Saved in:
| Main Authors: | Karkar, Chinmay, Chopra, Paras |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.18394 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
We're Different, We're the Same: Creative Homogeneity Across LLMs
by: Wenger, Emily, et al.
Published: (2025)
by: Wenger, Emily, et al.
Published: (2025)
Why LLMs Aren't Scientists Yet: Lessons from Four Autonomous Research Attempts
by: Trehan, Dhruv, et al.
Published: (2026)
by: Trehan, Dhruv, et al.
Published: (2026)
The Sequential Edge: Inverse-Entropy Voting Beats Parallel Self-Consistency at Matched Compute
by: Sharma, Aman, et al.
Published: (2025)
by: Sharma, Aman, et al.
Published: (2025)
Think Just Enough: Sequence-Level Entropy as a Confidence Signal for LLM Reasoning
by: Sharma, Aman, et al.
Published: (2025)
by: Sharma, Aman, et al.
Published: (2025)
Language Models Entangle Language and Culture
by: Jain, Shourya, et al.
Published: (2026)
by: Jain, Shourya, et al.
Published: (2026)
EsoLang-Bench: Evaluating Genuine Reasoning in Large Language Models via Esoteric Programming Languages
by: Sharma, Aman, et al.
Published: (2026)
by: Sharma, Aman, et al.
Published: (2026)
Hybrid Neural World Models
by: Lakshmanan, Pranav, et al.
Published: (2026)
by: Lakshmanan, Pranav, et al.
Published: (2026)
Building Interpretable Models for Moral Decision-Making
by: Goel, Mayank, et al.
Published: (2026)
by: Goel, Mayank, et al.
Published: (2026)
We're Tired, Y'all
by: Lee Skallerup Bessette, et al.
Published: (2024)
by: Lee Skallerup Bessette, et al.
Published: (2024)
Discovering Reinforcement Learning Interfaces with Large Language Models
by: Jaswal, Akshat Singh, et al.
Published: (2026)
by: Jaswal, Akshat Singh, et al.
Published: (2026)
METIS: Mentoring Engine for Thoughtful Inquiry & Solutions
by: Kumar, Abhinav Rajeev, et al.
Published: (2026)
by: Kumar, Abhinav Rajeev, et al.
Published: (2026)
ISO-Bench: Can Coding Agents Optimize Real-World Inference Workloads?
by: Nangia, Ayush, et al.
Published: (2026)
by: Nangia, Ayush, et al.
Published: (2026)
Were RNNs All We Needed?
by: Feng, Leo, et al.
Published: (2024)
by: Feng, Leo, et al.
Published: (2024)
What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
by: Zhang, Fan, et al.
Published: (2026)
by: Zhang, Fan, et al.
Published: (2026)
Crafting Interpretable Embeddings by Asking LLMs Questions
by: Benara, Vinamra, et al.
Published: (2024)
by: Benara, Vinamra, et al.
Published: (2024)
Asking the Right Questions about Information Technology: Until We Begin, We're Skating on Thin Philosophical Ice.
by: Buschman, John
Published: (1990)
by: Buschman, John
Published: (1990)
Reimagining Anomalies: What If Anomalies Were Normal?
by: Liznerski, Philipp, et al.
Published: (2024)
by: Liznerski, Philipp, et al.
Published: (2024)
Asking Forever: Universal Activations Behind Turn Amplification in Conversational LLMs
by: Coalson, Zachary, et al.
Published: (2026)
by: Coalson, Zachary, et al.
Published: (2026)
Solving The Dynamic Volatility Fitting Problem: A Deep Reinforcement Learning Approach
by: Gnabeyeu, Emmanuel, et al.
Published: (2024)
by: Gnabeyeu, Emmanuel, et al.
Published: (2024)
Britain. We're listening
Published: (1995)
Published: (1995)
We're Engaged! A Community-University Library Collaboration
by: Rolloff, Evelyn K.
Published: (2013)
by: Rolloff, Evelyn K.
