Heimdall automates translation of eBPF C programs to Rust with formal equivalence proofs for 94.1% of 102 tested programs using LLMs, static analysis, and Z3-based checking.
Jenga: Effective memory management for serving LLM with heterogeneity
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A perceptron model trained on kernel data and run via eBPF in the Linux page cache outperforms FIFO by up to 10% in insertion rate on some workloads with low overhead.
TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
SAECache uses a multi-queue semantic-aware eviction policy with fully adaptive online learning to improve TTFT by 1.4x-2.7x over LRU-style baselines in LLM prefix caching.
BatchWeave delivers an object-store-native data plane for distributed large foundation model training via transactional global batches and a decentralized adaptive commit algorithm.
PromptAudit evaluates five prompting strategies across five LLMs on 1000 CVEs and finds chain-of-thought prompting yields the strongest overall performance while adaptive chain-of-thought and self-consistency reduce effective results.
AnyPoC introduces a multi-agent system for generating and validating PoC tests from LLM bug reports, producing 1.3x more valid PoCs, rejecting 9.8x more false positives, and discovering 122 new bugs across 12 major projects.
A research roadmap analyzing the current state of search-based software engineering with foundation models, outlining challenges and directions across three integration aspects.
Empirical tests show open-source LLM agents underperform the Bandit SAST tool and are not ready to replace it for security scanning.
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BatchWeave: A Consistent Object-Store-Native Data Plane for Large Foundation Model Training
BatchWeave delivers an object-store-native data plane for distributed large foundation model training via transactional global batches and a decentralized adaptive commit algorithm.