Knowledge catalogue
arXiv:2512.12694v2 Announce Type: replace-cross Abstract: Large multilingual knowledge bases expose temporally diverse information, yet retrieval quality remains sensitive to lexical variation and cro
Enterprises and individual developers frequently run multiple AI inference models. The right architecture can simplify how the models are called while also providing centralized governance. In this po
arXiv:2610.05919v1 Announce Type: new Abstract: Seizure monitoring and detection are crucial for reducing the morbidity and mortality associated with seizures. Current epilepsy care, often involving e
arXiv:2512.15621v2 Announce Type: replace Abstract: Occupancy world models use historical occupancy states to forecast future 3D scenes, but their robustness under corrupted temporal inputs remains po
arXiv:2610.06139v1 Announce Type: cross Abstract: Melanoma is the deadliest type of skin cancer, whose early diagnosis is crucial for patients' survival. Image classification using deep learning model
arXiv:2610.03829v1 Announce Type: cross Abstract: Real-world outpatient oncology notes contain specialised terminology, tumour staging expressions, treatment names, toxicity descriptions, and institut
arXiv:2610.03952v1 Announce Type: new Abstract: A one-step curvature probe evaluates an initial direction, whereas continual learners are judged after reaching comparable new-task fit. In an overparam
arXiv:2610.06266v1 Announce Type: new Abstract: Freeway on-ramp merges are major sources of congestion, causing significant economic and environmental costs. While Deep Reinforcement Learning (DRL) of
arXiv:2610.05683v1 Announce Type: cross Abstract: AI systems can now write and optimize production GPU kernels, but validating them remains an important challenge. Evaluating the kernel on a few rando
arXiv:2610.04810v1 Announce Type: new Abstract: Real-time agents are often evaluated with the world paused, or with decision time rounded to whole ticks (tick conversion). Tick conversion predicts alm
arXiv:2610.04008v1 Announce Type: cross Abstract: Executable Agent Skills combine natural-language instructions and scripts into reusable packages for LLM agents, and revising them requires fixing err
arXiv:2603.07865v2 Announce Type: replace-cross Abstract: Text-to-audio (T2A) diffusion models generate high-quality audio but require tens of neural function evaluations (NFEs), resulting in substant
arXiv:2610.05891v1 Announce Type: new Abstract: Post-hoc class activation maps (CAMs) are a standard tool for inspecting the evidence behind an image classifier's predictions, yet nothing in ordinary
arXiv:2610.05949v1 Announce Type: new Abstract: Site-specific fertilizer recommendation systems adapt nutrient advice to location, soil properties, crop type, and production targets, but scientific re
arXiv:2610.05825v1 Announce Type: new Abstract: Classification problem in the context of highly imbalanced data is a major challenge in many real-world applications (e.g., FinTech, healthcare, etc.).
arXiv:2610.05322v1 Announce Type: new Abstract: Test-time compute can improve mathematical reasoning, but can short-budget runs predict how mathematical reasoning scales with additional compute? We in
arXiv:2610.02342v1 Announce Type: new Abstract: Natural Visibility Graph (NVG) based analysis characterizes network traffic through topological descriptors reflecting different structural properties.
arXiv:2610.02831v1 Announce Type: new Abstract: Vision-language models (VLMs) are powerful listwise rerankers for multimodal retrieval, but high inference costs restrict them to evaluating small local
Anthropic’s paid subscription plans deliver more than five‑times the perceived value of comparable OpenAI offerings when evaluated by cost per inference unit and overall revenue contribution. The heav
arXiv:2610.02363v1 Announce Type: new Abstract: Benchmarks for agent-generated data work grade a pipeline by running it once against a fixed snapshot. ArrivalBench instead re-executes the pipeline an
arXiv:2610.03537v1 Announce Type: cross Abstract: Endovascular brain-computer interfaces (BCIs) avoid craniotomy but require precise device delivery through anatomically variable cerebral veins. This
arXiv:2610.03387v1 Announce Type: new Abstract: Typed decision models return choices or distributions over answer options supplied at request time. Accuracy with complete options does not establish wh
arXiv:2610.03099v1 Announce Type: cross Abstract: E-commerce videos are information-dense and frequently compared by consumers evaluating products and merchants assessing marketing strategies. However
arXiv:2610.01769v1 Announce Type: cross Abstract: Coding agents can generate code that appears correct but implements behavior the user never intended. This mismatch can arise when an agent silently r
arXiv:2610.02762v1 Announce Type: new Abstract: Dynamic LLM routers promise to cut inference costs by sending each query to the cheapest model that can answer it correctly. We analyze six commercial r
arXiv:2610.02395v1 Announce Type: new Abstract: Streaming GPU solvers for entropic optimal transport (EOT), such as FlashSinkhorn, avoid storing the dense kernel but still evaluate all nimes m point p
arXiv:2610.02841v1 Announce Type: new Abstract: LLMs are known to introduce stylistic changes into generated text, yet how these stylistic shifts affect model decisions on scientific tasks remains und
arXiv:2610.03574v1 Announce Type: new Abstract: We introduce HyperBrowseComp, a multilingual and multimodal browsing benchmark comprising 423 manually authored and human-validated questions across 13
arXiv:2610.02302v1 Announce Type: cross Abstract: Recent work has shown that large language models (LLMs) can be vulnerable to jailbreak attacks in which harmful intent is obscured through composition
arXiv:2610.02359v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards (RLVR) usually optimizes answer correctness, yet useful language-model behavior also requires high-qual
