Abstraction Agent

Boning Li; Longbo Huang

cs.MASep 3, 2026

Abstraction Agent

Authors: Boning Li, Longbo Huang

Organizations: Institute for Interdisciplinary Information Sciences, Tsinghua University

Abstract

Information abstraction, which groups strategically similar private states into a tractable number of buckets, is essential for scaling game-solving algorithms to large imperfect-information games. Constructing effective abstractions, however, has traditionally required domain-specific evaluators such as hand-strength calculators or equity estimators, which demand expert knowledge and engineering effort and are unavailable for most less-studied games. We propose the Abstraction Agent, a zero-shot pipeline that uses a large language model (LLM) to discover continuous strategic features from a natural-language game description, score private states on these features, and cluster them into abstraction buckets, without any game-specific evaluator, training data, or game-tree traversal during abstraction construction. The pipeline runs in four phases: feature discovery with calibration anchors, batched private-state scoring, correlation-based feature selection, and $k$ -means clustering. The resulting abstractions reduce lifted-strategy exploitability by up to 62% relative to an expected-hand-strength baseline on heads-up no-limit Texas hold'em (HUNL) turn endgames, and beat a scalar rank baseline at every granularity on ROVER Trials, an original game absent from any pretraining corpus. Beyond these quantitative benchmarks, the pipeline transfers with unchanged prompts to four-card Pot-Limit Omaha, HUNL preflop and flop, and Riichi Mahjong, where the discovered features track each game's recognized strategic concepts. This is structured knowledge elicitation: converting implicit strategic knowledge in LLM parameters into explicit numerical features for downstream algorithmic computation. The code is available at https://github.com/lbn187/AbstractionAgent.

Figures & tables

Figure 1: The Abstraction Agent pipeline. Given a textual GameConfig and a set of private holdings, an LLM discovers strategic features (Phase 1), scores each holding on them (Phase 2), redundant dimensions are filtered (Phase 3), and holdings are clustered into

K

buckets (Phase 4). The pipeline uses no hand evaluators, training data, or game-tree traversal, and the same code applies to poker, Omaha, and Mahjong unchanged.

Board	$K$	Agent	EHS	Ratio
7 $\spadesuit$ 9 $\heartsuit$ 9 $\clubsuit$ T $\clubsuit$	20	0.0437	0.0335	1.30 $\pm$ 0.20
	50	0.0142	0.0192	0.74 $\pm$ 0.17
	100	0.0036	0.0098	0.36 $\pm$ 0.04
T $\spadesuit$ 6 $\heartsuit$ A $\heartsuit$ 7 $\clubsuit$	20	0.1359	0.1088	1.25 $\pm$ 0.23
	50	0.0483	0.0592	0.82 $\pm$ 0.10
	100	0.0102	0.0256	0.40 $\pm$ 0.08

Table 1: HUNL turn endgame exploitability (fraction of pot; mean over 10 seeds). Ratio = mean Agent / EHS

\pm

95% Student-

t

CI half-width.

$K$	Backbone	SG1	SG2
50	GPT-5.5	0.74	0.82
	Llama-3.3-70B	1.62	1.86
	Qwen-2.5-32B	1.48	2.01
100	GPT-5.5	0.36	0.40
	Llama-3.3-70B	1.07	1.36
	Qwen-2.5-32B	1.96	3.70

Table 2: Backbone dependence: mean exploitability ratio (Agent / EHS) over 10 seeds.

Domain-specific methods (require hand evaluator):
Method	SG1	SG2
Random	0.1314	0.3670
LLM-Embed (zero-shot)	0.0318	0.1847
Abstraction Agent (ours)	0.0142	0.0483
EHS	0.0192	0.0592
PA (EHS + Variance)	0.0053	0.0254

Table 3: Comparison of abstraction methods at

K{=}50

(exploitability as fraction of pot; agent is the 10-seed mean).

