Knowledge catalogue
arXiv:2605.03882v1 Announce Type: cross Abstract: Individuals frequently form deep attachments to physical objects (e.g., plush toys) that usually cannot sense or respond to their emotions. While AI c
arXiv:2605.04503v1 Announce Type: new Abstract: Image Difference Captioning (IDC) generates natural language descriptions that precisely identify differences between two images, serving as a key bench
arXiv:2605.04507v1 Announce Type: new Abstract: Negotiation agents must infer what their counterpart values, update those beliefs over dialogue turns, and choose actions under uncertainty. End-to-end
arXiv:2605.04458v1 Announce Type: new Abstract: Evaluation of long-form, citation-backed reports has lately received significant attention due to the wide-scale adoption of retrieval-augmented generat
arXiv:2605.04266v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) typically assumes a static or non-strategic reward model (RM). In iterative deployment, however, the p
arXiv:2605.04449v1 Announce Type: new Abstract: Dialogue State Tracking (DST) requires precise extraction of structured information from multi-domain conversations, a task where Large Language Models
arXiv:2605.04638v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is an important technique for ensuring the trustworthiness of LLMs, given their tendency to hallucinate. Existing state-
arXiv:2605.04064v1 Announce Type: cross Abstract: Reasoning capabilities are crucial for reliable medical visual question answering (VQA); however, existing datasets rarely include reasoning explanati
arXiv:2605.04475v1 Announce Type: new Abstract: Reliable autonomous driving requires scene understanding that is semantically consistent across heterogeneous sensors and verifiable at the reasoning st
arXiv:2605.04747v1 Announce Type: new Abstract: We introduce Knowledge-Free Correlated Agreement (KFCA) to reward client contributions in federated learning (FL) without relying on ground truth, a pub
arXiv:2605.04069v1 Announce Type: new Abstract: We introduce LAWS (Learning from Actual Workloads Symbolically), a self-certifying inference caching architecture that builds a growing library of certi
arXiv:2602.02543v3 Announce Type: replace Abstract: Sequential Locate-and-Edit (L&E) model editing can fail abruptly after many edits. We identify and formalize this failure as a positive norm-feedbac
arXiv:2509.24943v2 Announce Type: replace Abstract: Long videos, characterized by temporal complexity and sparse task-relevant information, pose significant reasoning challenges for AI systems. Althou
arXiv:2605.04003v1 Announce Type: cross Abstract: High-precision CNC machining of free-form aerospace components requires bounded compensations informed by inspection, simulation, and process knowledg
arXiv:2605.04542v1 Announce Type: new Abstract: Recent analyses question whether reinforcement learning (RL) is responsible for strong reasoning in large language models (LLMs). At the same time, dist
arXiv:2605.05159v1 Announce Type: new Abstract: We present our system for SemEval-2026 Task 9: Multilingual Polarization Detection, a binary classification task spanning 22 languages. Our approach fin
arXiv:2605.03308v1 Announce Type: new Abstract: Travel planning serves as a critical task for long-horizon reasoning, exposing significant deficits in LLMs. However, existing benchmarks and evaluation
arXiv:2605.03409v1 Announce Type: new Abstract: We present Robust Agent Compensation (RAC), a log-based recovery paradigm (providing a safety net) implemented through an architectural extension that c
arXiv:2511.06754v3 Announce Type: replace-cross Abstract: Inspired by how humans reason over discrete objects and their relationships, we explore whether compact object-centric and object-relation rep
arXiv:2605.04305v1 Announce Type: new Abstract: We introduce SWAN (Semantic Watermarking with Abstract Meaning Representation), a novel framework that embeds watermark signatures into the semantic str
arXiv:2605.04962v1 Announce Type: new Abstract: Foundation models have established unified representations for natural language processing, yet this paradigm remains largely unexplored for tabular dat
arXiv:2602.02315v2 Announce Type: replace Abstract: Large language models (LLMs) form implicit beliefs (posteriors over latent variables) from prompts, but we lack a mechanistic account of how these b
arXiv:2605.04078v1 Announce Type: new Abstract: Reasoning distillation aims to transfer multi-step reasoning capabilities from large language models to smaller, more efficient ones. While recent metho
arXiv:2507.20021v3 Announce Type: replace-cross Abstract: Recent ObjectNav systems credit large language models (LLMs) for sizable zero-shot gains, yet it remains unclear how much comes from language
arXiv:2605.03212v1 Announce Type: cross Abstract: Modeling latent clinical constructs from unconstrained clinical interactions is a unique challenge in affective computing. We present ADAPTS (Agentic
arXiv:2605.02911v1 Announce Type: new Abstract: Future sixth-generation (6G) mobile networks are envisioned to be equipped with a diverse set of powerful, yet highly specialized, optimization experts.
