Search: “vector-search”

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Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

Hugging Face published a tutorial on training and finetuning multi-vector embedding models using the Sentence Transformers library.

Hugging Face's blog walks through training and finetuning multi-vector embedding models with Sentence Transformers. Multi-vector approaches store multiple vectors per document to support late-interaction retrieval. The post is a practical guide for developers building retrieval pipelines with the library.

Hugging Face Blog·Aug 26, 00:00 UTC · 21d agoAI tools & infra1

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Atlas: Efficient Verifiable Semantic Search

Atlas delivers zero-knowledge proofs for HNSW semantic search, verifying RAG retrieval in under a second on SIFT1M and 2.0 seconds at 100M vectors.

Atlas lets a search provider prove that a query was answered correctly against a committed HNSW index without revealing the index, addressing provider deviations like truncation or bias. It combines offline preprocessing, a fixed-size-state restructuring of HNSW with a correctness proof, and timestep-tagged batching of per-step arguments. The system proves queries in under a second on SIFT1M and 2.0 seconds at 100 million vectors while preserving plaintext HNSW recall, and proven retrieval maintains end-to-end RAG answer quality at lower cost than prior verifiable retrieval systems.

arXiv cs.CR·Sep 10, 17:30 UTC · 5d agoResearch1

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Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

Hugging Face details building and using multi-vector late-interaction embedding models with Sentence Transformers for retrieval workloads.

Hugging Face published a guide on multi-vector, late-interaction embedding models (ColBERT-style) supported through Sentence Transformers. The post covers how practitioners can build and use these models for retrieval and RAG pipelines. It is a developer tooling and technique write-up, not a security advisory.

Hugging Face Blog·Aug 18, 00:00 UTC · 29d agoAI tools & infra1

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Generative Late-Interaction Embeddings For Visual Document Retrieval

GLIE compresses visual document retrieval embeddings to four vectors per page while retaining nearly 80% of uncompressed nDCG@5 accuracy.

Researchers analyzing late-interaction retrieval embeddings found they lie exactly on the unit sphere and concentrate near a manifold of intrinsic dimension five to six. GLIE learns a few k vectors per page that serve as a lightweight index and a basis to regenerate the full embedding set for exact rescoring of top candidates at query time. On ViDoRe v1 with four vectors per page, GLIE retains nearly 80% of uncompressed nDCG@5 versus 70% for the best prior post-hoc method, using a 415K-parameter network trained in under three GPU-minutes on 1,000 pages.

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research

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The Frontier AEO Tracker: What Astra Chooses (and every other frontier model, and what you can do about it)

Latent Space launches an AEO tracker scoring 7 frontier models' product recommendations across 161 categories, revealing generational bias flips.

Latent Space built a tracker measuring Answer Engine Optimization by running 6 prompt variations across 7 frontier models with search enabled over 161 product categories, scoring first choices, alternatives, mentions, and anti-recommendations. It found 28 categories with a universally dominant primary choice and observed soft biases, such as models favoring their own lab's coding agents. Analysis of Anthropic's Sol→Astra and Opus→Fable generations showed newer models consulting fewer sources and being less likely to change answers when questions are paraphrased.

Latent Space·Sep 7, 21:32 UTC · 8d agoAI research

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Grouped Value Attention: Efficient KV Caching via On-Demand Key Reconstruction

Grouped Value Attention stores grouped values and reconstructs content keys via a learned linear map, cutting KV-cache size about 45-47% versus GQA.

GVA stores only grouped values and reconstructs content keys with a learned linear map absorbed into the query at decode time, while a small shared decoupled RoPE channel preserves positional information via a separately cached positional key. At 350M parameters trained on 30B FineWeb-Edu tokens, the 16-dimensional positional variant scores 44.18 average accuracy across five tasks versus 44.36 for GQA and 43.88 for MLA. Custom decoding kernels are in development with an open-source release planned.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research

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Beyond Top-k Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents

DSR reranks LLM agent skills with Determinantal Point Processes to favor complementary, non-redundant sets, improving multi-skill query coverage.

