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Obsidian is not a note-taking app but a context layer for AI reasoning systems because it stores notes as plain text markdown files that AI can directly read without friction. The article outlines three builds to transform Obsidian into a reasoning substrate, including a CLAUDE.md file to prime AI with personal thinking patterns.
This paper introduces the Valid-Answer-Invalid-Reasoning (VAIR) benchmark to expose the production-evaluation gap in AI reasoning models, where models can generate correct answers but fail to detect flawed reasoning, revealing answer confirmation bias.
A health tracking service that reads signals like longevity, heart rate, sleep, and recovery against user baseline is now open to all, with auditable reasoning steps on Hugging Face and privacy protection via OpenMed_AI PII models.
This paper introduces UPBench, a benchmark to evaluate large language models on urban planning knowledge across four knowledge pillars and five cognitive levels, finding that models perform better on higher-order analysis than factual recall, and identifying epistemic limitations such as regulatory hallucination and phronetic deficit.
This paper introduces EngVQA, a multimodal benchmark for evaluating engineering reasoning in vision-language models, along with an 8-stage automatic evaluation framework that enables fine-grained analysis of reasoning failures. It reveals substantial limitations in current VLMs' engineering reasoning capabilities.
BiNSGPS is a framework that introduces bidirectional interaction between a multimodal LLM adviser and a symbolic solver for geometry problem solving, allowing feedback from the solver to correct errors and generate auxiliary hypotheses. It achieves state-of-the-art performance of 90.5% on Geometry3K and 90.1% on PGPS9K benchmarks.
Introduces context-dependent argumentation frameworks (CDAFs) that model how an agent can strategically influence which attacks succeed by choosing a context, enabling manipulation scenarios not possible in value-based argumentation. Defines the ACTIVATION-MANIPULATION decision problem and provides baseline complexity bounds.
A critical take on the scaling argument for AI reasoning, arguing that autoregressive LLMs cannot achieve correctness through more compute alone, and highlighting alternative architectures like EBMs and formal verification as superior for critical applications.
Aleph, a new formal reasoning AI system, leads major benchmarks, validating Yann LeCun's emphasis on Energy-Based Models for AI reasoning.
This paper introduces a quantitative notion of diversity of extensions in abstract argumentation based on symmetric difference, and provides a systematic complexity classification for related reasoning tasks.
GPT-5.5 was used by Epoch to identify fatal errors in approximately one-third of the FrontierMath benchmark problems, demonstrating the model's capability to sanity-check evaluation standards.
Tim Gowers reports using ChatGPT 5.5 Pro to attempt to solve open mathematical problems posed by Melvyn Nathanson.
This paper presents a full-pipeline recipe for teaching thinking models to reason with tools, achieving state-of-the-art performance on benchmarks like AIME 2025 when applied to Qwen3 models.
The article argues that effective AI agents require restraint and explicit 'stop conditions' rather than endless autonomy, highlighting Ling-2.6-1T as a model suited for conservative planning roles.
MIT CSAIL researchers introduce RLCR, a method using Brier scores in reinforcement learning to train AI models to output calibrated confidence estimates, significantly reducing overconfidence without sacrificing accuracy.
OpenAI submitted proof attempts for the First Proof challenge, a research-level math competition testing whether AI can produce correct, checkable proofs. The company's internal model successfully solved at least five of the ten problems, demonstrating significant progress in sustained reasoning and rigorous mathematical thinking.
Gemini 2.5 Deep Think achieved gold-medal level performance at the 2025 International Collegiate Programming Contest World Finals, solving 10 of 12 problems in the five-hour competition, demonstrating significant advances in abstract reasoning and problem-solving capabilities.
Google is rolling out Deep Think, a new reasoning capability in the Gemini app for Google AI Ultra subscribers, featuring parallel thinking techniques and achieving bronze-level performance on the 2025 IMO benchmark. The full gold-medal version is being shared with select mathematicians for research purposes.