Bibliography

This bibliography uses a compact author-year style for the Markdown/PDF build. The final submission can be converted to the institution's required BibTeX, APA, IEEE, or Chicago format without changing the thesis argument.

Language Models and Transformers

  • Jurafsky, D., and Martin, J. H. (2025). Speech and Language Processing (3rd ed. draft). Online manuscript, https://web.stanford.edu/~jurafsky/slp3/.
  • Sennrich, R., Haddow, B., and Birch, A. (2016). Neural machine translation of rare words with subword units. Association for Computational Linguistics. arXiv:1508.07909.
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems. arXiv:1706.03762.
  • Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J. (2017). Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. International Conference on Learning Representations. arXiv:1701.06538.
  • Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems. arXiv:2005.14165.
  • Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V. (2022a). Finetuned language models are zero-shot learners. International Conference on Learning Representations. arXiv:2109.01652.
  • Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y. (2020). The curious case of neural text degeneration. International Conference on Learning Representations. arXiv:1904.09751.
  • Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9). arXiv:2107.13586.
  • Schulhoff, S., Ilie, M., Balepur, N., Kahadze, K., Liu, A., Si, C., Li, Y., Gupta, A., Han, H., et al. (2024). The Prompt Report: A systematic survey of prompt engineering techniques. arXiv preprint arXiv:2406.06608.
  • Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D. (2023). Self-consistency improves chain of thought reasoning in language models. International Conference on Learning Representations. arXiv:2203.11171.
  • Sumers, T. R., Yao, S., Narasimhan, K., and Griffiths, T. L. (2024). Cognitive architectures for language agents. Transactions on Machine Learning Research. arXiv:2309.02427.

Large Language Model Agents and Planning

  • Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. International Conference on Learning Representations.
  • Zhou, A., Yan, K., Shlapentokh-Rothman, M., Wang, H., and Wang, Y.-X. (2023). Language Agent Tree Search unifies reasoning, acting, and planning in language models. arXiv preprint arXiv:2310.04406.
  • Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., and Yao, S. (2023). Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems.
  • Wang, G., Xie, Y., Jiang, Y., et al. (2023). Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291.
  • Zhou, S., Xu, F. F., Zhu, H., et al. (2024). WebArena: A realistic web environment for building autonomous agents. International Conference on Learning Representations.
  • Liu, X., Yu, H., Zhang, H., et al. (2024). AgentBench: Evaluating LLMs as agents. International Conference on Learning Representations.
  • Ma, C., Zhang, J., Zhu, Z., et al. (2024). AgentBoard: An analytical evaluation board of multi-turn LLM agents. Advances in Neural Information Processing Systems (Datasets and Benchmarks). arXiv:2401.13178.
  • Yao, S., Shinn, N., Razavi, P., and Narasimhan, K. (2024). τ-bench: A benchmark for tool-agent-user interaction in real-world domains. arXiv preprint arXiv:2406.12045.
  • Castanyer, R. C., Castro, P. S., and Berseth, G. (2026). Agentick: A unified benchmark for general sequential decision-making agents. arXiv preprint arXiv:2605.06869.
  • Xu, B., Peng, Z., Lei, B., Mukherjee, S., Liu, Y., and Xu, D. (2023). ReWOO: Decoupling reasoning from observations for efficient augmented language models. arXiv preprint arXiv:2305.18323.
  • Prasad, A., Koller, A., Hartmann, M., Clark, P., Sabharwal, A., Bansal, M., and Khot, T. (2024). ADaPT: As-needed decomposition and planning with language models. Findings of NAACL. arXiv:2311.05772.
  • Wang, Z., Cai, S., Chen, G., Liu, A., Ma, X., and Liang, Y. (2023). Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents. Advances in Neural Information Processing Systems. arXiv:2302.01560.
  • Wang, L., Xu, W., Lan, Y., Hu, Z., Lan, Y., Lee, R. K.-W., and Lim, E.-P. (2023). Plan-and-Solve prompting: Improving zero-shot chain-of-thought reasoning by large language models. Association for Computational Linguistics. arXiv:2305.04091.
  • Patil, S. G., Zhang, T., Wang, X., and Gonzalez, J. E. (2023). Gorilla: Large language model connected with massive APIs. arXiv preprint arXiv:2305.15334.

