:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Huan, Haoran, Prabhudesai, Mihir, Wu, Mengning, Jaiswal, Shantanu, Pathak, Deepak
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2509.03518
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Diffusion Beats Autoregressive in Data-Constrained Settings
by: Prabhudesai, Mihir, et al.
Published: (2025)

Iterative Refinement Improves Compositional Image Generation
by: Jaiswal, Shantanu, et al.
Published: (2026)

Aligning Text-to-Image Diffusion Models with Reward Backpropagation
by: Prabhudesai, Mihir, et al.
Published: (2023)

Self-Questioning Language Models
by: Chen, Lili, et al.
Published: (2025)

Unified Multimodal Discrete Diffusion
by: Swerdlow, Alexander, et al.
Published: (2025)

Video Diffusion Alignment via Reward Gradients
by: Prabhudesai, Mihir, et al.
Published: (2024)

Maximizing Confidence Alone Improves Reasoning
by: Prabhudesai, Mihir, et al.
Published: (2025)

Solving Physics Olympiad via Reinforcement Learning on Physics Simulators
by: Prabhudesai, Mihir, et al.
Published: (2026)

Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey
by: Agrawal, Garima, et al.
Published: (2023)

Measuring Visual Understanding in Telecom domain: Performance Metrics for Image-to-UML conversion using VLMs
by: Ranjani, HG, et al.
Published: (2025)

Zero-Shot Visual Reasoning by Vision-Language Models: Benchmarking and Analysis
by: Nagar, Aishik, et al.
Published: (2024)

Can LLMs be Fooled? Investigating Vulnerabilities in LLMs
by: Abdali, Sara, et al.
Published: (2024)

Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios
by: Jaiswal, Shantanu, et al.
Published: (2024)

Which LLMs are Difficult to Detect? A Detailed Analysis of Potential Factors Contributing to Difficulties in LLM Text Detection
by: Thorat, Shantanu, et al.
Published: (2024)

Teaming LLMs to Detect and Mitigate Hallucinations
by: Till, Demian, et al.
Published: (2025)

Can Multimodal LLMs Perform Time Series Anomaly Detection?
by: Xu, Xiongxiao, et al.
Published: (2025)

Intrinsic Explainability of Multimodal Learning for Crop Yield Prediction
by: Najjar, Hiba, et al.
Published: (2025)

Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer
by: Liu, Xingyu, et al.
Published: (2024)

LLMs Will Always Hallucinate, and We Need to Live With This
by: Banerjee, Sourav, et al.
Published: (2024)

Sharpness-Aware Minimization Can Hallucinate Minimizers
by: Park, Chanwoong, et al.
Published: (2025)

Evolutionary Policy Optimization
by: Wang, Jianren, et al.
Published: (2025)

Is Implicit Knowledge Enough for LLMs? A RAG Approach for Tree-based Structures
by: Gupte, Mihir, et al.
Published: (2025)

Marginals Before Conditionals
by: Sahasrabudhe, Mihir
Published: (2026)

Semantic Energy: Detecting LLM Hallucination Beyond Entropy
by: Ma, Huan, et al.
Published: (2025)

Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts
by: Wu, Zhaomin, et al.
Published: (2025)

SAPG: Split and Aggregate Policy Gradients
by: Singla, Jayesh, et al.
Published: (2024)

HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs
by: Zeng, Xinyue, et al.
Published: (2026)

A Concise Review of Hallucinations in LLMs and their Mitigation
by: Pulkundwar, Parth, et al.
Published: (2025)

Generation Constraint Scaling Can Mitigate Hallucination
by: Kollias, Georgios, et al.
Published: (2024)

Robust Hallucination Detection in LLMs via Adaptive Token Selection
by: Niu, Mengjia, et al.
Published: (2025)

Cost-Effective Hallucination Detection for LLMs
by: Valentin, Simon, et al.
Published: (2024)

Failure Modes of LLMs for Causal Reasoning on Narratives
by: Yamin, Khurram, et al.
Published: (2024)

Agent Factories for High Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardware Optimization?
by: Bhandwaldar, Abhishek, et al.
Published: (2026)

Generative Classifiers Avoid Shortcut Solutions
by: Li, Alexander C., et al.
Published: (2025)

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques
by: Singh, Shruti, et al.
Published: (2024)

Compressing LLMs: The Truth is Rarely Pure and Never Simple
by: Jaiswal, Ajay, et al.
Published: (2023)

Benchmarking Reliability of Deep Learning Models for Pathological Gait Classification
by: Jaiswal, Abhishek, et al.
Published: (2024)

Unlearners Can Lie: Evaluating and Improving Honesty in LLM Unlearning
by: Gu, Renjie, et al.
Published: (2026)

SparseSSM: Efficient Selective Structured State Space Models Can Be Pruned in One-Shot
by: Tuo, Kaiwen, et al.
Published: (2025)

Can Small Language Models Learn, Unlearn, and Retain Noise Patterns?
by: Scaria, Nicy, et al.
Published: (2024)