STRIDE injects distilled LLM reasoning as continuous cross-modal priors into TSFMs via mean-pooled hidden states, achieving SOTA forecasting (0.674 MASE, 0.454 CRPS) on GIFT-Eval and superior reasoning on TFRBench.
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SCURank ranks multiple summary candidates with Summary Content Units to outperform ROUGE and LLM-based methods in summarization distillation.
Skill-SD turns an agent's completed trajectories into dynamic natural-language skills that condition only the teacher in self-distillation, yielding 14-42% gains over RL and OPSD baselines on multi-turn agent benchmarks.
OmniThoughtVis curates 1.8M multimodal CoT samples via teacher distillation, difficulty annotation, and tag-based sampling, yielding consistent gains on nine reasoning benchmarks and allowing 4B models to match or beat undistilled 8B baselines.
A neuro-symbolic system converts legal clauses into deterministic typed graphs for consistent, auditable adjudication that cuts compute costs by over 90% versus direct large reasoning model use.
citing papers explorer
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Reasoning-Aware Training for Time Series Forecasting
STRIDE injects distilled LLM reasoning as continuous cross-modal priors into TSFMs via mean-pooled hidden states, achieving SOTA forecasting (0.674 MASE, 0.454 CRPS) on GIFT-Eval and superior reasoning on TFRBench.
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SCURank: Ranking Multiple Candidate Summaries with Summary Content Units for Enhanced Summarization
SCURank ranks multiple summary candidates with Summary Content Units to outperform ROUGE and LLM-based methods in summarization distillation.
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Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents
Skill-SD turns an agent's completed trajectories into dynamic natural-language skills that condition only the teacher in self-distillation, yielding 14-42% gains over RL and OPSD baselines on multi-turn agent benchmarks.
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OmniThoughtVis: A Scalable Distillation Pipeline for Deployable Multimodal Reasoning Models
OmniThoughtVis curates 1.8M multimodal CoT samples via teacher distillation, difficulty annotation, and tag-based sampling, yielding consistent gains on nine reasoning benchmarks and allowing 4B models to match or beat undistilled 8B baselines.
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Accurate Legal Reasoning at Scale: Neuro-Symbolic Offloading and Structural Auditability for Robust Legal Adjudication
A neuro-symbolic system converts legal clauses into deterministic typed graphs for consistent, auditable adjudication that cuts compute costs by over 90% versus direct large reasoning model use.