Prompt Optimisation and Reflective Evolution

  • Agrawal, L. A., Tan, S., Soylu, D., et al. (2025). GEPA: Reflective prompt evolution can outperform reinforcement learning. arXiv preprint arXiv:2507.19457.
  • Khattab, O., Singhvi, A., Maheshwari, P., et al. (2024). DSPy: Compiling declarative language model calls into self-improving pipelines. International Conference on Learning Representations.
  • Opsahl-Ong, K., Ryan, M. J., Purtell, J., Broman, D., Potts, C., Zaharia, M., and Khattab, O. (2024). Optimizing instructions and demonstrations for multi-stage language model programs (MIPRO). Conference on Empirical Methods in Natural Language Processing. arXiv:2406.11695.
  • Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., and Zhou, D. (2024). Large language models as optimizers. International Conference on Learning Representations.
  • Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., and Ba, J. (2023). Large language models are human-level prompt engineers. International Conference on Learning Representations.
  • Pryzant, R., Iter, D., Li, J., Lee, Y. T., Zhu, C., and Zeng, M. (2023). Automatic prompt optimization with “gradient descent” and beam search. Conference on Empirical Methods in Natural Language Processing.
  • Guo, Q., Wang, R., Guo, J., et al. (2024). Connecting large language models with evolutionary algorithms yields powerful prompt optimizers. International Conference on Learning Representations. arXiv:2309.08532.
  • Fernando, C., Banarse, D., Michalewski, H., Osindero, S., and Rocktäschel, T. (2024). Promptbreeder: Self-referential self-improvement via prompt evolution. International Conference on Machine Learning.
  • Madaan, A., Tandon, N., Gupta, P., et al. (2023). Self-Refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems.
  • Yuksekgonul, M., Bianchi, F., Boen, J., Liu, S., Huang, Z., Guestrin, C., and Zou, J. (2024). TextGrad: Automatic "differentiation" via text. arXiv preprint arXiv:2406.07496.
  • Sclar, M., Choi, Y., Tsvetkov, Y., and Suhr, A. (2024). Quantifying language models' sensitivity to spurious features in prompt design, or: How I learned to start worrying about prompt formatting. International Conference on Learning Representations. arXiv:2310.11324.
  • Lu, Y., Bartolo, M., Moore, A., Riedel, S., and Stenetorp, P. (2022). Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. Association for Computational Linguistics. arXiv:2104.08786.
  • Wang, X., Li, C., Wang, Y., et al. (2024). PromptAgent: Strategic planning with language models enables expert-level prompt optimization. International Conference on Learning Representations. arXiv:2310.16427.
  • Cheng, C.-A., Nie, A., and Swaminathan, A. (2024). Trace is the next AutoDiff: Generative optimization with rich feedback, execution traces, and LLMs. Advances in Neural Information Processing Systems. arXiv:2406.16218.
  • Kong, M., Wang, Z., Shu, Y., and Dai, Z. (2026). Meta-prompt optimization for LLM-based sequential decision making (EXPO). Conference on Empirical Methods in Natural Language Processing. arXiv:2502.00728.
  • Chang, Y., and Chen, X. (2026). Naive Prompt Optimization: Rethinking the need for complex prompt search. arXiv preprint arXiv:2608.27266.
  • Rishav, R., Pujari, P., and Rastogi, P. (2026). ContraPrompt: Contrastive prompt optimization via dyadic reasoning trace analysis. arXiv preprint arXiv:2604.17937.
  • Liu, S., Xia, Q., Xia, Q., Liu, Y., Yu, X., and Qu, R. (2026). Reflection in the dark: Exposing and escaping the black box in reflective prompt optimization. ACL Student Research Workshop. arXiv:2603.18388.
  • Chen, Z., Wang, C., Sun, W., Yang, G., Liu, X., Zhang, J. M., and Liu, Y. (2025). Promptware engineering: Software engineering for LLM prompt development. arXiv preprint arXiv:2503.02400.
  • Feffer, M., Xu, R., Sun, Y., and Bansal, M. (2024). Prompt exploration with prompt regression. arXiv preprint arXiv:2405.11083.
  • Ramnath, K., Zhou, K., Guan, S., et al. (2025). A systematic survey of automatic prompt optimization techniques. Conference on Empirical Methods in Natural Language Processing, pp. 33066–33098. arXiv:2502.16923.
  • Liu, L., Tang, P., Singh, K. Y., and Ghadar, S. (2026). ESPO: Error-structured prompt optimization via diagnose, diversify, and stabilize. Conference on Empirical Methods in Natural Language Processing. arXiv:2609.04197.
  • Singh, M., Yadav, V., and Blanco, E. (2026). Error taxonomy-guided prompt optimization. arXiv preprint arXiv:2602.00997.
  • Ngweta, L., Kate, K., Tsay, J., and Rizk, Y. (2025). Towards LLMs robustness to changes in prompt format styles. NAACL Student Research Workshop. arXiv:2504.06969.
  • Errica, F., Siracusano, G., Sanvito, D., and Bifulco, R. (2025). What did I do wrong? Quantifying LLMs' sensitivity and consistency to prompt engineering. North American Chapter of the Association for Computational Linguistics. arXiv:2406.12334.
  • Eiben, A. E., and Smith, J. E. (2015). Introduction to Evolutionary Computing (2nd ed.). Springer.

