XGBoost

2026-10-26

RESEARCH · 1 SOURCE · arXiv cs.AI

ELBP: Evidence-Grounded SLM architecture improves on-device behavioral prediction (arXiv:2610.32193v1)

This arXiv preprint introduces Evidence-Grounded Local Behavioral Prediction (ELBP), a four-layer architecture that combines deterministic event processing, dual-view behavioral representations, SLM-based prediction, and evidence verification with abstention to anticipate future assistance use from interaction events. In experiments with instruction-tuned models ≤3B parameters, Qwen2.5-3B plus ELBP reached a full-set F1 of 0.80 and PR-AUC of 0.84 (≈15.9% relative gain over raw-event prompting and 17.6% over XGBoost) and substantially reduced ECE; selective prediction with evidence verification achieved F1 0.84 at 91% coverage with 96% evidence support.

6.0

2026-10-21

MODELS · 1 SOURCE · Hugging Face

NVIDIA's Kumo Relational outperforms supervised ML on relational benchmarks with no task-specific training

Kumo Relational is a pre-trained relational foundation model from NVIDIA that makes predictions directly on relational databases by using up to 10,000 historical examples as in-context input and producing outcomes in a single forward pass, without task-specific training, feature engineering, or table flattening. NVIDIA reports it outperforms supervised relational deep-learning models and expert-engineered gradient-boosted pipelines across 41 tasks on 15 databases, beats AutoGluon by 8% MRR on the SAP SALT benchmark, scales to databases with 500B+ rows, and is available via NVIDIA's open-source GPU-native structured-data-models library or as an NVIDIA NIM; it also exposes a Predictive Query Language (PQL) for agent use.

8.0

2026-09-17

RESEARCH · 1 SOURCE · arXiv cs.AI

Paper proposes a confidence-gated hybrid (ensemble + LLM) for cost-effective emotion recognition in conversation

arXiv:2609.17977v1 compares a low-cost stacked ensemble, off-the-shelf LLM prompting (including GPT-4o-mini), and a confidence-gated hybrid that escalates low-confidence ensemble predictions to an LLM for dialogue-context emotion recognition. Across IEMOCAP, MELD, and CMU-MOSI the hybrid Pareto-dominates both pure systems (improving weighted F1 and cutting LLM spend by routing most turns to the near-zero-cost ensemble), with reported costs of roughly $10–85 vs $99–170 per million utterances for hybrid vs LLM-only pipelines.

7.0

2026-09-15

RESEARCH · 1 SOURCE · arXiv cs.AI

Solar Intelligence — a hybrid retrieval-augmented platform for solar analytics, evidence-grounded QA, and forecasting

This arXiv cs.AI paper introduces Solar Intelligence, a hybrid retrieval-augmented system that unifies structured solar analytics, evidence-grounded scientific question answering, and machine-learning forecasting. The platform integrates daily NASA POWER and Biosphere 2 sensor data with a curated research corpus, uses DuckDB for structured queries, fuses BM25 and ChromaDB dense embeddings via Reciprocal Rank Fusion for retrieval with grounding by llama3.2:3b, produces daily irradiance/temperature/wind forecasts with XGBoost, and is exposed via FastAPI, Streamlit, and an MCP server for use as an app, API, or agent tool.

5.0