# Belief Revision: The Adaptability of Large Language Models Reasoning

arXiv: https://arxiv.org/abs/2406.19764 · HTML: https://mlsanity.com/paper/2406.19764/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- Builds on 16 of its 78 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)

## Builds on (most cited first, 16 of 16)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Representation Engineering: A Top-Down Approach to AI Transparency](https://mlsanity.com/paper/2310.01405/) (2023)
- [Knowledge Neurons in Pretrained Transformers](https://mlsanity.com/paper/2104.08696/) (2021)
- [Textbooks Are All You Need II: phi-1.5 technical report](https://mlsanity.com/paper/2309.05463/) (2023)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery](https://mlsanity.com/paper/2405.09783/) (2024)
- [BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief](https://mlsanity.com/paper/2109.14723/) (2021)
- [Integrating Large Language Model for Improved Causal Discovery](https://mlsanity.com/paper/2306.16902/) (2023)
- [Building Efficient Universal Classifiers with Natural Language Inference](https://mlsanity.com/paper/2312.17543/) (2023)
- [Are language models rational? The case of coherence norms and belief revision](https://mlsanity.com/paper/2406.03442/) (2024)

## Built on by (most cited first, 30 of 30)

- [TurnBench-MS: A Benchmark for Evaluating Multi-Turn, Multi-Step Reasoning in Large Language Models](https://mlsanity.com/paper/2506.01341/) (2025)
- [BeliefShift: Benchmarking Temporal Belief Consistency and Opinion Drift in LLM Agents](https://mlsanity.com/paper/2603.23848/) (2026)
- [When Two LLMs Debate, Both Think They'll Win](https://mlsanity.com/paper/2505.19184/) (2025)
- [Reflection-Bench: Evaluating Epistemic Agency in Large Language Models](https://mlsanity.com/paper/2410.16270/) (2024)
- [Grounded Continuation: A Linear-Time Runtime Verifier for LLM Conversations](https://mlsanity.com/paper/2605.14175/) (2026)
- [When Should Models Change Their Minds? Contextual Belief Management in Large Language Models](https://mlsanity.com/paper/2605.30219/) (2026)
- [NRR-Core: Non-Resolution Reasoning as a Computational Framework for Contextual Identity and Ambiguity Preservation](https://mlsanity.com/paper/2512.13478/) (2025)
- [Faithful Summarisation under Disagreement via Belief-Level Aggregation](https://mlsanity.com/paper/2601.04889/) (2026)
- [Evaluating Evidence Grounding Under User Pressure in Instruction-Tuned Language Models](https://mlsanity.com/paper/2603.20162/) (2026)
- [DeltaLogic: Minimal Premise Edits Reveal Belief-Revision Failures in Logical Reasoning Models](https://mlsanity.com/paper/2604.02733/) (2026)
- [Do LLMs have core beliefs?](https://mlsanity.com/paper/2605.03255/) (2026)
- [TARL: Transaction-Aware Reliable Ledgers for Executable Memory Management in Long-Term Agents](https://mlsanity.com/paper/2608.03699/) (2026)
- [On Definite Iterated Belief Revision with Belief Algebras](https://mlsanity.com/paper/2505.06505/) (2025)
- [AssertBench: A Benchmark for Evaluating Self-Assertion in Large Language Models](https://mlsanity.com/paper/2506.11110/) (2025)
- [NRR-Phi: A Typed External Text-to-State Interface and Update Contract for Inspectable Ambiguity-State Maintenance](https://mlsanity.com/paper/2601.19933/) (2026)
- [Lifted State Hypothesis in Large Language Models](https://mlsanity.com/paper/2607.19360/) (2026)
- [Recompute or Reuse? Diagnosing and Mitigating Textual Shortcuts in VLM Self-Reflection](https://mlsanity.com/paper/2608.01930/) (2026)
- [A Taxonomy of Cognitive Capability Gaps in Generative and Agentic AI](https://mlsanity.com/paper/2608.02553/) (2026)
- [RENDER: Controlling Reader-Facing Evidence in LLM Memory Evaluation](https://mlsanity.com/paper/2608.23568/) (2026)
- [PCFBench: A Diagnostic Benchmark for Product Carbon Footprint Estimation](https://mlsanity.com/paper/2608.27716/) (2026)
- [Metacognitive Steering: Learning the Structure of Scientific Judgment](https://mlsanity.com/paper/2609.16245/) (2026)
- [Truth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance](https://mlsanity.com/paper/2609.26035/) (2026)
- [Epistemic Policy Divergence in Multi-Turn LLM Contamination: A Protocol-Gradient Investigation](https://mlsanity.com/paper/2609.35308/) (2026)
- [ArgGYM: A Procedural, Engine-Verified Benchmark for Structured Defeasible Reasoning](https://mlsanity.com/paper/2609.38409/) (2026)
- [StreamDecisionBench: Evaluating Decisions in Force on Evolving Language Streams](https://mlsanity.com/paper/2609.38612/) (2026)
- [Is This Evidence Decision-Critical? Learning to Verify Rule-Governed Decisions](https://mlsanity.com/paper/2609.39608/) (2026)
- [DoGBench: Can Agents Meet Expert Standards for User-Facing Documentation?](https://mlsanity.com/paper/2609.39909/) (2026)
- [Large language models exhibit unreliable updating of clinical judgment as patient evidence evolves](https://mlsanity.com/paper/2610.02684/) (2026)
- [Fresh at Decision, Stale at Action: Routing New Information to LLM Decisions Already Made](https://mlsanity.com/paper/2610.18126/) (2026)
- [Narrowing the Collaboration Gap, Probably](https://mlsanity.com/paper/2610.19215/) (2026)

## Abstract (arXiv)

The capability to reason from text is crucial for real-world NLP applications. Real-world scenarios often involve incomplete or evolving data. In response, individuals update their beliefs and understandings accordingly. However, most existing evaluations assume that language models (LMs) operate with consistent information. We introduce Belief-R, a new dataset designed to test LMs' belief revision ability when presented with new evidence. Inspired by how humans suppress prior inferences, this task assesses LMs within the newly proposed delta reasoning (Delta R) framework. Belief-R features sequences of premises designed to simulate scenarios where additional information could necessitate prior conclusions drawn by LMs. We evaluate ~30 LMs across diverse prompting strategies and found that LMs generally struggle to appropriately revise their beliefs in response to new information. Further, models adept at updating often underperformed in scenarios without necessary updates, highlighting a critical trade-off. These insights underscore the importance of improving LMs' adaptiveness to changing information, a step toward more reliable AI systems.