LLMs Can Infer Unspoken Intent in Collaborative Tasks
Researchers tested whether large language models can interpret incomplete instructions by reasoning about a human partner's mental state, matching human performance.
LLMs using chain-of-thought reasoning can infer incomplete instructions by modeling human intent, achieving parity with human performance.
- — Agents must interpret ambiguous instructions by inferring unspoken intentions from shared context.
- — Instruction Inference task measures how well agents reason about a principal's mental state.
- — Tomcat agent uses two approaches: few-shot chain-of-thought or commonsense prompting.
- — GPT-4o and DeepSeek-R1 variants matched human participant accuracy on intent and action optimality.
- — Study compared 52 human participants against LLM variants on goal-oriented collaborative scenarios.
- — Theory of Mind reasoning enables agents to bridge gaps between stated and actual instructions.
- — Performance measured via intent accuracy, action optimality, and planning optimality metrics.
Astrobobo tool mapping
- Knowledge Capture Document a real collaboration task where instructions are often incomplete. Record the context, the instruction, and the inferred intent. Use this as a seed dataset to test LLM inference on your own workflows.
- Focus Brief Before delegating a task, draft a 'mental model' section: what does the recipient need to know about your goals, constraints, and assumptions? Compare this to what you actually stated in the instruction.
- Daily Log Log instances where an agent (human or AI) misinterpreted your instruction. Note what context was missing. Over time, identify patterns in what you consistently under-communicate.
Frequently asked
- LLMs do not possess genuine understanding or consciousness. They recognize patterns in language that correlate with human intent based on training data. The study shows they can match human performance on inferring intent in specific scenarios, but this is statistical pattern-matching, not true mental modeling. They lack the embodied experience and social intuition humans develop over a lifetime.
cite ▸
APA
Fardin Saad, Pradeep K. Murukannaiah, Munindar P. Singh. (2026, April 20). LLMs Can Infer Unspoken Intent in Collaborative Tasks. Astrobobo Content Engine (rewrite of arxiv/cs.AI). https://astrobobo-content-engine.vercel.app/article/llms-can-infer-unspoken-intent-in-collaborative-tasks-96d67e
MLA
Fardin Saad, Pradeep K. Murukannaiah, Munindar P. Singh. "LLMs Can Infer Unspoken Intent in Collaborative Tasks." Astrobobo Content Engine, 20 Apr 2026, https://astrobobo-content-engine.vercel.app/article/llms-can-infer-unspoken-intent-in-collaborative-tasks-96d67e. Based on "arxiv/cs.AI", https://arxiv.org/abs/2507.02935.
BibTeX
@misc{astrobobo_llms-can-infer-unspoken-intent-in-collaborative-tasks-96d67e_2026,
author = {Fardin Saad, Pradeep K. Murukannaiah, Munindar P. Singh},
title = {LLMs Can Infer Unspoken Intent in Collaborative Tasks},
year = {2026},
url = {https://astrobobo-content-engine.vercel.app/article/llms-can-infer-unspoken-intent-in-collaborative-tasks-96d67e},
note = {Astrobobo rewrite of arxiv/cs.AI, https://arxiv.org/abs/2507.02935},
}