Pause or Fabricate? Teaching LLMs to Say "I Need More Information"
> Paper: *Pause or Fabricate? Training Language Models for Grounded Reasoning* (arXiv 2604.19656, 2026) > Authors: Yiwen Qiu, Linjuan Wu, Yizhou Liu, et al. (Zhejiang University & Microsoft Research Asia) > Paper: arxiv.org/abs/2604.19656
---
A Perfect Answer Built on a Fabricated Premise
Imagine asking an LLM a math question: "Xiao Ming has 5 apples and gives some to Xiao Hong, with 2 left. How many did Xiao Hong get?"
A reasonable person's first reaction would be: "Wait — you didn't tell me how many Xiao Ming gave away."
But most LLMs won't say that. They will confidently start reasoning: "Xiao Ming gave Xiao Hong 5 - 2 = 3 apples." The logic looks flawless, but the chain of reasoning rests on a fabricated premise. The model assumed "gave some" meant "gave all," then derived an answer that is "correct-looking but unreliable."
This is what the paper calls "ungrounded reasoning" — the problem isn't that the model can't reason, but that it doesn't know when it should stop and ask questions.
An Overlooked Capability: Inferential Boundary Awareness
The paper's core insight is simple: many LLM reasoning failures stem not from a lack of intelligence but from a lack of inferential boundary awareness — the ability to recognize whether the current information is sufficient to support valid reasoning.
Analogy: a math genius taking an exam reads "In triangle ABC, angle A = 60°, find angle B." A brilliant student wouldn't immediately start writing equations — they'd raise their hand and ask the teacher: "What other conditions are given?"
Today's LLMs are more like students who "make things up when unsure" — preferring to invent missing conditions rather than admit insufficient information.
GRIL: Teaching AI to Ask Before Answering
The paper proposes GRIL (Grounded Reasoning via Interactive Reinforcement Learning), a multi-turn reinforcement learning framework that splits reasoning into two stages:
Stage 1: Clarify and Pause The model first judges whether the input contains sufficient premises for reasoning. If information is missing, it actively pauses and requests clarification rather than pushing forward blindly.
Stage 2: Grounded Reasoning Only after confirming that the necessary premises are present does the model execute the actual reasoning task.
The key point of this design: it doesn't make the model "smarter" — it makes the model "more honest."
How Is It Trained? An Interactive "Exam Environment"
GRIL's training method is interesting. The researchers built an interactive environment simulating real-world scenarios with incomplete information:
- Data construction: premises are deliberately hidden in problems from math reasoning datasets such as GSM8K, creating "incomplete-information" questions
- Reward design: Stage 1 rewards correctly identifying insufficient information (pausing); Stage 2 rewards correct reasoning when information is complete
- Multi-turn interaction: the model can request missing information, receive supplements, and then continue reasoning
- Large gains in premise detection: GRIL-trained models significantly outperform baselines at recognizing incomplete-information problems
- Higher task success rates: on complete problems, GRIL models also reason more successfully — because the model has learned to distinguish "can reason" from "cannot reason"
- Far less ungrounded reasoning: the model no longer "bluffs," with fabricated-premise behavior dropping substantially
- Generalization: GRIL performs well on out-of-distribution domains, showing it learned genuine boundary awareness rather than memorizing problems
- Robustness to noise: GRIL handles noisy user-supplied clarifications fairly well
It's like training a student: instead of always giving complete problems, deliberately hand over partial ones and see whether the student can spot what's missing.
Results: The Data Speaks
The experimental results are impressive:
Why Does This Matter?
This research touches on a deep issue: AI reliability depends not only on how much it knows, but on whether it knows what it doesn't know.
In real applications, incomplete information is the norm, not the exception. Ask an AI a legal question and it may not know your jurisdiction; ask a medical question and it may not know your medical history. If the AI always "confidently fabricates," the smarter it gets, the more dangerous it becomes.
GRIL offers an important lesson: perhaps we shouldn't only pursue making AI answer better — we should first teach AI when not to answer.
It recalls Socrates' words: "The only thing I know is that I know nothing." Perhaps the hallmark of the next generation of AI will not be knowing all the answers, but knowing when to say "I need more information."
---
Paper | arxiv.org/abs/2604.19656
> Note: As of this writing, no public code repository has been found for this paper. If it is open-sourced later, check the authors' team pages.