This work presents a production retrieve-then-match cascade that spends computation in proportion to difficulty: retrieval surfaces plausible matches, a lightweight text cross-encoder auto-resolves the high-confidence majority, and an agentic multimodal vision-language model settles the ambiguous remainder.
Abstract
Product linking, the entity-resolution task of mapping merchant product records to canonical catalog products, consolidates fragmented listings so downstream search, recommendation, and advertising see one clean entry per product. At marketplace scale, billions of noisy, multi-category records must be resolved against tens of millions of canonical products, where scoring every candidate with a single model is either too weak for the hard cases or too costly for the easy ones. We present a production retrieve-then-match cascade that spends computation in proportion to difficulty: retrieval surfaces plausible matches, a lightweight text cross-encoder auto-resolves the high-confidence majority, and an agentic multimodal vision-language model settles the ambiguous remainder by inspecting product images and issuing web searches for evidence that is in neither record. The cross-encoder is distilled from millions of dual-VLM-consensus labels, retiring human annotation from the training set, and is calibrated to auto-accept links at a 98% precision bar validated against a smaller operator-certified audit. The agent is a self-hosted open-weight model that reaches a closed frontier VLM's precision at a four-point recall cost (88% versus 92%) for roughly one-seventh the per-pair cost, with no fine-tuning. Per-pair cost spans nearly five orders of magnitude from the cheap cross-encoder to the frontier VLM, so escalating only the hard tail to the agent raises end-to-end link coverage from the cheap stage's 68% to 77%.
LifeSciBench is introduced, a benchmark of 750 expert-authored tasks designed to evaluate whether language models can handle realistic life science research work, with each constituent task paired with a human expert-written rubric.
Amelia Liu, Andrew Ho, Anne Marie Droste et al.· bioRxiv· 2 citations
General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, together with state maintenance, failure recovery, and verifiable delivery. We call this \emph{working capability}: sustained, verifiable progress toward a real-world objective. Apodex 1.1 develops this capability along two complementary dimensions. \emph{Environment Scaling} expands the diversity and verifiability of executable file, search, and code environments, while \emph{Agentic Coordination Scaling} trains agents to decompose long-horizon tasks, delegate parallel work, integrate asynchronous results, and replan. A shared execution harness and AgentOS maintain task state and provenance across tools and agents, and training turns environment trajectories and coordination traces into reliable behavior. Across complex professional work, finance, scientific research, mathematics, coding, and search, Apodex 1.1 reaches the leading performance band despite using a substantially smaller model than many frontier systems. The 35B-parameter Apodex 1.1 Mini further retains strong working capability in a locally deployable form. These results ground agentic intelligence in useful, verifiable work completed over time and advance our goal of building a \emph{Heavy-Duty Solver} for ambitious, long-running tasks.
Apodex Team B. An, B. Li, B. Wang et al.· 1 citation
A rapidly advancing precision-therapy pipeline-including antisense oligonucleotides to upregulate the intact allele, AAV-based gene replacement, CRISPR-mediated transcriptional activation, epigenetic modulators, and rational pathway-targeted small molecules-offers realistic prospects for disease modification.
Autistic children experience disproportionately high rates of anxiety, yet common interventions such as cognitive behavioural therapy and traditional mindfulness practices may be less effective due to their cognitive and abstract demands. Yoga nidra, a form of guided meditation using concrete visualisations and breath awareness, may offer a more accessible alternative. This mixed methods study evaluated the feasibility and pilot efficacy of a co-designed six-week online yoga nidra intervention targeting anxiety in autistic children aged 8-14 years. Neurophysiological and psychological data were collected from 13 participants using parent- and self-reported measures of anxiety (ASC-ASD), intolerance of uncertainty (IUSC), and emotion dysregulation (EDI), alongside heart rate variability (HRV). Notably, the cohort included a minimally speaking child, which demonstrates the potential for the intervention to extend to autistic children with language support needs, a group who are often underrepresented in similar research. Results indicated no statistically significant changes in anxiety over time; however, medium effect sizes were observed in self- (Hedge's g = 0.55) and parent-reported anxiety (Hedge's g = 0.45), with three participants moving from clinically significant to non-significant anxiety levels post-intervention. Additionally, intolerance of uncertainty and emotion dysregulation demonstrated small to medium effect size reductions (IUSC, Hedge's g = 0.50; EDI-Reactivity, Hedge's g = 0.55; EDI-Dysphoria, Hedge's g = 0.25), suggesting potential benefits of yoga nidra in these areas. Greater attendance was significantly associated with self-reported reductions in anxiety (ASC-ASD-SR, r = .757, p < .05), but not the other outcome measures. Unexpectedly, HRV outcomes indicated reduced autonomic functioning post-intervention. Additionally, qualitative data from semi-structured interviews with three child participants and their mothers from the study were analysed using reflexive thematic analysis, revealing five key themes of their experience: Thinking About the Body is Hard; We Want More; Homework Sucks; Making Mindfulness Concrete; and On-Screen and At Home is Convenient. While the online format was appreciated for its accessibility, challenges with adherence and scheduling highlighted the need for more flexible delivery models. This study advances upon prior research by being the first to develop a co-designed yoga nidra intervention specifically for autistic children. Findings support the feasibility of yoga nidra as a complementary intervention for autistic children and suggest directions for future research, including larger trials and further co-design with the autistic community.
Tundi Loftus, Shu H Yau, Sophia Soares et al.· Research in Developmental Di...· 1 citation
TestifAI, a deep learning testing framework for efficient and accurate estimation of robustness against combinations of perturbations, is proposed and partial model tomography is introduced, a novel approach to reconstructing model behaviour in a multi-perturbation space from tests that apply only a small number of perturbations.
Arooj Arif, T. Hartung, E. Botoeva et al.· 1 citation
This work introduces a controlled testbed for studying how goal-directed persuaders shift elicited stances in networks of LLM agents grounded in real-world ego-network topologies, and argues for evaluating multi-agent persuasion as a trajectory- and exposure-level process.
Haoyi Qiu, Geng-Lin Liu, P. Venkit et al.· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
