Large language models (LLMs) are increasingly discussed as tools for peer review, but their value is often assessed through human-likeness, perceived usefulness, or textual overlap with reviewer comments. This study shifts attention from whether LLMs resemble human reviewers to what functions of scientific critique they perform. Using ICLR 2025 peer-review data, we compare human reviews with LLM reviews generated under baseline and expert prompts. We operationalize scientific critique through two review acts, weakness critique and scientific questioning, and annotate point-level review text using five theory-guided frameworks: Anderson's knowledge types, Toulmin's argumentation model, Graesser's question depth, SOLO cognitive complexity, and Hattie's feedback functions. The results reveal a differentiated critique profile. Human reviews placed greater emphasis on scientific framing and revision guidance, more often identifying higher-order weaknesses and asking questions oriented toward improvement. LLM reviews showed higher rates of explanatory depth, integrative reasoning, and explicit argument structuring. Expert prompting did not make LLM critique uniformly more human-like; it partially narrowed some gaps but mainly amplified LLM-specific tendencies toward integration and formal argumentation. These findings show that LLM-assisted peer review changes the functional composition of review text, making it important to distinguish LLM-amplified critique from areas requiring human prioritization and accountable judgement.
YunHong Yang, Mike Thelwall, Guoxiu He· 0 citations
Experiments show that EASD consistently improves accuracy over standard SD and reward-guided variants while maintaining comparable inference efficiency, suggesting that speculative decoding can serve not only as an acceleration method but also as an effective mechanism for improving reasoning quality.
Tiancheng Su, Meicong Zhang, Guoxiu He· 3 citations
Generating a rigorous paper introduction with large language models (LLMs) remains challenging, since it requires coordinating background, gap identification, method and contribution within a coherent narrative. Existing solutions externalize this process as multi-stage prompts or agent workflows which are expensive and vulnerable to cross-stage drift. We propose StructPO, a struct-aware policy learning framework that internalizes the entire multi-stage writing workflow into a single-pass policy controlled by explicit stage tokens. StructPO introduces struct-aware credit assignment to decouple local stage quality from global coherence and refinement-guided optimization to internalize revision behavior into the first-pass policy. Experiments show that StructPO improves semantic alignment, structural rationality and inference efficiency over workflow-based baselines, generalizes to out-of-domain settings, and remains competitive with GPT-5.1 in human evaluation when scaled to Qwen3-32B. These results show that internalizing academic writing workflows through fine-grained policy optimization offers a viable alternative to costly external orchestration.
Meicong Zhang, Tiancheng Su, Jiahao Cheng et al.· 0 citations
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