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Large language models (LLMs) may generate unreliable code on corner cases missed by testing, while formal verification can provide machine-checkable guarantees. Recently, researchers have proposed several benchmarks to evaluate the capabilities of LLMs in generating formally verifiable code, where LLMs need to formulat...
Multi-agent coding systems are designed to tackle complex software engineering tasks through collaboration. However, existing evaluations typically assume configured organizations are followed faithfully, whereas reality differs. This behavioral gap, combined with differences in underlying system components, prevents c...
This work proposes C3, a Controllable Competitive Collaboration framework featuring two novel mechanisms: Centralized Auction-Based Competition (CAB) for role allocation via bidding and Decentralized Communication-Aware Competition (DCC) for solution refinement through opponent-aware adaptation.
The proposed PSC, a novel grammar-constrained decoding method, can compute the complete vocabulary mask by checking the parser stack exactly once per decoding step, with time complexity independent of the vocabulary size.
An empirical study of router-side injection in coding agents, examining four intervention levels of increasing subtlety and developing SIDEL, a framework for trace recording, replay, injection, and defense evaluation, which is evaluated four representative coding agents, and further evaluates whitelist-based execution...
Adaptive Reuse of Cached Hidden States for Efficient Rollback (Archer) is introduced, a training-free KV caching method for rollback-capable DLMs that characterizes prompt reuse as a reversibility-aligned cache boundary, bounds its state-dependent approximation error, and gives a decoder-margin condition for preserving...
PRAXIS is a framework that enables agents to systematically extract, represent, and reuse tacit knowledge for domain code generation and outperforms state-of-the-art agents equipped with powerful agentic search capabilities, as well as experience-based and skill-based methods.
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