8e854125c2cdaa1dacac420ea6e3d000dcf9421eebe3cf2410e624",
  "private": false,
  "record": {
    "abstract": "With the recent advancements in reasoning capabilities, tool calling using MCP servers and Audio Language Models (ALMs), development and integration of multi-modal agents (with voice and text support) has come to the industry forefront. Cascading pipelines for voice agents still play a central role in the industry owing to their superior reasoning capabilities facilitated by LLMs. Although, cascading pipelines often present error propagation through the pipeline. We propose a framework, FOCAL to benchmark end-to-end reasoning, component-wise error propagation and error analysis for automated as well as human-assisted testing of multi-modal agents (voice to voice + text input). We also share two novel metrics viz. Reasoning and Semantic scores to evaluate efficacy of the agent in having meaningful conversations in voice mode.",
    "arxiv_id": "2601.07367",
    "authors": [
      "Aditya Choudhary",
      "Anupam Purwar"
    ],
    "categories": [
      "cs.SD"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "title": "FOCAL: A Novel Benchmarking Technique for Multi-modal Agents",
    "url": "https://arxiv.org/abs/2601.07367",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c272c839-4603-4c79-bee7-ca52fba51399",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}