arXiv:2606. 30850v1 Announce Type: new Abstract: Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty about their environment.
arXiv:2604. 18576v4 Announce Type: replace Abstract: We present the Bayesian Linguistic Forecaster (BLF), an agentic system for binary forecasting that achieves state-of-the-art performance on the ForecastBench benchmark.
Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully reflect the evidence behind a forecast. We ask whether internal representations offer a more direct window into both.
arXiv:2605. 24528v2 Announce Type: replace Abstract: Real world decision-making requires constructing mental models under uncertainty over evidence, over the underlying causal rules, and over the state of the world itself.
arXiv:2606. 05330v1 Announce Type: cross Abstract: Large language models can shift human beliefs across high-stakes domains, but most persuasion studies rely on pre/post belief change.
arXiv:2609.15849v1 Announce Type: cross Abstract: Can LLMs reason through new information like humans, or do they merely retrieve cached opinions? This is critical for silicon sampling, where LLM per...
arXiv:2510.15144v4 Announce Type: replace Abstract: Simulating human reasoning in open-ended tasks has long been a central aspiration in AI and cognitive science. While large language models now appr...
The paper introduces Bayesian Chronicle Agents (BCA), a lightweight belief layer that separates an LLM agent’s internal stance from its outward speech. Each stance is represented as a probability updated via a single Bayesian step per utterance, with a single prior‑strength parameter κ controlling stubbornness. By sweeping κ, the authors generate three controllable opinion‑dynamics regimes—consensus, persistent disagreement, and committed‑minority influence—matching Friedkin–Johnsen theory and demonstrating recoverable, auditable belief states across models.
arXiv:2607. 08046v1 Announce Type: cross Abstract: Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully reflect the evidence behind a forecast.
The paper proposes a distributional theory explaining how large language models (LLMs) incorporate external evidence into their decision-making process. It identifies three key predictions: (1) evidence is more persuasive when it aligns with the model’s prior beliefs, (2) models more readily accept errors from their own internal processes than from external sources, and (3) the same evidence can improve weaker models while harming stronger ones. Extensive experiments across ten million trials, twelve LLMs from four families, and eight domains—including quantum mechanics, physics, genetics, and molecular biology—confirm these predictions and reveal that evidence integration occurs late in the network as a structured sequence of steps rather than through a simple trust metric.
arXiv:2607. 06648v1 Announce Type: new Abstract: Latent reasoning methods perform multi-step inference entirely in the model's continuous hidden states, promising more compact and efficient reasoning.
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...
The paper introduces a computational model that encodes symbolic knowledge as mental programs combining natural language and source code, and uses LLM-guided Bayesian learning to sequentially infer these programs. It demonstrates that this approach satisfies data‑efficiency, uncertainty handling, and flexibility, reproducing human inductive learning and active inquiry behaviors such as anchoring and garden‑pathing. In contrast, pure LLMs and classic Bayesian models either fail the task, do not match human behavior, or require prohibitive computational resources.