arXiv AI
Jun 4
FALSIFYBENCH: Evaluating Inductive Reasoning in LLMs with Rule Discovery Games
arXiv:2606. 04751v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in scientific tasks.
LLMs Can See the Smoke but not the Fire: Evaluating Abductive Reasoning with Elenchos
arXiv:2607. 12733v1 Announce Type: new Abstract: Large language models (LLMs) excel at pattern recognition and text generation, but their capacity for abductive inference - inferring latent hypotheses that explain observed behavior - remains poorly understood.
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arXiv AI
Sep 1
DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmark
arXiv:2608.30413v1 Announce Type: new Abstract: Defeasible reasoning is a type of reasoning where inferences are drawn from plausible current evidence, but can be retracted upon the introduction of n...
Hugging Face Trending Papers
Aug 31
DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmark
Defeasible reasoning is a type of reasoning where inferences are drawn from plausible current evidence, but can be retracted upon the introduction of newer evidence. Although recent studies have exami...
arXiv AI
Aug 28
Do Language Models Follow Occam's Razor? An Evaluation of Parsimony in Inductive and Abductive Reasoning
The paper investigates whether large language models (LLMs) follow Occam's Razor when performing inductive and abductive reasoning. It introduces a synthetic framework for generating questions that require both types of reasoning and a new automated metric to evaluate the simplicity and correctness of generated hypotheses. Experiments show that while LLMs can handle simple scenarios, they struggle with complex world models and producing high‑quality, simplest hypotheses, even when using advanced reasoning techniques.
arXiv AI
Sep 7
Evidence Integration in Large Language Models
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 AI
Sep 10
Compliance vs. Sensibility: On the Reasoning Controllability in Large Language Models
arXiv:2604.27251v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) acquire reasoning capabilities through shared inference patterns in pre-training data, which are further elicite...
arXiv Machine Learning
1d ago
The Missing Primitive: Diagnosing and Repairing Mathematical Reasoning in Large Language Models
arXiv:2610. 02191v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated striking capabilities on frontier mathematical problems, it remains unclear whether they possess the structural mathematical understanding underlying their solutions.
arXiv AI
Aug 18
Divergent-Convergent Reasoning: Scaling Test-Time Compute through Structured Solution Synthesis
arXiv:2608. 15303v1 Announce Type: new Abstract: Test-time compute can substantially improve Large Language Model (LLM) reasoning performance, yet how and when additional compute helps remains poorly understood.
arXiv AI
Sep 15
Thought without systematicity? Evaluating reasoning models on rule induction tasks
The paper investigates whether current reasoning models exhibit systematicity—the idea that understanding one concept should extend to closely related variations—by extending rule induction tasks from cognitive science. Using task isomorphisms like recombination and substitution, the authors generate structurally equivalent task variants and test models on them. Results show that while models can solve the original tasks, they frequently fail on these equivalent variants, indicating a lack of systematicity in their reasoning abilities.
arXiv AI
Sep 3
Thinking effort aligns between humans and reasoning models in abductive reasoning
The study examines how the effort expended by large reasoning models (LRMs) compares to that of humans during abductive reasoning tasks. By analyzing reaction times and reasoning traces, the authors find that LRMs and humans exhibit similar patterns of effort and error types. They also demonstrate that decoding strategies allowing models to explore multiple reasoning paths further align the models’ reasoning costs with human effort.
arXiv AI
Aug 18
CEDAR-GRPO: Process-Aware Reinforcement Learning for General Abductive Reasoning in LLMs
arXiv:2608. 14791v1 Announce Type: new Abstract: Abductive reasoning, often characterized as inference to the best explanation, is central to explanation under uncertainty, from everyday sense-making and investigation to scientific discovery.
arXiv AI
Aug 20
Mechanistic Interpretability of Structure-Aware Numerical Reasoning in LLaMA 3.1 8B
The paper investigates how LLaMA 3.1‑8B models numerical sequence patterns, focusing on time‑series prediction. By designing a task that requires detecting structural cues—specifically first differences in a sequence—the authors show that the model performs well and internally computes and stores these differences. Probing and activation‑patching experiments reveal that LLaMA retrieves and applies the first‑difference via an induction‑like circuit, marking one of the first demonstrations of concept induction in large language models.