PropGuard is a propagation-aware framework for LLM-MAS that constructs dual-view spatio-temporal graphs, employs a GE-GRPO inspector to recover suspicious subgraphs, and applies source-guided remediation to lower attack success while preserving task performance.
Codecor: An llm-based self-reflective multi-agent framework for code generation
8 Pith papers cite this work. Polarity classification is still indexing.
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ARIADNE combines blackboard architecture with MCTS to coordinate strategy, code, test, evaluation, and repair stages, yielding higher Pass@1 scores than prior LLM baselines on APPS, CodeContests, and related benchmarks.
EnvGraph improves executable repository-level code generation by jointly modeling external dependencies and internal references through a dual-layer environment representation and targeted iterative alignment.
AutoPass uses evidence from compiler states and runtime feedback to guide LLM agents in tuning LLVM optimizations, delivering 1.043x and 1.117x geometric-mean speedups over -O3 on x86-64 and ARM64.
FASE approximates functional correctness via MST on structural and semantic dissimilarity graphs, reporting 25% better Spearman correlation and 19% better ROCAUC than LLM-based semantic entropy at 0.3% runtime cost on HumanEval and BigCodeBench.
TopOptAgents deploys six LLM agents in self-refining loops to automate the full topology optimization workflow and succeeds on problem classes where single LLMs fail.
A survey that organizes existing work on LLM-based agents around code as the central harness, structured in three layers of interfaces, mechanisms, and multi-agent scaling, with applications across domains and listed open challenges.
A comprehensive review of self-evolving AI agents that improve themselves over time, organized via a framework of inputs, agent system, environment, and optimizers, with domain-specific and safety discussions.
citing papers explorer
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PropGuard: Safeguarding LLM-MAS via Propagation-Aware Exploration and Remediation
PropGuard is a propagation-aware framework for LLM-MAS that constructs dual-view spatio-temporal graphs, employs a GE-GRPO inspector to recover suspicious subgraphs, and applies source-guided remediation to lower attack success while preserving task performance.
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ARIADNE: Agentic Reward-Informed Adaptive Decision Exploration via Blackboard-Driven MCTS for Competitive Program Generation
ARIADNE combines blackboard architecture with MCTS to coordinate strategy, code, test, evaluation, and repair stages, yielding higher Pass@1 scores than prior LLM baselines on APPS, CodeContests, and related benchmarks.
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Toward Executable Repository-Level Code Generation via Environment Alignment
EnvGraph improves executable repository-level code generation by jointly modeling external dependencies and internal references through a dual-layer environment representation and targeted iterative alignment.
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AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning
AutoPass uses evidence from compiler states and runtime feedback to guide LLM agents in tuning LLVM optimizations, delivering 1.043x and 1.117x geometric-mean speedups over -O3 on x86-64 and ARM64.
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FASE: Fast Adaptive Semantic Entropy for Code Quality
FASE approximates functional correctness via MST on structural and semantic dissimilarity graphs, reporting 25% better Spearman correlation and 19% better ROCAUC than LLM-based semantic entropy at 0.3% runtime cost on HumanEval and BigCodeBench.
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Self-Refining Topology Optimization via an LLM-Based Multi-Agent Framework
TopOptAgents deploys six LLM agents in self-refining loops to automate the full topology optimization workflow and succeeds on problem classes where single LLMs fail.
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Code as Agent Harness
A survey that organizes existing work on LLM-based agents around code as the central harness, structured in three layers of interfaces, mechanisms, and multi-agent scaling, with applications across domains and listed open challenges.
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A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
A comprehensive review of self-evolving AI agents that improve themselves over time, organized via a framework of inputs, agent system, environment, and optimizers, with domain-specific and safety discussions.