Heterogeneous Modeling Tools

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4 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Metacognitive Reasoning in Energy Based Models using Instance Based Learning Theory
Sep 30, 2026Tailia Malloy, Prateek Kumar Rajput, Serge Lionel Nikiema +2Metacognition Meta-Learning
Interdisciplinary Center for Security, Reliability, and Trust, University of Luxembourg, Luxembourg · Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh PA
Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection
Sep 29, 2026Liangyu Teng, Hengsong Liu, Juncen Guo +4Heterogeneous Modeling Tools Diversity
Fudan Institute on Networking Systems of AI
FedSocket: Recipient-Executable Knowledge Exchange for Heterogeneous Multimodal Federated Learning
Sep 29, 2026Xinyuan ZhaoHeterogeneous Modeling Tools
Sun Yat-sen University
Can Frozen Hyperspherical Features Guide the Selection of Pseudo Masks?
Sep 24, 2026Xinge Guo, Fengyang Xiao, Dingming Zhang +6Heterogeneous Modeling Tools
Duke University · Carnegie Mellon University · University of Alabama at Birmingham
COMED: The Missing Middle Between Routing and Collaboration in Multi-LLM Inference
Sep 22, 2026Norah Alballa, Wenxuan Zhang, Salma Kharrat +4Multi-Agent Large Language Model Systems Heterogeneous Modeling Tools
KAUST · LUMS
Mo' Models, Mo' Problems: How to best select model pools when designing Multi-Agent Systems
Sep 15, 2026Sara Vera Marjanović, Jiacheng Xu, Aleksandr Laptev +3Multi-Agent Communication Heterogeneous Modeling Tools
University of Copenhagen · Work done during internship at NVIDIA · NVIDIA
Calibration-Aware Uncertainty Cascades for Efficient Heterogeneous Model Collaboration
Sep 12, 2026Yilin Zhang, Han Jiang, Cai Xu +2Calibrated Uncertainty Heterogeneous Modeling Tools
School of Computer Science and Technology, Xidian University Xi’an, China
Accelerating Unified Multimodal Models with Core-Expansion Routing and Unified Computation Scheduling
Aug 29, 2026Wengyi Zhan, Chenqian Yan, Songwei Liu +2Multimodal Model Fast Inference
1Xiamen University · 2ByteDance
Agreement Before Diversity: Verification-First Complementarity for Heterogeneous Language-Model Coordination
Aug 5, 2026Ruitong Li, Binjie Guo, Aisheng Mo +3Diversity Agreement
1The University of Hong Kong · Zhejiang University · 3Independent Researcher
Sixteen models, fewer than two voices: measuring ensemble dispersion where no answer is uniquely correct
Jul 31, 2026Mario Vega-Barbas, Lidia Mora-Valenciano, Iván Pau +2Diversity Dispersion
ETSIS de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Madrid, Spain · Independent Researcher, Guadalajara, Spain · Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Huddinge, Stockholm, Sweden +4
TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings
Jul 27, 2026Ayeen Poostforoushan, Liane Vogel, Carsten BinnigText Embeddings Multi-Turn Benchmark
Independent Researcher · Technical University of Darmstadt · Technical University of Darmstadt & DFKI & hessian.AI
LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain
Jul 16, 2026Nenad Petrovic, Jiajie Zhang, Vahid Zolfaghari +1Interoperability Mechanics
Chair of Robotics, Artificial Intelligence and Real-Time Systems Technical University of Munich, Munich, Germany
Behavioural Signatures of Risk-Sensitive Decision-Making in Large Language Models
Jul 11, 2026Xuankun Rong, Wenke Huang, Bo Du +2Large Language Model Decisions Heterogeneous Modeling Tools
School of Computer Science, Wuhan University, Wuhan, China · College of Computing and Data Science, Nanyang Technological University, Singapore
Collective Intelligence with Foundation Models
Jul 6, 2026J. de Curtò, I. de ZarzàMulti-Agent Reasoning Heterogeneous Modeling Tools
Department of Computer Applications in Science & Engineering, BARCELONA Supercomputing Center, 08034 Barcelona, Spain · Escuela Técnica Superior de Ingeniería (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain · Human Centered AI, Data & Software, LUXEMBOURG Institute of Science and Technology, L-4362 Esch-sur-Alzette, Luxembourg
When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models
Jun 25, 2026Josef ChenHeterogeneous Modeling Tools Frontier Models
KAIKAKU
HyDRA: Hybrid Dynamic Routing Architecture for Heterogeneous LLM Pools
May 16, 2026Aashna Garg, Siddharth Singha Roy, Jinu Jang +2Large Language Model Routing Heterogeneous Modeling Tools
Microsoft
Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection
May 15, 2026Heqiang Wang, Weihong Yang, Zheyuan Yang +4Multimodal Industrial Anomaly Detection Schedulers
Pengcheng Laboratory, Shenzhen, 518066, China · Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
Agentic Systems as Boosting Weak Reasoning Models
May 13, 2026Varun Sunkaraneni, Pierfrancesco Beneventano, Riccardo Neumarker +2Large Reasoning Models Heterogeneous Modeling Tools
Texas A&M University · MIT
SDG-MoE: Signed Debate Graph Mixture-of-Experts
May 8, 2026Stepan Kulibaba, Kirill Labzin, Artem Dzhalilov +4Mixture-Of-Experts Architectures Mixture-Of-Experts
Innopolis University, Innopolis · Sirius University, Sirius · Innopolis University, Innopolis; Sirius University, Sirius +1
CellScientist: From Execution Feedback to Auditable Model-Revision Trajectories for Cellular Perturbation Prediction
May 8, 2026Mengran Li, Bo Li, Jiaying Wang +7Virtual Cell Single-Cell Perturbation Prediction
Center for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science and Innovation (HKISI)
FaaSMoE: A Serverless Framework for Multi-Tenant Mixture-of-Experts Serving
Apr 29, 2026Minghe Wang, Trever Schirmer, Mohammadreza Malekabbasi +1Mixture-Of-Experts Cloud
TU Berlin · Berlin, Germany
Scalable Inference Architectures for Compound AI Systems: A Production Deployment Study
Apr 28, 2026Srikanta Prasad S, Utkarsh AroraAgentic Inference Artificial Intelligence Deployment
Agentforce AI Platform, Salesforce India Pvt Ltd Bangalore, Karnataka, India
Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale
Apr 27, 2026Liang Guo, Ge Song, Litao Deng +9Heterogeneous Modeling Tools Interaction History
Meta Platforms, Inc., USA
OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling
Sep 28, 2025Jihu Guo, Tenghui Ma, Wei Gao +6Cpu-Gpu Hybrid Designs Heterogeneous Modeling Tools
FDU & Shanghai AI Laboratory · HKUST · Independent Researcher +2

