Select, Label, Evaluate: Active Testing in NLP
arXiv:2603.21840v2 Announce Type: replace Abstract: Human annotation cost and time remain significant bottlenecks in Natural Language Processing (NLP), with test data annotation being particularly exp
Knowledge catalogue
arXiv:2603.21840v2 Announce Type: replace Abstract: Human annotation cost and time remain significant bottlenecks in Natural Language Processing (NLP), with test data annotation being particularly exp
arXiv:2608.28383v1 Announce Type: cross Abstract: Hybrid attention dominates frontier LLMs, yet Vision Transformers (ViTs) in multimodal LLMs lack a satisfactory hybrid design, with no consensus on wh
arXiv:2608.28262v1 Announce Type: new Abstract: Entropic optimal transport (EOT) has been shown to offer a computationally tractable approximation to exact optimal transport. However, the standard Sin
arXiv:2608.27882v1 Announce Type: new Abstract: Tabular foundation models based on in-context learning have recently emerged as strong alternatives to task-specific model fitting. However, the current
arXiv:2509.19212v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) are increasingly deployed in real-world applications, yet their ability to make context-aware safety decisi
arXiv:2607.15058v2 Announce Type: replace Abstract: CAD-to-image alignment aims to estimate an object's 9D pose (rotation, translation, and anisotropic scale) from a single RGB image, with application
arXiv:2608.27783v1 Announce Type: cross Abstract: Speech LLMs are usually graded after they answer, although an operating system first has to decide whether a waveform should be sent to the model. We
arXiv:2608.27612v1 Announce Type: cross Abstract: In neuroevolution, indirect encoding generates neural network connectivity from a compact genome rather than specifying each connection. ES-HyperNEAT
arXiv:2603.19423v3 Announce Type: replace-cross Abstract: Large language model (LLM) agents increasingly rely on external tools (file operations, API calls, database transactions) to autonomously comp
Today, Transluce is releasing the most expansive independent evaluation to date of how AI systems respond to users experiencing mental health crises. We evaluated 77 model variants from OpenAI, Anthro
arXiv:2608.28138v1 Announce Type: new Abstract: Adapting video vision-language models (VLMs) is computationally expensive because video inputs produce a large number of visual tokens, making both fine
arXiv:2608.27514v1 Announce Type: new Abstract: Diffusion-based language models (dLLMs) enable parallel token generation through iterative denoising, but existing decoding strategies collapse to singl
arXiv:2608.28018v1 Announce Type: new Abstract: Knowledge-intensive reasoning requires Large Language Models (LLMs) to ground answers in provided evidence. When evidence is insufficient, it is desirab
arXiv:2608.27662v1 Announce Type: new Abstract: This work presents a generative continual learning framework based on growing self-organizing maps (GSOMs) that are augmented with learned distributiona
arXiv:2603.20111v2 Announce Type: replace Abstract: The Joint-Embedding Predictive Architecture (JEPA) is often seen as a non-generative alternative to likelihood-based self-supervised learning, empha
arXiv:2608.28549v1 Announce Type: new Abstract: Recent generative approaches to geometry estimation adapt pretrained image diffusion models and treat the task as image-conditioned generation. Leveragi
arXiv:2608.28008v1 Announce Type: new Abstract: In this paper, we propose a new token compression paradigm for video Multimodal Large Language Models (MLLMs), termed Visual Token Coding (VTC). Inspire
With b10726, the default --lazy-mode change keeps the 51B-parameter PLE n-gram embedding table of Qwen 3.8 Flash Next on disk: it is mmap'd and its rows are read on demand during inference, even with
arXiv:2608.28518v1 Announce Type: cross Abstract: We investigate whether automatic speech recognition (ASR) errors in user input can lead to unsafe outputs from Embodied AI (EAI) models. We find that
(not written by Claude, all errors and crappy text are result of too little coffee on a Sunday morning ;) Our home server is a 2018 Thinkstation P520, bought for about 600eur in 2023. It's been upgrad
Interesting paper on prompt optimization. They claim that a single-lineage prompt optimizer just matched GEPA on a smaller rollout budget. Prompt optimization has been drifting toward heavier machiner
TL;DR: Every public low-bit GGUF of this model is secretly ~4.70 bpw. Shim the rows to 256 and it becomes a real 3.07 bpw / 11.77 GiB file that runs 262K context on 16GB. Needs patched llama.cpp — not
PSA: For everyone using GLM-5.3 you probably want to use this model in reasoning_effort='high' mode. > 'max' - 34% for 77k tokens (not bad!) > 'high' - 32% for 50k tokens But 'max' is not as unusable
arXiv:2608.26234v1 Announce Type: cross Abstract: Retail electricity markets in deregulated systems face significant price volatility and complex interactions with forward and futures products, posing
arXiv:2608.27086v1 Announce Type: new Abstract: Enterprise AI deployment is a coordination problem across business units, application and AI teams, testing, platform engineering, infrastructure, secur
arXiv:2608.26177v1 Announce Type: cross Abstract: Long-form generation exposes fundamental limitations of large language models. Even 70B-parameter models exhibit length collapse at 16k-token outputs,
arXiv:2608.26949v1 Announce Type: new Abstract: Answering questions over real-world documents requires processing long inputs that interleave text with tables. Optical context compression, which repre
arXiv:2608.26618v1 Announce Type: cross Abstract: Model selection becomes particularly challenging under strong predictor dependence and model-class uncertainty, especially when there are exponentiall
arXiv:2608.26581v1 Announce Type: new Abstract: Low-bit quantization offers a promising avenue for reducing the computational and memory demands of Multimodal Large Language Models (MLLMs). Recent har
arXiv:2603.28410v2 Announce Type: replace Abstract: Preference-based many-objective Bayesian optimization typically assumes that all pairwise comparisons arise from a single latent utility function, d
arXiv:2608.26141v1 Announce Type: new Abstract: Multimodal large language models have demonstrated strong document reasoning capabilities by incorporating explicit thinking processes. While this capab
arXiv:2601.03605v2 Announce Type: replace Abstract: Despite the significant advancements of Large Language Models (LLMs), their factuality remains a critical challenge, creating a growing need for mor
Agent Seer synthesizes graded multi-turn tool-use eval scenarios from MCP specs alone—no examples, live tools, or domain tuning.
