KrishokChat is a citation-verified Bengali agricultural instruction dataset with 145,500 QA pairs from 290 knowledge nodes and a real-world 1,001-query Farmer Benchmark.
Deepseek- v4: Towards highly efficient million-token context intelligence.arXiv preprint arXiv:2606.19348, 2026
4 Pith papers cite this work. Polarity classification is still indexing.
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DOPD is an advantage-aware dual distillation method that dynamically assigns token supervision from either privileged teacher or student to transfer capability while mitigating non-replicable information asymmetry in on-policy distillation.
DRIFT is an online self-evolution policy optimization framework using Difficulty Routing, Rhythm Gating, success buffers, and two-stage curriculum learning that reports new SOTA results on five reasoning benchmarks.
HyperDFlash improves speculative decoding for hyper-connection LLMs via pre-collapse residual conditioning and a lightweight gated reducer from the target hc_head, outperforming MTP and DFlash in draft acceptance and speedup.
citing papers explorer
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KrishokChat: A Citation-Grounded Dataset and Benchmark for Bengali Agricultural Advisory
KrishokChat is a citation-verified Bengali agricultural instruction dataset with 145,500 QA pairs from 290 knowledge nodes and a real-world 1,001-query Farmer Benchmark.
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DOPD: Dual On-policy Distillation
DOPD is an advantage-aware dual distillation method that dynamically assigns token supervision from either privileged teacher or student to transfer capability while mitigating non-replicable information asymmetry in on-policy distillation.
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DRIFT: Difficulty Routing Self-DIstillation with Rhythm-Gated Exploration and Success BuFfer Training
DRIFT is an online self-evolution policy optimization framework using Difficulty Routing, Rhythm Gating, success buffers, and two-stage curriculum learning that reports new SOTA results on five reasoning benchmarks.
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HyperDFlash: Hyper-Connection-Aligned Block Speculative Decoding with Gated Residual Reduction
HyperDFlash improves speculative decoding for hyper-connection LLMs via pre-collapse residual conditioning and a lightweight gated reducer from the target hc_head, outperforming MTP and DFlash in draft acceptance and speedup.