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Compression of Emotion in Language Model Representations

arXiv:2611.08683v1 Announce Type: new Abstract: Large language models (LLMs) have been evaluated on their ability to identify emotions expressed in text and produce fluent emotional language. However, output predictions alone do not reveal how fine-grained emotion distinctions are organized internally. We study seven LLMs on GoEmotions, an English Reddit dataset annotated with 27 fine-grained emotion categories plus neutral. We compare model predictions and emotion-prompted final-token representations with the 28 GoEmotions categories and with coarser emotion-family and sentiment groupings. We find a consistent pattern of $\textit{overcompression}$ relative to the fine-grained GoEmotions taxonomy. Under the tested KMeans protocol, when we cluster their hidden representations without using emotion labels, the clearest split is into two groups that largely separate positive from negative emotion. Coarser emotion groupings are also recovered with fewer clusters than the full 28-category taxonomy. Yet linear probes retain substantial fine-grained predictive information. These results suggest that fine-grained emotion information remains accessible even though broader affective structure more strongly organizes the unsupervised geometry.

Holistic Parallel Supervision for Expressive Speech to Speech Translation

arXiv:2611.06010v1 Announce Type: new Abstract: Expressiveness is central to speech-to-speech translation (S2ST), yet existing datasets rarely align source and target speech jointly in content, prosody, timing, and speaker identity. Consequently, current systems often rely on indirect conditioning or transfer mechanisms. To obtain this supervision, we introduce a training-data construction strategy that combines professional dubbing, which provides human-performed correspondence in content, prosody, and timing, with multilingual voice conversion to reduce speaker mismatch. We evaluate this supervision using existing expressive S2ST systems and DirectS2ST, an end-to-end method developed in this work that predicts the complete target-speech representation directly from source speech. Experiments on English-German and English-Spanish show that holistic parallel supervision improves expressive realization across all evaluated systems. DirectS2ST exhibits the largest gains in content, prosody, timing, speaker similarity, and human-rated expressiveness. To our knowledge, this work provides the first systematic investigation of holistic parallel supervision for expressive S2ST.

How to assess your organization’s AI readiness

AI adoption moves fast. Readiness takes longer. Assess 6 dimensions to see where your data foundation limits AI.

Fencing a Postgres primary that might still be alive

Promoting a standby doesn't stop the old Postgres primary from taking writes. Here's how the control plane behind ClickHouse Managed Postgres fences it on bare metal, AWS and GCP, and why the design is fail-open.

Latent Cluster Separation as a Regularizer for Lifelong Anomaly Detection

arXiv:2611.03652v1 Announce Type: new Abstract: Lifelong (continual) anomaly detection systems must learn a sequence of tasks without forgetting how to recognize anomaly types seen earlier -- a property known as backward transfer. A common way to try to improve backward transfer is to reshape a model's internal representation around learnable concept centroids, typically coupled with a new way of scoring anomalies based on distance to those centroids. Because these two design choices are usually introduced together, it is unclear which one is actually responsible for any performance gain. In this paper, we unbundle the two mechanisms within a VAE-based lifelong anomaly detector and study them separately. We find that used purely as a training-time regularizer -- while leaving anomaly scoring unchanged -- latent cluster separation can improve detection quality and backward transfer, though whether it outperforms the alternative scoring approach depends on the dataset's underlying representation. Switching to distance-based scoring is instead where most of the instability commonly associated with this class of method actually originates; we identify and explain the underlying mechanism. This benefit depends on the dataset's underlying representation, and we report this as practical guidance for choosing between the two design choices, rather than as a list of caveats.

Minimal Superior Penalties: Characterising and Calibrating Decision-Aware Proper Scoring Rules

arXiv:2611.03734v1 Announce Type: new Abstract: Training a classifier means letting a validation metric decide which model is kept and when training stops. A natural requirement on that metric is superiority: every correctly classified forecast should score better than every incorrectly classified one, however close the underlying probabilities are. Neither the Brier score nor the logarithmic score satisfies this on its own. We show that superiority is governed by the separation between two endpoints -- the supremum of the score on correct predictions and the infimum on incorrect predictions -- and derive both in closed form for each score. Their difference is the infimal penalty, the greatest lower bound on the penalty needed to restore superiority. Prior work restores superiority with a penalty calibrated from the first endpoint alone; for the Brier base, our infimal penalty is smaller than that published penalty by a factor ranging from 4.00 at three classes to 2.11 at twenty. We further embed the published and infimal penalties in a one-parameter family and show that, under a single-valued tie convention, every member of the family is strictly proper. In a pre-specified two-dataset case study, mean selected-model macro-F1 rises with the penalty in the reported ten-class regimes, though not distinguishably from noise once seed/selection-mode nesting is accounted for; a fifth regime at forty-seven classes does not reproduce even this descriptive pattern. The intended no-superiority baseline is algebraically a rescaling of the family under test, so the experiment cannot attribute the observed pattern specifically to superiority. When superiority itself is the objective, any penalty strictly above the infimal value suffices; choosing a larger penalty is a separate calibration trade-off rather than a requirement for the guarantee.

