Cross-lingual transfer is a standard approach to low-resource sign language translation, but it is unclear what the transferred model actually provides. We fine-tune a pose-based model from American Sign Language (ASL) to Indian Sign Language (ISL) and decompose the resulting transfer across its encoder, decoder, and articulator streams. The matched ASL encoder-decoder pair reaches 3.61 BLEU-4 on ISL, while either component alone performs close to training from scratch. Content-word recall reveals a sharp frequency effect: frequently observed content improves with training and additional data, whereas rare and unseen content remains near zero, even after twenty epochs and a 49% increase in training data. Finally, comparing ASL and Chinese Sign Language (CSL) sources shows that the apparent source-language gap is substantially reduced when the output language is controlled, with remaining differences concentrated differently across articulators. Because the source checkpoints also differ in pretraining domain, we treat this final pattern as suggestive rather than causal. Together, the results show that transfer provides a coupled encoder-decoder starting point, but does not by itself solve the long tail of target-language content.

An autonomous vehicle has the privilege of abundant data drawn from many sources, but abundance raises a question of provenance: when we care about one specific event in the scene, which source actually carries the evi dence for it? Identifying that source matters be yond attribution, because a system that weighs every channel at once lets the irrelevant ones di lute the decision, and more input is not the same as more information. GoldenViewVQA poses this question in a controlled form, asking a sys tem to name which of six synchronised vehicle cameras carries the evidence for a question, and to answer that question. We introduce a novel two-stage system built on symbolic scene serialisation. We sweep all six views with a de tector fine-tuned on nuScenes, back-project ev ery detection onto the ground plane, associate detections across cameras into single objects that carry an explicit view-set, and render the scene as text. A language model then names the supporting view and the answer, reading either the serialisation alone (System 1) or the serialisation together with the six raw images (System 2). The pixels help the system locate evidence, not answer questions: System 2 lifts view macro-accuracy to 0.68, against 0.38 for System 1 and 0.14 for an always-CAM_FRONT baseline. Our best run reaches 0.78 joint accu racy, 0.83 view accuracy and 0.65 view macro accuracy on the official test split. Answer accu racy already reaches 0.95 with no image at all: option phrasing alone largely solves the answer head, leaving nearly all the measurable signal in the view head.

We describe team OdeD’s submission to LitTraceQA, the literature-grounded question answering task at GroundLM 2026. The task scores three coupled outputs: paper identifiers, coarse evidence locators, and answers in multiple-choice or table form. Our best official score on the 71-question held-out test split is 0.7649, against 0.4563 for our first submission and 0.5519 for our best fully automated one. Little of that improvement came from the retrieval-and-reading pipeline we began with; most of it came from two other sources. Reading the released evaluator showed the metric to be more asymmetric than it appears: paper F1 and evidence F1 carry weight 1/3 each while each of the three answer metrics carries 1/9. The evidence key is also a coarse tuple, so a correct value on a correct page scores zero when the source type or the visible object id is wrong. The 55-example development split then served to recover the dataset’s annotation conventions rather than as a tuning set, and our own submission history became a measuring instrument. Each macro metric is a mean of a small number of rational per-question values, so a controlled change to one prediction file often admits a single arithmetic explanation. Two predicted deltas returned exact to four decimals. We report the conventions this recovered, six heuristics it refuted, four bugs it exposed in our own verifiers, and the limitation that much of our final score reflects per-question auditing and leaderboard feedback rather than a system that would generalise.

We describe ENSAR, our submission to the AraSeg 2026 shared task on Arabic sentence segmentation, where a system must decide, for every word in a document, whether a sentence ends there. The four tracks strip punctuation, paragraph breaks, both, or neither. We start from an ensemble of five fine-tuned encoders decoded with a length-aware semi-Markov program. It is strong, but most of its mistakes are places where all five models are wrong and all five assign high confidence, so averaging them or moving a threshold changes nothing. ENSAR adds a reasoning stage on top. Two LLM juries, each holding a written policy learned from its own graded errors on the training split, debate every document and apply only the edits both endorse. Our system ranked first on three of the four tracks in both the closed and open settings.