arXiv AI August 11, 2026 By Nikolaus Holzer, William Fishell, Baishakhi Ray, Mark Santolucito

TempoBench: Reasoning Execution Without Causal Attribution Is Just Simulation

arXiv:2510. 27544v3 Announce Type: replace Abstract: Current training paradigms, optimized for long-horizon reasoning trace execution, have made Large Language Models (LLMs) excel at pattern matching and forward simulation of reasoning, but underperform at counterfactual causal understanding and reasoning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 9

TempoBench: Evaluating Temporal Causal Reasoning in Large Language Models

arXiv:2510. 27544v2 Announce Type: replace Abstract: Temporal reasoning involves understanding how systems evolve over time through input-driven state transitions.

By Nikolaus Holzer, William Fishell, Baishakhi Ray, Mark Santolucito

llms fine-tuning benchmarks

arXiv AI

Jul 10

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

arXiv:2607. 08093v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use.

By Andrej Leban, Yuekai Sun

llms agents benchmarks

arXiv AI

Jun 29

CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching

arXiv:2602. 20094v2 Announce Type: replace Abstract: As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground their reasoning in causality rather than spurious correlations.

By Yuzhe Wang, Yaochen Zhu, Jundong Li

llms benchmarks

Hugging Face Trending Papers

Jul 9

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use. Yet the relevant benchmark landscape largely divides into symbolic causal reasoning benchmarks without realistic data analysis or data analysis benchmarks without a principled causal data-generating structure.

llms agents benchmarks

arXiv AI

Jun 6

ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces

arXiv:2606. 05402v1 Announce Type: cross Abstract: Large reasoning models (LRMs) produce reasoning traces with non-linear structures, such as backtracking and self-correction, that complicate the evaluation and monitoring of the reasoning process.

By Jinu Lee, Shivam Agarwal, Amruta Parulekar, Siddarth Madala, Dilek Hakkani-Tur, Julia Hockenmaier

llms

arXiv AI

Jul 21

Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering

arXiv:2607. 18100v1 Announce Type: new Abstract: Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable.

By Sheldon Yu, Tong Yu, Xunyi Jiang, Rohan Surana, Gagan Mundada, Sungchul Kim, Lina Yao, Julian McAuley, Junda Wu

llms

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Related stories

TempoBench: Evaluating Temporal Causal Reasoning in Large Language Models

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces

Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering