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 […]
Near-Optimal Online Deployment and Routing for Streaming LLMs
arXiv:2506.17254v2 Announce Type: replace-cross Abstract: The rapid pace at which new large language models (LLMs) appear, and older ones become obsolete, forces providers to manage a streaming inventory under a strict concurrency cap and per-query cost budgets. We cast this as an online decision problem that couples stage-wise deployment (at fixed maintenance windows) with per-query […]
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. […]
Do Reasoning Models Enhance Embedding Models?
arXiv:2601.21192v1 Announce Type: new Abstract: State-of-the-art embedding models are increasingly derived from decoder-only Large Language Model (LLM) backbones adapted via contrastive learning. Given the emergence of reasoning models trained via Reinforcement Learning with Verifiable Rewards (RLVR), a natural question arises: do enhanced reasoning translate to superior semantic representations when these models serve as embedding initializations? […]
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 […]
Multi-modal Imputation for Alzheimer’s Disease Classification
arXiv:2601.21076v1 Announce Type: new Abstract: Deep learning has been successful in predicting neurodegenerative disorders, such as Alzheimer’s disease, from magnetic resonance imaging (MRI). Combining multiple imaging modalities, such as T1-weighted (T1) and diffusion-weighted imaging (DWI) scans, can increase diagnostic performance. However, complete multimodal datasets are not always available. We use a conditional denoising diffusion probabilistic […]
End-to-end audio-visual learning for cochlear implant sound coding simulations in noisy environments
arXiv:2508.13576v2 Announce Type: replace-cross Abstract: The cochlear implant (CI) is a successful biomedical device that enables individuals with severe-to-profound hearing loss to perceive sound through electrical stimulation, yet listening in noise remains challenging. Recent deep learning advances offer promising potential for CI sound coding by integrating visual cues. In this study, an audio-visual speech enhancement […]
Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models
arXiv:2601.22060v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable success across a broad range of vision tasks. However, constrained by the capacity of their internal world knowledge, prior work has proposed augmenting MLLMs by “reasoning-then-tool-call” for visual and textual search engines to obtain substantial gains on tasks requiring extensive factual information. […]
When should I search more: Adaptive Complex Query Optimization with Reinforcement Learning
arXiv:2601.21208v1 Announce Type: new Abstract: Query optimization is a crucial component for the efficacy of Retrieval-Augmented Generation (RAG) systems. While reinforcement learning (RL)-based agentic and reasoning methods have recently emerged as a promising direction on query optimization, most existing approaches focus on the expansion and abstraction of a single query. However, complex user queries are […]
Log2Motion: Biomechanical Motion Synthesis from Touch Logs
arXiv:2601.21043v1 Announce Type: cross Abstract: Touch data from mobile devices are collected at scale but reveal little about the interactions that produce them. While biomechanical simulations can illuminate motor control processes, they have not yet been developed for touch interactions. To close this gap, we propose a novel computational problem: synthesizing plausible motion directly from […]