ProcCtrlBench introduces an ontology of 11 defect types across 4 categories plus control preservation metrics to evaluate LLM coding agent trajectories on 200 cases from AndroidBench, TerminalBench, and SWE-bench-Verified.
ProcBench: Benchmark for multi-step reasoning and following procedure.ArXiv preprint, abs/2410.03117, 2024
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UNVERDICTED 3representative citing papers
Continual training recipe upcycles dense Qwen2.5-8B LLM to 4x channel-sparse model via predictor-gated bank-wise sparsity in SwiGLU FFN with a single-layer repair for long-context failure on RULER-CWE.
Strict generation directly from Task-Method-Knowledge models yields 96.5% grounded and 92.6% usable QA pairs across 23 topics, outperforming transcript-first and TMK-aware alternatives on representational grounding.
citing papers explorer
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ProcCtrlBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agents
ProcCtrlBench introduces an ontology of 11 defect types across 4 categories plus control preservation metrics to evaluate LLM coding agent trajectories on 200 cases from AndroidBench, TerminalBench, and SWE-bench-Verified.
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Continual LLM Upcycling: A Predictor-Gated Bank-Wise Sparsity Training Recipe for Dense-to-Sparse LLMs
Continual training recipe upcycles dense Qwen2.5-8B LLM to 4x channel-sparse model via predictor-gated bank-wise sparsity in SwiGLU FFN with a single-layer repair for long-context failure on RULER-CWE.
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Constructing Evaluation Datasets for Procedural Reasoning: Balancing Naturalness, Grounding, and Multi-Hop Coverage
Strict generation directly from Task-Method-Knowledge models yields 96.5% grounded and 92.6% usable QA pairs across 23 topics, outperforming transcript-first and TMK-aware alternatives on representational grounding.