causal-reasoning

A perspective paper proposing a four-stage research agenda (Ground, Structure, Verify, Act) for interpreting JEPA-based latent world models, arguing interpretability should shift from decoding representations to understanding how latent variables interact, evolve, and causally drive prediction.

ACED-Bench introduces a neuro-symbolic benchmark combining natural-language scientific scenarios with hidden probabilistic graphical models to evaluate whether LLM agents can actively design experiments, gather causal evidence, and make correct causal decisions across 180 core tasks and a 150-question qualification set.

Aether AI 展示了其首个因果机器人智能系统 CRIS-0 在真实家庭环境中的运行，该系统通过因果世界模型预测动作后果、维持任务上下文并调用统一工具接口来导航和适应现实变化。

该论文提出 VepAgent，一个将因果转移推理与工具增强强化学习结合的智能体框架，用于视频事件预测，并构建了 futurebench-4K 思维链数据集，在 FutureBench 和 NEPBench 上达到 SOTA 性能。

This arXiv paper proposes a protocol to measure the predictive credit of scientific explanations for experimental forecasts, finding that matched explanations did not significantly improve prediction accuracy across Tox21 and OpenML benchmarks, though some gains appeared under certain model replays.

FireWorldBench is a benchmark that evaluates complex physical world intelligence in multimodal large language models through coupled-field fire dynamics, focusing on tasks like forecasting, perception, and causal reasoning.

The paper introduces CaLR, a framework that reformulates reasoning as constrained latent optimization using causal topology to enhance diffusion language models, achieving state-of-the-art performance on complex benchmarks.

The paper proposes a deterministic framework that compiles games specified in VGDL into Dynamic Structural Causal Models to guarantee causal fidelity, enabling transparent causal pathways for counterfactual reasoning and causal reinforcement learning.

The paper presents C3T, a thread-structured temporal model for predicting sentiment shifts in social media conversation trees using counterfactual causal reasoning, and introduces the CaSiRe dataset for causal sentiment reasoning.

This paper presents a preliminary study evaluating the accuracy of large language models in verifying causal medical hypotheses, finding that while they exhibit strong recall, they often fail to provide valid scientific evidence or reject unsupported claims.

CaRGo-T introduces a graph-based reasoning framework to model causal relationships for improving multimodal humor comprehension in vision-language models, showing performance gains on humor understanding and detection tasks.

The paper proposes bipartite graphical causal models (BGCMs) to resolve ambiguities in causal interventions for systems at equilibrium with cyclic dependencies, generalizing existing frameworks like causal Bayesian networks and structural causal models.

A preregistered stress test on a small transformer shows that while latent causal structure can be localized, releasing it into behavior fails: the gate detector inverts out-of-distribution and linear release directions are bounded below sufficiency, dissociating localization from behavioral release.

This paper investigates how transformers organize causal knowledge, showing that type-level supervision induces a typed routing structure that is functionally decoupled from answer readout, with exact local editability and bit-exact revertibility.

This paper compares three methods for grounding a frozen LLM (Qwen2.5-32B-Instruct) in a wastewater treatment simulator for causal question answering, including live tool-use, structured parameter injection, and a small decoupled retrieval model, achieving high accuracy and cross-plant transfer.

This paper proposes Abstract Event Causal Rules (AECR), a relation-level causal abstraction paradigm that converts concrete cause-effect pairs into generalized causal logic. It introduces a multi-agent system for causal induction and an attention-based encoder that improves event prediction, especially for rare and unseen events.

This paper introduces CALVER, a training-free symbolic verifier that scores structured causal reasoning traces against Pearl's criteria to select the best candidate, outperforming plurality voting and other selection methods on causal reasoning benchmarks where multiple valid answers exist.

This paper tests whether increasing interventional data in pretraining improves LLMs' causal direction reasoning, using controlled Simpson's-paradox worlds. It finds that the training mixture does not govern interventional evidence use; instead the evidence type in the inference-time context is the decisive factor.

This arXiv paper introduces CausalVLBench, a benchmark for evaluating visual causal reasoning in large vision-language models across three tasks: causal structure inference, intervention target prediction, and counterfactual prediction. It evaluates open-source LVLMs on three causal representation learning datasets, revealing strengths and weaknesses.

This paper investigates whether language models can correctly judge how diagnostic evidence supports or challenges different causal claims, introducing paired prompts that vary only the causal target. Linear readouts from the penultimate transformer hidden state on models like Qwen2.5-7B-Instruct show moderate balanced accuracy (0.654-0.659) and recover 18–21 out of 49 pairs, indicating some linear decodability of causal relevance.