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 […]
Tackling GNARLy Problems: Graph Neural Algorithmic Reasoning Reimagined through Reinforcement Learning
arXiv:2509.18930v2 Announce Type: replace-cross Abstract: Neural Algorithmic Reasoning (NAR) is a paradigm that trains neural networks to execute classic algorithms by supervised learning. Despite its successes, important limitations remain: inability to construct valid solutions without post-processing and to reason about multiple correct ones, poor performance on combinatorial NP-hard problems, and inapplicability to problems for which […]
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 […]
Toward Robust Multilingual Adaptation of LLMs for Low-Resource Languages
arXiv:2510.14466v2 Announce Type: replace-cross Abstract: Large language models (LLMs) continue to struggle with low-resource languages, primarily due to limited training data, translation noise, and unstable cross-lingual alignment. To address these challenges, we propose LiRA (Linguistic Robust Anchoring for LLMs)-a plug-and-play framework that requires only lightweight fine-tuning on top of existing pretrained backbones. LiRA jointly optimizes […]
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. […]
FIT: Defying Catastrophic Forgetting in Continual LLM Unlearning
arXiv:2601.21682v1 Announce Type: cross Abstract: Large language models (LLMs) demonstrate impressive capabilities across diverse tasks but raise concerns about privacy, copyright, and harmful materials. Existing LLM unlearning methods rarely consider the continual and high-volume nature of real-world deletion requests, which can cause utility degradation and catastrophic forgetting as requests accumulate. To address this challenge, we […]
SAGE: Sequence-level Adaptive Gradient Evolution for Generative Recommendation
arXiv:2601.21452v1 Announce Type: cross Abstract: While works such as OneRec have validated the scaling laws of Large Language Models (LLMs) in recommender systems, they rely on a cumbersome separate vocabulary. This dependency prevents the model architecture from reusing native LLM vocabularies, resulting in high maintenance costs and poor scalability. In response, we aim to efficiently […]
SHARP: Social Harm Analysis via Risk Profiles for Measuring Inequities in Large Language Models
arXiv:2601.21235v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in high-stakes domains, where rare but severe failures can result in irreversible harm. However, prevailing evaluation benchmarks often reduce complex social risk to mean-centered scalar scores, thereby obscuring distributional structure, cross-dimensional interactions, and worst-case behavior. This paper introduces Social Harm Analysis via Risk […]
The Surprising Difficulty of Search in Model-Based Reinforcement Learning
arXiv:2601.21306v1 Announce Type: cross Abstract: This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compounding errors are the primary obstacles for model-based RL. We challenge this view, showing that search is not a plug-and-play replacement for a learned policy. Surprisingly, we find that search can harm performance even […]
QUARK: Robust Retrieval under Non-Faithful Queries via Query-Anchored Aggregation
arXiv:2601.21049v1 Announce Type: new Abstract: User queries in real-world retrieval are often non-faithful (noisy, incomplete, or distorted), causing retrievers to fail when key semantics are missing. We formalize this as retrieval under recall noise, where the observed query is drawn from a noisy recall process of a latent target item. To address this, we propose […]
Optimization and Mobile Deployment for Anthropocene Neural Style Transfer
arXiv:2601.21141v1 Announce Type: cross Abstract: This paper presents AnthropoCam, a mobile-based neural style transfer (NST) system optimized for the visual synthesis of Anthropocene environments. Unlike conventional artistic NST, which prioritizes painterly abstraction, stylizing human-altered landscapes demands a careful balance between amplifying material textures and preserving semantic legibility. Industrial infrastructures, waste accumulations, and modified ecosystems contain […]
Reputation as a Solution to Cooperation Collapse in LLM-based MASs
arXiv:2505.05029v3 Announce Type: replace Abstract: Cooperation has long been a fundamental topic in both human society and AI systems. However, recent studies indicate that the collapse of cooperation may emerge in multi-agent systems (MASs) driven by large language models (LLMs). To address this challenge, we explore reputation systems as a remedy. We propose RepuNet, a […]