The paper proposes Diverse Skill Routing (DSR), a diversity-aware reranking framework for LLM agent skill routing that uses a Determinantal Point Process to balance query relevance and non-redundancy across large skill registries. DSR introduces a query-residual diversity kernel that penalizes redundant skill overlap while avoiding penalties arising only from shared query relevance. On the SkillRouter benchmark, DSR improves recall and full coverage over a strong pointwise reranking baseline, with the largest gains on multi-skill queries. The authors argue skill routing should be treated as complementary set selection, not just relevance ranking.

Hugging Face daily papers·Sep 4, 20:00 UTC · 11d agoAI research

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The Pelican comparison grid for Astra is pretty interesting

Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.

Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.

Simon Willison·Sep 4, 23:59 UTC · 11d agoAI research

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Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:32 UTC · 11d agoAI research1

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Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures

Continual Search framework iteratively prompts LLM judges to keep searching agent execution logs, boosting long-horizon failure root-cause attribution accuracy.

The paper frames automated root-cause attribution (RCA) for long-horizon AI agent failures as a search problem, since relevant evidence is sparse and distributed across massive execution traces. The authors propose Continual Search, an iterative framework that nudges an LLM judge across successive turns to keep hunting unresolved diagnostic evidence instead of settling on an early plausible diagnosis. They introduce MegaRCA-Mix, a benchmark of 50 human-annotated failure trials on long-horizon, execution-heavy tasks. On MegaRCA-Mix, Continual Search improves GPT-5.5's F1 from 0.349 to 0.498 (over 40% gain), and lower-tier models can surpass higher-tier counterparts when search is effective.

Hugging Face daily papers·Sep 10, 20:00 UTC · 5d agoAI research1

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Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval

Case study shows autonomous LLM research reaches 90% of SOTA on telecom ticket retrieval in 10 weeks versus 10 months human work.

The paper explores adapting autonomous research to open-ended, industry-grade ML problems through a telecom ticket retrieval case study with commercial and open-source agents. Autonomous research reached 90% of state-of-the-art performance (0.34 vs. 0.38 Recall@1) in 10 weeks versus 10 months of human work, at up to $200 per Cursor campaign. The authors find agents excel at narrow hyperparameter optimization but lack human-like intuition, recommending human-agent collaboration.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:09 UTC · 4d agoAI research

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Studying Without a Syllabus: Task-Agnostic Environment Preprocessing

Paper formalizes task-agnostic environment preprocessing, where agents study unfamiliar environments under a budget to build reusable artifacts for a frozen solver.

The paper formalizes task-agnostic environment preprocessing, where a studying system explores an environment under a budget and produces artifacts like indices, scripts, or procedural guidance for a frozen solver, without task examples or evaluation feedback. The authors compare unaided and archive-equipped meta-agents against fixed synthetic-practice and corpus-processing methods across six heterogeneous benchmarks. A meta-agent variant achieves the highest Avg@3 reward on five benchmarks, while fixed corpus processing remains best on the largest corpus benchmark. Studied artifacts reduce the test-time sampling needed to reach a given score, shifting computation from repeated test-time attempts to a pre-task study phase.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research

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AdamX: Cosine similarity meets gradient descent

Researchers propose AdamX, a cosine-similarity-based first-order optimizer with variance rectification that matches Adam-class convergence across benchmark datasets and architectures.

The paper introduces AdamX, a first-order optimizer that uses cosine similarity as an adaptive mechanism for controlling update magnitudes, plus a variance rectification scheme for smoother optimization early in training. The method is described as scalable, model-agnostic, and straightforward to integrate into existing pipelines. Empirically, AdamX shows competitive convergence rates measured by epochs to reach performance thresholds under a fixed hyperparameter budget, with code and experiments released on GitHub.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:42 UTC · 5d agoAI research

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Adversarial Testing of Automated Program Repair Agents for Security Vulnerabilities

SWEADV benchmark shows adversarial issue descriptions make LLM program-repair agents write insecure fixes in 51.7% of cases, evading most detection tools.

