Heterogeneous agents achieve dense latent KV-cache communication via lightweight cross-model transformation and two-phase training, outperforming text at lower compute in context-aware settings and enabling context-unaware transfer.
arXiv preprint arXiv:2509.21164 , year=
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Persona-based Mixture-of-Thought data curation lets a 7B student outperform larger models on humor generation, while DPO and O-GRPO add no gain over SFT.
Early entropy dynamics during LLM decoding mark when explicit reasoning becomes beneficial, enabling the training-free EDRM router that selects strategies per instance and yields 41-55% token savings with accuracy gains across 15 benchmarks.
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See What I See, Know What I Think: Dense Latent Communication Across Heterogeneous Agents
Heterogeneous agents achieve dense latent KV-cache communication via lightweight cross-model transformation and two-phase training, outperforming text at lower compute in context-aware settings and enabling context-unaware transfer.
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HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillation
Persona-based Mixture-of-Thought data curation lets a 7B student outperform larger models on humor generation, while DPO and O-GRPO add no gain over SFT.
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When Do LLMs Reason? A Dynamical Systems View via Entropy Phase Transitions
Early entropy dynamics during LLM decoding mark when explicit reasoning becomes beneficial, enabling the training-free EDRM router that selects strategies per instance and yields 41-55% token savings with accuracy gains across 15 benchmarks.
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