Sep 30, 2026, cs.LGJ/K move · Enter open · S save

Metacognitive Reasoning in Energy Based Models using Instance Based Learning Theory

Tailia Malloy, Prateek Kumar Rajput, Serge Lionel Nikiema, Cleotilde Gonzalez+1

Interdisciplinary Center for Security, Reliability, and Trust, University of Luxembourg, Luxembourg · Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh PA

Metacognition Meta-Learning Heterogeneous Modeling Tools Multiple Instance Learning

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Metacognition involves reasoning about cognitive processes themselves. An example is in resource allocation where we choose how much time and effort to put into a reasoning task before we begin based on our confidence. Current Artificial Intelligence (AI) systems that rely on Large Language Models (LLMs) cannot estimate their uncertainty about an output without first responding, and cannot dynamically allocate resources to producing an output, making this type of metacognitive process difficult. A recently proposed alternative to classic transformer architectures that addresses these two concerns is the Energy Based Model (EBM) which allows for interpretable uncertainty modeling and dynamic allocation of compute resources. While EBMs can allow for control of these two processes, the actual metacognitive task of determining compute allocation based on uncertainty is not directly addressed. Instance-Based Learning Theory (IBLT) provides an approach to modeling human-like decisions from experience that has previously been applied to predicting human metacognitive reasoning. In this paper we introduce a framework for MEtacognitive Reasoning with Instance-based Learning Theory and Energy Dynamics (MERITED). Grounded in IBLT, this framework allows for control of the computational effort allocated in an EBM to allow for metacognitive control over reasoning effort based on uncertainty while remaining computationally efficient. This work has two main contributions, the training and open weight sharing of a 191M parameter reasoning EBM, and an implementation of the MERITED framework for dynamic compute allocation using an IBL model.

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