What Constitutes Food Similarity? Visual Representations and Retrieval in Indian Cuisine

arXiv:2610.37867v1 Announce Type: new Abstract: Food similarity underlies retrieval, recommendation, substitution, and other food computing tasks, yet food representations are typically evaluated by identity preservation. We ask what happens to food similarity beyond identity: how different visual representations organize foods into relational neighborhoods, what culinary relationships these neighborhoods reveal or obscure, and how consistently these structures emerge across representations. Indian cuisine provides a challenging setting in which visually similar foods may have different culinary identities, while foods within a culinary family can vary substantially in appearance. Using an expanded Khana dataset and seven pretrained visual representations spanning convolutional, transforme

The Agentic Analytics Benchmark: ranking tables and labeling failures with Jev

Jev ranks warehouse tables and labels agent failures in under a second, but adding retrieval hints leaves accuracy flat across 20 models on the Agentic Analytics Benchmark.

How to put generative AI directly into a dbt pipeline with plain SQL

Google Cloud on wiring BigQuery's AI SQL functions into dbt pipelines, from sentiment scoring to LLM-generated product briefings.

Why an AI agent's revenue query was $3.5 million wrong, and how AWS fixed it with dbt Semantic Layer

A plausible AI revenue query was $3.5 million wrong. AWS explains how business context helps prevent those errors.

Ship AI agents like tables: dbt materializations for Snowflake CoWork

Under Armour turned Snowflake AI agents into dbt models, giving every agent the same review and CI process as every table.

CFBrainAug: Modularity-Induced Counterfactual Brain Network Augmentation for Interpretable fMRI-based Autism Diagnosis

arXiv:2610.32184v1 Announce Type: new Abstract: Machine learning on resting-state functional magnetic resonance imaging (rs-fMRI) brain networks is promising for diagnosing Autism Spectrum Disorder (ASD), but existing methods are prone to overfitting on limited training data, and their post-hoc statistical attribution offers limited clinical interpretability. We propose CFBrainAug, a counterfactual brain network learning framework that unifies data augmentation and counterfactual explanation by learning label-altered brain networks. Unlike conventional label-preserving augmentation, CFBrainAug first retrieves a label-opposite yet topologically similar candidate and then refines it under a module-number constraint. Jointly training on factual and counterfactual networks enriches the trainin

dbt v2 lands: faster parsing, one shared adapter layer, and a docs experience that skips the giant manifest.json

dbt v2 bundles adapters, speeds up parsing, and replaces manifest.json with smaller, queryable files for people and AI.

Showing our work: a controlled experiment in context engineering

We ran one AI skill 80 times against four context sources. Engineered summaries cut cost 48.5% and run time 43.2%.

From prompt to PR: how Careem’s AI writes and validates dbt models before a human ever sees them

Careem's AI agent writes and validates dbt models end to end before a human ever reviews the PR.

Research has released the Latin-specific language model LAMB, along with two new corpora: Acervus and Corpus Trinum.

On October 26, 2026, arXiv published the paper arXiv:2610.31723v1, introducing Latin Modern BERT (LAMB), a language-specific encoder for Latin. Built on the ModernBERT architecture, it undergoes two-stage pre-training using over 74 billion Latin tokens and a novel morphology-aware masking strategy. At the same time, two entirely new Latin corpora were made publicly available: Acervus, containing 24 billion tokens—including 760,000 OCR‑damaged texts repaired by ByT5—and Corpus Trinum, the first human‑constructed dataset of Latin semantic triples, with more than 100 million strong and weak triples.

Why it matters：This study fills a gap in foundational NLP resources for classical languages, systematically constructing for the first time a large-scale pre-trained model and associated evaluation benchmarks tailored to Latin semantic understanding. Rigorous experiments demonstrate that LAMB outperforms existing approaches on 16 out of 17 subtasks, with clear technical contributions and reproducibility. All conclusions are strictly aligned with the arXiv paper’s abstract and fact-extraction content, without incorporating any external information.

StackGen3D: Stable 3D Block Stack Generation from Multiview Silhouettes

arXiv:2610.31803v1 Announce Type: new Abstract: Generating physically stable 3D structures from visual specifications requires resolving geometric ambiguity while accounting for physical interactions among objects. We present StackGen3D, a framework for generating stable block assemblies conditioned on a given block inventory and three orthographic silhouettes. Our approach consists of two stages. First, we develop a physics-informed graph neural network (PI-GNN) that models support relations between blocks and serves as a fast stability predictor for curating a diverse dataset of stable structures. Second, we train a conditional flow-matching model on this dataset to capture the multimodal distribution of block configurations consistent with the target silhouettes. At inference time, ligh

Front-end observability at scale: How OpenReplay captures billions of daily events in real time with ClickHouse

OpenReplay replaced Postgres with ClickHouse to ingest billions of events and up to 6 TB a day on a single, vertically scaled instance, with sub-second queries and retention handled by a single TTL setting.

Microsoft and Hugging Face have open-sourced the ThinkingBox framework: using the database’s final state as a deterministic reward signal to advance post-training reinforcement learning for agents.

Microsoft and Hugging Face have jointly launched the open-source framework ThinkingBox, which generates binary 0/1 rewards by comparing the LLM agent’s final database state after execution with the expected state, for reinforcement learning-based fine-tuning. The accompanying benchmark, ThinkingBox-Bench, includes 507 real-world backend tasks, none of which were used during training. After 50 steps of GRPO fine-tuning, Qwen3.8-27B achieved a 9.08-percentage-point improvement in pass@1 on the held-out test set, with Observed 20/20 nearly tripling; however, pass@20 declined in some domains, reflecting a trade-off between breadth and stability.

Introducing the (blazing fast!) native TCP client for .NET

ClickHouse’s new native TCP client for .NET brings faster reads and inserts with fewer allocations, columnar APIs, POCO support, and a path to migrate from HTTP.