arxiv arXiv cs.AI · 1d ago

Concept-Constrained Prompt Learning for Few-Shot CLIP Adaptation

CCPL introduces a lightweight framework that anchors class prompts to frozen concept prototypes, improving few-shot CLIP adaptation. It achieves better base-to-new performance on DTD and EuroSAT compared to CoOp, with consistent gains from text-space concept regularization, while maintaining neutrality on OxfordPets. The method uses concept dropout and controllable ensemble fusion at inference, with results sensitive to dataset semantics and protocol.

arxiv arXiv cs.AI · 1d ago

Context-Aware Distillation and Ablation for Text2DSL

A new Text2DSL system uses context-aware distillation with a structured context of BNF grammar, API specification, and closed identifier vocabulary. Ablation studies show that the vocabulary has the largest impact on semantic quality, while API and BNF significantly improve structural validity, confirming structured context as a critical, load-bearing component.

arxiv arXiv cs.AI · 1d ago

CWE-Level Generalisation in Syscall-Based HIDS

A one-class anomaly detector trained on normal behavior of CVEs sharing a CWE class can generalise to unseen CVEs within the same class, but effectiveness varies by CWE family. The CWE-307 detector achieves F1 = 0.6976 at 5% false positive rate, while CWE-89 and CWE-434 perform poorly, with F1 ≤ 0.21. Cross-CVE transfer is direction-dependent and driven more by the breadth of the source normal profile than the CWE category.

arxiv arXiv cs.AI · 1d ago

Text2DSL: LLM-Based Code Generation for Domain-Specific Languages

This paper introduces Text2DSL, a distinct task of generating domain-specific language code from natural language. Using the PolkitBench dataset of 4,204 validated pairs, it shows that structured context—such as BNF grammar and API specs—boosts syntactic and structural validity and CodeBLEU scores by 60% to 95% across different LLM models, without fine-tuning.

media r/LocalLLaMA · 1d ago

Baidu's Unlimited-OCR Transcribes Dozens of Pages in One Forward Pass

Baidu has released Unlimited-OCR, a model that transcribes dozens of pages in a single forward pass using Reference Sliding Window Attention (R-SWA). It builds on DeepSeek-OCR, inheriting its encoder, image compression, and MoE architecture, with only 500M active parameters per token. The model achieves 93.92% accuracy on OmniDocBench v1.6, outperforming DeepSeek-OCR's 87.01% on v1.5, though vendor-reported results warrant independent validation.

arxiv arXiv cs.AI · 1d ago

PaperClaw: Autonomous Research with Human-in-the-Loop Refinement

PaperClaw is a multi-agent system that autonomously conducts research from field selection to paper publication. It uses a validated, iterative propose-test-reflect loop, grounded in real references and runnable results, and supports human-in-the-loop refinement at any stage. Evaluation shows it produces strong papers both autonomously and with human oversight.

arxiv arXiv cs.LG · 1d ago

Optimal subsampling in RKHS for supervised learning

This paper proposes an optimal subsampling scheme in reproducing kernel Hilbert spaces, based on asymptotic analysis of an empirical risk minimizer with Horvitz-Thompson reweighting. The scheme, derived via the trace of the covariance operator, is shown to be implementable via plug-in and performs well on synthetic and real-world datasets.

arxiv arXiv cs.LG · 1d ago

TeaNet Improves Few-Shot Learning in Vibrational Spectroscopy

TeaNet, a task-enhanced augmentation network, reconstructs randomly masked spectra to generate augmented samples that preserve original spectral features while introducing domain-specific variations. This approach enables deep neural networks to identify discriminant wavenumbers more effectively, outperforming CNNs by 17% in challenging synthetic scenarios and offering improved interpretability in few-shot learning tasks.

arxiv arXiv cs.LG · 1d ago

Topological Neural Dynamics: Neuron-wise Sequence Modeling

Topological Neural Dynamics (TND) introduces a neuron-wise framework for sequence modeling, where each neuron evolves independently through a directed graph structure. In a single-player Pong behavior cloning task, TND achieves a mean of 17.47 consecutive catches per round, surpassing all baseline models by more than three times.

