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UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD
arXiv:2606.05058v1 Announce Type: new Abstract: Computer-Aided Design (CAD) underpins modern engineering and manufacturing by enabling the creation of precise, editable 3D models. However, CAD research typically studies tasks in isolation, and multi-modal, multi-task learning for CAD is hindered by the absence of a unified benchmark. To address this gap, we introduce UniCAD, a comprehensive benchmark for multi-modal CAD learning that covers point-to-CAD reconstruction, text/image-to-CAD...
Dreaming Of Others: Latent Teammate Modeling In World Models For Multi-Agent Reinforcement Learning
Announce Type: new Abstract: In cooperative multi-agent reinforcement learning (MARL), agents must coordinate with partners whose internal policies and intentions are not directly observable. While world models such as Dreamer have demonstrated strong generalization and sample efficiency in single-agent settings, their application to MARL remains limited by an inability to handle teammate-induced uncertainty. We propose a new perspective: treat teammates as structured, learnable components...
MASF: A Multi-Model Adaptive Selection Framework for Abstractive Text summarization
Announce Type: new Abstract: Automatic text summarization has become increasingly important due to the rapid growth of digital textual information. This paper presents a Multi-Model Adaptive Summarization Framework designed to improve the robustness and quality of abstractive text summarization. Relying on a single model often leads to inconsistent summarization quality across articles with varying structures and topics.
Closed-Form Spectral Regularization for Multi-Task Model Merging
arXiv:2606.07289v1 Announce Type: new Abstract: Model merging combines several independently fine-tuned experts into a single multi-task model without any training data, reducing the storage, serving, and decentralized-development costs of large foundation models. State-of-the-art merging methods formulate merging as a layer-wise quadratic interference minimization problem. Although this problem admits an exact closed-form pseudoinverse solution, that solution underperforms hundreds of...
Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation
Announce Type: new Abstract: Telehealth systems have become increasingly important for delivering accessible and timely medical information. Existing large language models often struggle to provide consistent and contextually appropriate medical responses across varying levels of case severity. This limitation highlights the need for models that can effectively adapt to the progressive complexity in medical queries.
When LLMs Learn to Be Consistently Wrong: A Multi-Model Study of Linear Representations of Synthetic Deception
arXiv:2605.30381v1 Announce Type: new Abstract: Deceptive alignment, in which models maintain accurate internal representations while deliberately producing false outputs, remains a central challenge in AI safety. While strategic deception is the primary long-term concern, synthetic dishonesty - induced via direct optimization on incorrect answers - provides a controlled testbed for studying the representational basis of learned deception.
Diverse efforts in the same direction: A multi-model comparison of climate-neutrality power sector pathways for the Nordic countries
arXiv:2603.26719v2 Announce Type: replace Abstract: The Nordic countries have adopted ambitious climate targets that imply far-reaching transformations of their power sectors, making energy system modelling a central input to long-term policy analysis. At the same time, comparing results across studies remains challenging due to differences in model structure, assumptions, and data. This paper presents a comparative assessment of Nordic power sector transition pathways across four Nordic...
DynaGraph: Lightweight Multi-Model Interaction Framework via Dynamic Topological Reconfiguration
arXiv:2605.29511v2 Announce Type: replace Abstract: Tackling complex reasoning tasks typically relies on massive monolithic LLMs, which suffer from severe computational redundancy. While task decomposition through structured pipelines or multi-agent collaborations offers an alternative, these approaches inevitably fall into a critical dilemma: predefined static topologies are highly vulnerable to cascading errors, whereas unconstrained dynamic agents suffer from trajectory divergence and...
Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulation
new Abstract: Agentic AI completes tasks through iterative planning, tool use, and reasoning based on observed outcomes. Despite its popularity, its system-level behavior remains poorly understood, particularly for complex datasets and agent architectures-owing to highly non-deterministic execution, prohibitive evaluation costs, and limited visibility into proprietary models. This paper presents GAIATrace, the first token-level trace dataset of two state-of-the-art agentic systems...
MIND: Multi-rationale INtegrated Discriminative Reasoning Framework for Multi-modal Large Models
arXiv:2512.05530v2 Announce Type: replace Abstract: Recently, multimodal large language models (MLLMs) have been widely applied to reasoning tasks. However, they suffer from limited multi-rationale semantic modeling, insufficient logical robustness, and susceptibility to misleading cues.