Knowledge catalogue
arXiv:2609.00747v1 Announce Type: new Abstract: Large language models increasingly generate research ideas, yet judging their novelty or feasibility at generation time does not establish whether they
arXiv:2609.01535v1 Announce Type: cross Abstract: This paper explores whether large language models (LLMs) can design video coding tools, a highly challenging task due to the intricate algorithmic cou
arXiv:2609.01552v1 Announce Type: new Abstract: Scientific equation discovery has long been central to scientific progress, proceeding through iterative cycles of hypothesis generation, observational
arXiv:2609.00457v1 Announce Type: new Abstract: Injecting frozen relational-encoder embeddings as soft tokens into a large language model (LLM) is a conceptually appealing fusion strategy: the encoder
arXiv:2510.26954v3 Announce Type: replace-cross Abstract: The Turing Test is no longer adequate for distinguishing human and machine intelligence. With advanced artificial intelligence systems already
arXiv:2609.00816v1 Announce Type: new Abstract: Scene text recognition is reported as 89--97% accurate on the six standard benchmarks, and the problem is widely treated as saturated. We present an alt
arXiv:2609.00959v1 Announce Type: new Abstract: Mixed-format medical visual question answering (VQA) requires stable option selection and machine-readable free-text output. The two formats fail differ
arXiv:2609.00445v1 Announce Type: cross Abstract: Deployed language model safeguards (safety fine-tuning, filtering, unlearning) vary by principal only outside the model weights: filters are reconfigu
arXiv:2609.00892v1 Announce Type: new Abstract: Rubric-based reinforcement learning decomposes open-ended instructions into prompt-specific, flexible rubrics, making it better suited than reinforcemen
arXiv:2609.01195v1 Announce Type: new Abstract: Entity matching (EM) requires fine-grained contextual understanding and domain knowledge. Recent work shows that large language models (LLMs) can serve
arXiv:2609.01425v1 Announce Type: new Abstract: Voucher abuse poses a major challenge in e-commerce, where malicious users exploit promotional vouchers for profit. Unfortunately, fraud patterns evolve
arXiv:2609.01040v1 Announce Type: cross Abstract: Machine learning systems deployed for credit, hiring, and resource distribution are increasingly subject to regulatory oversight from policies such as
arXiv:2605.05593v2 Announce Type: replace Abstract: Despite the remarkable success of Multimodal Large Language Models (MLLMs) across diverse tasks, the internal mechanisms governing how they encode a
arXiv:2609.00951v1 Announce Type: new Abstract: Collaborative perception shares information among multiple agents to obtain a comprehensive scene representation, enhancing the perceptual capability of
arXiv:2603.03146v2 Announce Type: replace-cross Abstract: Integrated communication and computation (IC^2) has emerged as a new paradigm for enabling efficient edge inference in sixth-generation (6G) n
arXiv:2609.01352v1 Announce Type: cross Abstract: Role-playing large language models (LLMs) are expected to adopt a character's style while also respecting that character's knowledge boundaries. Prior
arXiv:2609.00714v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems (MAS) have shown strong potential for solving complex tasks, yet their development forces a tradeof
arXiv:2609.01345v1 Announce Type: new Abstract: Inference cascades cut cost by answering most queries with a cheap model and escalating a hard tail to a frontier model that acts as verifier. A natural
arXiv:2609.01432v1 Announce Type: cross Abstract: Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentioning
arXiv:2609.00276v1 Announce Type: cross Abstract: Speech-based Alzheimer's disease (AD) detection increasingly relies on speech-enhanced and curated versions of the Pitt Corpus, where speech enhanceme
arXiv:2609.01111v1 Announce Type: new Abstract: Clinical LLM assistants must reason over multi-visit patient trajectories, yet whether the compact history representations used to scale them---retrieva
arXiv:2609.01575v1 Announce Type: new Abstract: Extracting structured fields from hundreds of millions of documents annually remains costly in regulated industries: bespoke OCR cascades cover only a f
arXiv:2609.01308v1 Announce Type: new Abstract: Cardiac magnetic resonance (CMR) imaging provides complementary information on cardiac anatomy, function, and tissue characterization across multiple se
arXiv:2609.00967v1 Announce Type: new Abstract: As large language models increasingly act through external tools, deciding when to call a tool has become a central problem alongside deciding how to us
arXiv:2609.00242v1 Announce Type: cross Abstract: Long-tail autonomous driving failures are often framed as rare-object recognition errors. We argue that this view is incomplete: the decision-critical
arXiv:2603.11915v2 Announce Type: replace Abstract: Theory of Mind (ToM)-the ability to reason about the mental states of oneself and others-is a cornerstone of human social intelligence. As Multimoda
arXiv:2609.00088v1 Announce Type: cross Abstract: LLM judges, models that score another system's output, can be gamed by the systems they score. Recent work identifies one defence that works: the judg
arXiv:2609.00250v1 Announce Type: cross Abstract: Many people now see AI systems as not just productivity tools but as social companions. Researchers are eager to study the consequences of AI companio
arXiv:2609.00759v1 Announce Type: new Abstract: Large language models (LLMs) increasingly handle in-context learning (ICL) tasks where a long, novel context defines the rules, knowledge, and output sc
arXiv:2510.14980v3 Announce Type: replace Abstract: Large language models (LLMs) have shown strong abilities in writing and revising programs, yet many program-synthesis benchmarks still evaluate prog
