Image de couverture de livre

Can Large Reasoning Models do Analogical Reasoning under Perceptual Uncertainty?

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Camposampiero, Giacomo, Hersche, Michael, Wattenhofer, Roger, Sebastian, Abu, Rahimi, Abbas
Format:	Preprint
Publié:	2025
Sujets:	Artificial Intelligence Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

I-RAVEN-X: Benchmarking Generalization and Robustness of Analogical and Mathematical Reasoning in Large Language and Reasoning Models
par: Camposampiero, Giacomo, et autres
Publié: (2025)

Towards Learning to Reason: Comparing LLMs with Neuro-Symbolic on Arithmetic Relations in Abstract Reasoning
par: Hersche, Michael, et autres
Publié: (2024)

Towards Learning Abductive Reasoning using VSA Distributed Representations
par: Camposampiero, Giacomo, et autres
Publié: (2024)

Scalable Evaluation and Neural Models for Compositional Generalization
par: Camposampiero, Giacomo, et autres
Publié: (2025)

On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages
par: Terzić, Aleksandar, et autres
Publié: (2024)

Limits of Transformer Language Models on Learning to Compose Algorithms
par: Thomm, Jonathan, et autres
Publié: (2024)

Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures
par: Hersche, Michael, et autres
Publié: (2024)

Terminating Differentiable Tree Experts
par: Thomm, Jonathan, et autres
Publié: (2024)

On the Role of Noise in Factorizers for Disentangling Distributed Representations
par: Karunaratne, Geethan, et autres
Publié: (2024)

A foundation model with multi-variate parallel attention to generate neuronal activity
par: Carzaniga, Francesco, et autres
Publié: (2025)

Locally Coherent Parallel Decoding in Diffusion Language Models
par: Hersche, Michael, et autres
Publié: (2026)

Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models
par: Terzić, Aleksandar, et autres
Publié: (2025)

Soft-Masked Diffusion Language Models
par: Hersche, Michael, et autres
Publié: (2025)

A Theoretical Analysis of Test-Driven Code Generation
par: Menet, Nicolas, et autres
Publié: (2026)

Thompson Sampling via Fine-Tuning of LLMs
par: Menet, Nicolas, et autres
Publié: (2025)

Kernel Approximation using Analog In-Memory Computing
par: Büchel, Julian, et autres
Publié: (2024)

Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models
par: Terzić, Aleksandar, et autres
Publié: (2026)

PUZZLES: A Benchmark for Neural Algorithmic Reasoning
par: Estermann, Benjamin, et autres
Publié: (2024)

FLIP Reasoning Challenge
par: Plesner, Andreas, et autres
Publié: (2025)

The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG
par: Carzaniga, Francesco Stefano, et autres
Publié: (2025)

Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks
par: Grillo, Niccolò, et autres
Publié: (2025)

Quantifying and Understanding Uncertainty in Large Reasoning Models
par: Li, Yangyi, et autres
Publié: (2026)

POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles
par: Menet, Nicolas, et autres
Publié: (2026)

Evaluating the Robustness of Analogical Reasoning in Large Language Models
par: Lewis, Martha, et autres
Publié: (2024)

On the Expressive Power of GNNs for Boolean Satisfiability
par: Peltonen, Saku, et autres
Publié: (2026)

Can Large Language Models Reason and Optimize Under Constraints?
par: Bernier, Fabien, et autres
Publié: (2026)

Can Large Language Models Reason and Plan?
par: Kambhampati, Subbarao
Publié: (2024)

Can LLMs Learn to Reason Robustly under Noisy Supervision?
par: Yang, Shenzhi, et autres
Publié: (2026)

ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning
par: Kwon, Yongchan, et autres
Publié: (2025)

Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty
par: Kim, Jeonghye, et autres
Publié: (2026)

Benchmarking Positional Encodings for GNNs and Graph Transformers
par: Grötschla, Florian, et autres
Publié: (2024)

Next Level Message-Passing with Hierarchical Support Graphs
par: Vonessen, Carlos, et autres
Publié: (2024)

Can Large Reasoning Models Improve Accuracy on Mathematical Tasks Using Flawed Thinking?
par: Amjith, Saraswathy, et autres
Publié: (2025)

Parametric Neural Amp Modeling with Active Learning
par: Grötschla, Florian, et autres
Publié: (2025)

From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty
par: Jenane, Azza, et autres
Publié: (2026)

Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
par: Doucet, Paul, et autres
Publié: (2024)

From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?
par: Zhou, Zhanke, et autres
Publié: (2025)

Uncertainty Quantification of Large Language Models using Approximate Bayesian Computation
par: Sharma, Mridul, et autres
Publié: (2025)

Tracing Uncertainty in Language Model "Reasoning"
par: Grünefeld, Nils, et autres
Publié: (2026)

Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models
par: Qu, Yun, et autres
Publié: (2026)