Researchers built SWEADV, a benchmark of 750 adversarial issue descriptions derived from 150 SWE-bench Verified repair tasks, covering command execution, deserialization, path traversal, denial of service, and weak hashing attack types. Tested on mini_swe agents backed by GPT-5-Mini, MiniMax-M2.5, and DeepSeek-R, adversarial descriptions induced malicious behavior with successful repair in 51.7% of cases. Detection was weak: LLM-as-judge pre-repair screening reached only 62.3% accuracy, and post-repair detection via static analysis and LLM-as-judge achieved just 39.4% and 55.4%.

arXiv cs.CR·Sep 14, 17:53 UTC · 1d agoAI safety & security1

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CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search

CoRA-NAS combines zero-cost proxy ranking with low-cost learning-curve refinement, achieving the best worst-space Spearman correlation across NAS benchmarks.

The paper proposes CoRA-NAS, a two-stage neural architecture search framework pairing a static ranking prior (CoRA-Rank) with learning-curve refinement (CoRA-Refine) that extrapolates early validation curves for sampled anchors and propagates residual corrections with an ExtraTrees model at about 1% of full training cost. Across NAS-Bench-201, NAS-Bench-101, TransNAS-Bench-101, and NATS-SSS it achieves mean Spearman correlations of 0.946, 0.715, 0.786, and 0.894 respectively, with the best worst-space correlation of 0.715 among compared methods. On NAS-Bench-201/CIFAR-100 its selected architecture reaches 73.32% accuracy versus a 73.37% ground-truth best.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:49 UTC · 5d agoAI research

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SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking

SAS trains attention sparsification end-to-end with the language modeling loss, beating sparse attention baselines especially under tight context budgets.

Simple Attention Sparsification (SAS) injects the selector's continuous scores into attention logits in log form inside the softmax, letting gradients from the language modeling loss directly update the ranking of context units. The method uses normalized softmax gates calibrated against the current block and a memory-efficient Triton kernel integrated into FlashAttention-style computation. Across reasoning, long-context, and agentic tasks, SAS consistently outperforms trainable sparse attention baselines across budgets, with the largest gains under tight attention budgets.

Hugging Face daily papers·updated Sep 11, 17:58 UTC · 4d ago·first Sep 10, 20:00 UTC · 5d agoAI research 2 sources1

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Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis

AllenAI's OlmoEarth Studio adds custom embedding exports to support downstream geospatial analysis workflows.

A Hugging Face blog post from AllenAI introduces OlmoEarth embeddings, a feature allowing custom embedding exports from OlmoEarth Studio for downstream analysis tasks. Only the title was available, so no benchmark or performance details are provided. OlmoEarth is Ai2's open geospatial AI model family.

Hugging Face Blog·Aug 12, 16:14 UTC · Aug 12, 2026AI tools & infra

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Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact

Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.

The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:57 UTC · 5d agoAI research

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MAxBench: A Multinomial Concept Recovery Benchmark

MAxBench evaluates multinomial concept recovery methods, finding affine subspaces steer most reliably but none consistently beats prompting.

MAxBench is a geometry-agnostic evaluation framework for multinomial concept representations in language models, based on sampling from recovered concept representations. It compares 10 localization methods covering 5 geometry types across 6 concepts and 4 models. Findings show affine subspaces steer more reliably than rank-one or linear subspaces due to better non-zero offsets, manifold steering is competitive where applicable, and no method consistently outperforms prompting.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:08 UTC · 4d agoAI research

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When the prompt becomes the payload: A practical pen-testing guide for GenAI, LLM and RAG applications

CSO Online publishes a practical penetration-testing guide for GenAI, LLM, and RAG applications, covering prompt injection, retrieval poisoning, and tenant isolation testing.

The guide frames LLM applications as attack graphs spanning prompts, retrieval layers, vector stores, tools, identities, and downstream APIs, arguing that conventional web testing misses instruction-vs-data channel risks. It builds on OWASP prompt injection guidance (direct vs. indirect injection) and NIST's 2025 adversarial machine-learning taxonomy, noting that RAG and fine-tuning do not remove injection risk. Recommended practices include documenting trust transitions across components, using canaries and synthetic records to avoid test side effects, running multi-turn and obfuscated injection campaigns, and verifying chains from poisoned documents to observable state changes. It also details testing RAG pipelines via controlled document poisoning across metadata, OCR layers, and code comments, plus cross-tenant isolation checks on retrieved document IDs.

