Theo X. Olausson

Theo X. Olausson (he/him)

I'm a Member of Technical Staff at Inception, where I work on reinforcement learning for diffusion language models.

I recently graduated from MIT, where I was advised by Armando Solar-Lezama. My PhD started with early work on self-correcting code generation [OIW+24, GLJ+24], abstraction learning [BOW+23, GWB+24] and combining language models with symbolic tools [OGL+23]. Later on, I shifted my focus to diffusion language models [JOB+26, OJW+26, BTM+26] and statistically guaranteed hallucination control [EOS+26]. During my PhD I also had the good fortune of working on these topics (and more) at Apple ML Research, the Center for Computational Mathematics at the Flatiron Institute, and Microsoft Research.

I'm passionate about community building and have previously served as co-President of the MIT EECS Graduate Students' Association. I'm also one of the lead organizers of VerifAI, the Workshop on AI Verification in the Wild (held at ICLR'25 and '26). Most of my free time is spent doing some variation of cycling, running or climbing. I also like film photography, gothic novels and writing music.

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Selected Publications * equal contribution

  1. Conformal Language Modeling via Posterior Sampling
    N. Emmenegger*, TX. Olausson*, A. Solar-Lezama, C. Podimata
    NeurIPS 2026 Oral @ AgenticUQ arXiv
  2. Learning Unmasking Policies for Diffusion Language Models
    M. Jazbec*, TX. Olausson*, L. Béthune, P. Ablin, M. Kirchhof, J. Monteiro, V. Turrisi, J. Ramapuram, M. Cuturi
    ICML 2026 Oral Spotlight Best Paper @ DeLTa 2026 arXiv
  3. Is Self-Repair a Silver Bullet for Code Generation?
    TX. Olausson, JP. Inala, C. Wang, J. Gao, A. Solar-Lezama
    ICLR 2024 arXiv
  4. LINC: A Neuro-Symbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers
    TX. Olausson*, A. Gu*, B. Lipkin*, C. Zhang*, A. Solar-Lezama, JB. Tenenbaum, R. Levy
    EMNLP 2023 Outstanding Paper arXiv

All Publications (11)

  1. Conformal Language Modeling via Posterior Sampling
    N. Emmenegger*, TX. Olausson*, A. Solar-Lezama, C. Podimata
    NeurIPS 2026 Oral @ AgenticUQ arXiv
  2. The Design Space of Tri-Modal Masked Diffusion Models
    L. Béthune, V. Turrisi, BK. Mlodozeniec, PR. Lopez, L. Boominathan, ..., TX. Olausson, ..., J. Ramapuram
    NeurIPS 2026 Spotlight arXiv
  3. A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models
    TX. Olausson*, M. Jazbec*, X. Wang, A. Solar-Lezama, CA. Naesseth, S. Mandt, E. Nalisnick
    COLM 2026 arXiv
  4. Learning Unmasking Policies for Diffusion Language Models
    M. Jazbec*, TX. Olausson*, L. Béthune, P. Ablin, M. Kirchhof, J. Monteiro, V. Turrisi, J. Ramapuram, M. Cuturi
    ICML 2026 Oral Spotlight Best Paper @ DeLTa 2026 arXiv
  5. Amortizing Maximum Inner Product Search with Learned Support Functions
    TX. Olausson, J. Monteiro, M. Klein, M. Cuturi
    ICML 2026 arXiv
  6. The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations?
    A. Gu, WD. Li*, N. Jain*, TX. Olausson*, C. Lee*, K. Sen, A. Solar-Lezama
    Findings of ACL 2024 arXiv
  7. Is Self-Repair a Silver Bullet for Code Generation?
    TX. Olausson, JP. Inala, C. Wang, J. Gao, A. Solar-Lezama
    ICLR 2024 arXiv
  8. LILO: Learning Interpretable Libraries by Compressing and Documenting Code
    G. Grand, L. Wong, M. Bowers, TX. Olausson, JB. Tenenbaum, J. Andreas
    ICLR 2024 arXiv
  9. LINC: A Neuro-Symbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers
    TX. Olausson*, A. Gu*, B. Lipkin*, C. Zhang*, A. Solar-Lezama, JB. Tenenbaum, R. Levy
    EMNLP 2023 Outstanding Paper arXiv
  10. Top-Down Synthesis For Library Learning
    M. Bowers, TX. Olausson, L. Wong, G. Grand, JB. Tenenbaum, K. Ellis, A. Solar-Lezama
    POPL 2023 arXiv
  11. HeteroGen: Automatic Synthesis of Heterogeneous Cache Coherence Protocols
    N. Oswald, V. Gavrielatos, V. Nagarajan, T. Olausson, DJ. Sorin, R. Carr
    HPCA 2022 IEEE Micro Top Picks