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The survey provides the first structured taxonomy of Multimodal Code Intelligence, connecting mature code-generation benchmarks to emerging agentic settings and identifying verification gaps that current text-to-code evaluations do not address.
Existing code-layer scanners miss between 89% and 100% of instruction-layer threats like Prompt Injection and Memory Poisoning in LLM agent skills, and SKILLVETBENCH's LLM-as-Judge approach closes that gap with zero false negatives across 78 confirmed-malicious skills in benchmark testing.
CoAgent replaces the abort-and-retry waste of OCC and the blocking delays of 2PL with an advisory protocol that lets LLM agents self-repair conflicts, achieving serializable correctness while preserving meaningful concurrency gains that classical mechanisms cannot sustain.
The findings show that agent+tool evaluations cannot assume the agent adds judgment on top of the tool — and that the gap between parrot behavior and optimal action widens, not shrinks, as LLM capability scales.
The pattern replaces fragile prose-based guardrails with tool-scoped enforcement and parallel clean contexts, directly addressing the context dilution and incorrect cross-repo edits that occur when a single agent session spans multiple repositories.
ALE's sub-25% pass rates across all leading models reveal a substantial gap between current AI capabilities and reliable real-world task performance across professional domains.
A new memory infrastructure layer in the agentic tooling space.
The conversation surfaces "east-west" data exfiltration as a concrete, named security risk that enterprise microservice architectures face specifically because of autonomous agents — a threat distinct from traditional perimeter-focused security models.
Termem allows different AI coding agents to share session history within a directory, removing the isolation that normally prevents one agent from seeing another's prior context.
CSTS addresses a core bottleneck in agentic LLM development by replacing manual skill engineering with an automated, multi-model collective process that explicitly tests whether skills transfer across models — a property the paper identifies as critical for robust generalization.