Backbone	$K$	Agent/Rank	Agent/PA
GPT-5.5	5	0.920 $\pm$ 0.074	1.043 $\pm$ 0.106
	10	0.804 $\pm$ 0.038	0.940 $\pm$ 0.067
	20	0.667 $\pm$ 0.070	0.913 $\pm$ 0.106
	30	0.663 $\pm$ 0.065	0.916 $\pm$ 0.111
Qwen-2.5-32B	5	1.157 $\pm$ 0.085	1.319 $\pm$ 0.160
	10	0.969 $\pm$ 0.059	1.128 $\pm$ 0.054

Table 4: ROVER Trials: mean exploitability ratio (Agent / baseline) over 10 seeds

\pm

95% Student-

t

CI half-width.

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Pattern	Count	Description
Double-suited (2+2)	3,081	Two suited pairs, two flush draws.
Single-suited (2+1+1)	7,098	One suited pair; one flush draw.
Triple-suited (3+1)	3,718	Three of one suit, one weaker flush draw.
Rainbow (1+1+1+1)	1,820	All four suits differ; no flush potential.
Monotone (4+0)	715	Same suit; one flush draw (must-use-two).

Appendix

Table 5: PLO4 suit pattern classification (canonical hand counts out of 16,432).

Type	Example	Strategic Properties
Premium pairs + suited	AA-KK ds	Top pair + flush draws; strongest
Top rundowns	AKQJ, KQJT	Max straight connectivity
Suited connectors	8h7h6s5s	Flush + straight; strong playability
High pairs + danglers	AA-xx rainbow	High pair, limited coordination
Mid rundowns	T987, 9876	Good connectivity, lower nut potential
Suited aces	As-xxx	Nut flush draw potential

Appendix

Table 6: PLO4 structural hand types with examples and strategic properties.

ID	Board	Street	Pot
1	$7\spadesuit\,9\heartsuit\,9\clubsuit\,T\clubsuit$	Turn	500
2	$T\spadesuit\,6\heartsuit\,A\heartsuit\,7\clubsuit$	Turn	4780

Appendix

Table 7: Libratus subgame specifications.

Game	$N$	$K$	Key Result
HUNL Turn	${\sim}$ 1K	20–100	up to 62% $\downarrow$ vs EHS
ROVER Trials	125	5–30	beats baseline
PLO4 Preflop	16,432	30	PLO features
HUNL Flop	1,176	20	Draw/made sep.
HUNL Preflop	169	10	Premium groups
Mahjong	200	10	Chance features

Appendix

Table 8: Cross-game summary of the six evaluation settings.

Cluster	Representative Hands	Characteristics
0 (8)	22, 33, 44, 55, 66, 77, 88, 99	Small/medium pairs (set-mining value)
1 (19)	A2s–A5s, 54s–76s	Suited aces + suited connectors
2 (19)	32o–T2o	Offsuit junk (weakest hands)
3 (12)	QTo, KTo, ATo, QJo, KJo, AJo	Offsuit broadways
4 (25)	K2s–K4s, Q2s–Q4s, T4s–J4s	Suited high + low kicker
5 (10)	ATs, QJs, KJs, AJs, KQs	Premium suited broadways

Appendix

Table 9: Complete HUNL preflop clusters (

K=10

, 169 canonical hands, sorted by ID).

Feature	SG1	SG2
Current_Made_Str.	0.87	0.93
Improvement_Pot.	0.27	$-$ 0.30
Vuln._To_Outdraws	0.68	0.31
Nut_Ceiling	0.68	0.14
River_Stability	$-$ 0.36	0.52
Blocker_Value	0.65	0.46

Appendix

Table 10: Pearson correlation between LLM features and EHS across turn subgames.

