| % ===================================================================== |
| % refs.bib -- The Precision Ladder (ThakiCloud AI Research) |
| % Every arXiv entry verified against the arXiv API (title + authors + |
| % date) on 2026-09-27. ThakiCloud self-citations verified via HTTP 200 |
| % on both the Hugging Face dataset page and the research blog post on |
| % 2026-09-27. BFCL leaderboard URL verified HTTP 200 on 2026-09-27. |
| % Freshness: 24 of 31 entries (77.4%) dated 2026-03 or later; |
| % 7 entries (22.6%) older (2022-2026-01). |
| % ===================================================================== |
|
|
| % --- ThakiCloud prior work (self-citations; URLs verified 2026-09-27) --- |
|
|
| @techreport{thaki2026toolcallcliff, |
| title = {The Tool-Call Cliff: Measuring the Accuracy Decay of Agentic Structured Output Under Low-Bit Quantization in Self-Hosted H200 Serving}, |
| author = {{ThakiCloud AI Research (Hyojung Han)}}, |
| year = {2026}, |
| month = sep, |
| institution = {ThakiCloud}, |
| url = {https://thakicloud.com/tech-blog/ko/research/tool-call-quantization-cliff/}, |
| note = {Dataset: \url{https://huggingface.co/datasets/thaki-AI/daily-paper-2026-09-17-tool-call-quantization-cliff}} |
| } |
|
|
| @techreport{thaki2026verbositytax, |
| title = {The Reasoning Verbosity Tax: Per-Turn Cost-Quality Frontiers of Explicit vs. Compact Chain-of-Thought in Self-Hosted Agentic LLMs on H200}, |
| author = {{ThakiCloud AI Research (Hyojung Han)}}, |
| year = {2026}, |
| month = sep, |
| institution = {ThakiCloud}, |
| url = {https://thakicloud.com/tech-blog/ko/research/reasoning-verbosity-tax-agentic-cost/}, |
| note = {Dataset: \url{https://huggingface.co/datasets/thaki-AI/daily-paper-2026-09-11-reasoning-verbosity-tax-agentic-cost}} |
| } |
|
|
| @techreport{thaki2026validatordividend, |
| title = {The Validator's Dividend: Measuring the Cost-Quality Frontier of Validation-Gated Resampling versus k-Sample Consensus for Agentic Tool-Call Output on Self-Hosted H200}, |
| author = {{ThakiCloud AI Research (Hyojung Han)}}, |
| year = {2026}, |
| month = sep, |
| institution = {ThakiCloud}, |
| url = {https://thakicloud.com/tech-blog/ko/research/validator-dividend-tool-call-consensus/}, |
| note = {Dataset: \url{https://huggingface.co/datasets/thaki-AI/daily-paper-2026-09-23-validator-dividend-tool-call-consensus}} |
| } |
| |
| @techreport{thaki2026precisiontier, |
| title = {Precision-Tier Routing: Per-Request Quantization-Level Selection for Cost-Optimal LLM Serving on H200}, |
| author = {{ThakiCloud AI Research (Hyojung Han)}}, |
| year = {2026}, |
| month = aug, |
| institution = {ThakiCloud}, |
| url = {https://thakicloud.com/tech-blog/ko/research/precision-tier-llm-routing/}, |
| note = {Dataset: \url{https://huggingface.co/datasets/thaki-AI/daily-paper-2026-08-08-precision-tier-llm-routing}} |
| } |
| |
| @techreport{thaki2026ensamblerouter, |
| title = {Cross-Tier Model Ensemble Voting for Skill Routing: A Cost--Quality Study Beyond Single-Signal Retrieval Fusion}, |
| author = {{ThakiCloud AI Research (Hyojung Han)}}, |
| year = {2026}, |
| month = aug, |
| institution = {ThakiCloud}, |
| url = {https://thakicloud.com/tech-blog/ko/research/ensemble-tier-voting-skill-router/}, |
| note = {Dataset: \url{https://huggingface.co/datasets/thaki-AI/daily-paper-2026-08-06-ensemble-tier-voting-skill-router}} |
| } |
| |
| % --- 2026 references dated 2026-03 or later (verified via arXiv API 2026-09-27) --- |
| |
| @misc{kozyrev2026gdn, |
| title = {Why Gated DeltaNet Survives 4-Bit Quantization: {NVFP4} {W4A4} for the Recurrent Half of a Hybrid 27B {LLM}}, |
| author = {Kozyrev, Sergii and Maiboroda, Davyd}, |
| year = {2026}, |
| month = {9}, |
| eprint = {2609.04098}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.LG} |
| } |
| |
| @misc{ocgquant2026ocg, |
| title = {{OCGQuant}: Outlier-Companion Grouping for {NVFP4} Quantization}, |
| author = {Yao, Yishan and Li, Binjun and Yi, Hanling and Li, Pengyu and Liu, Xiaoqing and Yang, Zihan}, |
| year = {2026}, |
| month = {8}, |
| eprint = {2609.00066}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.LG} |
| } |
| |
| @misc{jang2026flatscore, |