Reinforcement Learning, Preference Optimisation, and Self-Improvement

  • Sutton, R. S., and Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
  • Christiano, P. F., Leike, J., Brown, T., Marber, M., Legg, S., and Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems.
  • Ouyang, L., Wu, J., Jiang, X., et al. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems.
  • Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
  • Shao, Z., Wang, P., Zhu, Q., et al. (2024). DeepSeekMath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300.
  • Guo, D., Yang, D., Zhang, H., et al. (2025). DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv preprint arXiv:2501.12948.
  • Chen, Z., Deng, Y., Yuan, H., Ji, K., and Gu, Q. (2024). Self-play fine-tuning converts weak language models to strong language models. International Conference on Machine Learning.
  • Huang, J., Chen, X., Mishra, S., Zheng, H. S., Yu, A. W., Song, X., and Zhou, D. (2023). Large language models cannot self-correct reasoning yet. arXiv preprint arXiv:2310.01798.

Chain-of-Thought and Distillation

  • Wei, J., Wang, X., Schuurmans, D., et al. (2022b). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems.
  • Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y. (2022). Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems.
  • Zelikman, E., Wu, Y., Mu, J., and Goodman, N. (2022). STaR: Bootstrapping reasoning with reasoning. Advances in Neural Information Processing Systems.
  • Mukherjee, S., Mitra, A., Jawahar, G., Agarwal, S., Palangi, H., and Awadallah, A. (2023). Orca: Progressive learning from complex explanation traces of GPT-4. arXiv preprint arXiv:2306.02707.
  • Hsieh, C.-Y., Li, C.-L., Yeh, C.-K., et al. (2023). Distilling step-by-step: Outperforming larger language models with less training data and smaller model sizes. Association for Computational Linguistics.
  • Turpin, M., Michael, J., Perez, E., and Bowman, S. R. (2023). Language models don't always say what they think: Unfaithful explanations in chain-of-thought prompting. Advances in Neural Information Processing Systems. arXiv:2305.04388.
  • Lanham, T., Chen, A., Radhakrishnan, A., et al. (2023). Measuring faithfulness in chain-of-thought reasoning. arXiv preprint arXiv:2307.13702.
  • Wang, Y. (2026). Doing what they say, not what they reason: Locating the faithfulness gap in LLM agents. arXiv preprint arXiv:2606.00476.
  • Loré, N., Ilami, S., and Heydari, B. (2024). Large model strategic thinking, small model efficiency: Transferring theory of mind in large language models. arXiv preprint arXiv:2408.05241.

