The two main benchmarks for LLM instructed code editing over-represent Python, miss common real-world domains and edit types, and have test coverage issues that limit what they measure.
From code foundation models to agents and applications: A comprehensive survey and practical guide to code intelligence
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Coding agents struggle to infer least-privilege file permissions by omitting needed accesses while granting unused or sensitive ones, but Sufficiency-Tightness Decomposition improves sensitive-task success by up to 15.8% and reduces attacks.
RSCB-MC is a risk-sensitive contextual bandit memory controller for LLM coding agents that chooses safe actions including abstention, achieving 60.5% proxy success with 0% false positives and low latency in 200-case validation.
InCoder-32B-Thinking uses error-feedback synthesized thinking traces and a code world model to reach top open-source scores on general and industrial code benchmarks including 81.3% on LiveCodeBench and 84.0% on CAD-Coder.
ReCreate automatically creates and adapts domain LLM agents by turning past interaction experiences into scaffold edits via reasoning and hierarchical abstraction.
citing papers explorer
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Edit, But Verify: An Empirical Audit of Instructed Code-Editing Benchmarks
The two main benchmarks for LLM instructed code editing over-represent Python, miss common real-world domains and edit types, and have test coverage issues that limit what they measure.
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Do Coding Agents Understand Least-Privilege Authorization?
Coding agents struggle to infer least-privilege file permissions by omitting needed accesses while granting unused or sensitive ones, but Sufficiency-Tightness Decomposition improves sensitive-task success by up to 15.8% and reduces attacks.
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Learning When to Remember: Risk-Sensitive Contextual Bandits for Abstention-Aware Memory Retrieval in LLM-Based Coding Agents
RSCB-MC is a risk-sensitive contextual bandit memory controller for LLM coding agents that chooses safe actions including abstention, achieving 60.5% proxy success with 0% false positives and low latency in 200-case validation.
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InCoder-32B-Thinking: Industrial Code World Model for Thinking
InCoder-32B-Thinking uses error-feedback synthesized thinking traces and a code world model to reach top open-source scores on general and industrial code benchmarks including 81.3% on LiveCodeBench and 84.0% on CAD-Coder.
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ReCreate: Reasoning and Creating Domain Agents Driven by Experience
ReCreate automatically creates and adapts domain LLM agents by turning past interaction experiences into scaffold edits via reasoning and hierarchical abstraction.