Continuous language diffusion works by entering high-margin decoder basins where frozen T5 embeddings recover 93-96% of native decisions and linear readouts reach 97.9% agreement, implying models should be evaluated as representation-decoder systems.
T5gemma 2: Seeing, reading, and understanding longer
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2026 9representative citing papers
KaLM-Reranker-V1 uses encoder–decoder FBNL with Matryoshka pooling to match Qwen3-class reranking quality at substantially lower online cost.
Introduces V-RAGBench benchmark and CARVE method that selects per-chunk retrieval configurations via parallel retrievers and adaptive reranking, outperforming eight VideoRAG baselines.
BoostLLM trains sequential PEFT adapters in a boosting framework with tree path inputs to improve LLM performance on few-shot tabular classification, matching or exceeding XGBoost.
Attention-based models can retrieve evidence intrinsically by using decoder attention to score and reuse their own pre-encoded chunks, outperforming separate retrieval pipelines on QA benchmarks.
LinguDistill recovers approximately 10% of lost performance on language benchmarks in VLMs by selectively distilling from a frozen LM teacher using KV-cache sharing, while preserving vision performance.
With matched data and sizes, CTC+LLM shallow fusion beats tight speech-LLM integration on in-domain ASR, while prefix LLMs win average WER on out-of-domain HuggingFace sets.
S5-TTS introduces a streaming T5-TTS variant with lookahead-causal masking and interleaved multi-source distillation that achieves comparable quality to full-context models while cutting end-to-end latency.
Motif-Video 2B reaches 83.76% on VBench, outperforming a 14B-parameter model with 7x fewer parameters and far less training data through shared cross-attention and a three-part backbone.
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Retrieval from Within: An Intrinsic Capability of Attention-Based Models
Attention-based models can retrieve evidence intrinsically by using decoder attention to score and reuse their own pre-encoded chunks, outperforming separate retrieval pipelines on QA benchmarks.