AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 7 of 55 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
As coding agents take on long-horizon software evolution tasks spanning multiple files and stages, longer execution trajectories introduce two coupled challenges: (1) accumulated histories strain context budgets, and (2) repository changes can invalidate earlier execution evidence. Existing approaches address these cha...
OBJECTIVE To delineate the prognostic architecture of primary Sjögren's disease (pSjD) through an integrated analysis of clinical-immunologic clusters and anti-RNP/anti-Ro52 antibody profiles. METHODS This ambispective cohort study enrolled 1293 patients with pSjD from China-Japan Friendship Hospital (2013-2023). Uns...
TRACE is presented, a training-free layer that treats re-entry as an eligibility decision rather than a storage or retrieval operation, reconciling a departure checkpoint against absence-period updates, resolving explicit and implicit invalidation, and releasing a bounded Return View only when it covers the returning r...
Traditional search engines struggle to synthesize fragmented information for complex queries, while generative AI search engines face challenges in relevance, comprehensiveness, and presentation. To address these limitations, we introduce Xinyu AI Search, a novel system that incorporates a query-decomposition graph to...
This paper proposes MSCE, a training-free Memory--Skill Co-Evolution framework that organizes agent experience into grounded step traces, reusable procedural policies, and declarative environmental cognition, and introduces reflection-weighted value backfilling.
Memory-Aware Propagation and Link Enforcement Guard, MAPLE-Guard, a memory-link guard for memory-enabled MAS, suggests that memory-aware link enforcement covers a gap left by prompt-level and topology-level defenses.
To measure evolved-state generation, LongEvoRoleBench is introduced, which pairs four long-dialogue corpora for cross-episode evolution with four short-dialogue corpora as within-scene state-tracking checks, under a unified next-utterance protocol.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.