Published: (2013)
WeGeFT: Weight-Generative Fine-Tuning for Multi-Faceted Efficient Adaptation of Large Models
by: Savadikar, Chinmay, et al.
Published: (2023)
by: Savadikar, Chinmay, et al.
Published: (2023)
Britain. We're with you, sort of
Published: (2001)
Published: (2001)
Avoiding Catastrophe in Online Learning by Asking for Help
by: Plaut, Benjamin, et al.
Published: (2024)
by: Plaut, Benjamin, et al.
Published: (2024)
Can LLMs Separate Instructions From Data? And What Do We Even Mean By That?
by: Zverev, Egor, et al.
Published: (2024)
by: Zverev, Egor, et al.
Published: (2024)
History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting
by: Khanna, Sarthak, et al.
Published: (2025)
by: Khanna, Sarthak, et al.
Published: (2025)
Black-box Optimization of LLM Outputs by Asking for Directions
by: Zhang, Jie, et al.
Published: (2025)
by: Zhang, Jie, et al.
Published: (2025)
Scalable Complexity Control Facilitates Reasoning Ability of LLMs
by: Hang, Liangkai, et al.
Published: (2025)
by: Hang, Liangkai, et al.
Published: (2025)
Reinforcement Learning for Ballbot Navigation in Uneven Terrain
by: Salehi, Achkan
Published: (2025)
by: Salehi, Achkan
Published: (2025)
Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning
by: Chen, Liang, et al.
Published: (2025)
by: Chen, Liang, et al.
Published: (2025)
Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
by: Hu, Yifan, et al.
Published: (2025)
by: Hu, Yifan, et al.
Published: (2025)
Towards Greener Nights: Exploring AI-Driven Solutions for Light Pollution Management
by: Varshney, Paras, et al.
Published: (2024)
by: Varshney, Paras, et al.
Published: (2024)
Safe Learning Under Irreversible Dynamics via Asking for Help
by: Plaut, Benjamin, et al.
Published: (2025)
by: Plaut, Benjamin, et al.
Published: (2025)
View From Above: A Framework for Evaluating Distribution Shifts in Model Behavior
by: Chopra, Tanush, et al.
Published: (2024)
by: Chopra, Tanush, et al.
Published: (2024)
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities
by: Niv, Itay, et al.
Published: (2025)
by: Niv, Itay, et al.
Published: (2025)
LLMs Will Always Hallucinate, and We Need to Live With This
by: Banerjee, Sourav, et al.
Published: (2024)
by: Banerjee, Sourav, et al.
Published: (2024)
Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning
by: Madaan, Divyam, et al.
Published: (2024)
by: Madaan, Divyam, et al.
Published: (2024)
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
by: Chakrabarti, Kushal, et al.
Published: (2021)
by: Chakrabarti, Kushal, et al.
Published: (2021)
Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem
by: Chakrabarti, Kushal, et al.
Published: (2020)
by: Chakrabarti, Kushal, et al.
Published: (2020)
The Pitfalls of Benchmarking in Algorithm Selection: What We Are Getting Wrong
by: Petelin, Gašper, et al.
Published: (2025)
by: Petelin, Gašper, et al.
Published: (2025)
Similar Items
-
We're Different, We're the Same: Creative Homogeneity Across LLMs
by: Wenger, Emily, et al.
Published: (2025) -
Why LLMs Aren't Scientists Yet: Lessons from Four Autonomous Research Attempts
by: Trehan, Dhruv, et al.
Published: (2026) -
The Sequential Edge: Inverse-Entropy Voting Beats Parallel Self-Consistency at Matched Compute
by: Sharma, Aman, et al.
Published: (2025) -
Think Just Enough: Sequence-Level Entropy as a Confidence Signal for LLM Reasoning
by: Sharma, Aman, et al.
Published: (2025) -
Language Models Entangle Language and Culture
by: Jain, Shourya, et al.
Published: (2026)