| title = {Flat Score, Amplified Failures: How the Error Budget Masks Damage in Quantized {LLM} Agents}, |
| author = {Jang, Jiwon and Yang, Kisu and Lim, Heuiseok and Park, Hyunwoo}, |
| year = {2026}, |
| month = {7}, |
| eprint = {2607.27275}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| @misc{lian2026quantization, |
| title = {Quantization Inflates Reasoning: Token Inflation as a Hidden Cost of Low-Bit Reasoning Models}, |
| author = {Lian, Xinyu and Krichene, Walid and Huang, Beichen and Tanaka, Masahiro and Ruwase, Olatunji and Zhang, Li and Zhang, Minjia}, |
| year = {2026}, |
| month = {6}, |
| eprint = {2606.25519}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.LG} |
| } |
| |
| @misc{bouchard2026escalation, |
| title = {Is Escalation Worth It? A Decision-Theoretic Characterization of {LLM} Cascades}, |
| author = {Bouchard, Dylan}, |
| year = {2026}, |
| month = {5}, |
| eprint = {2605.06350}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.LG} |
| } |
| |
| @misc{kotte2026ucci, |
| title = {{UCCI}: Calibrated Uncertainty for Cost-Optimal {LLM} Cascade Routing}, |
| author = {Kotte, Varun}, |
| year = {2026}, |
| month = {5}, |
| eprint = {2605.18796}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.LG} |
| } |
| |
| @misc{moslem2026clusterrouteescalate, |
| title = {Cluster, Route, Escalate: Cascaded Framework for Cost-Aware {LLM} Serving}, |
| author = {Moslem, Yasmin and Kacmajor, Magdalena and Nedumpozhimana, Vasudevan and Abbas, Ammar and Panahi, Solmaz and Lynch, David and Nie, Zhuangzhuang and Agapitos, Alexandros and Milenovic, Aleksandar and Song, Hongmeng and Shi, Yucheng and Pan, Yue and Buffini, Patricia and Kelleher, John D.}, |
| year = {2026}, |
| month = {6}, |
| eprint = {2606.27457}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| @misc{varshney2026llmrouter, |
| title = {{LLM} Router: Rethinking Routing with Prefill Activations}, |
| author = {Varshney, Tanay and Surla, Annie and Xu, Michelle and Krishnan, Gomathy Venkata and Jeblick, Maximilian and Austin, David and Vaidya, Neal and Onofrio, Davide}, |
| year = {2026}, |
| month = {3}, |
| eprint = {2603.20895}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.LG} |
| } |
| |
| @misc{vellaisamy2026energy, |
| title = {Characterization of Request and Token Energy Costs for {LLM} Inference Workloads on {GPU} Platforms}, |
| author = {Vellaisamy, Prabhu and Lam, Vanessa and Blanton, Shawn and Shen, John Paul}, |
| year = {2026}, |
| month = {8}, |
| eprint = {2608.28044}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.AR} |
| } |
| |
| @misc{qi2026brief, |
| title = {Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents}, |
| author = {Qi, Xuan}, |
| year = {2026}, |
| month = {4}, |
| eprint = {2604.02155}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| @misc{taheri2026small, |
| title = {Small Reasoning Models are Instruction Followers in Function Calling}, |
| author = {Taheri, Yalda and Heydari, Mohammad Hassan and Naaman, Erfan and Fatemi, Afsaneh}, |
| year = {2026}, |
| month = {8}, |
| eprint = {2608.22472}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| @misc{shen2026triaxialkv, |
| title = {{TriAxialKV}: Toward Extreme Low-Precision {KV}-Cache Quantization for Agentic Inference Tasks}, |
| author = {Shen, Hanzhang and Wu, Haoran and Zhao, Yiren and Mullins, Robert}, |
| year = {2026}, |
| month = {5}, |
| eprint = {2605.17170}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.LG} |
| } |
| |
| @misc{rath2026quantbias, |
| title = {Quantization Undoes Alignment: Bias Emergence in Compressed {LLMs} Across Models and Precision Levels}, |
| author = {Rath, Plawan Kumar and Maliakkal, Rahul}, |
| year = {2026}, |
| month = {5}, |
| eprint = {2605.15208}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| @misc{park2026vsraq, |
| title = {Value-and-Structure Alignment for Routing-Consistent Quantization of Mixture-of-Experts Models}, |
| author = {Park, Hancheol and Lee, Geonho and Piao, Tairen and Kim, Tae-Ho}, |
| year = {2026}, |
| month = {6}, |
| eprint = {2606.05688}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| @misc{koikeakino2026ttq, |
