# Concurrent Work Disclosure

Per ACL Rolling Review policy, we disclose two concurrent submissions by overlapping authors that involve poker and LLMs. Neither paper is cited in our submission because (1) they were developed independently and in parallel (all finished in 2026.5), (2) their research questions, methods, and contributions do not overlap with ours, and (3) the author lists of these works are not identical to ours—we were unable to obtain complete concurrent-submission information from all co-authors before our submission deadline, and could only confirm the existence of these works after the deadline had passed.

## Paper 1: Zero-Shot Game Abstraction: LLM-Guided Information Abstraction for Imperfect-Information Games

**Venue:** ACL 2025 ARR (concurrent submission)

**Summary:** This paper uses LLMs to *construct information abstractions* for game-solving algorithms. It proposes a pipeline that prompts an LLM to discover strategic features, score private states, and cluster them into abstraction buckets—replacing hand-crafted evaluators (e.g., equity calculators). The output is an abstraction fed into a traditional CFR solver; the LLM is not involved at game time.

**Relationship to our work:** The two papers operate at fundamentally different levels:
- **Paper 1** uses LLMs as an offline abstraction tool *before* solving. The final player is a CFR solver, not an LLM.
- **Our paper** uses LLMs as the online decision-maker *during* play, guided by a deterministic context engine and expert-designed prompt skills. No solver is involved.

There is no shared method, code, experimental setup, or result. The only surface-level similarity is that both involve LLMs and poker, but the research questions are orthogonal: theirs asks "can LLMs replace domain-specific evaluators for abstraction construction?" while ours asks "can structured prompting close the gap between LLM knowledge and situated action in sequential decision-making?"

## Paper 2: Solver-Guided Reasoning for Mixed-Equilibrium Strategies

**Venue:** NeurIPS 2026 (concurrent submission)

**Summary:** This paper addresses LLMs' inability to play mixed strategies by training them on solver output. It proposes Mixed-Strategy Decision Trees (MDT) that distill solver equilibria into sparse rules, then fine-tunes or prompts LLMs to follow these rules. The goal is to reason the solver-generated equilibrium mixed strategies.

**Relationship to our work:** The two papers differ in both methodology and goal:
- **Paper 2** uses solver output to *train/fine-tune* LLMs toward equilibrium play. It requires millions of solver queries and targets frequency-level accuracy.
- **Our paper** is training-free and solver-free. We use expert-designed prompt skills to guide a frozen LLM at inference time. We do not attempt to reproduce equilibrium mixed strategies; instead, we bound the action space and provide strategic context to reduce catastrophic errors.

There is no shared method, code, experimental setup, or result. The only surface-level similarity is that both involve LLMs and poker, but the research questions are orthogonal: theirs asks "can LLMs reason the mixed solver strategy?" while ours asks "can structured prompting close the gap between LLM knowledge and situated action in sequential decision-making?"

## Summary

| Dimension | Paper 1 | Paper 2 | Our Paper |
|-----------|---------|---------|-----------|
| LLM role | Offline feature extraction | Training target | Online decision-maker |
| Solver involvement | Output feeds into CFR | Provides training signal | None |
| Training required | No | Yes | No |
| Game-time agent | CFR solver | Fine-tuned LLM | Frozen LLM + prompt skills |
| Core contribution | Abstraction construction | Equilibrium reasoning | Situated skill activation |

We confirm that our submission was developed independently. The concurrent works address different research questions with different methods, and their existence does not diminish the novelty or contribution of our paper.
