arXiv:2512.20173v1 Announce Type: new Abstract: Offline Preference-based Reinforcement Learning (PbRL) learns rewards and policies aligned with human preferences without the need for extensive reward engineering and direct interaction with human annotators. However, ensuring safety remains a critical challenge across many domains and tasks. Previous works on safe RL from human feedback (RLHF) first learn reward […]
A Branch-and-Price Algorithm for Fast and Equitable Last-Mile Relief Aid Distribution
arXiv:2512.19882v1 Announce Type: new Abstract: The distribution of relief supplies to shelters is a critical aspect of post-disaster humanitarian logistics. In major disasters, prepositioned supplies often fall short of meeting all demands. We address the problem of planning vehicle routes from a distribution center to shelters while allocating limited relief supplies. To balance efficiency and […]
TongSIM: A General Platform for Simulating Intelligent Machines
arXiv:2512.20206v1 Announce Type: new Abstract: As artificial intelligence (AI) rapidly advances, especially in multimodal large language models (MLLMs), research focus is shifting from single-modality text processing to the more complex domains of multimodal and embodied AI. Embodied intelligence focuses on training agents within realistic simulated environments, leveraging physical interaction and action feedback rather than conventionally […]
PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research
arXiv:2512.19799v1 Announce Type: new Abstract: Advances in LLMs have produced agents with knowledge and operational capabilities comparable to human scientists, suggesting potential to assist, accelerate, and automate research. However, existing studies mainly evaluate such systems on well-defined benchmarks or general tasks like literature retrieval, limiting their end-to-end problem-solving ability in open scientific scenarios. This is […]
MemR$^3$: Memory Retrieval via Reflective Reasoning for LLM Agents
arXiv:2512.20237v1 Announce Type: new Abstract: Memory systems have been designed to leverage past experiences in Large Language Model (LLM) agents. However, many deployed memory systems primarily optimize compression and storage, with comparatively less emphasis on explicit, closed-loop control of memory retrieval. From this observation, we build memory retrieval as an autonomous, accurate, and compatible agent […]
Multi-LLM Thematic Analysis with Dual Reliability Metrics: Combining Cohen’s Kappa and Semantic Similarity for Qualitative Research Validation
arXiv:2512.20352v1 Announce Type: cross Abstract: Qualitative research faces a critical reliability challenge: traditional inter-rater agreement methods require multiple human coders, are time-intensive, and often yield moderate consistency. We present a multi-perspective validation framework for LLM-based thematic analysis that combines ensemble validation with dual reliability metrics: Cohen’s Kappa ($kappa$) for inter-rater agreement and cosine similarity for […]
$D^3$ETOR: $D$ebate-Enhanced Pseudo Labeling and Frequency-Aware Progressive $D$ebiasing for Weakly-Supervised Camouflaged Object $D$etection with Scribble Annotations
arXiv:2512.20260v1 Announce Type: cross Abstract: Weakly-Supervised Camouflaged Object Detection (WSCOD) aims to locate and segment objects that are visually concealed within their surrounding scenes, relying solely on sparse supervision such as scribble annotations. Despite recent progress, existing WSCOD methods still lag far behind fully supervised ones due to two major limitations: (1) the pseudo masks […]
Improving Local Training in Federated Learning via Temperature Scaling
arXiv:2401.09986v3 Announce Type: replace-cross Abstract: Federated learning is inherently hampered by data heterogeneity: non-i.i.d. training data over local clients. We propose a novel model training approach for federated learning, FLex&Chill, which exploits the Logit Chilling method. Through extensive evaluations, we demonstrate that, in the presence of non-i.i.d. data characteristics inherent in federated learning systems, this […]
Methods for Analyzing RNA Pseudoknots via Chord Diagrams and Intersection Graphs
arXiv:2512.19939v1 Announce Type: new Abstract: RNA molecules are known to form complex secondary structures including pseudoknots. A systematic framework for the enumeration, classification and prediction of secondary structures is critical to determine the biological significance of the molecular configurations of RNA. Chord diagrams are mathematical objects widely used to represent RNA secondary structures and to […]
FGDCC: Fine-Grained Deep Cluster Categorization — A Framework for Intra-Class Variability Problems in Plant Classification
arXiv:2512.19960v1 Announce Type: new Abstract: Intra-class variability is given according to the significance in the degree of dissimilarity between images within a class. In that sense, depending on its intensity, intra-class variability can hinder the learning process for DL models, specially when such classes are also underrepresented, which is a very common scenario in Fine-Grained […]