Noah Syrkis
noah[at]syrkis.com
talks
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works
LAB LOG
January 6, 2026
Copenhagen, Denmark
Parabellum is a large-scale, vectorized, faster than-real-time war-game developed in collaboration with the Swiss Military
LAB LOG
Noah Syrkis
January 6, 2026
1 |
2026-02-02
1 |
2026-02-02
▶
Handed in MIIII (feel 6.5 - 7 out of 10)
▶
Next: PRISM paper for UAI (Feb. 25th)
▶
Focus should be on uncertainty
▶
PRISM
1
is a Gemma
[1]
LM program
[2]
▶
tripper2
to is down. . . .
[3] L. Kurscheidt, P. Morettin, R. Sebas
tiani, A. Passerini, and A. Vergari, “A Prob
abilistic Neuro-symbolic Layer for Algebraic
Constraint Satisfaction.”
Neuro-symbolic Layer for Constraints (i.e.,
never fly into wall)
[4] Y. Yu and H. Chen, “Decentralized On
line Learning in General-Sum Stackelberg
Games,” in
Proceedings of the Fortieth Con
ference on Uncertainty in Artificial Intelli
gence
, PMLR, Sep. 2024, pp. 4056–4077.
Group of units with commander
𝒞
. To see or
not to see
𝒞
’s reward
[5] C. Qiu, H. Fu, K. Li, J. Zhang, and
X. Wang, “Enhanced Equilibria-Solving via
Private Information Pre-Branch Structure in
Adversarial Team Games.”
On ex ante coordination in adversarial
games (like HIVE chat )
Table 1: Previous intersting papers in UAI
1
Unrelated to OpenAI’s PRISM
1 of
2
References
[1]
G. Team
et al.
, “Gemma 3 Technical Report,” no. arXiv:2503.19786. arXiv, Mar. 2025. doi:
10.48550/arXiv.2503.19786
.
[2]
I. Schlag
et al.
, “Large Language Model Programs,” no. arXiv:2305.05364. arXiv, May 2023. doi:
10.48550/arXiv.2305.05364
.
[3]
L. Kurscheidt, P. Morettin, R. Sebastiani, A. Passerini, and A. Vergari, “A Probabilistic Neuro-
symbolic Layer for Algebraic Constraint Satisfaction.”
[4]
Y. Yu and H. Chen, “Decentralized Online Learning in General-Sum Stackelberg Games,” in
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence
, PMLR, Sep.
2024, pp. 4056–4077.
[5]
C. Qiu, H. Fu, K. Li, J. Zhang, and X. Wang, “Enhanced Equilibria-Solving via Private Information
Pre-Branch Structure in Adversarial Team Games.”
2 of
2
LAB LOG
Noah Syrkis
January 6, 2026
1 |
2026-02-02
1 |
2026-02-02
▶
Handed in MIIII (feel 6.5 - 7 out of 10)
▶
Next: PRISM paper for UAI (Feb. 25th)
▶
Focus should be on uncertainty
▶
PRISM
1
is a Gemma
[1]
LM program
[2]
▶
tripper2
to is down. . . .
[3] L. Kurscheidt, P. Morettin, R. Sebas
tiani, A. Passerini, and A. Vergari, “A Prob
abilistic Neuro-symbolic Layer for Algebraic
Constraint Satisfaction.”
Neuro-symbolic Layer for Constraints (i.e.,
never fly into wall)
[4] Y. Yu and H. Chen, “Decentralized On
line Learning in General-Sum Stackelberg
Games,” in
Proceedings of the Fortieth Con
ference on Uncertainty in Artificial Intelli
gence
, PMLR, Sep. 2024, pp. 4056–4077.
Group of units with commander
𝒞
. To see or
not to see
𝒞
’s reward
[5] C. Qiu, H. Fu, K. Li, J. Zhang, and
X. Wang, “Enhanced Equilibria-Solving via
Private Information Pre-Branch Structure in
Adversarial Team Games.”
On ex ante coordination in adversarial
games (like HIVE chat )
Table 1: Previous intersting papers in UAI
1
Unrelated to OpenAI’s PRISM
1 of
2
References
[1]
G. Team
et al.
, “Gemma 3 Technical Report,” no. arXiv:2503.19786. arXiv, Mar. 2025. doi:
10.48550/arXiv.2503.19786
.
[2]
I. Schlag
et al.
, “Large Language Model Programs,” no. arXiv:2305.05364. arXiv, May 2023. doi:
10.48550/arXiv.2305.05364
.
[3]
L. Kurscheidt, P. Morettin, R. Sebastiani, A. Passerini, and A. Vergari, “A Probabilistic Neuro-
symbolic Layer for Algebraic Constraint Satisfaction.”
[4]
Y. Yu and H. Chen, “Decentralized Online Learning in General-Sum Stackelberg Games,” in
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence
, PMLR, Sep.
2024, pp. 4056–4077.
[5]
C. Qiu, H. Fu, K. Li, J. Zhang, and X. Wang, “Enhanced Equilibria-Solving via Private Information
Pre-Branch Structure in Adversarial Team Games.”
2 of
2