arxiv arXiv cs.LG · 1d ago

NASDAQ: Normalized Observation Space Dynamics-Augmented Q-Learning

NASDAQ addresses low-dimensional observation challenges in reinforcement learning by normalizing observation spaces to balance reconstruction losses. It integrates value learning with short-term value and next observation prediction, achieving competitive or superior performance with less training time across domains.

arxiv arXiv cs.LG · 1d ago

Diagnostics for MORL Policy Selection

We propose a diagnostic workflow to reveal behavioral variation in multi-objective reinforcement learning policies. The method highlights differences in policy trajectories beyond expected returns, offering quantitative and visual tools for policy inspection. Validated on grid worlds and scaled to continuous control tasks, it effectively captures behavioral diversity under increasing complexity.

arxiv arXiv cs.LG · 1d ago

MedTS-TTT: Test-Time Training for Medical Time Series

MedTS-TTT introduces a test-time training framework for medical time series classification. Built on CLSA-TTT and a Gated Convolutional Backbone, it enables rapid, single-step adaptation without iterative optimization. On four public datasets, it achieves 11 top-1 rankings out of 12 evaluations across nine baselines and three metrics.

media r/LocalLLaMA · 1d ago

KaLM-Reranker-V1: Fast and Efficient Document Reranking

KaLM-Reranker-V1 is a fast but not late-interaction reranker that decouples query and passage computation while maintaining strong relevance modeling through cross-attention. It achieves state-of-the-art performance on BEIR, outperforms industrial models like Qwen3-Reranker, and shows excellent results on MIRACL and LMEB, with the 0.27B Nano model remaining competitive against 7-12B models.

arxiv arXiv cs.LG · 1d ago

Unsupervised anomaly detection with reservoir computers

A Kolmogorov--Smirnov test on reservoir computer output weights detects regime changes in nonlinear systems. The method distinguishes visually identical attractors, resolves parameter drifts seven times smaller than deep-learning baselines, and identifies ventricular flutter in ECG recordings.

arxiv arXiv cs.LG · 1d ago

Sea-Scan: ML-based Dark Vessel Detection with Weak Supervision

Sea-Scan uses machine learning to detect and localize dark vessels from unlabeled data. It achieves a 97.8% detection rate with only a 1.98% false-trigger rate, using weak supervision from imperfect AIS labels.

arxiv arXiv cs.LG · 1d ago

Ramanujan Graph Rewiring Alleviates GNN Over-Squashing

Ramanujan Propagation uses Ramanujan graphs to reduce over-squashing in Graph Neural Networks by ensuring non-negative resistance curvature. The method preserves local connectivity while enabling efficient long-range information flow, outperforming nine state-of-the-art rewiring techniques.

arxiv arXiv cs.LG · 1d ago

Reservoir Computing for Feature-Free Audio Signal Processing

This paper explores Reservoir Computing as a feature-free method for raw audio signal classification. It shows that parallel deep reservoir architectures outperform shallow and sequential ones in accuracy while maintaining low complexity, enabling efficient, low-power audio processing with minimal preprocessing.

arxiv arXiv cs.LG · 1d ago

Open-Data Framework Identifies Urban Power Grid Topology

A new framework uses public infrastructure and OpenStreetMap data to reconstruct urban power grid topology from transmission to building-level connections. It successfully maps the grid for 7,330 buildings in Oslo's Alna district, enabling detailed power system analysis such as flow optimization and resilience studies.

arxiv arXiv cs.LG · 1d ago

SOHET: Transformer for Heterogeneous Event Streams

SOHET introduces a hierarchical transformer architecture with event-type-specific tabular encoders and self-supervised pre-training. It outperforms existing methods by 5.8% on Booking.com's fraud detection task and achieves state-of-the-art results on 6 out of 8 EBES benchmark tasks.

arxiv arXiv cs.LG · 1d ago

Predictive Repair Management Using Multi-Head Attention and Online Learning

A deep learning framework using multi-head attention and online learning accurately predicts repair durations by integrating categorical and numerical historical data. The model achieves 78% accuracy on real-world repair data from 2013 to 2020, outperforming feed-forward neural networks and random forests, with attention weights revealing key feature interactions.