Unified streaming and offline ASR aims to deliver full-context accuracy and low-latency incremental decoding with a single model. Transformer/Conformer systems are effective, but long-form streaming inference still suffers from growing attention masks, historical caches, and memory states. Latency-Controlled BiMamba (LC-BiMamba) offers attention-free bidirectional modeling, yet its backward branch is confined to fixed chunks, tying the model to one latency. Extending it to dynamic chunk training expands mini-batches and slows training. We propose TC-BiMamba, whose Trans-Chunk formulation casts local-future backward modeling as sequence-level dynamic-chunk training over complete sequences. TC-BiMamba supports multiple chunk-wise latency settings, keeps the encoder/CTC path memory-stable on long-form inputs, and improves training scalability. On AISHELL-1, AISHELL-2, and LibriSpeech, it outperforms U2++ and remains competitive with externally reported LC-BiMamba results using fewer parameters, while achieving 1.3 times training speedup and 50% GPU memory reduction.

We study timestamped speaker-attributed automatic speech recognition (SA-ASR) for long-form, multi-party speech with overlap. In this setting, chunk-wise inference must preserve meeting-level speaker identity consistency while producing time-stamped, speaker-labeled transcripts. Prior Speech-LLM systems tend to prioritize either local diarization or global labeling, lacking the ability to jointly model fine-grained temporal boundaries and robust cross-chunk identity linking. We propose G-STAR, an end-to-end framework that couples a cache-conditioned speaker-tracking module with a Speech-LLM transcription backbone. The tracker provides structured speaker cues with temporal grounding, and the LLM generates attributed text conditioned on these cues. G-STAR supports component-wise optimization and joint end-to-end training, enabling flexible learning under heterogeneous supervision and domain shift. Under chunk-wise decoding protocols, experiments on both oracle-segmented local evaluation and full-meeting global evaluation show strong speaker-attributed transcription performance.