Zero-shot reasoning for simulating scholarly peer-review

Khalid M. Saqr

Home/Archive/arXiv

arXiv · 2510.02027

Zero-shot reasoning for simulating scholarly peer-review

Khalid M. Saqr

View original source ↗Read source document ↗Export metadata ↓

Abstract

Scholarly publishing requires scalable scrutiny supported by auditable evidence. This paper presents a two-component benchmark of xPeer, the peer-review simulation engine delivered through the xPeerd.com web front-end. The operational component analyzes 352 of 500 simulation records retained under stable-task criteria across disciplines and review modes. The human-reference component releases 1,108 version-1 F1000Research manuscript records with linked human reports and applies a prespecified two-human/two-xPeer comparison. Human-review text and recommendations remained outside the generation input, and source joining occurred after xPeer outputs had been persisted. This procedure defines workflow-level review withholding; prior model exposure falls outside the recorded design. Among 802 records with exactly two human reports, 271 contained two usable xPeer reviewer fields, giving a complete-pair availability rate of 33.8%. Under deterministic extraction rules, median manuscript-level report length was 1,889 words for xPeer and 763 for humans, while median concern count was 41 and 13, respectively. xPeer reports showed higher targeting, category coverage, and executability. Human reports showed higher explicit-reasoning language, lexical manuscript attestation, taxonomy-based scientific relevance, and lower mean within-source redundancy. Cross-source lexical concern matching and recommendation agreement were low. The evidence therefore defines distinct observable review profiles and a transparent reproducibility baseline. Scientific correctness of individual concerns, autonomous editorial use, and cross-system superiority require expert adjudication and common-protocol testing. The study-level dataset and exact version-pinned reproducibility record are archived on Zenodo

Explore related subjects

cs.AI cs.ET

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Khalid M. Saqr. 2026-08-05. Zero-shot reasoning for simulating scholarly peer-review. https://arxiv.org/abs/2510.02027

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals

Biosignals collected from wearable devices are widely utilized in healthcare applications. Machine learning models used in these applications often rely on features extracted from biosignals due to their effectiveness, lower data dimensionality, and wide compatibility across various model architectures. However, existing feature extraction methods often lack task-specific contextual knowledge, struggle to identify optimal features in high-dimensional combinatorial feature space, and are prone to automated code generation and execution errors. In this paper, we propose DeepFeature, the first LLM-empowered, context-aware feature generation framework for wearable biosignals. DeepFeature introduces a multi-source feature generation mechanism that integrates the inherent ability of LLMs, expert knowledge and inter-feature interactions. It also employs an iterative feature refinement process that uses feature assessment-based feedback for feature re-selection. Additionally, DeepFeature utilizes a robust multi-layer filtering and verification approach for feature description-to-code translation to ensure that the feature extraction functions run without crashing. Experimental evaluation results show that DeepFeature achieves the highest average AUROC across eight tasks under both sample-level and subject-level settings, outperforming the best baselines by 4.60% and 4.61%, respectively. DeepFeature achieves the most pronounced gains on the PPG-BP tasks, while remaining competitive with the best-performing baselines on Epilepsy, WESAD, and our self-collected SEN dataset.

cs.AI↗

arXiv2026

Modality-Guided Mixture of Structured Experts with Entropy-Triggered Routing for Multimodal Recommendation

Multimodal recommenders combine collaborative behavior with visual and textual item evidence, whose usefulness varies across user-item interactions. Independently trained source-specific diagnostic probes partition held-out interactions into behavior-, appearance-, semantics-, and mixed-evidence regimes across five benchmarks, within which capacity-matched fixed fusion rules exhibit systematic regime-dependent performance crossovers. This diagnostic observation motivates MAGNET, a multimodal graph recommender with two core mechanisms. First, a calibrated expert bank organizes trainable experts by anchor source (behavior, appearance, or semantics) and fusion family (dominant, balanced, or complementary), while an interaction-conditioned router selects a sparse composition using all three evidence sources. Second, an entropy-triggered, coverage-aware progressive schedule decouples population-level routing-mass coverage from per-instance decisiveness, transitioning from broad routing exploration to confident specialization once sufficient coverage is sustained, while preserving coverage thereafter. MAGNET separately encodes the observed interaction graph and a filtered content-induced structural view, applying cross-view alignment after independent propagation. We evaluate MAGNET on four Amazon domains and the non-Amazon MicroLens-100K benchmark, including tail-item and low-history warm-start user evaluation alongside matched expert-design controls. Under the shared-feature, fixed-split protocol, MAGNET-DV exceeds the strongest protocol-compatible non-MAGNET baseline in every reported main-table cell, supported by seed-wise difference tests. Both the fixed and KL-anchored structured variants achieve higher five-seed mean NDCG@20 than matched homogeneous and free-mixture alternatives across all five datasets. MAGNET supports route-level diagnostics through explicit expert semantics.

cs.AI↗

arXiv2026

From Refusal Tokens to Refusal Control: Discovering and Steering Category-Specific Refusal Directions

Language models are commonly fine-tuned for safety alignment to refuse harmful prompts. One approach fine-tunes them to generate categorical refusal tokens that distinguish different refusal types before responding. In this work, we leverage a version of Llama 3 8B fine-tuned with these categorical refusal tokens to enable inference-time control over fine-grained refusal behavior, improving both safety and reliability. We show that refusal token fine-tuning induces separable, category-aligned directions in the residual stream, which we extract and use to construct categorical steering vectors with a lightweight probe that determines whether to steer toward or away from refusal during inference. In addition, we introduce a learned low-rank combination that mixes these category directions in a whitened, orthonormal steering basis, resulting in a single controllable intervention under activation-space anisotropy, and show that this intervention is transferable across same-architecture model variants without additional training. Across benchmarks, both categorical steering vectors and the low-rank combination consistently reduce over-refusals on benign prompts while increasing refusal rates on harmful prompts, highlighting their utility for multi-category refusal control.

cs.AI↗