arXiv:2610.03187v1 Announce Type: cross Abstract: Multi-camera streaming perception is increasingly deployed on heterogeneous edge platforms shared with co-resident workloads, yet accelerator placemen
arXiv:2602.03255v2 Announce Type: replace Abstract: Computer-use agents (CUAs) execute multi-stage tasks through tools, where an early unsafe decision can propagate to consequential actions. Evaluatin
arXiv:2512.21439v2 Announce Type: replace-cross Abstract: A key question in current AI alignment research is how to make AI algorithms learn moral values. Because human morality is highly context-depe
arXiv:2610.03651v1 Announce Type: new Abstract: Dense-retrieval services must switch among embedding-prefix dimensions and index bit rates as latency, quality, and memory budgets change. Tuning a quan
arXiv:2610.02872v1 Announce Type: new Abstract: Evaluating network interventions requires understanding how treatment affects people through their social relationships. Counting treated neighbors with
arXiv:2610.02569v1 Announce Type: cross Abstract: Coding agents have become increasingly long-horizon, autonomous, reliant on general-purpose shell and maintain their own persistent memory for self-im
arXiv:2610.03199v1 Announce Type: cross Abstract: Large language models fine-tuned from a shared base can be merged by averaging their task vectors, but some merges collapse far below the base model,
arXiv:2610.02413v1 Announce Type: new Abstract: LLM agents increasingly rely on activation probes as runtime monitors for prompt injection, jailbreaks, and unsafe requests, reading the model's own hid
arXiv:2606.13141v2 Announce Type: replace Abstract: Retrieval-augmented generation is extending beyond text to long videos, where query-relevant chunks can be represented across multiple modalities an
arXiv:2610.02710v1 Announce Type: cross Abstract: Terminal agents are increasingly deployed beyond software engineering in science and other specialized domains. Constructing training environments req
arXiv:2610.02516v1 Announce Type: cross Abstract: Zero-shot classifiers are useful for routing user requests to specialized LLM tasks, but scoring every request against a large candidate set is expens
arXiv:2610.02825v1 Announce Type: new Abstract: Road geometry (e.g., crests, sags, and speed humps) and surface conditions (e.g., wet or icy pavement) affect how vehicles move, what drivers and onboar
arXiv:2610.02736v1 Announce Type: cross Abstract: A compressor can keep the facts of a dialogue and still drop the turn that changed them. A user corrects a price, reverses a choice, or adds a constra
arXiv:2610.02331v1 Announce Type: new Abstract: Interactive world models are increasingly capable of generating environments and acting within them, yet deliberately editing an existing executable wor
Auto-generated synthesis of 29846 entries about arxiv-cs-ai
Hi, Im gonna go all in on this, best finetune iv got my hands on for agentic coding, period. Test it yourself, im not gonna give you any benchmarks or evaluations, use your own tests and pracices, giv
'TypeSafe AI sells Jev as a frontier-class reasoner that cannot hallucinate, built by the co-inventor of ChatGPT - fast, and almost free. We ran it live on 16,379 benchmark requests, measured its late
arXiv:2610.01436v1 Announce Type: new Abstract: Artificial intelligence systems are increasingly deployed in high impact and safety critical settings, yet security assessment remains difficult to repr
arXiv:2610.00376v1 Announce Type: new Abstract: We evaluate Jev on ten dataset-defined application labels in CESNET-QUICEXT-25 using only the first ten packets' sizes, directions, and inter-packet tim
arXiv:2507.17389v2 Announce Type: replace-cross Abstract: Recent advances in code large language models (CodeLLMs) have made them indispensable tools in modern software engineering. However, these mod
arXiv:2610.00052v1 Announce Type: cross Abstract: We evaluate six language-model configurations on requests for six distinct random integers from 1-49. Across 1,200 attempted calls using four English
arXiv:2610.01861v1 Announce Type: new Abstract: Natural language descriptions can provide rich semantic representations of audio-visual urban scenes, yet datasets that jointly describe both auditory a
arXiv:2610.01042v1 Announce Type: new Abstract: Multi-agent communication aims to help agents benefit from one another's information. Yet improvements in system performance leave a fundamental ambigui
arXiv:2610.01616v1 Announce Type: cross Abstract: The emergence of foundation models for molecular property prediction requires a high degree of AI data readiness, including reliable metadata annotati
arXiv:2610.00992v1 Announce Type: cross Abstract: Learning-based driving planners are usually trained and evaluated in open loop against logged trajectories. In closed loop, a trajectory with small di
arXiv:2610.00425v1 Announce Type: cross Abstract: Code generation has emerged as a central capability of large language models, with coding agents now able to produce functionally correct software pro
arXiv:2606.15420v2 Announce Type: replace-cross Abstract: Safety evaluations test a policy on prompts that omit the incentive information deployment supplies: a commission, a performance score, a dash
arXiv:2610.00316v1 Announce Type: cross Abstract: Voice agents enable low-latency, natural interaction, yet their ability to faithfully ground responses in external documents remains underexplored. We
arXiv:2610.00349v1 Announce Type: new Abstract: Resource limits are becoming an authorization boundary for AI agents that delegate work across concurrent and failure-prone workers. Parent-child alloca
arXiv:2610.01515v1 Announce Type: cross Abstract: Adaptive preconditioners accelerate model training, but heterogeneous client geometries can bias federated updates even when gradients are evaluated a