AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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9,568 papers
SMAT (Simple MAT) is introduced, which jointly optimizes expert loss and expected loss at simulated merged parameters generated by sampling scaling coefficients, masks, and additive noise, and improves the mean score across five merging methods.
CalibHyper is proposed, a chance-corrected relational hypergraph method based on the joint label distribution that achieves ROC-AUC competitive with the strongest reported results.
Training-free KV-cache compression methods such as SnapKV, H2O, and PyramidKV evict tokens at the end of prefill, aiming to preserve the attention mass that future queries are expected to use -optimizing what to keep. We show that this objective fails in two distinct ways. (1) Compensation: restoring the evicted attent...
On-Policy Attention Self-Distillation (OPASD), which complements token-level supervision with solution-conditioned attention distillation, shows that solution-conditioned attention provides a complementary supervision signal that makes on-policy self-distillation more accurate, stable, and compute-efficient.
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This work studies language-model synthesis of Clifford circuits, which prepare the stabilizer states used in quantum error correction and admit exact classical verification, in a target-conditioned framework.
This work studies interventions at four levels of group-policy RL pipelines---data, rollout, reward, and advantage---and conducts extensive RL training on saturated reasoning data only, demonstrating that don't waste your saturated data: with the right strategies they can be recycled into useful RL training signals in...
Tory of Scene (ToS), a training-free reasoning schema in which each agent reads its public role, the only difference between the agents, and the task context they all observe, is proposed, which outperforms all six baselines on every benchmark, and each baseline falls far behind it in at least one setting.
LLM post-training combines supervised fine-tuning (SFT), a mode-covering forward-KL objective, with reinforcement learning (RL), a mode-seeking reverse-KL objective, with reinforcement learning (RL), a mode-seeking reverse-KL objective to exploit anisotropy, a division of labor that post-training can exploit.
The memory usage and decoding latency of LLM inference grow rapidly with context length. To reduce these costs, key-value (KV) cache compression methods selectively retain cached states based on token importance or differences in attention patterns across heads. However, we discover that retrieval capability varies sub...
Large language models can generate several records within one autoregressive completion, making earlier answers available as context for later records. This paper shows that such shared-completion batching can distort relationships among variables in the resulting synthetic data, using controlled tests on synthetic sur...
HM-Router is introduced, a routing method that jointly selects a model and harness for each query, with an interaction term inspired by canonical polyadic tensor decomposition to capture how their compatibility varies with the query.
Safety auditing under compression should rely on several judges matched to the serving context rather than on keyword rates alone, because the gap is not universal and weakens under short fillers and paired SnapKV, so safety auditing should rely on several judges matched to the serving context rather than on keyword ra...
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Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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