Cost In CS.LG Papers

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 30 papers for Cost In CS.LG Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on GSM8K, NyayaBench and metric focus on cost, accuracy. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Top Papers Reporting This Metric

• How Do Latent Reasoning Methods Perform Under Weak and Strong Supervision?

  Yingqian Cui, Zhenwei Dai, Bing He, Zhan Shi, Hui Liu · Feb 25, 2026

  Automatic Metrics Coding

  Latent reasoning has been recently proposed as a reasoning paradigm and performs multi-step reasoning through generating steps in the latent space instead of the textual space.

• SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

  Patrick Tser Jern Kon, Archana Pradeep, Ang Chen, Alexander P. Ellis, Warren Hunt · Feb 25, 2026

  Automatic Metrics Coding

  Our approach combines supervised fine-tuning on expert-augmented trajectories with agentic reinforcement learning that explicitly discourages degenerative looping and unproductive expert collaboration.

• JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning

  Ruichen Xu, Ying-Jun Angela Zhang, Jianwei Huang · Feb 25, 2026

  Automatic Metrics General

  Extensive evaluations on MNIST and CIFAR-10 demonstrate that JSAM achieves up to 15% improvement in test accuracy compared to existing unbiased selection mechanisms while maintaining cost efficiency across varying data heterogeneity levels.

• From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators

  Zhihao Li, Yu Feng, Zhilu Lai, Wei Wang · Feb 25, 2026

  Automatic Metrics General

  On standard PDE benchmarks and real datasets, our method attains state-of-the-art competitive accuracy while providing intrinsic interpretability.

• Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG

  Inderjeet Singh, Vikas Pahuja, Aishvariya Priya Rathina Sabapathy, Chiara Picardi, Amit Giloni · Feb 24, 2026

  Automatic Metrics General

  Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics across retrieval, planning, and generation components.

• Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

  Mohammadreza Ghaffarzadeh-Esfahani, Nahid Yousefian, Ebrahim Heidari-Farsani, Ali Akbar Omidvarian, Sepehr Ghahraei · Feb 24, 2026

  Automatic Metrics MedicineMultilingual

  Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language processing (NLP).

• Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-training

  Anas Barakat, Souradip Chakraborty, Khushbu Pahwa, Amrit Singh Bedi · Feb 24, 2026

  Automatic Metrics MathCoding

  Pass@k is a widely used performance metric for verifiable large language model tasks, including mathematical reasoning, code generation, and short-answer reasoning.

• Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning

  Zhangjie Xia, Yu Yang, Pan Xu · Feb 24, 2026

  Simulation Env General

  Off-dynamics offline reinforcement learning (RL) aims to learn a policy for a target domain using limited target data and abundant source data collected under different transition dynamics.

• Motivation is Something You Need

  Mehdi Acheli, Walid Gaaloul · Feb 24, 2026

  Automatic Metrics General

  Inspired by the interplay of emotions and cognition in the human brain and more specifically the SEEKING motivational state, we design a dual-model framework where a smaller base model is trained continuously, while a larger motivated model

• Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference

  Anna Hart, Chi Han, Jeonghwan Kim, Huimin Zhao, Heng Ji · Feb 24, 2026

  Automatic Metrics General

  Modern Protein Language Models (PLMs) apply transformer-based model architectures from natural language processing to biological sequences, predicting a variety of protein functions and properties.

• Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference

  Arindam Khaled · Feb 23, 2026

  Automatic Metrics Math

  In this work, we propose "Pyramid MoA", a hierarchical Mixture-of-Agents architecture that uses a lightweight Router to dynamically escalate queries only when necessary.

• Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning

  Abhinaba Basu · Feb 21, 2026

  Automatic Metrics Multilingual

  Personal AI agents incur substantial cost via repeated LLM calls.

• Luna-2: Scalable Single-Token Evaluation with Small Language Models

  Vatsal Goel, Rishon Dsouza, Nikhil Ega, Amey Ramesh Rambatla, Rob Friel · Feb 20, 2026

  Automatic Metrics Coding

  Real-time guardrails require evaluation that is accurate, cheap, and fast - yet today's default, LLM-as-a-judge (LLMAJ), is slow, expensive, and operationally non-deterministic due to multi-token generation.

• Sink-Aware Pruning for Diffusion Language Models

  Aidar Myrzakhan, Tianyi Li, Bowei Guo, Shengkun Tang, Zhiqiang Shen · Feb 19, 2026

  Automatic Metrics Coding

  Diffusion Language Models (DLMs) incur high inference cost due to iterative denoising, motivating efficient pruning.

• Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning

  Jyotin Goel, Souvik Maji, Pratik Mazumder · Feb 19, 2026

  Automatic Metrics General

  Instruction-following language models are trained to be helpful and safe, yet their safety behavior can deteriorate under benign fine-tuning and worsen under adversarial updates.