arXiv:2608.26198v1 Announce Type: new Abstract: Operating a scientific instrument such as an atomic force microscope (AFM) requires continuous expert decision-making. A trained user defines the experi
arXiv:2608.26178v1 Announce Type: new Abstract: There is growing interest in whether language models have stable preferences, for technical, safety, and philosophical reasons. We test 20 language mode
arXiv:2608.26991v1 Announce Type: new Abstract: Powerful code agents can execute scripts, call tools, and manage files, yet many important applications remain accessible primarily through graphical us
arXiv:2608.26379v1 Announce Type: cross Abstract: Retrieval evaluation for retrieval-augmented generation (RAG) is increasingly designed around whether retrieved passages contain evidence that can sup
arXiv:2608.27424v1 Announce Type: cross Abstract: Static scanners are increasingly used to identify executable or otherwise unsafe content in machine- learning artifacts, yet conventional evaluation m
arXiv:2601.08444v2 Announce Type: replace Abstract: Table reasoning, a task to answer questions by reasoning over data presented in tables, is an important topic due to the prevalence of knowledge sto
arXiv:2608.27420v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) significantly improves LLM reasoning but often causes a drop in policy entropy, leading to narrowe
arXiv:2608.27149v1 Announce Type: new Abstract: Conversational AI systems, such as chatbots and virtual assistants, are becoming increasingly important to digital business processes. However, the esta
arXiv:2608.26548v1 Announce Type: cross Abstract: This paper considers a stationary camera calibration problem, which estimates the camera orientation angles yaw, pitch and roll, using a drone traject
arXiv:2608.26147v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong potential for medical reasoning, yet the scarcity and cost of expert-annotated data constrain their progr
arXiv:2608.26375v1 Announce Type: cross Abstract: Standard machine-learning training minimizes a loss function over a dataset, but does not guarantee that the resulting model will satisfy predefined r
arXiv:2608.27214v1 Announce Type: new Abstract: Open World Object Detection (OWOD) built on multimodal foundation models often suffers from semantic ambiguity caused by unidirectional text-to-vision m
arXiv:2601.04778v2 Announce Type: replace-cross Abstract: Video-language models (VLMs) achieve strong multimodal understanding but remain prone to hallucinations, especially when reasoning about actio
arXiv:2608.26357v1 Announce Type: new Abstract: Large language models (LLMs) exhibit uneven multilingual performance, especially when dealing with low-resource languages. Inference-time intervention o
arXiv:2510.12979v2 Announce Type: replace Abstract: Large language models (LLMs) augmented with multi-step reasoning and action generation abilities have shown promise in leveraging external tools to
arXiv:2608.26574v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) offer a promising alternative to autoregressive generation by decoding multiple tokens in parallel through itera
arXiv:2608.27034v1 Announce Type: new Abstract: This paper proposes a fully differentiable jitter correction method for X-ray phase-contrast micro computed tomography using a deep learning-based image
arXiv:2608.27226v1 Announce Type: new Abstract: We introduce a self-supervised framework for learning joint visuospatial representations from RGB-D observations. While modern vision foundation models
arXiv:2608.26750v1 Announce Type: new Abstract: Data lakes rely on metadata to remain usable, yet this meta data is often limited or weakly informative for column relationship discovery, especially in
arXiv:2608.26686v1 Announce Type: new Abstract: Despite the growing number of public datasets, annotated medical images remain scarce. Supervised learning methods achieve strong performance on many be
arXiv:2608.27371v1 Announce Type: new Abstract: This paper introduces Embodied Scene Rearrangement Planning (ESRP), a novel task requiring embodied agents to rearrange furniture in 3D scenes to match
arXiv:2608.26531v1 Announce Type: new Abstract: The advent of vision foundation models, notably the Segment Anything Model (SAM), has catalyzed significant advancements in natural image segmentation.
arXiv:2608.26430v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) strategies such as Low-Rank Adaptation (LoRA) are effective solutions for fine-tuning large-scale pre-trained mod
arXiv:2608.26129v1 Announce Type: cross Abstract: Scientific peer review datasets have trained AI systems exclusively on Computer Science and Machine Learning venues, producing models that critique ab
arXiv:2608.27029v1 Announce Type: cross Abstract: Training molecular machine-learning models on ephemeral or memory-constrained accelerator instances can require repeatedly retrieving preprocessed mol
arXiv:2606.08357v2 Announce Type: replace Abstract: Diffusion language models generate text through iterative denoising, offering a powerful alternative to autoregressive generation. However, discrete
arXiv:2608.26856v1 Announce Type: cross Abstract: Although Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in Medical Visual Question Answering (Med-VQA), their relia