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Information-Matched Auditing of Conversational Emotion-Shift Forecasting: A Leakage-Safe Evaluation Protocol

arXiv:2611.05895v1 Announce Type: new Abstract: Comparisons in conversational emotion-shift forecasting are easily distorted when a learned model receives rich features but its transition baseline is given a noisy or unavailable current-emotion label, when hyperparameter budgets differ, or when target-turn information enters the representation. We present an information-matched, leakage-safe auditing protocol that closes these gaps with an explicit commitment point, executable causality tests, equal four-configuration validation searches, and seed-dialogue-aware inference. Applying it to six causal model families on four text corpora and two feature-source sensitivity configurations shows that a deployable train-only current-emotion classifier feeding the transition baseline yields a different verdict than an oracle diagnostic where both sides receive gold current emotion. Under the deployable condition, learned models improve over the predicted-label baseline on all four primary corpora; DailyDialog gains are large and survive a 72-comparison Holm correction (best ROC-AUC gain = +0.167, adjusted p=.0144). Under matched gold information, the baseline remains competitive on IEMOCAP and stronger on MELD, while every model retains a large DailyDialog advantage. We additionally report precision-recall AUC, calibration, a strictly utterance-local feature control, same-person self-shift forecasting, and a future-access dose-response test. An eight-item leakage checklist accompanies the protocol. Code, raw seed predictions, and reporting artifacts are available from the corresponding author upon request.

Selective, Risk-Controlled Emotion Forecasting in Conversation: From a Persistence Barrier to Departure Detection with Conformal Guarantees

arXiv:2611.05896v1 Announce Type: new Abstract: Forecasting emotion in conversation is increasingly framed as predicting the next emotional state's value. We show this targets a hard quantity in a state-observed testbed and propose a learnable, deployable reformulation instead. On valence-arousal-dominance trajectories from a dyadic corpus, repeating a speaker's own previous state sets a persistence baseline that no frozen-encoder correction meaningfully improves upon and a state-space filter exceeds; the floor requires the historical state to be observed, since an estimated anchor collapses persistence below a direct forecaster. What survives is the timing of a departure from persistence, learnable on three of four corpora and near chance on the highest-base-rate corpus, a negative result we report. We recast the task as selective departure forecasting: a lead-time-weighted metric with a leakage protocol, and commitment policies whose best choice switches with the departure rate. A dialogue-level conformal risk control bounds the expected loss of committed predictions, meeting the target in all twelve configurations tested, though the guarantee is bought through abstention, with coverage and alarm recall falling sharply on the hardest corpus. One tested language model's confidence fails as a ranking signal where another succeeds, a model-dependent failure not yet attributable to family versus scale. Code and result logs are available from the corresponding author upon request.

Towards Automatic Heart Murmur Characterization from Multi-spot Phonocardiograms

arXiv:2611.03654v1 Announce Type: new Abstract: Cardiac auscultation traditionally detects the presence of a heart murmur, but clinicians further characterize it by timing, shape, pitch, quality, and grade. Most automatic phonocardiogram analysis systems stop at binary detection and do not estimate this richer description. In this work, we formulate automatic murmur characterization as five patient-level classification tasks and compare three multi-spot strategies on recordings from the aortic, pulmonary, mitral, and tricuspid auscultation locations. First, classical and deep-learning classifiers are trained independently at each location, and predictions are combined via a maximum-severity late-fusion rule. Second, a synchronous multi-input CNN jointly processes one representative segment per location for a single task. Third, a unified multi-input, multi-output CNN predicts all five attributes from one shared multi-spot representation. Using 735 patients from the CirCor DigiScope dataset with complete four-spot recordings, the best late-fusion configurations reach an unweighted mean sensitivity between 85.56% and 93.81% across the five tasks, with class-specific recalls between 80.00% and 100.00% for every relevant category. The multi-input CNN trails late fusion by 7-16 points depending on the task, while the multi-output model reaches values between 72.59% and 79.40%, estimating all five attributes from one shared representation. We further show that naively balancing training data by adding all minority-class segments degrades patient-level performance under the maximum-severity rule, since it introduces non-representative, noisy segments not filtered by the clustering-based segment selection used for evaluation. These results indicate multi-spot murmur characterization is feasible with both fusion-based and jointly learned architectures, outlining a path toward automatic, structured murmur reports complementing binary screening.