arXiv:2605.02906v1 Announce Type: new Abstract: In the field of software operations, Large Language Models (LLMs) have attracted increasing attention. However, existing research has not yet achieved e
arXiv:2605.03111v1 Announce Type: cross Abstract: Social-educational robots designed for socially interactive pedagogical support, such as the Robot Study Companion (RSC), rely on responsive, privacy-
arXiv:2605.02454v1 Announce Type: cross Abstract: Software engineering increasingly involves making high-stakes decisions under uncertainty, using signals from code, field data, and socio-technical pr
arXiv:2512.17817v3 Announce Type: replace Abstract: While 3DGS has emerged as a high-fidelity scene representation, encoding rich, general-purpose features directly from its primitives remains under-e
arXiv:2605.01892v1 Announce Type: new Abstract: European financial institutions face mounting regulatory pressure while their security operations centres remain constrained not by data or staffing but
arXiv:2605.03408v1 Announce Type: new Abstract: Reinforcement learning systems rely on environment interfaces that specify observations and reward functions, yet constructing these interfaces for new
arXiv:2508.13881v2 Announce Type: replace Abstract: Existing driving style recognition systems largely depend on low-level sensor-derived features for training, neglecting the rich semantic reasoning
arXiv:2605.01030v2 Announce Type: new Abstract: We present a machine-checked formalization of structurally governed AI workflow architectures and prove that effect-level governance can be imposed with
arXiv:2605.02921v1 Announce Type: cross Abstract: As LLMs continue to shape real-world applications, automated jailbreak generation becomes essential to reveal safety weaknesses and guide model improv
arXiv:2601.07885v2 Announce Type: replace-cross Abstract: Emoticons are widely used in digital communication to convey affective intent, yet their safety implications for Large Language Models (LLMs)
arXiv:2605.03460v1 Announce Type: cross Abstract: Time series (TS) reasoning models (TSRMs) have shown promising capabilities in general domains, yet they consistently fail on financial domain, which
arXiv:2605.03387v1 Announce Type: new Abstract: Large language models perform well on high-resource pairs but are less reliable for Japanese-Chinese sentences containing noun-modifying clause construc
arXiv:2604.14607v2 Announce Type: replace Abstract: We study the overall process of automatic formalization of GDPR provisions using large language models, within a human-in-the-loop verification fram
arXiv:2602.19171v2 Announce Type: replace-cross Abstract: Parametric CAD sequences are reusable because dimensional and geometric constraints govern how parameter changes propagate. Existing CAD gener
arXiv:2605.00957v1 Announce Type: cross Abstract: Achieving the right amount of trust in AI systems is important, but challenging. The problem is exacerbated with the rise of Large Language Models (LL
arXiv:2605.01486v1 Announce Type: new Abstract: Legal consultation is a high-stakes, knowledge-intensive task that requires agents to identify relevant legal issues, retrieve authoritative support, an
arXiv:2602.00933v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) is rapidly becoming the standard interface for Large Language Models (LLMs) to discover and invoke external t
arXiv:2605.01235v1 Announce Type: cross Abstract: Driven by the escalating global burden of mental health conditions, music-based interventions have attracted significant attention as a non-invasive,
arXiv:2602.05048v2 Announce Type: replace Abstract: Joint planning through language-based interactions is a key area of human-AI teaming. Planning problems in the open world often involve various aspe
arXiv:2507.22268v3 Announce Type: replace-cross Abstract: We study the problem of inferring substitutable and complementary items, which underpins applications such as alternative and follow-up purcha
arXiv:2605.02971v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit severe multilingual safety misalignment: they possess strong safeguards in high-resource languages but remain hig
arXiv:2605.03799v1 Announce Type: new Abstract: This preprint presents a systematic, research-oriented practicum that guides the reader through the entire modern NLP pipeline: from tokenisation and ve
arXiv:2605.01562v1 Announce Type: cross Abstract: The Object-Oriented Method for Requirements Authoring and Management (OOMRAM) is a requirements reuse framework that relies on exact identifier matchi
arXiv:2605.01189v1 Announce Type: new Abstract: Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transpar
arXiv:2605.03058v1 Announce Type: new Abstract: A key goal of explainable AI (XAI) is to express the decision logic of large language models (LLMs) in symbolic form and link it to internal mechanisms.
NEW paper from Microsoft Research. (bookmark it) The entire interpretability literature is built around human readers. As more analysis gets delegated to agents, the right target of interpretability s
arXiv:2605.02092v1 Announce Type: new Abstract: The automation of scientific research workflows has emerged as a transformative frontier in artificial intelligence, yet existing autonomous research ag
arXiv:2603.20562v2 Announce Type: replace Abstract: Large language models (LLMs) are now widely used as judges, yet their decisions can change under presentation choices that should be irrelevant. We
AI coding agents are rapidly becoming ubiquitous across the software industry, fundamentally changing how developers write, test, and debug daily code. While these tools excel at localized, self-conta
arXiv:2605.02937v1 Announce Type: new Abstract: Deep learning in de novo protein design has achieved atomic-level fidelity. However, existing models remain largely non-deliberative: they directly synt
arXiv:2605.03863v1 Announce Type: cross Abstract: The visual environment is a fundamental yet unquantified determinant of mental health. While the concept of the environmental exposome is well establi
arXiv:2509.12464v2 Announce Type: replace Abstract: Reasoning language models such as DeepSeek-R1 produce long chain-of-thought traces during inference time which make them costly to deploy at scale.
arXiv:2605.03514v1 Announce Type: new Abstract: The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph tasks. As a wid
arXiv:2605.03092v1 Announce Type: new Abstract: While sentiment analysis is the staple of financial NLP, capturing the nuances of 'why' behind that sentiment remains a challenge. There have been attem