·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
AI is more likely than humans to form biases when hiring43m◆Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal5h◆Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?5h◆Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes5h◆From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems5h◆The AI Fiction Paradox5h◆Decoupled Alignment for Robust Plug-and-Play Adaptation5h◆Hybrid coupling with operator inference and the overlapping Schwarz alternating method5h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents5h◆EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections5h◆SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents5h◆Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery5h◆How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI5h◆PolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment5h◆ADS-C: Antidistillation Sampling for Classification5h◆Do Generative Models Keep Time? A Time-Aware Evaluation of Synthetic Sequential Tabular Data5h◆QUADS: Stabilizing NVFP4 Reinforcement Learning for MoE via QUantization-error Alignment across Dual Sides5h◆Data-Native Global Optimization for Big Data K-means Clustering5h◆A Semiparametric Framework for Stochastic Fundamental Diagram Modeling5h◆Constrained Hebbian Learning Supports Efficient Representational Allocation under Structural Constraints5h◆AI is more likely than humans to form biases when hiring43m◆Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal5h◆Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?5h◆Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes5h◆From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems5h◆The AI Fiction Paradox5h◆Decoupled Alignment for Robust Plug-and-Play Adaptation5h◆Hybrid coupling with operator inference and the overlapping Schwarz alternating method5h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents5h◆EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections5h◆SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents5h◆Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery5h◆How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI5h◆PolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment5h◆ADS-C: Antidistillation Sampling for Classification5h◆Do Generative Models Keep Time? A Time-Aware Evaluation of Synthetic Sequential Tabular Data5h◆QUADS: Stabilizing NVFP4 Reinforcement Learning for MoE via QUantization-error Alignment across Dual Sides5h◆Data-Native Global Optimization for Big Data K-means Clustering5h◆A Semiparametric Framework for Stochastic Fundamental Diagram Modeling5h◆Constrained Hebbian Learning Supports Efficient Representational Allocation under Structural Constraints5h◆
News/AIMO Interpretability Challenge
arxiv
PublishedJuly 16, 2026 at 4:00 AM
—neutral

AIMO Interpretability Challenge

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.13899v1 Announce Type: new Abstract: We propose the AIMO Interpretability Challenge, a competition on distinguishing robust from spurious reasoning in frontier mathematical language models based on the models' internal mechanisms. The challenge is motivated by a central limitation of stan

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivBeyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal5harxivDo Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?5harxivBeyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes5harxivFrom Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems5h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews