Software-based fault isolation (SFI) enables in-process isolation through compiler instrumentation of memory accesses, and is a critical part of WebAssembly (Wasm). We present two optimizations that improve SFI performance and scalability: Segue uses x86-64 segmentation to reduce the cost of instrumentation on memory accesses, e.g., it eliminates 44.7% of Wasm's overhead on a Wasm-compatible subset of SPEC CPU 2006, and reduces overhead of Wasm-sandboxed font rendering in Firefox by 75%; ColorGuard leverages memory tagging (e.g., MPK), to enable up to a 15× increase in the number of Wasm instances that can run concurrently in a single address space, improving efficiency for high scale server-side workloads. We also explore the challenges of deploying these optimizations in three production toolchains: Wasm2c, WAMR and Wasmtime.

The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges. This becomes particularly evident when LLMs inadvertently generate harmful or toxic content, either unintentionally or because of intentional inducement. Existing alignment methods usually direct LLMs toward the favorable outcomes by utilizing human-annotated, flawless instruction-response pairs. Conversely, this study proposes a novel alignment technique based on mistake analysis, which deliberately exposes LLMs to erroneous content to learn the reasons for mistakes and how to avoid them. In this case, mistakes are repurposed into valuable data for alignment, effectively helping to avoid the production of erroneous responses. Without external models or human annotations, our method leverages a model's intrinsic ability to discern undesirable mistakes and improves the safety of its generated responses. Experimental results reveal that our method outperforms existing alignment approaches in enhancing model safety while maintaining the overall utility.

Mispronunciation detection and diagnosis (MDD) is a popular research focus in computer-aided pronunciation training (CAPT) systems.End-to-end (e2e) approaches are becoming dominant in MDD.However an e2e MDD model usually requires entire speech utterances as input context, which leads to significant time latency especially for long paragraphs.We propose a streaming e2e MDD model called CoCA-MDD.We utilize conv-transformer structure to encode input speech in a streaming manner.A coupled cross-attention (CoCA) mechanism is proposed to integrate frame-level acoustic features with encoded reference linguistic features.CoCA also enables our model to perform mispronunciation classification with whole utterances.The proposed model allows system fusion between the streaming output and mispronunciation classification output for further performance enhancement.We evaluate CoCA-MDD on publicly available corpora.CoCA-MDD achieves F1 scores of 57.03% and 60.78% for streaming and fusion modes respectively on L2-ARCTIC.For phone-level pronunciation scoring, CoCA-MDD achieves 0.58 Pearson correlation coefficient (PCC) value on SpeechOcean762.

Transformer has achieved competitive performance against state-of-the-art end-to-end models in automatic speech recognition (ASR), and requires significantly less training time than RNN-based models.The original Transformer, with encoderdecoder architecture, is only suitable for offline ASR.It relies on an attention mechanism to learn alignments, and encodes input audio bidirectionally.The high computation cost of Transformer decoding also limits its use in production streaming systems.To make Transformer suitable for streaming ASR, we explore Transducer framework as a streamable way to learn alignments.For audio encoding, we apply unidirectional Transformer with interleaved convolution layers.The interleaved convolution layers are used for modeling future context which is important to performance.To reduce computation cost, we gradually downsample acoustic input, also with the interleaved convolution layers.Moreover, we limit the length of history context in self-attention to maintain constant computation cost for each decoding step.We show that this architecture, named Conv-Transformer Transducer, achieves competitive performance on LibriSpeech dataset (3.6% WER on test-clean) without external language models.The performance is comparable to previously published streamable Transformer Transducer and strong hybrid streaming ASR systems, and is achieved with smaller look-ahead window (140 ms), fewer parameters and lower frame rate.