AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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9,568 papers
This work introduces MemeCF, a cue-focused benchmark of 9,895 memes across harm, hate, and sarcasm, with annotations identifying the modality and rationale of the pivotal evidence.
Reordering a set of mathematical rules without changing its meaning should preserve the correct answer, but must a model's internal representations stay invariant too? We investigate this question using synthetic multi-step function-composition problems, each presented under multiple rule orderings with the same correc...
This work extends balanced-tree recursive operators from sequence encoding to autoregressive prediction, enabling all prefix representations to be computed with logarithmic depth and linear runtime.
Mixture-of-Experts (MoE) training requires global load balance to prevent expert under-utilization and local balance for efficient expert-parallel execution. Existing distributed Quantile Balancing (QB) uses shard-dependent or approximate global quantiles, while token-independent expert biases cannot ensure microbatch-...
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Solutions based on large language models (LLMs) often rely on temperature sampling to improve accuracy and stability by aggregating multiple samples from the completion distribution. However, this memoryless approach is inherently suboptimal: because it lacks awareness of prior generations and their evaluations, it pro...
Direct transfer of Step Law systematically overestimates the optimal learning rate: the median ratio eta_SL / eta* is approximately 4.0x, with a range of 2.4x to 6.6x.
Long-horizon agentic tasks require an agent to modify an environment through a sequence of tool calls, with success determined by the final state. The standard recipe assigns a single outcome reward at the end and compares trajectories sampled for the same task. As a result, a group with no successful trajectory yields...
It is argued that the Linear Representation Hypothesis is not one hypothesis but a family of claims distinguished by representation equivalence, and this framework clarifies how assumptions can change across metrics, reading points, and analysis stages, and is used to audit common representation quantities and recent i...
ChipMEM, a verification-grounded memory layer for EDA agents, is introduced, a verification-grounded memory layer for EDA agents that combines cross-task procedural memory with within-trajectory statistical guidance.
This paper proposes CONCAT, a training-free multi-agent collaboration framework based on CONsensus and Confidence-driven Ad hoc Teaming to efficiently organize agent interactions and achieves up to 2.02x higher efficiency than LLM-Debate and outperforms training-aware methods such as AgentDropout.
Large language models (LLMs) are increasingly being used as ad hoc therapists. While prior research has found that LLMs outperform human counselors in generating single-turn empathetic responses, fewer studies have compared their behaviors across multi-turn sessions. In this study, we compare the session-level behavior...
This paper proposes methods to extract over 50 types of events from a Dutch historical corpus spanning the 17th and 18th centuries, and proposes creating expert classifiers for subgroups of the events present in the training data based on similar frequency in the training data or on semantic relatedness.
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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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