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.
Jenga: Effective memory management for serving LLM with heterogeneity
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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.
FM-Agent is the first framework to automate compositional Hoare reasoning for large systems by having LLMs derive natural-language function specs from caller intent and then generate tests that found 522 new bugs in systems up to 143k lines of code.
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.
citing papers explorer
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TIDAL: Recovering Temporal Phase for Cloud Block Storage Placement from LLM-Derived Semantics
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.
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Not All Tokens Are Worth Caching: Learning Semantic-Aware Eviction for LLM Prefix Caches
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.
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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.
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FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning
FM-Agent is the first framework to automate compositional Hoare reasoning for large systems by having LLMs derive natural-language function specs from caller intent and then generate tests that found 522 new bugs in systems up to 143k lines of code.
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AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection
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.
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Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap
A research roadmap analyzing the current state of search-based software engineering with foundation models, outlining challenges and directions across three integration aspects.