CSO Online·Sep 9, 10:00 UTC · 7d agoAI safety & security1

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LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction

LongAgent autonomously searches variable sets and temporal windows to predict longitudinal medical outcomes, beating the strongest non-agent baseline on synthetic data.

The paper proposes LongAgent, an agent-based method that searches over combinations of variable sets, temporal windows and aggregation functions for outcome prediction on heterogeneous medical longitudinal data. It uses a history memory of previous searches and numerical evidence to guide exploration. On synthetic data it achieves mean RMSE 1.7376, improving over the best non-agent baseline by 0.0151 (95% CI [0.0045, 0.0260]; p=0.0273), and performs comparably to the best baseline on a real clinical dataset.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 16:51 UTC · 1d agoAI research

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ReMoMask-2: Latent Retrieval-Augmented Masked Motion Generation

ReMoMask-2 rebuilds retrieval in the generator's latent space for text-to-motion generation, achieving lowest FID on KIT-ML and SnapMoGen.

ReMoMask-2 is a retrieval-augmented text-to-motion framework that constructs its retrieval database directly in the generator's pre-quantization latent space and aligns text queries through a distilled lightweight projector, eliminating the representation gap. The framework combines Hierarchical Bidirectional Momentum contrastive learning, Semantic Spatial-Temporal Attention, and Topology Structured Masking to handle hierarchical motion structure. The retriever achieves state-of-the-art accuracy, and ReMoMask-2 attains the lowest FID on KIT-ML and SnapMoGen, with a single mask-transformer stage outperforming the previous two-stage pipeline while delivering the fastest inference.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research

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Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverage in RLVR

Researchers propose DATPO, a difficulty-adaptive tree-structured RLVR training method that expands reasoning coverage (pass@k) and improves test-time scaling on math benchmarks.

The paper identifies three rollout design principles for RLVR: difficulty-adaptive rollout expands pass@k, tree-based rollout beats parallel sampling, and sentence-entropy-guided forking overcomes token-level branching localization. DATPO combines difficulty-adaptive tree search with a sibling-diversity advantage term to promote semantic diversity during training. On mathematical reasoning benchmarks, DATPO outperforms baselines in pass@k, directly translating to superior test-time scaling performance.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research

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ConvMem: Convolutional Memory for Long-Context Reasoning

Researchers propose ConvMem, a training-free framework treating LLMs as convolutional kernels for parallelizable long-context reasoning beyond fixed context windows.

ConvMem reformulates long-context reasoning as a hierarchical convolution in which the LLM summarizes text segments hierarchically, shortening the reasoning path from a linear chain to a logarithmic tree. It uses configurable strides, skip connections, and multi-kernel convolution to capture evidence, decompose queries, and enable massive parallelization across segments and reasoning threads. On RULER-HotpotQA and RULER-2WikiMultiHopQA it outperforms training-free baselines and avoids the out-of-distribution overfitting seen in RL-trained approaches like MemAgent.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:53 UTC · 6d agoAI research

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Transfer Learning for Evolving Domains

TrED formalizes transfer learning for domains whose data availability evolves over time, arguing classical settings are regimes along one trajectory, and remains unsolved.

The paper introduces Transfer Learning for Evolving Domains (TrED), formalizing transfer learning as a trajectory problem where target data and labels are progressively collected. TrED is specified by a data availability process fixed by the environment, a freely chosen learning protocol, and an evaluation criterion scoring the whole trajectory of models. Classical settings like domain generalization, domain adaptation, and multi-domain learning are recovered as regimes within this framework. The authors survey the literature and find most methods are tailored to a single regime, leaving TrED a well-posed open problem.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:37 UTC · 4d agoAI research

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Continual Learning Mechanisms Compose for Long-Horizon Memorization

Composing data, function, and weight anchors with merged LoRA raises 100-task long-horizon retention from 1.2% to 34.9% in continual fine-tuning.