Efficient Fine-Tuning and Compact Models

  • Hu, E. J., Shen, Y., Wallis, P., et al. (2022). LoRA: Low-rank adaptation of large language models. International Conference on Learning Representations.
  • Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L. (2023). QLoRA: Efficient finetuning of quantized LLMs. Advances in Neural Information Processing Systems.
  • Liquid AI. (2025). LFM2 technical report. arXiv preprint arXiv:2511.23404. Cited by the model card of LFM2.5-1.2B-Thinking as the report of its architecture.
  • Yang, A., Li, A., Yang, B., et al. (2025). Qwen3 technical report. arXiv preprint.
  • Google DeepMind. (2026). Gemma 4 model card. Model documentation cited for the Gemma-4 edge and 31B instruction-tuned variants.

Infrastructure

  • Doosthosseini, A., Decker, J., Nolte, H., and Kunkel, J. (2026). SAIA: a seamless Slurm-native solution for HPC-based services. The Journal of Supercomputing, 82(7), 403. https://doi.org/10.1007/s11227-026-08508-3
  • He, H., and Thinking Machines Lab. (2025). Defeating nondeterminism in LLM inference. Thinking Machines Lab: Connectionism. https://doi.org/10.64434/tml.20250910
  • Song, Y., Wang, G., Li, S., and Lin, B. Y. (2024). The good, the bad, and the greedy: Evaluation of LLMs should not ignore non-determinism. arXiv preprint arXiv:2407.10457.
  • Wimbauer, A., Möller, J., Imgrund, E., and Rieck, K. (2026). Fingerprinting inference systems of large language models. arXiv preprint arXiv:2605.29979.
  • Atil, B., Aykent, S., Chittams, A., et al. (2025). Non-determinism of "deterministic" LLM settings. arXiv preprint arXiv:2408.04667.
  • Mohammadi, M., Li, Y., Lo, J., and Yip, W. (2025). Evaluation and benchmarking of LLM agents: A survey. ACM SIGKDD Conference on Knowledge Discovery and Data Mining. arXiv:2507.21504.
  • Willard, B. T., and Louf, R. (2023). Efficient guided generation for large language models (Outlines). arXiv preprint arXiv:2307.09702.

Statistics

  • Wilson, E. B. (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22(158), 209–212.
  • Brown, L. D., Cai, T. T., and DasGupta, A. (2001). Interval estimation for a binomial proportion. Statistical Science, 16(2), 101–133.

