SkCC compiles LLM skills via SkIR to achieve portability across agent frameworks, reduce adaptation effort from O(m×n) to O(m+n), and enforce security with reported gains in task success rates and token efficiency.
Mahoney, Kurt Keutzer, and Amir Gholami
6 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
Slipstream uses asynchronous compaction with trajectory-grounded judge validation to improve long-horizon agent accuracy by up to 8.8 percentage points and reduce latency by up to 39.7%.
PlanCompiler uses a typed node registry, static validation, and deterministic compilation to reach 278/300 successes on structured LLM pipeline benchmarks, outperforming GPT-4.1 and Claude Sonnet baselines at lower cost.
Tree Training serializes tree trajectories via DFS and uses redundancy-free partitioning to compute weighted per-token losses exactly once per token, achieving up to 6.2x training speedup on dense and MoE models.
SGLang is a new system that speeds up structured LLM programs by up to 6.4x using RadixAttention for KV cache reuse and compressed finite state machines for output decoding.
Plan-and-Act trains a dedicated Planner on synthetic plan-annotated trajectories to generate high-level plans that an Executor follows, reaching 57.58% success on WebArena-Lite and 81.36% on WebVoyager.
citing papers explorer
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SkCC: Portable and Secure Skill Compilation for Cross-Framework LLM Agents
SkCC compiles LLM skills via SkIR to achieve portability across agent frameworks, reduce adaptation effort from O(m×n) to O(m+n), and enforce security with reported gains in task success rates and token efficiency.
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Slipstream: Trajectory-Grounded Compaction Validation for Long-Horizon Agents
Slipstream uses asynchronous compaction with trajectory-grounded judge validation to improve long-horizon agent accuracy by up to 8.8 percentage points and reduce latency by up to 39.7%.
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PlanCompiler: A Deterministic Compilation Architecture for Structured Multi-Step LLM Pipelines
PlanCompiler uses a typed node registry, static validation, and deterministic compilation to reach 278/300 successes on structured LLM pipeline benchmarks, outperforming GPT-4.1 and Claude Sonnet baselines at lower cost.
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Tree Training: Accelerating Agentic LLMs Training via Shared Prefix Reuse
Tree Training serializes tree trajectories via DFS and uses redundancy-free partitioning to compute weighted per-token losses exactly once per token, achieving up to 6.2x training speedup on dense and MoE models.
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SGLang: Efficient Execution of Structured Language Model Programs
SGLang is a new system that speeds up structured LLM programs by up to 6.4x using RadixAttention for KV cache reuse and compressed finite state machines for output decoding.
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Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
Plan-and-Act trains a dedicated Planner on synthetic plan-annotated trajectories to generate high-level plans that an Executor follows, reaching 57.58% success on WebArena-Lite and 81.36% on WebVoyager.