Cluster	Representative Hands	Characteristics
0 (590)	9 a 7 b 5 a 5 b (ds), 6 a 6 b 5 a 3 b (ds)	Low-rank suited coordination holdings
1 (505)	A a J b J c 7 b (ss), A a J b J c 6 b (ss)	High-pair heavy
2 (629)	K a T b 9 a 5 b (ds), A a T a 5 b 2 b (ds)	Low-rank suited coordination holdings
3 (324)	A a A b K c 5 c (ss), A a A b 9 c 7 c (ss)	Premium paired / AA-heavy holdings
4 (743)	Q a 9 a 6 b 3 b (ds), J a T b 7 a 3 a (ts)	Low-rank suited coordination holdings
5 (468)	Q a T b 9 c 6 a (ss), Q a J b 8 a 8 c (ss)	Mixed suited coordination holdings

Appendix

Table 11: Complete PLO4 preflop clusters (

K=30

, 16,432 canonical hands; abstract suit labels

a

–

d

Cluster	Representative Hands	Characteristics
16 (133)	J a 9 b 7 c 5 d (r), Q a T b 9 c 7 d (r)	Rainbow low-to-mid coordination holdings
17 (178)	K a K b T c 4 d (r), A a A b Q c 7 d (r)	Premium paired / AA-heavy holdings
18 (624)	Q a J a 8 b 2 a (ts), 5 a 5 b 2 a 2 b (ds)	Low-rank suited coordination holdings
19 (387)	7 a 5 b 2 c 2 d (r), A a 7 b 3 c 2 d (r)	Rainbow low-to-mid coordination holdings
20 (896)	9 a 7 a 2 a 2 b (ts), Q a 7 a 7 b 2 a (ts)	Low-pair heavy
21 (618)	A a K b 9 a 9 b (ds), K a Q b T b 2 a (ds)	Mixed suited coordination holdings

Appendix

Table 12: PLO4 preflop clusters (continued).

Cluster	Representative Hands	Characteristics
0 (80)	Ad 9s, Ad 9d, Ad 8c	Top pair A, strong kicker
1 (16)	Js Ts, Js Td, Jd Tc	Nut straight (Broadway)
2 (162)	9s 4d, 9s 3c, 9s 2h	Air (9x low kicker, no draw)
3 (138)	Ah 5h, Ah 4h, Ks 9s	Medium made + backdoor draws
4 (25)	9d 9c, 8s 8h, 6s 6d	Underpairs (below board)
5 (8)	Ad Ac, Ks Kd, Qs Qc	Sets (AA/KK/QQ)

Appendix

Table 13: Complete HUNL flop clusters (board A

\spadesuit

\heartsuit

\diamondsuit

;

K=20

, 1,176 hands, sorted by ID).

Variant	$K{=}20$		$K{=}50$
Variant	SG1	SG2	SG1	SG2
Rules-only	1.20	1.21	0.73	0.91
Range-aware	1.30	1.25	0.74	0.82
Feature-discovery	1.35	1.18	0.76	0.92

Appendix

Table 14: Prompt-variant robustness: mean Agent / EHS exploitability ratio over 10 seeds (GPT-5.5).

Feature Removed	Mean ARI
Vulnerability_To_Outdraws	0.7558
Blocker_Value	0.7607
Current_Made_Strength	0.7638
Nut_Ceiling	0.7716
Improvement_Potential	0.7866
River_Stability	0.7959

Appendix

Table 15: Feature importance via leave-one-out ablation (mean ARI; lower = larger impact).

Feature	Description
Current_Speed	Shanten + effective tiles
Acceptance_Breadth	Tile types improving hand
Hand_Value_Potential	Scoring upside (yaku, dora)
Wait_Quality	Quality of the winning wait
Defensive_Resilience	Avoid dealing into opponents
Next_Draw_Swing	How one draw changes value (not selected)

Appendix

Table 16: Features discovered for Riichi Mahjong mid-game (turn 8, East round).

Size	Representative Hands	Dominant Trait
27	11578m 156p 13347s GR	Wide acceptance, 3–4 shanten
20	112467899m 4p 16s W	High speed + value
22	11149m 5579p 668s WH	Slow, high defense
16	1129m 39p 4777s WGR	High value, slow
23	1136m 2578p 1455s S	Average hands
33	1119m 1p 157889s WR	Most defensive

Appendix

Table 17: Complete Riichi Mahjong clusters (

K=10

, 200 hands).

Imperfect-Information Games

Imperfect-information games study strategic decisions when players have private information or cannot fully observe the state or actions.

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