Knowledge catalogue
arXiv:2609.10018v1 Announce Type: new Abstract: EdgeAI systems are increasingly employing computer vision applications to enable intelligent, on-device decision-making in real-time. However, these dep
arXiv:2601.15295v2 Announce Type: replace-cross Abstract: Interactive narrative (IN) authors craft spaces of divergent narrative possibilities for players to explore, with the player's input determini
arXiv:2609.07194v1 Announce Type: new Abstract: We present EmoMed - a multimodal medical consultation agent that adapts its responses based on users' emotional states while maintaining clinical accura
arXiv:2609.07139v1 Announce Type: new Abstract: A transformer can make an attribute linearly decodable in its residual stream at a depth where that attribute does not yet influence the output. This ga
arXiv:2609.09357v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) enables inference on private data without revealing it to the server, but evaluating an entire input under FHE is e
arXiv:2609.10387v1 Announce Type: new Abstract: Deformable convolution networks have recently become popular for many computer vision tasks, especially for semantic segmentation, because of their exce
arXiv:2609.05576v1 Announce Type: new Abstract: The paradigm of LLMs has rapidly shifted from passive language interfaces to autonomous Claw-like agents that execute long-horizon tasks across stateful
arXiv:2507.04453v4 Announce Type: replace Abstract: Online alignment of large language models (LLMs) is dominated by reinforcement learning from human feedback (RLHF) with gradient-based optimizers su
arXiv:2609.08943v1 Announce Type: cross Abstract: Automatic fact-checking systems assess the veracity of claims given evidence from relevant documents. Large Language Models (LLMs) have demonstrated s
arXiv:2609.06027v1 Announce Type: cross Abstract: Search-augmented LLM agents are increasingly used for consumer decisions, making them vulnerable to Generative Engine Optimization (GEO) poisoning. Ex
arXiv:2609.09788v1 Announce Type: new Abstract: Choosing when to retrain a deployed classifier requires assessing subgroup error rates across the sequence of models used, including periods between upd
arXiv:2505.19333v2 Announce Type: replace Abstract: Current evaluations of Large Language Model (LLM) steering techniques focus on task-specific performance, overlooking how well steered representatio
arXiv:2504.18376v3 Announce Type: replace-cross Abstract: Natural Language Inference (NLI) is a central task in natural language understanding with applications in fact-checking, question answering, a
arXiv:2609.06731v1 Announce Type: cross Abstract: Temporal relation extraction determines whether an event occurs before, after, or simultaneously with another event, and therefore relies on accuratel
arXiv:2510.22115v2 Announce Type: cross Abstract: We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Des
arXiv:2609.09081v1 Announce Type: new Abstract: Mid-training, the stage between pre-training and alignment, is where a model's per-domain data composition is typically set by data availability rather
arXiv:2609.08267v1 Announce Type: new Abstract: Hallucination detection is crucial for large language models (LLMs), as hallucinated content creates significant barriers in applications requiring fact
arXiv:2609.05801v1 Announce Type: new Abstract: A document modeled as a discrete sequence of tokens can be thought of as being generated from a composition of texts from different domains; a README fi
arXiv:2609.08790v1 Announce Type: cross Abstract: Threat hunting increasingly depends on converting unstructured knowledge (e.g., Cyber Threat Intelligence reports) into actionable hunt leads: concise
arXiv:2609.09184v1 Announce Type: new Abstract: Vision-language model (VLM) confidence may change in aggregate when visual evidence is degraded while remaining structurally inconsistent within individ
arXiv:2608.27824v2 Announce Type: replace Abstract: Evidential argumentation extends Dung's abstract argumentation by requiring arguments and interactions to be backed by chains of evidence rooted in
arXiv:2609.08435v2 Announce Type: new Abstract: In persistent interactions, long contexts may encode an evolving process rather than a fixed record: later events can revise or revoke earlier informati
arXiv:2609.09860v1 Announce Type: new Abstract: Balanced k-shot sampling draws exactly k labeled examples per class. We show that it induces an exact, provable degeneracy in a family of small-sample d
arXiv:2609.09466v1 Announce Type: new Abstract: Online gradient descent is usually studied through external regret, where the learner competes with fixed alternatives. Recent work shows that first-ord
arXiv:2609.09133v1 Announce Type: new Abstract: Execution feedback can guide coding agents toward correct repository repairs, but only when the tests capture the behavior requested by the issue. Agent
arXiv:2508.04125v2 Announce Type: replace-cross Abstract: The application of Large Language Models (LLMs) is growing in the productive completion of Software Engineering tasks. Yet, studies investigat
arXiv:2609.06155v1 Announce Type: cross Abstract: Neural models, including dense retrievers, have been widely adopted in Information Retrieval (IR), often delivering state-of-the-art performance. Desp
arXiv:2609.06085v1 Announce Type: cross Abstract: Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear and
arXiv:2602.14536v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have seen remarkable advancements, achieving state-of-the-art results in diverse applications. Fine-tuning, an im
arXiv:2609.06063v1 Announce Type: new Abstract: AI Agents are increasingly deployed in real-world settings, where they interact with external tools and make sequential decisions with limited human ove
