Today’s MT digest highlights how translation research is expanding from benchmark tasks into real-world communication, representation learning, and cultural access. A clear theme is robustness: several works probe whether meaning and similarity survive translation, generation, or transfer across modalities, often through geometric views of model behavior. Another is democratization, with efforts aimed at making multilingual experiences smoother for global audiences while opening historically specialized texts to broader communities.

Language-free Experience at Expo 2025 Osaka

In line with the Global Communication Plan 2025, we have pursued the development of multilingual translation technologies to realize a language-barrier-free experience at Expo 2025 Osaka. Our work includes the advancement of simultaneous interpretation systems emphasizing high translation quality and low latency. Key achievements include chunk-based input segmentation, context-aware translation, and multi-engine machine translation technologies. Through demonstration deployments and collaboration with private companies, our technologies have led to real-world applications, with several services and systems showcased at Expo 2025 Osaka.

Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus

We investigate the extent to which cosine similarity between paragraph embeddings is invariant under machine translation, using the Manifesto Corpus of over 2,800 political party platforms in 28 languages translated to English via the EU eTranslation service. Rather than measuring translation-induced semantic shift directly we measure the stability of pairwise similarity relationships across embedding models, and use inter-model disagreement on original-language text as a calibrated invariance threshold. This yields a per-language non-inferiority test for four hypotheses about how translation interacts with embedding choice, with verdicts that distinguish languages where translation demonstrably preserves semantic structure from those where it demonstrably degrades it and from those where the available evidence does not resolve the question. The framework is corpus- and pipeline-agnostic and extends naturally to downstream tasks. Applied to our data, it identifies ten languages with translation invariance and four with detectable distortion.

Borrowed Geometry: Computational Reuse of Frozen Text-Pretrained Transformer Weights Across Modalities

Frozen Gemma 4 31B weights pretrained exclusively on text tokens, unmodified, transfer across modality boundaries through a thin trainable interface. (1) OGBench scene-play-singletask-task1-v0: $+4.33$pt over published GCIQL at $n=3$ with std 0.74 -- a published-SOTA win on a robotic manipulation task the substrate has never seen. (2) D4RL Walker2d-medium-v2: Decision-Transformer parity ($76.2 \pm 0.8$, $n=3$) at $0.43\times$ DT's trainable count, with the frozen substrate compressing to a 5L slice ($+1.66$pt over the 6L baseline at $n=3$). (3) Associative recall as the cleanest pretraining-load-bearing case: the frozen slice + a 113K-parameter linear interface reaches L30 best-checkpoint per-bit error 0.0505 ($n=2$); a 6.36M-parameter from-scratch trained transformer at matched capacity ($1/\sqrt{d_k}$ scaling, two seeds, LR sweep) cannot solve the task at all under the protocol (best L30 = 0.4395), an $8.7\times$ advantage. Architecture-alone falsifications: a frozen random transformer with correct $1/\sqrt{d_k}$ scaling stays at random-chance loss for 50k steps; a random-init Gemma slice fails OGBench cube-double-play-task1 entirely (0.89% across $n=3$ where pretrained reaches 60%). A dual-measurement protocol -- text-activation probing on 95 English sentences plus task-ablation on a non-language target -- names individual heads independently identifiable on both protocols: head L26.28 scores $3.7\times$ the slice mean for English token-copying and is the #2 most-critical head for binary copy ablation ($Δ$ L30 $= +0.221$); three further heads (L27.28, L27.2, L27.3) classify by the same protocol. The mechanism is single-model and the cross-modality results are single-task within their respective benchmarks; cross-model replication is structurally constrained because Gemma 4 31B is the only model on the small-scale Pareto frontier as of April 2026.

Escaping Mode Collapse in LLM Generation via Geometric Regulation

Mode collapse is a persistent challenge in generative modeling and appears in autoregressive text generation as behaviors ranging from explicit looping to gradual loss of diversity and premature trajectory convergence. We take a dynamical-systems view and reinterpret mode collapse as reduced state-space accessibility caused by geometric collapse: during generation, the model's internal trajectory becomes confined to a low-dimensional region of its representation space. This implies mode collapse is not purely a token-level phenomenon and cannot be reliably solved by symbolic constraints or probability-only decoding heuristics. Guided by this perspective, we propose Reinforced Mode Regulation (RMR), a lightweight, online state-space intervention that regulates dominant self-reinforcing directions in the Transformer value cache (implemented as low-rank damping). Across multiple large language models, RMR substantially reduces mode collapse and enables stable, high-quality generation at extremely low entropy rates (down to 0.8 nats/step), whereas standard decoding typically collapses near 2.0 nats/step.

Democratizing the medieval English legal tradition

The record of the beginning of the most widespread legal system in the world is contained in millions of pages of handwritten text. Most of the records of the first centuries of the Anglo-American legal system are hand-written in a highly abbreviated form of medieval Latin which only a few dozen scholars in the world are trained to read. In this interdisciplinary project, we construct a dataset of 4029 lines of text across 193 medieval criminal and civil cases. We then use the dataset to train an open-source end-to-end pipeline for transcribing these manuscripts. We first train standard neural network architectures for line segmentation and handwriting recognition (R-Blla and CNN+LSTM with CTC decoding, respectively) and show that they can already achieve 79% word accuracy, despite the relatively small training set and the challenge of expanding abbreviations. We then demonstrate that simple post-processing significantly boosts accuracy: adding an n-gram language model to the CTC decoder improves word accuracy to 82%, while asking Gemini Pro 3 to correct mistakes boosts accuracy to 88%. Finally, we compare the CNN+LSTM architecture with TrOCR, a transformer-based OCR architecture, demonstrating that TrOCR shows comparable word accuracy but worse character accuracy due to its over-willingness to guess, making it harder for humans to infer the correct reading. We incorporated our pipeline into a web portal (glyphmachina.com), opening up the English legal tradition to legal scholars, medievalists, and students.