# Same Game, Different Story: A Minimal Conservative Strategic Robustness Benchmark for Large Language Model Agents arXiv: https://arxiv.org/abs/2607.19670 ยท HTML: https://mlsanity.com/paper/2607.19670/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. - Builds on 25 of its 50 references that are in the map. - First posted on arXiv 22 Jul 2026. ## Placed in - LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/) ## Builds on (most cited first, 25 of 25) - [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022) - [Playing repeated games with Large Language Models](https://mlsanity.com/paper/2305.16867/) (2023) - [Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis](https://mlsanity.com/paper/2312.05488/) (2023) - [Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing](https://mlsanity.com/paper/2309.05898/) (2023) - [LLMs Are Not Intelligent Thinkers: Introducing Mathematical Topic Tree Benchmark for Comprehensive Evaluation of LLMs](https://mlsanity.com/paper/2406.05194/) (2024) - [On Jailbreaking Quantized Language Models Through Fault Injection Attacks](https://mlsanity.com/paper/2507.03236/) (2025) - [Collective Reasoning Among LLMs: A Framework for Answer Validation Without Ground Truth](https://mlsanity.com/paper/2502.20758/) (2025) - [Revisiting the Reliability of Language Models in Instruction-Following](https://mlsanity.com/paper/2512.14754/) (2025) - [Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models](https://mlsanity.com/paper/2411.16797/) (2024) - [Sanitizing Manufacturing Dataset Labels Using Vision-Language Models](https://mlsanity.com/paper/2506.23465/) (2025) - [ASSERTIFY: Utilizing Large Language Models to Generate Assertions for Production Code](https://mlsanity.com/paper/2411.16927/) (2024) - [Equal Access, Unequal Interaction: A Counterfactual Audit of LLM Fairness](https://mlsanity.com/paper/2602.02932/) (2026) - [Streamlining Security Vulnerability Triage with Large Language Models](https://mlsanity.com/paper/2501.18908/) (2025) - [Prioritizing App Reviews for Developer Responses on Google Play](https://mlsanity.com/paper/2502.01520/) (2025) - [LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Models](https://mlsanity.com/paper/2511.02866/) (2025) - [Geometry-Aware Decoding with Wasserstein-Regularized Truncation and Mass Penalties for Large Language Models](https://mlsanity.com/paper/2602.10346/) (2026) - [Assessing Foundational Medical 'Segment Anything' (Med-SAM1, Med-SAM2) Deep Learning Models for Left Atrial Segmentation in 3D LGE MRI](https://mlsanity.com/paper/2411.05963/) (2024) - [GUSL-Dehaze: A Green U-Shaped Learning Approach to Image Dehazing](https://mlsanity.com/paper/2510.20266/) (2025) - [Research Novelty in Information Systems Journals After ChatGPT: Differences Across Institutional Language Contexts](https://mlsanity.com/paper/2603.22510/) (2026) - [Vertex-Softmax: Tight Transformer Verification via Exact Softmax Optimization](https://mlsanity.com/paper/2605.10974/) (2026) - [Can MLLMs Critique Like Humans? Evaluating Open-Ended Aesthetic Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2606.29689/) (2026) - [Automated detection of circadian-dependent epileptic biomarkers for seizure localization using machine learning and signal processing](https://mlsanity.com/paper/2510.15717/) (2025) - [Enhancing Demand-Oriented Regionalization with Agentic AI and Local Heterogeneous Data for Adaptation Planning](https://mlsanity.com/paper/2511.10857/) (2025) - [Truthful AI Advisors: A Pre-Specified Benchmark for Large Language Model Honesty Under Preference Misalignment](https://mlsanity.com/paper/2606.01456/) (2026) - [Do Large Language Model Voters Strategize? An Oracle-Based Benchmark for Manipulation under Voting Rules](https://mlsanity.com/paper/2606.21001/) (2026) ## Built on by (most cited first, 1 of 1) - [Passing Coarse Marginal Checks Can Be Cheap: Persona Mixtures and Imprecise Treatment-Response Estimates in an LLM Persona Panel](https://mlsanity.com/paper/2608.00979/) (2026) ## Abstract (arXiv) Large language model agents are increasingly deployed in settings where the value of an action depends on what other agents do. This creates a strategic reliability problem: the same game may be described as a business negotiation, a friendly compromise, a diplomatic exchange, or an abstract payoff matrix, and the model may choose different actions even when the incentives are unchanged. This paper introduces Same Game, Different Story, a benchmark for strategic robustness: invariance of model-induced action distributions under payoff-preserving language changes. The empirical analysis uses a deliberately narrow, literature-calibrated comparison from Lor`e and Heydari's peer-reviewed study: business framing versus friend-sharing framing across GPT-3.5, GPT-4, and LLaMa-2 in four social-dilemma games, with 300 initializations per retained model-game-context cell. The retained design comprises 24 of the source study's 60 cells, representing 7,200 decisions. Because trial-level files were not available from the article, the analysis is presented as a secondary calibration based on reconstructed published rates, not as new model runs. As a conservative sensitivity analysis, effect magnitudes are attenuated by 30% toward the null: action shifts are multiplied by 0.70, and non-robustness, defined as one minus the robustness score, is multiplied by 0.70. Under this attenuation, pooled strategic robustness is 0.783 with a 95% bootstrap interval from 0.774 to 0.790, and friend-sharing framing raises cooperation by 0.307 with a 95% bootstrap interval from 0.297 to 0.316 relative to business framing. The analysis supports the narrower claim that social-relational framing can change strategic choices even when incentives are held fixed, without extending the analysis to a broader suite of contextual or cross-benchmark comparisons.