Multi-agent AI system formalizes entire 500-page graduate algebraic combinatorics textbook into Lean, creating 130K lines of code in one week at human-expert cost.
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Veritas detects memory corruption vulnerabilities in stripped binaries by combining static value-flow slicing, dual-view LLM reasoning, and multi-agent runtime validation, reporting 90% recall, zero false positives on 623 exhaustive cases, and discovery of a real Apple CVE.
DPC selects correct text-to-SQL outputs by enforcing execution consistency between SQL and Python on an adversarially constructed minimal distinguishing database.
R&B-EnCoRe uses self-supervised importance-weighted variational inference to distill action-predictive reasoning datasets that improve VLA performance on manipulation, navigation, and driving tasks without external verifiers.
Talk-to-Your-Slides uses language-driven structured data manipulation with a hierarchical architecture to edit slides, reporting 34% faster processing, 34% better instruction fidelity, and 87% lower cost than GUI-based baselines on text-centric tasks while releasing the TSBench benchmark.
citing papers explorer
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Automatic Textbook Formalization
Multi-agent AI system formalizes entire 500-page graduate algebraic combinatorics textbook into Lean, creating 130K lines of code in one week at human-expert cost.
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Veritas: A Semantically Grounded Agentic Framework for Memory Corruption Vulnerability Detection in Binaries
Veritas detects memory corruption vulnerabilities in stripped binaries by combining static value-flow slicing, dual-view LLM reasoning, and multi-agent runtime validation, reporting 90% recall, zero false positives on 623 exhaustive cases, and discovery of a real Apple CVE.
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DPC: Training-Free Text-to-SQL Candidate Selection via Dual-Paradigm Consistency
DPC selects correct text-to-SQL outputs by enforcing execution consistency between SQL and Python on an adversarially constructed minimal distinguishing database.
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Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning
R&B-EnCoRe uses self-supervised importance-weighted variational inference to distill action-predictive reasoning datasets that improve VLA performance on manipulation, navigation, and driving tasks without external verifiers.
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Talk to Your Slides: High-Efficiency Slide Editing via Language-Driven Structured Data Manipulation
Talk-to-Your-Slides uses language-driven structured data manipulation with a hierarchical architecture to edit slides, reporting 34% faster processing, 34% better instruction fidelity, and 87% lower cost than GUI-based baselines on text-centric tasks while releasing the TSBench benchmark.