Game and Embodied-Agent Benchmarks

  • Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature.
  • Silver, D., Huang, A., Maddison, C. J., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature.
  • Silver, D., Schrittwieser, J., Simonyan, K., et al. (2017). Mastering the game of Go without human knowledge. Nature.
  • Côté, M.-A., Kádár, Á., Yuan, X., et al. (2019). TextWorld: A learning environment for text-based games. Computer Games Workshop.
  • Küttler, H., Nardelli, N., Miller, A. H., et al. (2020). The NetHack Learning Environment. Advances in Neural Information Processing Systems.
  • Hafner, D. (2022). Benchmarking the spectrum of agent capabilities. International Conference on Learning Representations.
  • Samvelyan, M., Kirk, R., Kurin, V., et al. (2021). MiniHack the planet: A sandbox for open-ended reinforcement learning research. Advances in Neural Information Processing Systems.
  • Paglieri, D., et al. (2025). BALROG: Benchmarking agentic LLM and VLM reasoning on games. International Conference on Learning Representations.
  • Wu, Y., Tang, X., Mitchell, T. M., and Li, Y. (2023). SmartPlay: A benchmark for LLMs as intelligent agents. arXiv preprint arXiv:2310.01557.
  • Park, D., Kim, M., Choi, B., et al. (2025). Orak: A foundational benchmark for training and evaluating LLM agents on diverse video games. arXiv preprint arXiv:2506.03610.
  • Guertler, L., et al. (2025). TextArena. arXiv preprint arXiv:2504.11442.
  • Jia, J., Yuan, Z., Pan, J., McNamara, P. E., and Chen, D. (2024). Decision-making behavior evaluation framework for LLMs under uncertain context. Advances in Neural Information Processing Systems. arXiv:2406.05972.
  • Hartley, J., Hamill, C., Batra, D., Seddon, D., Okhrati, R., and Khraishi, R. (2025). How personality traits shape LLM risk-taking behaviour. arXiv preprint arXiv:2503.04735.
  • Han, Y., Wang, Y., Qian, L., et al. (2026). Can LLM agents be CFOs? Benchmarking long-horizon resource allocation in an uncertain enterprise environment (EnterpriseArena). arXiv preprint arXiv:2603.23638.
  • Lin, Y., Wang, Z., Liu, M., et al. (2026). BAGEN: Are LLM agents budget-aware? arXiv preprint arXiv:2606.00198.
  • Jeurissen, D., Perez-Liebana, D., Gow, J., Cakmak, D., and Kwan, J. (2024). Playing NetHack with LLMs: Potential and limitations as zero-shot agents. arXiv preprint arXiv:2403.00690.
  • Zhang, A. L., et al. (2025). VideoGameBench: Can vision-language models complete popular video games? arXiv preprint arXiv:2505.18134.
  • Weng, X. (2025). Magic Tower: A novel benchmark for evaluating large language models in strategic game playing. Unpublished undergraduate research report, The Chinese University of Hong Kong / UNIST summer research. Code: github.com/xx-Weng/Magic_Tower. (Grey literature; a probabilistic-combat variant of the game, not the deterministic 24-layer tower used here.)
  • Expert walkthrough. (n.d.). Walkthrough of the 24-floor Magic Tower [community text in Chinese, author unknown]. Copy available from the author on request. (Grey literature.)
  • Belle, N., Barnes, D., Amayuelas, A., Bercovich, I., Wang, X. E., and Wang, W. (2025). Agents of Change: Self-evolving LLM agents for strategic planning. arXiv preprint arXiv:2506.04651.

Classical Planning, Search, and Robotics

  • Russell, S., and Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
  • Fikes, R. E., and Nilsson, N. J. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence.
  • Ghallab, M., Nau, D., and Traverso, P. (2004). Automated Planning: Theory and Practice. Morgan Kaufmann.
  • Yao, S., Yu, D., Zhao, J., et al. (2023). Tree of Thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems.
  • Hao, S., Gu, Y., Ma, H., Hong, J. J., Wang, Z., Wang, D. Z., and Hu, Z. (2023). Reasoning with language model is planning with world model. Conference on Empirical Methods in Natural Language Processing.
  • Liu, B., Jiang, Y., Zhang, X., et al. (2023). LLM+P: Empowering large language models with optimal planning proficiency. arXiv preprint arXiv:2304.11477.
  • Rana, K., Haviland, J., Garg, S., Abou-Chakra, J., Reid, I., and Suenderhauf, N. (2023). SayPlan: Grounding large language models using 3D scene graphs for scalable task planning. arXiv preprint arXiv:2307.06135.
  • Huang, W., Xia, F., Xiao, T., et al. (2023). Inner Monologue: Embodied reasoning through planning with language models. Conference on Robot Learning.
  • Singh, I., Blukis, V., Mousavian, A., et al. (2023). ProgPrompt: Generating situated robot task plans using large language models. IEEE International Conference on Robotics and Automation.
  • Valmeekam, K., Marquez, M., Olmo, A., Sreedharan, S., and Kambhampati, S. (2023). On the planning abilities of large language models. arXiv preprint arXiv:2305.15771.
  • Kambhampati, S., Valmeekam, K., Guan, L., Verma, M., Stechly, K., Bhambri, S., Saldyt, L., and Murthy, A. (2024). LLMs can't plan, but can help planning in LLM-Modulo frameworks. International Conference on Machine Learning (position paper). arXiv:2402.01817.
  • Gundawar, A., Verma, M., Guan, L., Valmeekam, K., Bhambri, S., and Kambhampati, S. (2024). Robust planning with compound LLM architectures: An LLM-Modulo approach. arXiv preprint arXiv:2411.14484.
  • Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., and Liang, P. (2024). Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics.

Project-Specific Technical Sources