arXiv:2606. 12721v1 Announce Type: new Abstract: Inferring others' beliefs requires more than reading surface signals; it requires tracking who told them what, in what order, and how credibly.
arXiv:2606. 13607v1 Announce Type: new Abstract: When large language models (LLMs) fail to generalize or make haphazard errors in reasoning, it is often taken as evidence that LLMs are not truly reasoning, but rather performing a kind of pattern matching.
When large language models (LLMs) fail to generalize or make haphazard errors in reasoning, it is often taken as evidence that LLMs are not truly reasoning, but rather performing a kind of pattern matching. The implication is that people's behavior does not exhibit the same types of failures because human reasoning uses principled and abstract world models.
The paper defends the 'Whole Hog Thesis', arguing that sophisticated large language models such as ChatGPT are full linguistic and cognitive agents, possessing understanding, beliefs, desires, knowledge, and intentions. It rejects low‑level computational starting points and instead builds its case from high‑level behavioral observations, using Holistic Network Assumptions to link actions to mental states. The authors systematically rebut common objections—such as hallucinations and planning errors—by showing these resemble human fallibility and by challenging the necessity of traditional conditions like embodiment or semantic grounding.
arXiv:2606. 16944v1 Announce Type: new Abstract: Theory of mind (ToM), the capacity to ascribe mental states to others and use those ascriptions for prediction and inference, is widely assumed to be essential for effective human-machine integration.
arXiv:2609.07943v1 Announce Type: new Abstract: There is significant uncertainty about whether abstractions like beliefs or desires usefully describe the behavior of large language models (LLMs). In...
arXiv:2608. 09638v1 Announce Type: new Abstract: Theory of Mind (ToM) is essential for agent interactions, yet existing evaluations either rely on static scenarios that oversimplify mental-state reasoning or interactive settings that provide limited diagnostic insight.
arXiv:2607. 07003v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently exhibit sycophancy, where they agree with a user's statement even when incorrect.
arXiv:2609.17496v1 Announce Type: new Abstract: LLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (...
The paper introduces Epistemic Probabilistic Language Agents (EPLA), a neuro‑symbolic architecture designed to enable coordination among multi‑agent large language models (LLMs) under uncertainty. EPLA employs a Symbolic Guard that provides structured diagnostic feedback, allowing the LLM to generate typed actions while the Guard controls their execution against an authoritative symbolic state. The authors formalize an epistemic layer using gossip testbeds and epistemic lottery gossip models, combining view‑based call histories with agent‑indexed probability weights to address gaps in social behavior and coordination mechanisms for agentic LLMs.
Semantic Bayesian World Models (SBWMs) propose a shift from static knowledge graphs to a dynamic, probabilistic fabric of beliefs that can be updated via Bayesian conditioning and influenced by actions. The approach aims to bridge the gap between crisp factual assertions and the probabilistic reasoning of foundation models and autonomous agents, enabling richer inference in scenarios such as home‑security decisions, actuarial estimates, and planning tasks. Realizing SBWMs requires new tools for belief annotation, probabilistic entailment, semantic calibration, and protocols for belief exchange among agents.
The paper introduces epistemic memory, a validity-maintenance layer for intelligent systems that tracks when stored knowledge remains applicable. It formalizes a dynamic epistemic quotient and shows that fixed semantic representations inevitably incur error as epistemic boundaries shift. The authors propose Observable Belief Memory (OBM), which combines current epistemic quotients, belief over quotient classes, and within-class provenance, and demonstrate that explicit epistemic tracking improves robustness under changing observation conditions.