8 papers
Beyond Factual Knowledge: Benchmarking and Learning Step-Level Procedural Rule Reasoning in Large Language Models
Bohan Yu, Pengfei Cao, Chen Han +7
Large language models (LLMs) excel at text understanding and generation, yet still struggle to reliably understand and apply externally provided procedural rules at scale. To evalu…
Quantization Degradation in Large Language Models: A Signal-Noise Perspective
Chenxi Zhou, Pengfei Cao, Jinyu Ye +5
Post-training quantization reduces the deployment cost of large language models, yet how severely a quantized model degrades is not determined by bit-width alone. We systematically…
LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis
Chenhao Yuan, Yinhao Xu, Shuwen Xu +8
Synthesizing long-context supervised fine-tuning (SFT) data is a scalable way to enhance the long-context understanding of large language models (LLMs), yet existing approaches sha…
DecoEvo: Score-Decoupled Co-Evolution of Solver and Rubric-Generator Skills in Text Space
Jiangwang Chen, Zixin Song, Junlin Liu +10
Text-space optimization adapts large language models (LLMs) by editing external natural-language artifacts rather than model weights, so the optimized artifacts remain inspectable…
From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search
Junlin Liu, Jiangwang Chen, Zixin Song +7
Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this with outcome-based reinforc…
PReM: Learning What to Preserve and When to Refresh for Context Compression
Bohan Yu, Lei Shen, Chenxi Zhou +5
Efficient long-context inference is not only about reducing memory cost, but also about keeping useful contextual evidence accessible as generation proceeds. However, existing comp…