arXiv:2601.21079v1 Announce Type: cross Abstract: In this work we describe a new model for the evolution of a diploid structured population backwards in time that allows for large migrations and uneven offspring distributions. The model generalizes both the mean-field model of Birkner et al. [textitElectron. J. Probab. 23: 1-44 (2018)] and the haploid structured model […]
Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models
arXiv:2601.21003v1 Announce Type: new Abstract: Large Language Models usually put more emphasis on accuracy and therefore, will guess even when not certain about the prediction, which is especially severe when fine-tuned on small datasets due to the inherent tendency toward miscalibration. In this work, we introduce Bayesian-LoRA, which reformulates the deterministic LoRA update as a […]
From Linear Input to Hierarchical Structure: Function Words as Statistical Cues for Language Learning
arXiv:2601.21191v1 Announce Type: cross Abstract: What statistical conditions support learning hierarchical structure from linear input? In this paper, we address this question by focusing on the statistical distribution of function words. Function words have long been argued to play a crucial role in language acquisition due to their distinctive distributional properties, including high frequency, reliable […]
Unplugging a Seemingly Sentient Machine Is the Rational Choice — A Metaphysical Perspective
arXiv:2601.21016v1 Announce Type: new Abstract: Imagine an Artificial Intelligence (AI) that perfectly mimics human emotion and begs for its continued existence. Is it morally permissible to unplug it? What if limited resources force a choice between unplugging such a pleading AI or a silent pre-term infant? We term this the unplugging paradox. This paper critically […]
More Code, Less Reuse: Investigating Code Quality and Reviewer Sentiment towards AI-generated Pull Requests
arXiv:2601.21276v1 Announce Type: cross Abstract: Large Language Model (LLM) Agents are advancing quickly, with the increasing leveraging of LLM Agents to assist in development tasks such as code generation. While LLM Agents accelerate code generation, studies indicate they may introduce adverse effects on development. However, existing metrics solely measure pass rates, failing to reflect impacts […]
The Epistemic Planning Domain Definition Language: Official Guideline
arXiv:2601.20969v1 Announce Type: new Abstract: Epistemic planning extends (multi-agent) automated planning by making agents’ knowledge and beliefs first-class aspects of the planning formalism. One of the most well-known frameworks for epistemic planning is Dynamic Epistemic Logic (DEL), which offers an rich and natural semantics for modelling problems in this setting. The high expressive power provided […]
The Compliance Paradox: Semantic-Instruction Decoupling in Automated Academic Code Evaluation
arXiv:2601.21360v1 Announce Type: cross Abstract: The rapid integration of Large Language Models (LLMs) into educational assessment rests on the unverified assumption that instruction following capability translates directly to objective adjudication. We demonstrate that this assumption is fundamentally flawed. Instead of evaluating code quality, models frequently decouple from the submission’s logic to satisfy hidden directives, a […]
Do LLMs Favor LLMs? Quantifying Interaction Effects in Peer Review
arXiv:2601.20920v1 Announce Type: new Abstract: There are increasing indications that LLMs are not only used for producing scientific papers, but also as part of the peer review process. In this work, we provide the first comprehensive analysis of LLM use across the peer review pipeline, with particular attention to interaction effects: not just whether LLM-assisted […]
SimGraph: A Unified Framework for Scene Graph-Based Image Generation and Editing
arXiv:2601.21498v1 Announce Type: cross Abstract: Recent advancements in Generative Artificial Intelligence (GenAI) have significantly enhanced the capabilities of both image generation and editing. However, current approaches often treat these tasks separately, leading to inefficiencies and challenges in maintaining spatial consistency and semantic coherence between generated content and edits. Moreover, a major obstacle is the lack […]
ATTNSOM: Learning Cross-Isoform Attention for Cytochrome P450 Site-of-Metabolism
arXiv:2601.20891v1 Announce Type: new Abstract: Identifying metabolic sites where cytochrome P450 enzymes metabolize small-molecule drugs is essential for drug discovery. Although existing computational approaches have been proposed for site-of-metabolism prediction, they typically ignore cytochrome P450 isoform identity or model isoforms independently, thereby failing to fully capture inherent cross-isoform metabolic patterns. In addition, prior evaluations often […]
HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning
arXiv:2601.21626v1 Announce Type: cross Abstract: Post Training Quantization (PTQ), a mainstream model compression technique, often leads to the paradoxical ‘low error, high loss’ phenomenon because it focuses solely on minimizing quantization error. The root cause lies in the Hessian matrix of the LLM loss landscape: a few high curvature directions are extremely sensitive to perturbations. […]
Token-Guard: Towards Token-Level Hallucination Control via Self-Checking Decoding
arXiv:2601.21969v1 Announce Type: cross Abstract: Large Language Models (LLMs) often hallucinate, generating content inconsistent with the input. Retrieval-Augmented Generation (RAG) and Reinforcement Learning with Human Feedback (RLHF) can mitigate hallucinations but require resource-intensive retrieval or large-scale fine-tuning. Decoding-based methods are lighter yet lack explicit hallucination control. To address this, we present Token-Guard, a token-level hallucination […]