Cover Image

Compute Where it Counts: Self Optimizing Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Akhauri, Yash, Abdelfattah, Mohamed S.
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Attamba: Attending To Multi-Token States
by: Akhauri, Yash, et al.
Published: (2024)

Regression Language Models for Code
by: Akhauri, Yash, et al.
Published: (2025)

Encodings for Prediction-based Neural Architecture Search
by: Akhauri, Yash, et al.
Published: (2024)

On Latency Predictors for Neural Architecture Search
by: Akhauri, Yash, et al.
Published: (2024)

ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models
by: Akhauri, Yash, et al.
Published: (2024)

TokenButler: Token Importance is Predictable
by: Akhauri, Yash, et al.
Published: (2025)

Radial Networks: Dynamic Layer Routing for High-Performance Large Language Models
by: Dotzel, Jordan, et al.
Published: (2024)

xKV: Cross-Layer KV-Cache Compression via Aligned Singular Vector Extraction
by: Chang, Chi-Chih, et al.
Published: (2025)

FlashDLM: Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion
by: Hu, Zhanqiu, et al.
Published: (2025)

Analyzing Fairness of Computer Vision and Natural Language Processing Models
by: Rashed, Ahmed, et al.
Published: (2024)

SplitReason: Learning To Offload Reasoning
by: Akhauri, Yash, et al.
Published: (2025)

Language Models Need Inductive Biases to Count Inductively
by: Chang, Yingshan, et al.
Published: (2024)

Self-Calibrating Language Models via Test-Time Discriminative Distillation
by: Hedna, Mohamed Rissal, et al.
Published: (2026)

Local Prompt Optimization
by: Jain, Yash, et al.
Published: (2025)

The Expressive Capacity of State Space Models: A Formal Language Perspective
by: Sarrof, Yash, et al.
Published: (2024)

CAMO: A Class-Aware Minority-Optimized Ensemble for Robust Language Model Evaluation on Imbalanced Data
by: Ehab, Mohamed, et al.
Published: (2026)

Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model Alignment
by: Yin, Yueqin, et al.
Published: (2024)

Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models
by: Zhao, Zheng, et al.
Published: (2024)

Simultaneous Computation and Memory Efficient Zeroth-Order Optimizer for Fine-Tuning Large Language Models
by: Wang, Fei, et al.
Published: (2024)

Self-Play Preference Optimization for Language Model Alignment
by: Wu, Yue, et al.
Published: (2024)

High-Stakes Personalization: Rethinking LLM Customization for Individual Investor Decision-Making
by: Sawant, Yash Ganpat
Published: (2026)

Self-Taught Self-Correction for Small Language Models
by: Moskvoretskii, Viktor, et al.
Published: (2025)

Faster LLM Inference via Sequential Monte Carlo
by: Emara, Yahya, et al.
Published: (2026)

UnStar: Unlearning with Self-Taught Anti-Sample Reasoning for LLMs
by: Sinha, Yash, et al.
Published: (2024)

LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization
by: Chen, Guanzheng, et al.
Published: (2025)

GLLM: Self-Corrective G-Code Generation using Large Language Models with User Feedback
by: Abdelaal, Mohamed, et al.
Published: (2025)

Kanana: Compute-efficient Bilingual Language Models
by: Kanana LLM Team, et al.
Published: (2025)

Where Does Toxicity Live? Mechanistic Localization and Targeted Suppression in Language Models
by: Beniwal, Himanshu, et al.
Published: (2026)

An Analysis of Embedding Layers and Similarity Scores using Siamese Neural Networks
by: Bingi, Yash, et al.
Published: (2023)

Where is the signal in tokenization space?
by: Geh, Renato Lui, et al.
Published: (2024)

Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models
by: Zhao, Siyan, et al.
Published: (2026)

Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models
by: Zhang, Wenxuan, et al.
Published: (2024)

Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models
by: Chiang, Hung-Yueh, et al.
Published: (2025)

Self-AMPLIFY: Improving Small Language Models with Self Post Hoc Explanations
by: Bhan, Milan, et al.
Published: (2024)

RaZeR: Pushing the Limits of NVFP4 Quantization with Redundant Zero Remapping
by: Chen, Yuzong, et al.
Published: (2025)

Resolving Discrepancies in Compute-Optimal Scaling of Language Models
by: Porian, Tomer, et al.
Published: (2024)

Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
by: Zhang, Qingru, et al.
Published: (2023)

Predicting Where Steering Vectors Succeed
by: Billa, Jayadev
Published: (2026)

Where Do Reasoning Models Refuse?
by: Yamaguchi, Kureha, et al.
Published: (2025)

Self-Training Large Language Models with Confident Reasoning
by: Jang, Hyosoon, et al.
Published: (2025)