arXiv:2609.09367v1 Announce Type: new Abstract: Divergence-based regularization and Sharpness-Aware Minimization (SAM) are two prominent approaches for improving generalization in deep learning, both
arXiv:2609.08310v1 Announce Type: cross Abstract: The success of generative retrieval has largely been attributed to the use of Semantic IDs, which improve over arbitrary item-level identifiers such a
arXiv:2609.05806v1 Announce Type: new Abstract: Emotion Recognition in Conversations (ERC) aims to identify speakers' emotions in multi-turn dialogue. Accurate emotion recognition can support a wide r
arXiv:2602.13047v2 Announce Type: replace Abstract: Conversational speech reveals early signs of cognitive decline, including dementia and mild cognitive impairment (MCI). AI models show promise for s
arXiv:2609.09439v1 Announce Type: cross Abstract: Ensuring the satisfaction of Signal Temporal Logic (STL) specifications under uncertainty is challenging, as reachability-based monitoring provides gu
arXiv:2609.08407v1 Announce Type: new Abstract: In this study, we identify depth-dependent prefix redundancy in final-readout LLM embedding models, notably across representative backbones including Qw
arXiv:2410.08776v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) face significant security risks despite their advanced capabilities. While techniques like Reinforcement Learning
arXiv:2602.20773v2 Announce Type: replace Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and diff
arXiv:2609.08418v1 Announce Type: new Abstract: Training capable cyber agents is often treated primarily as a problem of model scale, yet open-weight post-training is constrained more directly by the
arXiv:2609.10498v1 Announce Type: new Abstract: Recovering 3D human pose from monocular sports broadcasts remains challenging when players must be localized in a shared metric world coordinate system
arXiv:2609.10032v1 Announce Type: new Abstract: Although the impact resistance of concrete has been studied extensively, a framework linking mesoscale heterogeneity to full-field stress-tensor predict
arXiv:2606.23346v2 Announce Type: replace-cross Abstract: We perform a realistic KiDS-Legacy mock analysis with field-level neural compression and simulation-based inference using just 60 N-body simul
arXiv:2609.07603v1 Announce Type: new Abstract: Financial scenarios are diverse and complex, spanning varying data conditions, tool configurations, and workflows. Yet existing CUA, Computer-Using Agen
arXiv:2609.07699v2 Announce Type: cross Abstract: Curating Web corpora for regional language variants like European Portuguese (PT-PT) is heavily bottlenecked by dialectal overlap (mainly with PT-BR)
arXiv:2609.09768v1 Announce Type: cross Abstract: In Retrieval-Augmented Generation (RAG) systems, a large number of retrieved chunks are concatenated to form the input context so that users can recei
arXiv:2609.06095v1 Announce Type: cross Abstract: Motivation: Undergraduate computing students increasingly turn to generative AI (GenAI) tools to understand abstract concepts through analogies. Analo
arXiv:2609.09905v1 Announce Type: cross Abstract: Preference alignment for flow and diffusion models now spans online reinforcement learning and offline preference optimization, but the relation betwe
arXiv:2609.10243v1 Announce Type: new Abstract: Embodied AI, including vision-language-action and world-action models, must operate reliably in the physical world. Yet methods that perform well in sim
arXiv:2609.10439v1 Announce Type: new Abstract: Large Language Models (LLMs) can memorize and reproduce sensitive, copyrighted, or otherwise undesirable training content, creating privacy, safety, and
arXiv:2609.09883v1 Announce Type: new Abstract: Depth pruning reduces large language model (LLM) inference cost by removing complete Transformer blocks. Activation-based methods collect hidden states
arXiv:2609.10273v1 Announce Type: cross Abstract: Open-loop multilegged locomotion over rough terrain has been interpreted as matter transport over a noisy channel: leg-ground interactions are discret
arXiv:2609.09826v1 Announce Type: new Abstract: Parkinson's disease is a progressive neurodegenerative disorder characterised by gradual deterioration of movement control. Automated freezing-of-gait (
arXiv:2609.07153v1 Announce Type: cross Abstract: Minimal-pair benchmarks such as BLiMP evaluate linguistic knowledge by testing whether language models (LMs) prefer acceptable sentences over minimall
arXiv:2609.10278v1 Announce Type: new Abstract: Recent progress in deep learning has significantly advanced facial landmark detection. However, most existing methods process features in a spatial-doma
arXiv:2609.10405v1 Announce Type: new Abstract: Robot actions are temporally correlated trajectories whose frequency components encode motion at different scales with highly non-uniform energy distrib
arXiv:2609.07034v1 Announce Type: cross Abstract: Frequency estimation is the problem of estimating individual tone frequencies from noisy multi-tone sinusoidal signals. Existing frequency estimation
arXiv:2609.07925v2 Announce Type: new Abstract: We present FrogNano, a 4B coding agent designed to tackle software engineering (SWE) tasks efficiently and effectively, even under resource-constrained
arXiv:2609.07673v1 Announce Type: cross Abstract: Citation-based measures of scientific influence typically treat citations as uniform signals, ignoring the different roles that cited works play in a
arXiv:2609.06403v1 Announce Type: new Abstract: Test-time scaling produces cohorts of reasoning rollouts, yet there is no standard label-free account of how their internal computation reorganizes as i
arXiv:2609.07984v1 Announce Type: new Abstract: Process mining has long turned event logs into process knowledge: discovered models, conformance evidence, bottleneck diagnoses, and runtime predictions