arXiv:2609.01200v1 Announce Type: new Abstract: When the visual encoder and the language decoder of a vision-language model (VLM) run on different compute nodes, the intermediate visual-token embeddin
arXiv:2607.19011v2 Announce Type: replace-cross Abstract: Multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms, s
arXiv:2609.00960v1 Announce Type: new Abstract: Magnetic resonance images of the same subject look markedly different across field strengths, which complicates the comparison and pooling of data acros
arXiv:2609.00863v1 Announce Type: new Abstract: Engineering inverse design is often limited by the high computational cost of iterative solvers for optimization problems constrained by partial differe
arXiv:2609.00605v1 Announce Type: cross Abstract: Machine unlearning for large language models (LLMs) often assumes that a pre-defined forget set matches what the model has memorized, but this frequen
arXiv:2609.00804v1 Announce Type: new Abstract: Exploring unknown environments with multiple UAVs requires coordination under intermittent communication, making decentralized operation a baseline assu
arXiv:2609.00576v1 Announce Type: new Abstract: Item-sensitivity, defined as whether a model's choice depends on the specific input rather than on its own output prior, is widely reported as evidence
arXiv:2606.14617v3 Announce Type: replace Abstract: Safe physical human--robot interaction on floating-base robots requires interaction regulation under changing contact constraints. We develop a cont
arXiv:2609.00682v1 Announce Type: new Abstract: Intelligent vehicles increasingly support adaptive applications beyond driving themselves, ranging from context-aware ADAS and automated driving to in-c
arXiv:2609.00925v1 Announce Type: cross Abstract: Language models can ignore prompt evidence when it conflicts with memorized knowledge. Post-training can make models follow such evidence more reliabl
arXiv:2609.00460v1 Announce Type: new Abstract: Recent Relational Deep Learning architectures have been proposed as foundation models for multi-table relational data, yet they impose constrained neigh
arXiv:2609.00749v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents require context assembly: the runtime must decide what to include in each prompt, in what order, and when
arXiv:2609.01406v1 Announce Type: new Abstract: Multimodal models are increasingly the default option for perception at the network edge, yet they are trained almost entirely in the datacenter, becaus
arXiv:2609.00621v1 Announce Type: new Abstract: Prompt optimization can improve multi-agent LLM systems, but the prompts being optimized often serve two entangled roles: generating task-relevant conte
arXiv:2512.20003v2 Announce Type: replace Abstract: Sampling from unnormalized probability densities is a pervasive challenge across the computational and physical sciences. Diffusion models provide a
arXiv:2609.00709v1 Announce Type: cross Abstract: Large Vision-Language Models produce fluent image descriptions but offer limited semantic control: users cannot reliably specify whether captions shou
arXiv:2609.00226v1 Announce Type: new Abstract: Automatic academic paper-to-slide generation is inherently iterative, because creating an effective presentation requires repeated cycles of generation,
arXiv:2609.00054v1 Announce Type: new Abstract: Formal Concept Analysis (FCA) is an approach for conceptual classification building and rule discovery from a binary table describing a set of objects b
arXiv:2609.00441v1 Announce Type: new Abstract: Effective manager-employee communication is critical for retaining high performers and developing underperformers, yet training managers in these skills
arXiv:2609.01161v1 Announce Type: new Abstract: Large language models can reproduce memorized text verbatim, yet copyright defenses are usually evaluated under incompatible protocols. We introduce Cop
arXiv:2609.01600v1 Announce Type: cross Abstract: Dynamic agent harnesses let language models change the software that shapes their own execution. This flexibility brings a new reasoning burden: a loc
arXiv:2609.00370v1 Announce Type: cross Abstract: Surrogate safety measures allow road-safety assessment from trajectory data in which crashes are absent, yet the dominant proximity measures - time-to
arXiv:2609.00366v1 Announce Type: cross Abstract: High test accuracy and good aggregate calibration do not show whether an individual prediction is structurally supported by its evidence. In tabular d
arXiv:2609.00508v1 Announce Type: new Abstract: Social media fact-checking has long been challenged by evidence-level and aggregation-level conflicts, where erroneous evidence mimics authoritative new
arXiv:2609.00995v1 Announce Type: new Abstract: Recovering 3D digital humans from a single 2D image is an ill-posed computer vision problem due to the loss of depth information. Probabilistic 3D human
arXiv:2609.00446v1 Announce Type: cross Abstract: Conventional federated learning (FL) relies on parameter averaging, which forces clients to be doubly homogeneous: it demands an identical architectur
arXiv:2609.00590v1 Announce Type: new Abstract: The next generation of mobile networks is envisioned as fully AI-native, with AI-RAN architectures embedding small language models (SLMs) to perform rea
arXiv:2609.00683v1 Announce Type: new Abstract: Creative generation tasks, such as narrative writing and scientific ideation, demand both high-quality outputs and distinct responses across independent
arXiv:2609.00272v1 Announce Type: new Abstract: Most advances in keypoint descriptions address monomodal settings, where image variations arise from viewpoint, illumination, or contrast changes. Multi
arXiv:2609.00808v1 Announce Type: cross Abstract: As far-right actors increasingly exploit online platforms to disseminate ideology and mobilize supporters, civil society organizations (CSOs) play a v