METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models

• URL: http://arxiv.org/abs/2604.11502v2
• Date: Thu, 16 Apr 2026 13:47:36 GMT
• Title: METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models
• Abstract summary: Contextual causal reasoning is a critical yet challenging capability for Large Language Models.<n>Existing benchmarks fail to ensure context consistency or cover the full causal hierarchy.<n>We pioneer METER to systematically benchmark LLMs across all three levels of the causal ladder.
• Score: 61.33372454250959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contextual causal reasoning is a critical yet challenging capability for Large Language Models (LLMs). Existing benchmarks, however, often evaluate this skill in fragmented settings, failing to ensure context consistency or cover the full causal hierarchy. To address this, we pioneer METER to systematically benchmark LLMs across all three levels of the causal ladder under a unified context setting. Our extensive evaluation of various LLMs reveals a significant decline in proficiency as tasks ascend the causal hierarchy. To diagnose this degradation, we conduct a deep mechanistic analysis via both error pattern identification and internal information flow tracing. Our analysis reveals two primary failure modes: (1) LLMs are susceptible to distraction by causally irrelevant but factually correct information at lower level of causality; and (2) as tasks ascend the causal hierarchy, faithfulness to the provided context degrades, leading to a reduced performance. We belive our work advances our understanding of the mechanisms behind LLM contextual causal reasoning and establishes a critical foundation for future research. Our code and dataset are available at https://github.com/SCUNLP/METER .

Related papers

• RepoReasoner: Evaluating Repository-Level Code Reasoning Ability of Long-Context Language Models [54.53236295237077]
  We introduce RepoReasoner, a benchmark for evaluating repository-level code reasoning.<n> Output Prediction measures fine-grained, stateful execution reasoning across files.<n>Call Chain Prediction evaluates high-level architectural dependency understanding under noisy context.
  arXiv  Detail & Related papers  (2026-07-28T17:12:41Z)
• From Early Encoding to Late Suppression: Interpreting LLMs on Character Counting Tasks [49.57538588967748]
  Large language models (LLMs) exhibit failures on elementary symbolic tasks such as character counting in a word, despite excelling on complex benchmarks.<n>We uncover a consistent phenomenon across modern architectures, including LLaMA, Qwen, and Gemma.<n>We show that symbolic reasoning failures in LLMs are not due to missing representations or insufficient scale, but arise from structured interference within the model's computation graph.
  arXiv  Detail & Related papers  (2026-04-01T11:40:12Z)
• How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities [75.10343190811592]
  Large Language Models (LLMs) are increasingly deployed in socially sensitive domains.<n>Our benchmark offers a principled and interpretable framework for safe and controllable behavior.
  arXiv  Detail & Related papers  (2026-03-03T03:50:13Z)
• Large Language Model Reasoning Failures [3.139060394530521]
  We present the first comprehensive survey dedicated to reasoning failures in Large Language Models (LLMs)<n>We introduce a novel categorization framework that distinguishes reasoning into embodied and non-embodied types.<n>For each reasoning failure, we provide a clear definition, analyze existing studies, explore root causes, and present mitigation strategies.
  arXiv  Detail & Related papers  (2026-02-05T20:29:26Z)
• FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning [20.27406245916013]
  We propose a novel framework that unifies the structural inductive biases of decision forests with the semantic reasoning capabilities of large language models (LLMs)<n>Our method is two-fold. First, we introduce a semantic splitting criterion in which the LLM evaluates candidate partitions based on their coherence over both labeled and unlabeled data, enabling the induction of more robust and generalizable tree structures under few-shot supervision.<n>Second, we propose a one-time in-context inference mechanism for leaf node stabilization, where the LLM distills the decision path and its supporting examples into a concise, deterministic prediction, replacing noisy empirical estimates with semantically informed outputs
  arXiv  Detail & Related papers  (2026-01-16T14:08:51Z)
• Exploring the Meta-level Reasoning of Large Language Models via a Tool-based Multi-hop Tabular Question Answering Task [0.0]
  We take a more structured approach, distinguishing meta-level reasoning from object-level reasoning.<n>We design a novel question answering task based around the values of geopolitical indicators for various countries over various years.<n>We find that LLMs demonstrate good meta-level reasoning on our task, yet are flawed in some aspects of task understanding.
  arXiv  Detail & Related papers  (2026-01-12T16:29:21Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)
• Evaluating LLMs' Mathematical and Coding Competency through Ontology-guided Interventions [47.83142414018448]
  We focus on two popular reasoning tasks: arithmetic reasoning and code generation. We introduce (i) a general ontology of perturbations for math and coding questions, (ii) a semi-automatic method to apply these perturbations, and (iii) two datasets. We show a significant performance drop across all the models against perturbed questions.
  arXiv  Detail & Related papers  (2024-01-17T18:13:07Z)
• Concise and Organized Perception Facilitates Reasoning in Large Language Models [31.238220405009617]
  Exploiting large language models (LLMs) to tackle reasoning has garnered growing attention.<n>It still remains highly challenging to achieve satisfactory results in complex logical problems, characterized by plenty of premises within the context and requiring multi-hop reasoning.<n>In this work, we first examine the mechanism from the perspective of information flow and reveal that LLMs confront difficulties akin to human-like cognitive biases when dealing with disordered and irrelevant content in reasoning tasks.
  arXiv  Detail & Related papers  (2023-10-05T04:47:49Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Integrating Large Language Model for Improved Causal Discovery [25.50313039584238]
  Large Language Models (LLM) have been used for causal analysis across various domain-specific scenarios.<n>We propose an error-tolerant LLM-driven causal discovery framework.
  arXiv  Detail & Related papers  (2023-06-29T12:48:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.