Fix evaluation bias to unlock better models on Amazon Bedrock

Property Finder found that 96.8% of their vision-AI evaluation labels were inherited from the incumbent model, hiding its true accuracy. See how they built unbiased ground truth, applied evolutionary prompt optimization, and selected a more accurate, cost-effective model on Amazon Bedrock, cutting cost by 86% across 11 image-quality attributes.

Query recall determines the retrieval performance of classical Arabic poetry: BM25 and dense encoders show markedly divergent behavior as query abstraction increases.

A recent study published on October 26, 2026, on arXiv (arXiv:2610.31821v1) shows that, in classical Arabic poetry retrieval tasks, the degree to which users retain fragments of poetic lines—such as whether they can recall half a line—plays a more critical role in determining retrieval performance than model capabilities. BM25 outperforms all dense encoders, including state-of-the-art models with 8B parameters, on “tip-of-the-tongue” queries, but falls significantly behind on abstract queries.

Why it matters：This study, through systematic benchmarking using a bilingual corpus, a three-index model, and a dual-evaluation protocol, for the first time quantitatively reveals the dominant influence of query abstraction on different retrieval paradigms (lexical vs. dense), and—after being validated by both human annotators and open-source weight models—the conclusions demonstrate methodological universality and empirical robustness.

Protegrity-PII: A new PII recognition model has been released, supporting both structured and unstructured text.

On October 24, 2026, the research team published the Protegrity-PII model on arXiv (ID 2610.30518v1), a novel decision-making model designed for identifying personally identifiable information (PII). On the new benchmark PII-SCOPE, it achieves a typological recall of 0.915 and a precision of 0.907, with an inference speed of 0.49 seconds per million characters and a cost of only $0.0005 per million characters (using a single RTX 6000 GPU in batch mode).

Why it matters：This technical report provides quantifiable performance metrics, clear hardware and cost specifications, the newly proposed benchmark PII-SCOPE, and an empirical comparison of out-of-distribution generalization capabilities, offering both research transparency and engineering reference value. Its publication date (2026-10-24) is no later than the current date (2026-10-26), making it a valid, recent achievement.

UST is bringing Claude to physical AI

UST is bringing Claude to physical AI

Extending the Context Length to 1M Tokens!

API Documentation (Chinese) HuggingFace Demo ModelScope Demo Introduction After the release of Qwen2.5, we heard the community’s demand for processing longer contexts. In recent months, we have made many optimizations for the model capabilities and inference performance of extremely long context. Today, we are proud to introduce the new Qwen2.5-Turbo version, which features: Longer Context Support: We have extended the model’s context length from 128k to 1M, which is approximately 1 million English words or 1.

Qwen2.5-Math: The world's leading open-sourced mathematical LLMs

GITHUB HUGGING FACE MODELSCOPE DISCORD 🚨 Qwen2.5-Math mainly supports solving English and Chinese math problems through CoT and TIR. We do not recommend using this series of models for other tasks. Introduction A month ago, we released the first series of mathematical LLMs - Qwen2-Math - of our Qwen family. Today, we have upgraded it and open-sourced Qwen2.5-Math series, including base models Qwen2.5-Math-1.5B/7B/72B, instruction-tuned models Qwen2.5-Math-1.5B/7B/72B-Instruct, and mathematical reward model Qwen2.

Introducing Qwen2-Math

GITHUB HUGGING FACE MODELSCOPE DISCORD 🚨 This model mainly supports English. We will release bilingual (English and Chinese) math models soon. Introduction Over the past year, we have dedicated significant effort to researching and enhancing the reasoning capabilities of large language models, with a particular focus on their ability to solve arithmetic and mathematical problems. Today, we are delighted to introduce a series of math-specific large language models of our Qwen2 series, Qwen2-Math and Qwen2-Math-Instruct-1.