| title = {{TTQ}: Activation-Aware Test-Time Quantization to Accelerate {LLM} Inference On The Fly}, |
| author = {Koike-Akino, Toshiaki and Liu, Jing and Wang, Ye}, |
| year = {2026}, |
| month = {3}, |
| eprint = {2603.19296}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.LG} |
| } |
| |
| @misc{su2026sra, |
| title = {Skill Retrieval Augmentation for Agentic {AI}}, |
| author = {Su, Weihang and Long, Jianming and Ai, Qingyao and He, Qiaozhi and Tang, Yichen and Wang, Changyue and Tu, Yiteng and Wang, Yingbo and Liu, Yiqun}, |
| year = {2026}, |
| month = {4}, |
| eprint = {2604.24594}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| @misc{zou2026cobra, |
| title = {{CoBRA}: Learning Tool-Use Boundaries via Counterfactual Margins}, |
| author = {Zou, Wenhao and Liu, Xianglong and Bi, Wendong and Wang, Hanjie and Zhao, Simin and Zhi, Gong}, |
| year = {2026}, |
| month = {9}, |
| eprint = {2609.00967}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| @misc{collura2026grammar, |
| title = {Stay Within Your Bounds: Distance-Guided Decoding for Guaranteed Context-Free Grammar Compliance}, |
| author = {Collura, Vincenzo and Tit, Karim and Giunchiglia, Eleonora and Papadakis, Mike and Cordy, Maxime}, |
| year = {2026}, |
| month = {8}, |
| eprint = {2608.28229}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| @misc{jim2026recursive, |
| title = {What Survives When You Compress a Recursive Reasoner for the Edge?}, |
| author = {Jim, Pearse and Kolawole, Steven and Busoye, Opegbemi Matthias and Bagai, Glory and Smith, Virginia}, |
| year = {2026}, |
| month = {6}, |
| eprint = {2606.26488}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| % --- Older entries (2022 -- 2026-01; 7 of 31 refs = 22.6\%) --- |
| |
| @misc{cheng2023frugalgpt, |
| title = {FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance}, |
| author = {Chen, Lingjiao and Zaharia, Matei and Zou, James}, |
| year = {2023}, |
| month = {5}, |
| eprint = {2305.05176}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| @misc{bfcl2024leaderboard, |
| title = {Berkeley Function Calling Leaderboard (BFCL)}, |
| author = {{UC Berkeley (Gorilla project)}}, |
| year = {2024}, |
| url = {https://gorilla.cs.berkeley.edu/leaderboard.html}, |
| note = {Dataset: \url{https://huggingface.co/datasets/gorilla-llm/Berkeley-Function-Calling-Leaderboard}} |
| } |
| |
| @misc{yang2025qwen3, |
| title = {{Qwen3} Technical Report}, |
| author = {Yang, An and Li, Anfeng and Yang, Baosong and Zhang, Beichen and Hui, Binyuan and Zheng, Bo and Yu, Bowen and Gao, Chang and Huang, Chengen and others}, |
| year = {2025}, |
| month = {5}, |
| eprint = {2505.09388}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| @misc{dong2025compressed, |
| title = {Can Compressed {LLMs} Truly Act? An Empirical Evaluation of Agentic Capabilities in {LLM} Compression}, |
| author = {Dong, Peijie and Tang, Zhenheng and Liu, Xiang and Li, Lujun and Chu, Xiaowen and Li, Bo}, |
| year = {2025}, |
| month = {5}, |
| eprint = {2505.19433}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
| @misc{frantar2022gptq, |
| title = {{GPTQ}: Accurate Post-Training Quantization for Generative Pre-trained Transformers}, |
| author = {Frantar, Elias and Ashkboos, Saleh and Hoefler, Torsten and Alistarh, Dan}, |
| year = {2022}, |
| month = {10}, |
| eprint = {2210.17323}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.LG} |
| } |
| |
| @misc{kwon2023vllm, |
| title = {Efficient Memory Management for Large Language Model Serving with PagedAttention}, |
| author = {Kwon, Woosuk and Li, Zhuohan and Zhuang, Siyuan and Sheng, Ying and Zheng, Lianmin and Yu, Cody Hao and Gonzalez, Joseph E. and Zhang, Hao and Stoica, Ion}, |
| year = {2023}, |
| month = {9}, |
| eprint = {2309.06180}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.LG}, |
| note = {SOSP 2023; vLLM} |
| } |
| |
| @misc{meng2026arcquant, |
| title = {{ARCQuant}: Boosting {NVFP4} Quantization with Augmented Residual Channels for {LLMs}}, |
| author = {Meng, Haoqian and Luo, Yilun and Zhao, Yafei and Liu, Wenyuan and Zhang, Peng and Ma, Xindian}, |
| year = {2026}, |
| month = {1}, |
| eprint = {2601.07475}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL} |
| } |
| |
