mlsanityData to 7 Oct 2026

Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies

arXiv 2510.22095 ↗First posted 25 Oct 2025cs.AI cs.CLcited by 5

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

Brain-Computer Interfaces (BCIs) offer a direct communication pathway between the human brain and external devices, holding significant promise for individuals with severe neurological impairments. However, their widespread adoption is hindered by critical limitations, such as low information transfer rates and extensive user-specific calibration. To overcome these challenges, recent research has explored the integration of Large Language Models (LLMs), extending the focus from simple command decoding to understanding complex cognitive states. Despite these advancements, deploying agentic AI faces technical hurdles and ethical concerns. Due to the lack of comprehensive discussion on this emerging direction, this position paper argues that the field is poised for a paradigm extension from BCI to Brain-Agent Collaboration (BAC). We emphasize reframing agents as active and collaborative partners for intelligent assistance rather than passive brain signal data processors, demanding a focus on ethical data handling, model reliability, and a robust human-agent collaboration framework to ensure these systems are safe, trustworthy, and effective.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 69 of its 128 references that are in the map. First posted on arXiv 25 Oct 2025.

Builds on

69 of 128 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025A Survey of LLM-based Agents in Medicine: How far are we from Baymax?16 Feb
  2. 2025Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks19 Jan

Other references · 67

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50 most cited of 67 All 67 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

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  2. 2026BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understandingcited by 2
  3. 2026Distributionally Robust Set Representation Learning Under Inference-Time Element Corruptioncited by 0
  4. 2026BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understandingcited by 0
  5. 2026An Investigation of the NeurIPS and ICML 2025 Position Trackscited by 0

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