The paper proposes a minimal structural framework for cognitive representation and problem solving, emphasizing the role of context, invariance recognition, and representative selection. It introduces a Knowledge Space composed of a Concept Graph and a Procedure Graph to formalize these operations without relying on complex inference or learning mechanisms. Illustrative examples and a weak-solver demonstration show how proper representational organization can simplify problem solving and delineate success and failure conditions.
By Antal Jakov\'ac, Andr\'as Telcs
The paper introduces a multi-stage rule‑chaining framework for the Abstraction and Reasoning Corpus (ARC), aiming to model cognitive generalization by inferring abstract rules from few examples. It combines three solvers—a deterministic rule discovery module, a pattern‑composition engine, and a structural abstraction layer—executed sequentially in a fallback hierarchy that reuses earlier reasoning traces to improve interpretability and generalization. The system achieved over 95% accuracy on ARC tasks, demonstrating strong performance across deterministic, compositional, and abstract categories.
By Deblina Kar
arXiv:2605.11388v2 Announce Type: replace
Abstract: Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambigu...
By Dean Light, Michael Theologitis, Kshitish Ghate, Shuyue Stella Li, Benjamin Newman, Chirag Shah, Aylin Caliskan, Pang Wei Koh, Dan Suciu, Yulia Tsvetkov
ConceptARC at Scale introduces a generator that produces thousands of ARC-style tasks focused on specific spatial and semantic concepts, expanding the original benchmark by tenfold. The new dataset, ConceptARC-100, contains 1,600 manually reviewed tasks across 16 concepts, each with 100 tasks. Evaluation of large reasoning models on this dataset reveals that they reliably solve certain concepts, struggle with others, and develop internal representations that cluster by transformation operations such as recoloring, removal, or translation.
By Philipp Mondorf, Adrien Deli\`ege, Solim LeGris, Claas Beger, Ryan Yi, Barbara Plank, Brenden M. Lake, Todd M. Gureckis
The paper introduces a self-demonstration-driven method for mapping relational database schemas to ontologies, addressing challenges such as semantic heterogeneity and cryptic schema naming. It combines neuro-symbolic task decomposition with pattern-guided, dependency-aware demonstrations to improve LLM performance on schema-ontology mapping. Experiments on the RODI benchmark show state‑of‑the‑art results, outperforming existing methods by up to 25 percentage points in F1 score.
By Siddhesh Thombre, Manasi Patwardhan, Sunita Sarawagi
arXiv:2607. 20520v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on mathematical problem solving, yet prior work often treats representationally equivalent formulations as interchangeable and conflates reasoning errors with interface failures.
By Sagnik Nath, Edith Aurora Graf, Liang Zhang, Diego Zapata-Rivera
arXiv:2609.09233v1 Announce Type: cross
Abstract: How can language model agents effectively leverage libraries of reusable knowledge to solve long-horizon tasks? Recent work has increasingly focused...
By Wasu Top Piriyakulkij, Rachel Lawrence, Alicia Curth, Sushrut Karmalkar, Niranjani Prasad
The paper introduces SALR, a schema‑anchored latent reasoning approach for generating logical forms in knowledge‑base question answering. SALR delays explicit schema commitments by generating continuous thoughts in hidden states and aligns these thoughts with a codebook of KB schema elements, guided by an alignment objective derived from gold logical forms. Experiments on GrailQA and WebQSP demonstrate that SALR consistently outperforms strong baselines, notably improving compositional question performance by 2.86 F1 points over TIARA.
By Guangze Gao, Zixuan Li, Sikui Zhang, Chunfeng Yuan, Wenjuan Li, Bing Li, Xiaolong Jin, Weiming Hu
arXiv:2607. 19592v1 Announce Type: new Abstract: Self-improving AI systems typically treat the agent as the object that improves, by optimizing prompts, workflows, harnesses, or even the agent's own code.
By Xuefei Julie Wang, Lauren Hyoseo Yoon, Chengrui Qu, Amanda Zichang Wang, Atharva Sehgal, Eric Mazumdar, Yisong Yue
arXiv:2607. 15845v1 Announce Type: new Abstract: Workflow generation in visual creation systems such as ComfyUI demands not only syntactic accuracy but also expert-level reasoning over modular compositions.
By Zhendong Li, Lei Sun, Ruibo Ming, He Zhang, Danda Pani Paudel, Luc Van Gool, Jinjin Gu
arXiv:2604. 27960v2 Announce Type: replace Abstract: Recent large language models (LLMs) have achieved impressive reasoning milestones but continue to struggle with high computational costs, logical inconsistencies, and sharp performance degradation on high-complexity problems.
By Adam Ishay, Joohyung Lee
arXiv:2607. 15845v2 Announce Type: replace Abstract: Workflow generation in visual creation systems such as ComfyUI demands not only syntactic accuracy but also expert-level reasoning over modular compositions.
By Zhendong Li, Lei Sun, Ruibo Ming, He Zhang, Danda Pani Paudel, Luc Van Gool, Jinjin Gu