Communicate While Thinking with Inspected Peer Assistance During Ongoing Multiagent Reasoning

Multi-agent collaboration can improve the reasoning of large language models (LLMs) through external insights. When peer feedback arrives only after an agent completes its local chain-of-thought (CoT) reasoning, intermediate errors may already have shaped later work, and correction relies on post-hoc revision. We propose CoTIE (CoT-State Inter-Agent Exchange), a collaboration mechanism that brings targeted peer assistance into unfinished local reasoning. In CoTIE, requests are formed from the agent's current state, replies are inspected in a separate model call, and only accepted information is added before the agent continues the same local reasoning. Naive CoTIE leaves peer requests and reply assessment to ordinary tool calls. Full CoTIE adds dedicated procedures for request triggering, state disclosure, peer selection, and record admission. Across six task scenarios with DeepSeek-V3.2 and Qwen3-235B-A22B, Full CoTIE achieves the highest mean score among the compared methods. Full CoTIE improves scores over Naive CoTIE by 1.8–7.1 points at 17.5–40.0% more tokens. Full CoTIE uses fewer tokens than the native workflow of MultiAgentBench (MAB) in all MAB settings and more tokens than output-level message passing in five of six general reasoning settings. In a controlled MAB Coding experiment with DeepSeek-V3.2 and fixed accepted peer information, delivery during unfinished reasoning scores 12.6 points higher than delivery at the start of revision, after candidate submission or the drafting limit, under matched stage budgets. Early delivery also uses 19.2% fewer tokens from the shared checkpoint through final submission. Regenerating the peer reply at revision narrows the score gap to 11.3 points. Manual review of continuations from 50 matched timing checkpoints finds fewer code edits based on incorrect assumptions and less error-related rework for early than late delivery. The code is available at https://anonymous.4open.science/r/CoTIE-review-ICLR/.