arXiv:2606. 19364v1 Announce Type: new Abstract: The prefill stage of Large Language Model (LLM) inference is a growing contributor to cloud-scale energy cost.
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arXiv:2608. 13076v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands.
arXiv:2609.13154v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have made prompts increasingly large and complex. Techniques such as chain-of-thought reasoning (Wei et...
arXiv:2606. 24083v1 Announce Type: cross Abstract: "Talk short.
arXiv:2607. 23915v1 Announce Type: cross Abstract: We examine how prompt tone affects both accuracy of the LLM answers and inference cost as reflected in output-token consumption.
ParaBridge is a self‑distillation method that uses a temporary paralinguistic instruction scaffold during training to teach a speech language model when non‑lexical cues should influence dialogue responses. By providing dense, full‑vocabulary next‑token targets from the scaffolded view while the scaffold‑free model generates its own replies, ParaBridge stabilizes inference‑time behavior without requiring curated dialogues or external reward models. Experiments on Qwen3‑Omni show significant gains on safety and empathy benchmarks while preserving general performance across multiple tests.
The paper evaluates three approaches for emotion recognition in conversation— a low‑cost stacked ensemble, an off‑the‑shelf LLM prompt, and a confidence‑gated hybrid that escalates only uncertain ensemble predictions to the LLM. Across three datasets (IEMOCAP, MELD, CMU‑MOSI), the hybrid consistently outperforms each pure system, achieving higher weighted F1 scores while routing most traffic through the inexpensive ensemble. This results in significant cost savings (≈$10‑85 per million utterances) and provides an interpretable escalation signal tied to emotion or sentiment shifts.