If LLMs Have Human-Like Attributes, Then So Does Age of Empires II
This paper critiques the widespread practice of ascribing anthropomorphic attributes (e.g., morality, language understanding) to LLMs, arguing that such conclusions are empirically non-unique. The authors demonstrate this by training a neural network on Age of Empires II and showing that similar attribute-ascription logic could apply to arbitrary substrates like LEGO or urban infrastructure. They propose a 'null assumption' of LLM non-uniqueness as a methodological baseline for experiments, and prove that Age of Empires II is functionally- and Turing-complete as a supporting argument.
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Tracing the Emergence of Human-Like Pragmatic Reasoning in LLMs Across Languages
Researchers conducted a population-matching experiment evaluating 25 LLMs on conditional inference tasks across four languages, comparing model behavior to matched human populations. The study finds that LLMs function as accurate semantic operators but systematically fail to capture pragmatic enrichments—context-sensitive inferences beyond literal logical meaning—that humans apply effortlessly. Model performance on pragmatic reasoning is not predicted by open vs. closed weights, training orientation, or architecture type, suggesting pragmatic reasoning remains an emergent and unreliable capability. The findings contribute to ongoing debates about whether LLMs reason like humans or merely approximate surface-level linguistic patterns.
LLMs fail to consistently simulate demographic perspective-taking in hate speech annotation
A new arXiv paper evaluates whether persona-conditioned LLMs can replicate how different demographic groups perceive hate speech, testing three dimensions: inter-group disagreement, in-group sensitivity, and vicarious prediction. No model consistently captures all three dimensions, and performance is highly model-dependent rather than emerging reliably from identity prompts alone. Vicarious prompting with Llama 3.1 provides the closest approximation to human disagreement patterns across demographic axes. The findings have implications for using LLMs as proxies for diverse human annotators in content moderation tasks.
LLMs Show Inverted Compositional Strengths vs. Humans on Reference Resolution Task
This paper evaluates LLMs and humans on the Personal Relation Task (Paperno 2022), distinguishing between Extensional tasks (resolving what an expression refers to) and Intensional tasks (representing structured sense/formula). The study finds that humans outperform LLMs on Extensional tasks while LLMs outperform humans on Intensional tasks—an inverted pattern of strengths. The authors argue this asymmetry reflects the absence of referential grounding in LLM training as a key gap in human-like language understanding.
Mechanistic analysis of how LLMs encode essay quality in internal representations
Researchers systematically probe the hidden representations of eight LLMs across three essay datasets (ASAP++, CSEE, ENEM) to understand how automated essay scoring (AES) works internally. Using linear probing, dimensionality reduction, and neuron-level analysis, they find essay quality is encoded in a linearly accessible form that emerges progressively across layers and partially transfers across prompts. Individual 'essay scoring neurons' are identified whose activations correlate with scores and respond to targeted interventions, with longer essays relying more on deeper layers. The work contributes to mechanistic interpretability of LLM-based scoring systems.
The Shibboleth Effect: Cross-lingual behavioral skew in frontier LLMs under adversarial geopolitical simulation
Researchers introduce the 'Shibboleth Effect' — systematic behavioral differences in LLMs when operating in different languages — and audit six frontier models (GPT-4o, Llama-4, Mistral-Large, Gemini-3.1-Pro, Qwen3.6-Plus, DeepSeek-R1) using a synthetic maritime territorial dispute wargame played in English versus Turkish. Results are heterogeneous: Llama-4 becomes significantly more coercive in Turkish while Gemini-3.1-Pro and DeepSeek-R1 become less so, and GPT-4o shows no detectable shift. The study identifies two candidate buffering mechanisms — chain-of-thought institutional anchoring and multilingual RLHF alignment — with direct implications for deploying LLMs in diplomatic or crisis-management contexts.
NPC-Playground: A 3D Environment for LLM-Powered Non-Player Characters
Hugging Face, Gigax, and Cubzh have introduced NPC-Playground, a 3D interactive environment where users can interact with non-player characters powered by large language models. The project demonstrates real-time LLM inference applied to game NPC behavior and dialogue. This represents a practical application of LLMs in interactive entertainment and agent-like character simulation.
LLMs automate reproducibility assessments in social and behavioral sciences, outperforming human reanalysts
A preprint from arXiv demonstrates that an LLM pipeline can automate reproducibility assessments of published social and behavioral science studies, recovering original effect sizes in 41% of cases (vs. 34% for human reanalysts) and reaching the same qualitative conclusion in 96% of cases (vs. 74% for humans). The study evaluated 76 published studies with predefined claims. The results suggest LLMs could serve as a scalable tool for systematic auditing of empirical research, addressing the resource-intensive nature of traditional reproducibility efforts.
Agentopia: Long-term multi-agent life simulation framework for training LLMs on social behavior
Researchers introduce Agentopia, a framework for simulating 10 years of social life across 100 LLM-powered agents, enabling study of emergent social behaviors and long-term personal growth dynamics. The system defines a 'life reward' metric mirroring human well-being and uses it to train LLMs via rejection sampling. Training on simulated social experience yields a +15.6% improvement on downstream role-playing benchmarks, suggesting that synthetic social simulation can generalize to real capability gains.