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9 storiesInnovation
Paper reports compiler-guided search improves Lean theorem-proving efficiency
An arXiv preprint proposes an adaptive proof-search method for context-dependent Lean 4 projects. Its authors report a 12.8-percentage-point average pass-rate improvement within a pass@32 budget while using 21.9% fewer LLM calls than pass@k baselines.arxiv.orgInnovation
Benchmark Separates Geometry Solving From Diagram Construction
A new open-source benchmark tests whether foundation models can construct faithful geometry diagrams, not merely solve the underlying problems. Its authors report that evaluated models achieved an average compile success rate of 36.14%, exposing a gap between mathematical answers and usable visual constructions.arxiv.orgInnovation
Alignment Is All You Need: Instruction-Free Training for General Audio-Language Models
A new approach to training multimodal large language models (MLLMs) eliminates the need for extensive task-specific supervision.arxiv.orgSecurity
Are LLMs Safe Beyond Text: Do Emojis Expose Gaps in Safety Evaluation
This work examines emoji-augmented prompts as a test case for gaps in safety evaluation of large language models (LLMs).arxiv.orgEnterprise
Bridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement
Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities for proactive customer re-engagement.arxiv.orgInnovation
New benchmark shows Vietnam’s exam rubric can change how language models rank
A paper introduces THPT-Ladder, a 632-item benchmark that applies Vietnam’s 2025 national exam grading scheme to language models and reports materially different scores from standard proportional-accuracy measures.arxiv.orgInnovation
Netflix paper outlines a lifecycle for LLM judges evaluating recommendation explanations
An arXiv paper describes how Netflix built, deployed and continuously monitored an LLM judge for recommendation explanations, reporting viewing and engagement gains in a five-week A/B test involving tens of millions of members.arxiv.orgInnovation
Survey frames self-evolving AI agents as dynamic graph transformations
A new arXiv survey proposes viewing an AI agent’s memories, tools, skills, workflows and relationships as a graph that changes over time, and calls for graph-aware evaluation and governance.arxiv.orgInnovation
FACET proposes an environment-grounded method for training terminal agents
A new arXiv preprint presents FACET, a framework for generating executable terminal tasks whose instructions, environments, solutions and verifiers are designed to remain consistent.arxiv.org
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