Cost In CS.AI Papers

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 53 papers for Cost In CS.AI Papers. Dominant protocol signals include automatic metrics, simulation environments, LLM-as-judge, with frequent benchmark focus on Retrieval, GSM8K 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

• Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

  Roy Miles, Aysim Toker, Andreea-Maria Oncescu, Songcen Xu, Jiankang Deng · Feb 26, 2026

  Automatic Metrics MathCoding

  This modular pipeline separates exploration (diffusion) from evaluation and solution synthesis, avoiding monolithic unified hybrids while preserving broad search.

• Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue

  Ning Gao, Wei Zhang, Yuqin Dai, Ling Shi, Ziyin Wang · Feb 26, 2026

  Automatic Metrics General

  The rapid evolution of Large Language Models (LLMs) has accelerated the transition from conversational chatbots to general agents.

• 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.

• When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models

  Satyam Kumar Navneet, Joydeep Chandra, Yong Zhang · Feb 25, 2026

  Automatic Metrics General

  Large Language Models (LLMs) are increasingly used to ``professionalize'' workplace communication, often at the cost of linguistic identity.

• 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.

• Sparsity Induction for Accurate Post-Training Pruning of Large Language Models

  Minhao Jiang, Zhikai Li, Xuewen Liu, Jing Zhang, Mengjuan Chen · Feb 25, 2026

  Automatic Metrics Math

  Large language models have demonstrated capabilities in text generation, while their increasing parameter scales present challenges in computational and memory efficiency.

• 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.

• The Headless Firm: How AI Reshapes Enterprise Boundaries

  Tassilo Klein, Sebastian Wieczorek · Feb 24, 2026

  Automatic Metrics General

  We argue that agentic AI induces a structural change in how coordination costs scale: in prior modular systems, integration cost grew with interaction topology (O(n^2) in the number of components); in protocol-mediated agentic systems, inte

• 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.

• SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

  David Anugraha, Vishakh Padmakumar, Diyi Yang · Feb 24, 2026

  Automatic Metrics Coding

  Based on this formulation, we introduce SparkMe, a multi-agent LLM interviewer that performs deliberative planning via simulated conversation rollouts to select questions with high expected utility.

• 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

• CAMEL: Confidence-Gated Reflection for Reward Modeling

  Zirui Zhu, Hailun Xu, Yang Luo, Yong Liu, Kanchan Sarkar · Feb 24, 2026

  Automatic Metrics General

  Reward models play a fundamental role in aligning large language models with human preferences.

• 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.

• KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration

  Mohammad Amanlou, Erfan Shafiee Moghaddam, Yasaman Amou Jafari, Mahdi Noori, Farhan Farsi · Feb 23, 2026

  Automatic Metrics Math

  Results show that KNIGHT enables token- and cost-efficient generation from a reusable graph representation, achieves high quality across these criteria, and yields model rankings aligned with MMLU-style benchmarks, while supporting topic-sp

• To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

  Zaifu Zhan, Min Zeng, Shuang Zhou, Yiran Song, Xiaoyi Chen · Feb 23, 2026

  Automatic Metrics Medicine

  Two open-source LLMs (Llama-3.1-8B and Qwen-2.5-7B) were evaluated on four biomedical QA benchmarks-HeadQA, MedQA-USMLE, MedMCQA, and PubMedQA.

• Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics

  Yue Pan, Xingyao Wang, Hanyue Zhang, Liwei Liu, Changxin Li · Feb 23, 2026

  Automatic Metrics MedicineCoding

  The model's high sensitivity was further corroborated by additional follow-up data, confirming its efficacy in predicting HF deterioration and its potential to secure patient safety in remote, home-based settings.

• 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.

• Can Large Language Models Replace Human Coders? Introducing ContentBench

  Michael Haman · Feb 23, 2026

  Automatic Metrics Coding

  This paper introduces ContentBench, a public benchmark suite that helps answer this replacement question by tracking how much agreement low-cost LLMs achieve and what they cost on the same interpretive coding tasks.

• Next Reply Prediction X Dataset: Linguistic Discrepancies in Naively Generated Content

  Simon Münker, Nils Schwager, Kai Kugler, Michael Heseltine, Achim Rettinger · Feb 22, 2026

  Automatic Metrics General

  The increasing use of Large Language Models (LLMs) as proxies for human participants in social science research presents a promising, yet methodologically risky, paradigm shift.

• 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.

• 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.

• ReIn: Conversational Error Recovery with Reasoning Inception

  Takyoung Kim, Jinseok Nam, Chandrayee Basu, Xing Fan, Chengyuan Ma · Feb 19, 2026

  Automatic Metrics LawMedicine

  Conversational agents powered by large language models (LLMs) with tool integration achieve strong performance on fixed task-oriented dialogue datasets but remain vulnerable to unanticipated, user-induced errors.

• Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents

  Wenxuan Ding, Nicholas Tomlin, Greg Durrett · Feb 18, 2026

  Simulation Env Coding

  Each problem has latent environment state that can be reasoned about via a prior which is passed to the LLM agent.

• 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.

• Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework

  Mengze Hong, Chen Jason Zhang, Zichang Guo, Hanlin Gu, Di Jiang · Feb 17, 2026

  Automatic Metrics General

  Existing approaches either rely on modular system designs with extensive agent orchestration or employ over-simplified instruction schemas, providing limited guidance and poor generalizability.

• Protecting Language Models Against Unauthorized Distillation through Trace Rewriting

  Xinhang Ma, William Yeoh, Ning Zhang, Yevgeniy Vorobeychik · Feb 16, 2026

  Automatic Metrics General

  Knowledge distillation is a widely adopted technique for transferring capabilities from LLMs to smaller, more efficient student models.

• Breaking Data Efficiency Dilemma: A Federated and Augmented Learning Framework For Alzheimer's Disease Detection via Speech

  Xiao Wei, Bin Wen, Yuqin Lin, Kai Li, Mingyang gu · Feb 16, 2026

  Automatic Metrics MedicineCoding

  Early diagnosis of Alzheimer's Disease (AD) is crucial for delaying its progression.

• Buy versus Build an LLM: A Decision Framework for Governments

  Jiahao Lu, Ziwei Xu, William Tjhi, Junnan Li, Antoine Bosselut · Feb 13, 2026

  Automatic Metrics General

  This paper provides a strategic framework for making this decision by evaluating these options across dimensions including sovereignty, safety, cost, resource capability, cultural fit, and sustainability.

• Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters

  Ailin Huang, Ang Li, Aobo Kong, Bin Wang, Binxing Jiao · Feb 11, 2026

  Simulation Env MathCoding

  We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency.

• Fast-weight Product Key Memory

  Tianyu Zhao, Llion Jones · Jan 2, 2026

  Automatic Metrics General

  Notably, in Needle-in-a-Haystack evaluations, FwPKM generalizes to 128K-token contexts despite being trained on only 4K-token sequences.

• Knowledge Distillation with Structured Chain-of-Thought for Text-to-SQL

  Khushboo Thaker, Yony Bresler · Dec 18, 2025

  Automatic Metrics General

  Deploying accurate Text-to-SQL systems at the enterprise level faces a difficult trilemma involving cost, security and performance.

• Aerial Vision-Language Navigation with a Unified Framework for Spatial, Temporal and Embodied Reasoning

  Huilin Xu, Zhuoyang Liu, Yixiang Luomei, Feng Xu · Dec 9, 2025

  Simulation Env General

  Extensive experiments on the AerialVLN and OpenFly benchmark validate the effectiveness of our method.

• 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.

• FML-bench: Benchmarking Machine Learning Agents for Scientific Research

  Qiran Zou, Hou Hei Lam, Wenhao Zhao, Yiming Tang, Tingting Chen · Oct 12, 2025

  Automatic Metrics General

  Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments.

• EconCausal: A Context-Aware Causal Reasoning Benchmark for Large Language Models in Social Science

  Donggyu Lee, Hyeok Yun, Meeyoung Cha, Sungwon Park, Sangyoon Park · Oct 8, 2025

  Automatic MetricsSimulation Env General

  To address this, we introduce EconCausal, a large-scale benchmark comprising 10,490 context-annotated causal triplets extracted from 2,595 high-quality empirical studies published in top-tier economics and finance journals.

• BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals

  Chenqi Li, Yu Liu, Timothy Denison, Tingting Zhu · Oct 2, 2025

  Automatic Metrics General

  Biosignals offer valuable insights into the physiological states of the human body.

• 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.

• Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents

  Yaorui Shi, Yuxin Chen, Siyuan Wang, Sihang Li, Hengxing Cai · Sep 27, 2025

  Automatic Metrics General

  To tackle these challenges, we present ReMemR1, which integrates the mechanism of memory retrieval into the memory update process, enabling the agent to selectively callback historical memories for non-linear reasoning.

• EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis

  Mohammad Hossein Samaei, Faryad Darabi Sahneh, Lee W. Cohnstaedt, Caterina Scoglio · Sep 24, 2025

  Llm As JudgeSimulation Env General

  We introduce EpidemIQs, a novel multi-agent LLM framework that integrates user inputs and autonomously conducts literature review, analytical derivation, network modeling, mechanistic modeling, stochastic simulations, data visualization and

• Diffusion Language Models Know the Answer Before Decoding

  Pengxiang Li, Yefan Zhou, Dilxat Muhtar, Lu Yin, Shilin Yan · Aug 27, 2025

  Automatic Metrics MathCoding

  Empirical evaluations of LLaDA-8B and Dream-7B across multiple tasks show that Prophet reduces the number of decoding steps by up to 3.4x while preserving high generation quality.

• 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

• 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.

• "Don't Do That!": Guiding Embodied Systems through Large Language Model-based Constraint Generation

  Amin Seffo, Aladin Djuhera, Masataro Asai, Holger Boche · Jun 4, 2025

  Simulation Env MathCoding

  Recent advancements in large language models (LLMs) have spurred interest in robotic navigation that incorporates complex spatial, mathematical, and conditional constraints from natural language into the planning problem.

• Cost-of-Pass: An Economic Framework for Evaluating Language Models

  Mehmet Hamza Erol, Batu El, Mirac Suzgun, Mert Yuksekgonul, James Zou · Apr 17, 2025

  Automatic Metrics General

  We then define the frontier cost-of-pass: the minimum cost-of-pass achievable across available models or the human-expert(s), using the approx.

• A Scalable Framework for Evaluating Health Language Models

  Neil Mallinar, A. Ali Heydari, Xin Liu, Anthony Z. Faranesh, Brent Winslow · Mar 30, 2025

  Automatic Metrics Medicine

  As LLM-driven health applications are increasingly adopted, rigorous and efficient one-sided evaluation methodologies are crucial to ensure response quality across multiple dimensions, including accuracy, personalization and safety.

• 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.

• Intermittent Semi-Working Mask: A New Masking Paradigm for LLMs

  HaoYuan Hu, Mingcong Lu, Di Luo, XinYa Wu, Jiangcai Zhu · Aug 1, 2024

  Automatic Metrics Math

  Across extensive evaluations, ISM outperforms causal baselines not only on multi-turn dialogue, but also on context-intensive tasks like mathematical reasoning.

• 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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