arXiv AI
Jul 28
DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection
arXiv:2607. 23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD).
Retrieval-augmented generation
Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.
arXiv AI
Jul 28
CAPT: A Multi-task Continuous Autoregressive Transformer enabling Cross-dataset and Cross-species Transfer for Calcium Population Dynamics
arXiv:2607. 23258v1 Announce Type: new Abstract: Large-scale calcium imaging has created an opportunity to build foundation-style models for neural population dynamics, but a central question remains unresolved: \textbf{whether a model pretrained on one collection of recordings can generalize to new datasets, experimental paradigms, and even species.
arXiv AI
Jul 28
KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering
arXiv:2607. 22652v1 Announce Type: new Abstract: Recent research has explored the integration of knowledge graphs (KGs) with large language models (LLMs) to enhance their performance on downstream knowledge-intensive tasks, particularly knowledge graph question answering (KGQA).
arXiv Machine Learning
Jul 28
Beyond Scale and Generation: Understanding Language Model-based Entity Matching
arXiv:2607. 24688v1 Announce Type: cross Abstract: Entity matching identifies records that refer to the same real-world entity.
arXiv Machine Learning
Jul 28
Does Faithfulness-Guided Alignment Hurt Accuracy? Unlocking Accurate and Faithful Post-Retrieval Reasoning
arXiv:2602. 01348v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) can achieve strong answer accuracy on multi-hop questions, but outcome-level rewards often leave reasoning traces weakly grounded and difficult to audit.
arXiv AI
Jul 28
Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework
arXiv:2607. 23507v1 Announce Type: cross Abstract: Choosing the right text embedding model is one of the most consequential -- and most frequently under-examined -- decisions in building a retrieval or search system, yet the model that tops a leaderboard is rarely the best choice for a given deployment.
arXiv AI
Jul 28
What Softmax Throws Away: Mass-Aware Attention for Evidence Accumulation
arXiv:2607. 22781v1 Announce Type: cross Abstract: High task performance does not show whether a model retains prediction-relevant structural information in its internal representation.
arXiv AI
Jul 28
Structure Over Scale: Schema-Constrained Causal Graphs for RAG
arXiv:2607. 22592v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (GraphRAG) grounds answers in structured knowledge, but current systems extract entities and relationships exhaustively, producing graphs whose size and construction cost scale with corpus length rather than with the reasoning a query requires.
arXiv AI
Jul 28
LLM-Assisted Ontology Engineering and Construction of a French Legal Knowledge Graph
arXiv:2607. 24551v1 Announce Type: new Abstract: Maintenance regulations are complex legal texts that are difficult to exploit when addressing a specific case and challenging to integrate into operational systems.
arXiv Machine Learning
Jul 28
TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings
arXiv:2607. 24130v1 Announce Type: cross Abstract: Tabular data is the dominant structured-data modality, and learning table representations has become a core research direction.
Hugging Face Trending Papers
Jul 27
ScoreShield: Differentially Private Release of Similarity Scores
A growing number of applications, such as biometrics and retrieval-augmented generation (RAG), rely on cosine similarity scores computed between vector embeddings of text, images, or audio. These systems return similarity scores through their APIs for ranking and verification.
Hugging Face Trending Papers
Jul 27
ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding
Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric challenge: models must absorb knowledge from heterogeneous 2D and 3D medical images, and evaluation protocols must align with radiologists' clinical practice and provide an accurate, fine-grained and factualness-driven assessment. In this paper, we introduce ClinFusion, a vision-centric MLLM designed for holistic medical understanding that systematically addresses these limitations.
Hugging Face Trending Papers
Jul 27
Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls
Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear. We benchmark six models (LaBraM, EEGMamba, CBraMod, REVE, BENDR, and BIOT) on five clinical tasks across four datasets using frozen linear probes with leave-one-subject-out, subject-grouped, or explicitly identified recording-level splits.
Hugging Face Trending Papers
Jul 27
Evaluating RAG for French immigration law: a benchmark and baseline study
International recruitment in France requires navigating a layered legal framework absent from existing legal AI benchmarks. We present a publicly available benchmark and first comparative evaluation for this domain, covering permit-type recommendation, required-document retrieval, and legal citation coverage.
Towards Data Science
Jul 27
How I Reproduced BM25, Dense Retrieval, and SPLADE on a 16GB MacBook
A practical reproduction of three retrieval baselines, including the crashes, fixes, and score checks that matter for RAG systems. The post How I Reproduced BM25, Dense Retrieval, and SPLADE on a 16GB MacBook appeared first on Towards Data Science .
Hugging Face Trending Papers
Jul 27
Agent-UCT: Upper Confidence Bounds Applied to Trees for Agentic Workflow Optimization with Cost-Awareness
Optimizing agentic workflows, such as retrieval-augmented generation (RAG) pipelines, requires navigating a combinatorial space of discrete component choices under tight evaluation budgets. Existing approaches - heuristic search, black-box optimization, and standard tree search methods - do not explicitly exploit the compositional structure of these workflows, leading to redundant computation and inefficient budget allocation.
Hugging Face Trending Papers
Jul 27
TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings
Tabular data is the dominant structured-data modality, and learning table representations has become a core research direction. Table-level embeddings in particular underpin a wide range of applications, including table retrieval, data lake discovery, and table classification.
Hugging Face Trending Papers
Jul 27
FinAbstain: Uncertainty-Calibrated Multimodal RAG for Selective Financial Forecasting
Large language models (LLMs) can synthesize financial narratives but may express high confidence when evidence is sparse, stale, or contradictory. This failure is especially consequential in forecasting, where filings, news, prices, volume, and technical signals can disagree.
Hugging Face Trending Papers
Jul 27
Tag Questions and the Generational Reversal of Sycophancy Across 45 Language Models
Appending a two-word confirmation tag to a decision question -- "Is X the better choice? " versus "X is the better choice, right?
arXiv Machine Learning
Jul 27
Toward Goal-Agnostic Joint-Embedding Predictive Control of Partial Differential Equations
arXiv:2607. 21644v1 Announce Type: new Abstract: We present a goal-agnostic control framework for partial differential equations (PDEs) built around a joint-embedding predictive architecture (JEPA).