- Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation
Mohsen Hariri, Amirhossein Samandar, Michael Hinczewski, Vipin Chaudhary · Oct 5, 2025 · Citations: 0
Rubric Rating Automatic MetricsSimulation Env
We present a principled Bayesian evaluation framework that replaces Pass@k and average accuracy over N trials (avg@N) with posterior estimates of a model's underlying success probability and credible intervals, yielding stable rankings and…
- $V_1$: Unifying Generation and Self-Verification for Parallel Reasoners
Harman Singh, Xiuyu Li, Kusha Sareen, Monishwaran Maheswaran, Sijun Tan · Mar 4, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
On code generation (LiveCodeBench, CodeContests, SWE-Bench) and math reasoning (AIME, HMMT) benchmarks, V_1-Infer improves Pass@1 by up to 10% over pointwise verification and outperforms recent test-time scaling methods while being…
- How Reliable is Language Model Micro-Benchmarking?
Gregory Yauney, Shahzaib Saqib Warraich, Swabha Swayamdipta · Oct 9, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
We introduce a meta-evaluation measure for micro-benchmarking which investigates how well a micro-benchmark can rank two models as a function of their performance difference on the full benchmark.
- Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models
Abraham Paul Elenjical, Vivek Hruday Kavuri, Vasudeva Varma · Feb 21, 2026 · Citations: 0
Pairwise Preference Human Eval
We introduce a psychologically grounded metacognitive framework that operationalizes Ann Brown's regulatory cycle (Planning, Monitoring, and Evaluation) as a structured prompting architecture, and study its integration within a lightweight…
- Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback
Xiaoying Zhang, Yipeng Zhang, Hao Sun, Kaituo Feng, Chaochao Lu · Jun 3, 2025 · Citations: 0
Critique Edit Automatic Metrics
We show that plateaued RL models can successfully refine failed solutions when given natural language critiques.
- Learning When to Sample: Confidence-Aware Self-Consistency for Efficient LLM Chain-of-Thought Reasoning
Juming Xiong, Kevin Guo, Congning Ni, Chao Yan, Katherine Brown · Mar 9, 2026 · Citations: 0
Automatic Metrics
Recent self-consistency-based approaches further improve accuracy but require sampling and aggregating multiple reasoning trajectories, leading to substantial additional computational overhead.
- D-COT: Disciplined Chain-of-Thought Learning for Efficient Reasoning in Small Language Models
Shunsuke Ubukata · Feb 25, 2026 · Citations: 0
Automatic Metrics
In this study, we propose Disciplined Chain-of-Thought (D-CoT), a novel framework that enforces a structured reasoning process using control tags -- such as <TEMP_LOW> for fact-checking and <TEMP_HIGH> for multi-perspective exploration --…
- Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference
Bo-Wei Chen, Chung-Chi Chen, An-Zi Yen · Feb 25, 2026 · Citations: 0
Automatic Metrics
Experiments on the Massive Multitask Language Understanding (MMLU) benchmark show that our approach achieves accuracy comparable to the largest model while reducing computational costs by 20\% to 40\%.
- Inducing Epistemological Humility in Large Language Models: A Targeted SFT Approach to Reducing Hallucination
Cem Uluoglakci, Tugba Taskaya Temizel · Mar 18, 2026 · Citations: 0
Pairwise Preference
We also release HypoTermQA-Enhanced, a benchmark for hallucination tendency strengthened through multiple validations.
- Long Grounded Thoughts: Synthesizing Visual Problems and Reasoning Chains at Scale
David Acuna, Chao-Han Huck Yang, Yuntian Deng, Jaehun Jung, Ximing Lu · Nov 7, 2025 · Citations: 0
Pairwise Preference
We introduce a framework able to synthesize vision-centric problems spanning diverse levels of complexity, and the resulting dataset with over 1M high-quality problems including: reasoning traces, preference data, and instruction prompts…
- Are Large Language Models Truly Smarter Than Humans?
Eshwar Reddy M, Sourav Karmakar · Mar 17, 2026 · Citations: 0
- TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
Alliot Nagle, Jakhongir Saydaliev, Dhia Garbaya, Michael Gastpar, Ashok Vardhan Makkuva · Mar 13, 2026 · Citations: 0
- NeuroLoRA: Context-Aware Neuromodulation for Parameter-Efficient Multi-Task Adaptation
Yuxin Yang, Haoran Zhang, Mingxuan Li, Jiachen Xu, Ruoxi Shen · Mar 12, 2026 · Citations: 0
- PostTrainBench: Can LLM Agents Automate LLM Post-Training?
Ben Rank, Hardik Bhatnagar, Ameya Prabhu, Shira Eisenberg, Karina Nguyen · Mar 9, 2026 · Citations: 0
- In-Context Environments Induce Evaluation-Awareness in Language Models
Maheep Chaudhary · Mar 4, 2026 · Citations: 0
- Tool Verification for Test-Time Reinforcement Learning
Ruotong Liao, Nikolai Röhrich, Xiaohan Wang, Yuhui Zhang, Yasaman Samadzadeh · Mar 2, 2026 · Citations: 0
- CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning
Xinyu Zhu, Yihao Feng, Yanchao Sun, Xianzhi Du, Pingzhi Li · Mar 1, 2026 · Citations: 0
- Sparks of Cooperative Reasoning: LLMs as Strategic Hanabi Agents
Mahesh Ramesh, Kaousheik Jayakumar, Aswinkumar Ramkumar, Pavan Thodima, Aniket Rege · Jan 26, 2026 · Citations: 0
- MobileLLM-R1: Exploring the Limits of Sub-Billion Language Model Reasoners with Open Training Recipes
Changsheng Zhao, Ernie Chang, Zechun Liu, Chia-Jung Chang, Wei Wen · Sep 29, 2025 · Citations: 0
- Latent Self-Consistency for Reliable Majority-Set Selection in Short- and Long-Answer Reasoning
Jungsuk Oh, Jay-Yoon Lee · Aug 25, 2025 · Citations: 0