Cover Image

Supporting Assessment of Novelty of Design Problems Using Concept of Problem SAPPhIRE

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Singh, Sanjay, Chakrabarti, Amaresh
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Development and Evaluation of a Retrieval-Augmented Generation Tool for Creating SAPPhIRE Models of Artificial Systems
by: Majumder, Anubhab, et al.
Published: (2024)

A Study on Effect of Reference Knowledge Choice in Generating Technical Content Relevant to SAPPhIRE Model Using Large Language Model
by: Bhattacharya, Kausik, et al.
Published: (2024)

Assessing the Variety of a Concept Space Using an Unbiased Estimate of Rao's Quadratic Index
by: Majumder, Anubhab, et al.
Published: (2024)

NoveltyRank: A Retrieval-Augmented Framework for Conceptual Novelty Estimation in AI Research
by: Yan, Zhengxu, et al.
Published: (2025)

How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning
by: Dutta, Subhabrata, et al.
Published: (2024)

Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publications
by: Lin, Ethan, et al.
Published: (2024)

Jailbreaking as a Reward Misspecification Problem
by: Xie, Zhihui, et al.
Published: (2024)

Reading Between the Lines: The One-Sided Conversation Problem
by: Ebert, Victoria, et al.
Published: (2025)

Audio Video Verbal Analysis (AVVA) for Capturing Classroom Dialogues
by: Upadhyay, Vivek, et al.
Published: (2026)

Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation
by: Zhou, Yujun, et al.
Published: (2025)

Solving the Inverse Alignment Problem for Efficient RLHF
by: Krishna, Shambhavi, et al.
Published: (2024)

SciMON: Scientific Inspiration Machines Optimized for Novelty
by: Wang, Qingyun, et al.
Published: (2023)

Latent Performance Profiling of Large Language Models
by: Chakraborty, Tanmoy, et al.
Published: (2026)

RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems
by: Qu, Yuxiao, et al.
Published: (2025)

Reasoning Beyond Limits: Advances and Open Problems for LLMs
by: Ferrag, Mohamed Amine, et al.
Published: (2025)

ThetaEvolve: Test-time Learning on Open Problems
by: Wang, Yiping, et al.
Published: (2025)

Detection Without Correction: A Robust Asymmetry in Activation-Based Hallucination Probing
by: Roy, Dip, et al.
Published: (2026)

The Zero Body Problem: Probing LLM Use of Sensory Language
by: Hicke, Rebecca M. M., et al.
Published: (2025)

Enhancing LLM Problem Solving with REAP: Reflection, Explicit Problem Deconstruction, and Advanced Prompting
by: Lingo, Ryan, et al.
Published: (2024)

MULTIVERSE: Exposing Large Language Model Alignment Problems in Diverse Worlds
by: Jin, Xiaolong, et al.
Published: (2024)

Interpretation of the Intent Detection Problem as Dynamics in a Low-dimensional Space
by: Sanchez-Karhunen, Eduardo, et al.
Published: (2024)

†DAGGER: Distractor-Aware Graph Generation for Executable Reasoning in Math Problems
by: Nazi, Zabir Al, et al.
Published: (2026)

MathChat: Converse to Tackle Challenging Math Problems with LLM Agents
by: Wu, Yiran, et al.
Published: (2023)

Learn Hard Problems During RL with Reference Guided Fine-tuning
by: Wu, Yangzhen, et al.
Published: (2026)

Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs
by: Hu, Zhiyuan, et al.
Published: (2026)

XFinBench: Benchmarking LLMs in Complex Financial Problem Solving and Reasoning
by: Zhang, Zhihan, et al.
Published: (2025)

A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs
by: Merchant, Humzah, et al.
Published: (2025)

What's New in My Data? Novelty Exploration via Contrastive Generation
by: Isonuma, Masaru, et al.
Published: (2024)

SAGE: A Novelty Gate for Efficient Memory Evolution in Agentic LLMs
by: Wang, Sijia, et al.
Published: (2026)

Toward LLM-Supported Automated Assessment of Critical Thinking Subskills
by: Peczuh, Marisa C., et al.
Published: (2025)

Neural Diversity Regularizes Hallucinations in Language Models
by: Chakrabarti, Kushal, et al.
Published: (2025)

On the Utility of Domain-Adjacent Fine-Tuned Model Ensembles for Few-shot Problems
by: Alam, Md Ibrahim Ibne, et al.
Published: (2024)

Teaching LLM to Reason: Reinforcement Learning from Algorithmic Problems without Code
by: Bao, Keqin, et al.
Published: (2025)

Problems with Chinchilla Approach 2: Systematic Biases in IsoFLOP Parabola Fits
by: Czech, Eric, et al.
Published: (2026)

Efficient Continual Pre-training of LLMs for Low-resource Languages
by: Nag, Arijit, et al.
Published: (2024)

Easy Problems That LLMs Get Wrong
by: Williams, Sean, et al.
Published: (2024)

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem
by: Wang, Yubo, et al.
Published: (2025)

SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning
by: Liang, Xiao, et al.
Published: (2025)

Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems
by: Chen, Justin Chih-Yao, et al.
Published: (2026)

When Not to Answer: Evaluating Prompts on GPT Models for Effective Abstention in Unanswerable Math Word Problems
by: Saadat, Asir, et al.
Published: (2024)