• Sign Lock-In: Randomly Initialized Weight Signs Persist and Bottleneck Sub-Bit Model Compression

  Akira Sakai, Yuma Ichikawa · Feb 19, 2026

  Automatic Metrics General

  Sub-bit model compression seeks storage below one bit per weight; as magnitudes are aggressively compressed, the sign bit becomes a fixed-cost bottleneck.

• Multi-Objective Alignment of Language Models for Personalized Psychotherapy

  Mehrab Beikzadeh, Yasaman Asadollah Salmanpour, Ashima Suvarna, Sriram Sankararaman, Matteo Malgaroli · Feb 17, 2026

  Automatic Metrics Medicine

  While AI systems show therapeutic promise, current alignment approaches optimize objectives independently, failing to balance patient preferences with clinical safety.

• MAEB: Massive Audio Embedding Benchmark

  Adnan El Assadi, Isaac Chung, Chenghao Xiao, Roman Solomatin, Animesh Jha · Feb 17, 2026

  Simulation Env CodingMultilingual

  We introduce the Massive Audio Embedding Benchmark (MAEB), a large-scale benchmark covering 30 tasks across speech, music, environmental sounds, and cross-modal audio-text reasoning in 100+ languages.

• Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss

  Ang Lv, Jin Ma, Yiyuan Ma, Siyuan Qiao · Dec 29, 2025

  Automatic Metrics General

  Mixture-of-Experts (MoE) models lack explicit constraints to ensure the router's decisions align well with the experts' capabilities, which ultimately limits model performance.

• Group Representational Position Encoding

  Yifan Zhang, Zixiang Chen, Yifeng Liu, Zhen Qin, Huizhuo Yuan · Dec 8, 2025

  Automatic Metrics MathLaw

  We present GRAPE (Group Representational Position Encoding), a unified framework for positional encoding based on group actions.

• Inference-Cost-Aware Dynamic Tree Construction for Efficient Inference in Large Language Models

  Yinrong Hong, Zhiquan Tan, Kai Hu · Oct 30, 2025

  Automatic Metrics Coding

  Large Language Models (LLMs) face significant inference latency challenges stemming from their autoregressive design and large size.

• mini-vec2vec: Scaling Universal Geometry Alignment with Linear Transformations

  Guy Dar · Sep 27, 2025

  Automatic Metrics General

  We build upon vec2vec, a procedure designed to align text embedding spaces without parallel data.

• Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration

  Zhicheng Yang, Zhijiang Guo, Yinya Huang, Yongxin Wang, Dongchun Xie · Aug 19, 2025

  Automatic Metrics General

  Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models, yet its full potential is hindered by two under-explored dimensions: Depth-the hardest pr

• DOTResize: Reducing LLM Width via Discrete Optimal Transport-based Neuron Merging

  Neha Verma, Kenton Murray, Kevin Duh · Jul 6, 2025

  Automatic Metrics General

  Structured pruning methods designed for Large Language Models (LLMs) generally focus on identifying and removing the least important components to optimize model size.

• Complexity-aware fine-tuning

  Andrey Goncharov, Daniil Vyazhev, Petr Sychev, Edvard Khalafyan, Alexey Zaytsev · Jun 26, 2025

  Automatic Metrics General

  General-purpose Large Language Models (LLMs) are frequently fine-tuned through supervised fine-tuning (SFT) to enhance performance in specific domains.

• Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay

  Yifan Sun, Jingyan Shen, Yibin Wang, Tianyu Chen, Zhendong Wang · Jun 5, 2025

  Automatic Metrics Coding

  Reinforcement learning (RL) has become an effective approach for fine-tuning large language models (LLMs), particularly to enhance their reasoning capabilities.

• vCache: Verified Semantic Prompt Caching

  Luis Gaspar Schroeder, Aditya Desai, Alejandro Cuadron, Kyle Chu, Shu Liu · Feb 6, 2025

  Automatic Metrics General

  We release the vCache implementation and four benchmarks to support future research.

• GRASP: Replace Redundant Layers with Adaptive Singular Parameters for Efficient Model Compression

  Kainan Liu, Yong Zhang, Ning Cheng, Zhitao Li, Shaojun Wang · Dec 31, 2024

  Automatic Metrics General

  Recent studies have demonstrated that many layers are functionally redundant in large language models (LLMs), enabling model compression by removing these layers to reduce inference cost.

• Renaissance: Investigating the Pretraining of Vision-Language Encoders

  Clayton Fields, Casey Kennington · Nov 11, 2024

  Automatic Metrics Coding

  To conduct these experiments, we introduce a VL evaluation framework called Renaissance.

• Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning

  Yong Liu, Zirui Zhu, Chaoyu Gong, Minhao Cheng, Cho-Jui Hsieh · Feb 24, 2024

  Automatic Metrics Coding

  While fine-tuning large language models (LLMs) for specific tasks often yields impressive results, it comes at the cost of memory inefficiency due to back-propagation in gradient-based training.

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