Knowledge catalogue
arXiv:2609.26242v1 Announce Type: new Abstract: When a data license expires, deleting stored records does not remove influence encoded in a trained forecaster. Machine unlearning seeks to remove this
arXiv:2609.26637v1 Announce Type: new Abstract: The rapid capability gains of frontier language models are widely attributed to improved reasoning abilities, yet this cannot be verified as raw CoT tra
arXiv:2609.25058v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) adapts large language models (LLMs) to downstream tasks while updating only a small fraction of their pretrained
arXiv:2609.25939v1 Announce Type: new Abstract: The performance of large language model (LLM) workflows often depends on selecting a small set of in-context demonstrations to guide model behavior on n
arXiv:2609.26094v1 Announce Type: new Abstract: Multi-step causal reasoning requires chaining inferences where each step constrains the next. An early error propagates silently, and a correct answer r
arXiv:2609.25453v1 Announce Type: new Abstract: Medical image analysis remains fundamentally challenging because of the intricate geometric and topological structures present in medical data. Conventi
arXiv:2609.26399v1 Announce Type: new Abstract: Scene safety understanding plays a life-or-death role in situational awareness in various critical domains. Traditional methods that rely on learning di
arXiv:2609.25537v1 Announce Type: new Abstract: Large language model (LLM) inference is constrained by the quadratic scaling of self-attention and the linear scaling of the KV cache, increasing latenc
arXiv:2606.13591v2 Announce Type: replace-cross Abstract: Multiagent language model systems, such as collaborative reasoning and debate, produce multiple correlated candidate answers and confidence si
arXiv:2609.23894v1 Announce Type: cross Abstract: Agentic AI extends LLM security beyond generated content to persistent state, autonomous actions, tool use, and interactions with humans and other age
arXiv:2609.24016v1 Announce Type: new Abstract: Commercial large language models are increasingly deployed across African fintech infrastructure for fraud detection and customer communication, yet no
arXiv:2312.06632v2 Announce Type: replace Abstract: Artificial intelligence is rapidly advancing scientific discovery, but this progress carries risks of misuse, such as the creation of harmful substa
arXiv:2609.26086v1 Announce Type: new Abstract: An agentic retrieval system issues a sequence of search queries and must decide, at each step, whether the evidence collected so far is enough to stop.
arXiv:2609.24174v1 Announce Type: new Abstract: Long-horizon language agents receive sparse terminal feedback, while intermediate rubrics provide structured but potentially misspecified assessments of
arXiv:2609.26056v1 Announce Type: new Abstract: Vision-Language Models (VLMs) offer promising capabilities for automated sports coaching but face a fundamental limitation: they implicitly compare agai
arXiv:2609.24369v1 Announce Type: cross Abstract: Deception plays a central role in Intelligence operations, yet it remains difficult to analyse systematically without expert knowledge of reasoning pa
arXiv:2609.25017v1 Announce Type: new Abstract: Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains,
arXiv:2609.26737v1 Announce Type: cross Abstract: Detection of overlapping communities is essential for modelling networks in which nodes participate simultaneously in multiple structural or functiona
arXiv:2609.25429v1 Announce Type: new Abstract: In this paper, we introduce the Directional Total Variation-Regularized Implicit Neural Representation (DTV-INR), an advanced variational paradigm that
arXiv:2609.26658v1 Announce Type: cross Abstract: Integrating heterogeneous datasets within data lakes is a critical challenge, particularly for semantically related tables that lack the explicit attr
arXiv:2609.22851v2 Announce Type: cross Abstract: Large Audio Language Models (LALMs) utilize either continuous features or discrete tokens, yet the optimal representation paradigm for general audio u
arXiv:2609.24092v1 Announce Type: new Abstract: Real-world document processing systems rely on rigid, predefined schemas, yet critical target fields often lack direct visual counterparts on the page.
arXiv:2609.26034v1 Announce Type: new Abstract: Water treatment research is expanding rapidly, but much of the knowledge acquired from this research remains scattered across unstructured literature. T
arXiv:2609.25405v1 Announce Type: cross Abstract: Modern information retrieval increasingly employs both embedding and generative models to handle complex queries. However, current serving systems suf
arXiv:2609.26422v1 Announce Type: new Abstract: Detecting emotions is necessary for building systems that can accurately and adaptively interact with humans. Speech Emotion Recognition (SER) has becom
arXiv:2609.26751v1 Announce Type: new Abstract: Large language models are increasingly used to generate SystemVerilog Assertions from natural-language specifica- tions and register-transfer-level desi
arXiv:2609.25788v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) promise a paradigm shift toward zero-shot forecasting by eliminating task-specific training. However, existing wor
arXiv:2609.25615v1 Announce Type: new Abstract: Architectural floor plans remain a high-friction barrier to archive digitization and early design-model preparation because heterogeneous graphics encod
arXiv:2605.06368v2 Announce Type: replace-cross Abstract: Despite extensive research into mitigating distribution shifts, many existing algorithms yield inconsistent performance, often failing to outp
arXiv:2609.25978v1 Announce Type: cross Abstract: Saliency maps are widely used to explain deep learning predictions in medical imaging, yet visually plausible explanations do not necessarily reflect
arXiv:2609.25082v1 Announce Type: new Abstract: Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-fro
arXiv:2609.26796v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive LLMs by enabling non-autoregressive text gene
arXiv:2609.25716v1 Announce Type: new Abstract: Reference-based image quality assessment (IQA) metrics aim to reflect how humans perceive the perceptual distance between a pair of images. To learn how
arXiv:2609.22664v1 Announce Type: cross Abstract: Research on large language model agents for penetration testing is evaluated almost entirely by capability: whether the agent captures a flag or repro
arXiv:2609.22719v1 Announce Type: cross Abstract: Business requirements for enterprise software systems are rarely captured in structured form; the logic resides instead in source code, configuration
arXiv:2609.25655v1 Announce Type: new Abstract: As large language models (LLMs) scale rapidly, dense full-parameter adaptation becomes increasingly expensive, motivating sparse and modular architectur
arXiv:2609.25186v1 Announce Type: cross Abstract: The rising global prevalence of mental health conditions, together with longstanding barriers in traditional healthcare, such as limited resources, hi
arXiv:2609.25034v1 Announce Type: new Abstract: Central bank press conferences are not merely information releases --- they are structured narratives. We study whether the shape of sentiment within a
arXiv:2609.25669v1 Announce Type: new Abstract: Adoption and diffusion of neologisms in online communities have received renewed attention in recent years. As internet slang terms such as APT, referri
arXiv:2609.26067v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) replace scalar edge weights with learnable univariate functions, increasing flexibility but also parameter memory bec
arXiv:2609.22619v1 Announce Type: new Abstract: Tracking recovery of walking function requires detecting meaningful gait change across rehabilitation sessions, yet objective 3D measurement remains con
arXiv:2609.26118v1 Announce Type: new Abstract: Latent action models (LAMs) learn action-related representations from action-free videos via self-supervised future prediction, offering a scalable para
arXiv:2507.14077v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) algorithms are a critical part of state-of-the-art digital health technology for diabetes management. Yet, access
arXiv:2609.23315v1 Announce Type: cross Abstract: Graph databases are frequently positioned as categorically necessary for connected-data workloads, yet the systems dimension along which they actually
arXiv:2609.26621v1 Announce Type: new Abstract: Greedy decoding from large language models is commonly treated as deterministic. We show it is not precision-invariant: the same model, prompt, and deco
arXiv:2609.24831v1 Announce Type: new Abstract: Agents have attracted considerably increasing attention due to the power of executing both Reasoning and Acting (ReAct) in open and dynamic environments
arXiv:2609.26590v1 Announce Type: cross Abstract: Self-attention-based vision backbones perform well on dense prediction, but the quadratic computational cost of global softmax attention limits their
arXiv:2609.26360v1 Announce Type: new Abstract: Embodied Question Answering (EQA) requires an agent to explore a previously unseen environment, gather relevant information, and answer questions about
arXiv:2502.02552v2 Announce Type: replace Abstract: This paper proposes a hierarchical Bayesian multitask learning model that is applicable to the general multi-task binary classification learning pro
SWE‑Serve evaluates AI coding agents on 53 inference‑engineering tasks derived from SGLang pull requests across six functional domains. Among 19 tasks that launch a live server, patches that pass loca
arXiv:2609.25055v1 Announce Type: new Abstract: In this report we present results of the ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains. This competition aimed to adv
arXiv:2609.25836v1 Announce Type: new Abstract: Multi-task optimization (MTO) addresses a set of optimization tasks simultaneously, often suffering from inaccurate inter-task relationship estimation u
arXiv:2609.26257v1 Announce Type: new Abstract: Continual learning (CL) aims to incrementally acquire knowledge from sequential data while avoiding catastrophic forgetting. Recently, prompt tuning has
arXiv:2609.25927v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as an efficient alternative to autoregressive models, yet aligning them via Reinforcement Learning
Interesting paper on agent memory stored as a linked markdown wiki. Lots of great ideas and insights if you work with LLM Wikis. Wikis are useful for agents because each page holds dense text and the
Introducing Contrastive Language Model (CLM): an ultra-fast System One Model trained with a contrastive learning objective that connects states and actions. CLM-8B is pre-trained on internet-scale dat
NV‑Reason‑CT is a purpose‑built vision‑language model for volumetric CT that uses a full 3‑D Vision Transformer encoder coupled with a Qwen 3.5‑4B language model to generate radiologist‑style chain‑of
arXiv:2609.26275v1 Announce Type: new Abstract: The vehicle routing problems with real-world constraints (we consider vehicles capacity limits, time windows constrains, pickup-and-delivery multi-depo
arXiv:2609.24346v1 Announce Type: new Abstract: Graph Retrieval-Augmented Generation (GraphRAG) has remarkably enhanced large language models on complex reasoning by leveraging structured entity topol
arXiv:2609.26381v1 Announce Type: new Abstract: Transforming scholarly PDFs into machine-readable fulltext remains a bottleneck for large-scale information systems. Recent vision-based parsers improve