Tootfinder

This https://arxiv.org/abs/2505.18492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Enumerate-Conjecture-Prove: Formally Solving Answer-Construction Problems in Math Competitions
Mathematical reasoning lies at the heart of artificial intelligence, underpinning applications in education, program verification, and research-level mathematical discovery. Mathematical competitions, in particular, present two challenging problem types: theorem-proving, requiring rigorous proofs of stated conclusions, and answer-construction, involving hypothesizing and formally verifying mathematical objects. Large Language Models (LLMs) effectively generate creative candidate answers but str…

This https://arxiv.org/abs/2505.10573 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Measurement to Meaning: A Validity-Centered Framework for AI Evaluation
While the capabilities and utility of AI systems have advanced, rigorous norms for evaluating these systems have lagged. Grand claims, such as models achieving general reasoning capabilities, are supported with model performance on narrow benchmarks, like performance on graduate-level exam questions, which provide a limited and potentially misleading assessment. We provide a structured approach for reasoning about the types of evaluative claims that can be made given the available evidence. For…

LeanLTL: A unifying framework for linear temporal logics in Lean
Eric Vin, Kyle A. Miller, Daniel J. Fremont
https://arxiv.org/abs/2507.01780 https://

LeanLTL: A unifying framework for linear temporal logics in Lean
We propose LeanLTL, a unifying framework for linear temporal logics in Lean 4. LeanLTL supports reasoning about traces that represent either infinite or finite linear time. The library allows traditional LTL syntax to be combined with arbitrary Lean expressions, making it straightforward to define properties involving numerical or other types. We prove that standard flavors of LTL can be embedded in our framework. The library also provides automation for reasoning about LeanLTL formulas in a wa…

Hecto: Modular Sparse Experts for Adaptive and Interpretable Reasoning
Sanskar Pandey, Ruhaan Chopra, Saad Murtaza Bhat, Ark Abhyudaya
https://arxiv.org/abs/2506.22919

Hecto: Modular Sparse Experts for Adaptive and Interpretable Reasoning
Mixture-of-Experts (MoE) models enable conditional computation by routing inputs to specialized experts, but these experts rely on identical inductive biases, thus limiting representational diversity. This static computation pathway is inefficient for inputs that require different types of reasoning and limits specialization and interpretability. We propose Hecto, a lightweight MoE architecture that leverages architectural heterogeneity by combining a GRU expert for temporal reasoning and an FF…

Reasoning about Bounded Reasoning
Shuige Liu, Gabriel Ziegler
https://arxiv.org/abs/2506.19737 https://arxiv.org/pdf/2506.19737

Reasoning about Bounded Reasoning
Interactive decision-making relies on strategic reasoning. Two prominent frameworks are (1) models of bounded reasoning, exemplified by level-$k$ models, which keep reasoning implicit, and (2) epistemic game theory, which makes reasoning explicit. We connect these approaches by "lifting" static complete-information games into incomplete-information settings where payoff types reflect players' reasoning depths as in level-$k$ models. We introduce downward rationalizability, defined via minimal b…

Reasoning with Exploration: An Entropy Perspective
Daixuan Cheng, Shaohan Huang, Xuekai Zhu, Bo Dai, Wayne Xin Zhao, Zhenliang Zhang, Furu Wei
https://arxiv.org/abs/2506.14758

Reasoning with Exploration: An Entropy Perspective
Balancing exploration and exploitation is a central goal in reinforcement learning (RL). Despite recent advances in enhancing language model (LM) reasoning, most methods lean toward exploitation, and increasingly encounter performance plateaus. In this work, we revisit entropy -- a signal of exploration in RL -- and examine its relationship to exploratory reasoning in LMs. Through empirical analysis, we uncover strong positive correlations between high-entropy regions and three types of explora…

Towards Bug-Free Distributed Go Programs
Zhengqun Koo
https://arxiv.org/abs/2506.15135 https://arxiv.org/pdf/2506.15135

Towards Bug-Free Distributed Go Programs
Programmers of distributed systems need to reason about concurrency to avoid races. However, reasoning about concurrency is difficult, and unexpected races show up as bugs. Data race detection in shared memory systems is well-studied (dynamic data race detection [13], behavioral types [15], dynamic race detection [31]). Similar to how a data race consists of reads and writes not related by happens-before at a shared memory location, a communication race consists of receives and sends not relate…

How Well Can Reasoning Models Identify and Recover from Unhelpful Thoughts?
Sohee Yang, Sang-Woo Lee, Nora Kassner, Daniela Gottesman, Sebastian Riedel, Mor Geva
https://arxiv.org/abs/2506.10979

How Well Can Reasoning Models Identify and Recover from Unhelpful Thoughts?
Recent reasoning models show the ability to reflect, backtrack, and self-validate their reasoning, which is crucial in spotting mistakes and arriving at accurate solutions. A natural question that arises is how effectively models can perform such self-reevaluation. We tackle this question by investigating how well reasoning models identify and recover from four types of unhelpful thoughts: uninformative rambling thoughts, thoughts irrelevant to the question, thoughts misdirecting the question a…

This https://arxiv.org/abs/2505.10573 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Measurement to Meaning: A Validity-Centered Framework for AI Evaluation
While the capabilities and utility of AI systems have advanced, rigorous norms for evaluating these systems have lagged. Grand claims, such as models achieving general reasoning capabilities, are supported with model performance on narrow benchmarks, like performance on graduate-level exam questions, which provide a limited and potentially misleading assessment. We provide a structured approach for reasoning about the types of evaluative claims that can be made given the available evidence. For…