The paper investigates whether language models that produce the same probabilistic predictions after different types of evidence (textual or activation interventions) actually share the same underlying belief states. Using a Bernoulli inference task and an analytic Bayesian reference, the authors compare four textual routes and two activation-level interventions across three open-weight models, measuring both next-outcome continuations and explicit probabilistic estimates. They find that the format of evidence influences which readouts reflect its content, and that the effective evidence weight—how many new observations a belief is worth—varies widely across routes, with some elicitation questions altering inferred weights by orders of magnitude. In a one-shot steering scenario, an intervention that matches a natural record’s immediate prediction loses most of its effect when new evidence is introduced, demonstrating that agreement in current predictions does not guarantee equivalent belief updating.
Language models can produce similar probabilistic predictions after observing outcomes, reading summaries, receiving instructions, or undergoing activation interventions. Whether these routes induce e...
Large language models (LLMs) are increasingly used for clinical reasoning, yet their ability to revise judgments as patient evidence changes is uncertain. In this study, researchers evaluated longitudinal belief updating using real intensive‑care patient trajectories and found that conditioning on prior judgments often increased prediction error. Controlled experiments revealed two failure modes: models were more responsive to worsening than improving respiratory evidence, and they shifted estimates significantly when prior risk was altered, indicating a causal influence of prior beliefs. Prompting did not improve reliability, and an Evidence‑Validated Longitudinal Update (EVLU) approach produced fewer but more trustworthy revisions, highlighting a reliability‑coverage trade‑off.
The study investigates whether prompting language models to provide five weighted estimates in a single response can reduce bias in their judgments. Across various bias tasks—anchoring, hindsight, base‑rate, conjunction, and status‑quo/default—the authors compare direct numerical answers with a multi‑estimate format, testing different source framings, random cues, and probability‑consistency constraints on three models (gpt‑6.1‑sol, claude‑sonnet‑5.5, and Qwen3‑8B). Results show that while direct elicitation reduces irrelevant‑anchor indices and status‑quo effects in two configurations, multi‑estimate elicitation does not consistently debias or attenuate responses, and the observed effects are limited by noise and lack of equivalence.
Faithfully understanding the internal process that occurs as models form their chain of thought is an important problem with the rise in model capabilities. However, this remains challenging due to th...
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.
The paper introduces a method for analyzing how interventions in a prompt affect a language model’s reasoning by using behavioral counterfactuals. It trains a module to edit the residual stream so that the next-token distributions for a fixed response match between a source and a counterfactual prompt, outperforming standard activation reconstruction. The resulting attribution maps and visualizations help identify causally important locations for counterfactual recovery and guide hypothesis testing with activation patching, as demonstrated in a case study on Gemma-4-E4B-it and DeepSeek-R1-0528-Qwen3-8B.
arXiv:2610.17310v1 Announce Type: new Abstract: Language-model agents are expected to adapt to others whose behavior changes through interaction. Yet adaptation may rely on learned behavioral pattern...
arXiv:2610.27745v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judgment assistants and as stand-in participants in psychology experiments, yet little is known...
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:2603. 01437v2 Announce Type: replace Abstract: As chain of thought (CoT) has become central to scaling reasoning capabilities in large language models (LLMs), it has also emerged as a promising tool for interpretability, suggesting the opportunity to understand model decisions through verbalized reasoning.
The paper investigates how visual interventions—training‑free edits applied to multimodal large language models—affect hallucinations and prediction quality. By applying a visual‑residual edit across successive decoder layers and matching its RMS strength, the authors identify an Evidence Anchoring Boundary (EAB) where the intervention’s influence drops sharply; effects before the EAB are 13.78–26.10× larger than after it. They further compare counterfactual subtraction across different question types and introduce calibrated selective routing, which only alters predictions when calibration supports an intervention, improving attribute accuracy by 1.56–1.81 percentage points while avoiding the harms of universal subtraction.
arXiv:2610.33237v1 Announce Type: new Abstract: How can a language model know whether it has enough evidence to answer? Language models tend to answer even when the evidence they have is insufficient...