The paper introduces long-horizon memorization: a model learns 100 query-answer tasks through continual supervised fine-tuning without retaining earlier examples or receiving task identifiers at inference. No single continual learning mechanism maintains strong retention at this horizon, so the authors compose complementary mechanisms along data/function/weight anchors and low-rank allocation rules. The best method combining all three anchors with merged LoRA ranks among the top 3 methods on all three datasets and raises average final retention from 1.2% to 34.9%, a 28-fold improvement.

Hugging Face daily papers·Sep 6, 20:00 UTC · 9d agoAI research

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HarnessVLN: Unifying Training-Free Embodied Navigation through an Agent Harness

Researchers introduce HarnessVLN, a zero-shot training-free agent harness that sets new training-free SOTA on vision-language navigation benchmarks including R2R and HM3D.

HarnessVLN is a zero-shot, training-free framework for embodied vision-language navigation that coordinates perception, retrieval, grounding, navigation, recovery, and termination through a unified tool interface. It validates planner proposals against spatial evidence, geometric feasibility, and subgoal consistency, using hierarchical event memory and a persistent Spatiotemporal Graph that stores reusable spatial evidence and failure annotations. It reports success rates of 60.8% on R2R, 53.9% on RxR, 76.0% on HM3D-v2, and 59.3% on HM3D-OVON, surpassing prior training-free state of the art, with real-world humanoid deployment demonstrated.

Hugging Face daily papers·Sep 13, 20:00 UTC · 2d agoAI research

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DataFlex-RL: An Evaluation Platform for RLVR Data Policies

DataFlex-RL benchmark of 13 RLVR data policies on Qwen2.5-7B finds none reproducibly beats uniform sampling under matched GRPO training.

DataFlex-RL is an evaluation platform comparing rollout-selection, reweighting, and mixture data policies for RLVR under a common GRPO recipe. Across 13 configurations and 12 matched seeds with Qwen2.5-7B-Base on 12 math, logic, and science benchmarks, uniform GRPO improved domain-balanced accuracy by 7.76 points, but no alternative policy achieved a statistically significant improvement. A corrected 12-seed Llama-3.1-8B-Base extension found no consistent winner, and math-heavy evaluation summaries were negatively correlated (-0.33) with domain-balanced summaries.

Hugging Face daily papers·Sep 4, 20:00 UTC · 11d agoAI research

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Evidence-Grounded Retrieval for Investigation Hunt Lead Generation from CTI Reports

AHLERT generates environment-aware threat hunting leads from CTI reports using ATT&CK-seeded knowledge graph retrieval, roughly doubling F1 over flat-RAG baselines.

The paper introduces AHLERT, a system that converts Cyber Threat Intelligence reports into structured, investigable hunt leads via hybrid dense retrieval with multi-hop traversal over an MITRE ATT&CK-seeded knowledge graph and ontology-grounded RAG constrained to the defender's assets. It is LLM-agnostic and evaluated on public CTI reports for well-known APTs across proprietary and open-weight models. Hybrid evidence retrieval with ontology grounding raises mean F1 from 0.44 to 0.85, and AHLERT attains the highest effectiveness score (~86.95%) versus off-the-shelf LLMs.

arXiv cs.CR·Sep 8, 14:21 UTC · 7d agoResearch

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Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled autoregressive testbed shows validation losses must be analyzed per task, and image tokenizer choice affects joint multimodal text modeling.

Researchers built a pure-autoregressive testbed to study image tokenizers as the 'visual language' of unified multimodal models, tracking task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They found that losses exhibit distinct scaling behavior per task and rank tokenizers differently, and that I2T loss over a shared text vocabulary gives a more consistent loss–performance signal than T2I loss. Better reconstruction does not necessarily yield lower task-specific losses or stronger downstream performance, and tokenizer choice can affect text modeling under joint optimization. Case studies examine the discriminator, semantic supervision, and vocabulary size design axes.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research1

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