Knowledge catalogue
arXiv:2609.00002v1 Announce Type: new Abstract: World models enable language-model agents to predict environment dynamics and plan before acting. In text environments, the model must learn symbolic ac
arXiv:2609.00251v1 Announce Type: new Abstract: As people increasingly interact with LLM assistants in daily life, continually adapting to individual preferences has become essential for effective lon
arXiv:2609.00003v1 Announce Type: new Abstract: Machine unlearning studies the removal of knowledge from an AI model, making the system forget a concept it previously learned. Despite rapid progress i
arXiv:2512.05539v3 Announce Type: replace Abstract: The visible parts of a scene are determined by occlusion among overlapping surfaces. Here we consider 'dead leaves' models, which replicate this by
arXiv:2609.00161v1 Announce Type: new Abstract: World models have made remarkable progress in action-conditioned future prediction for embodied agents, yet still struggle to model physically plausible
arXiv:2609.00563v1 Announce Type: cross Abstract: Animals and robots encounter mud at the water-land interface. Like sand, mud can stay solid or flow like a fluid. Unlike sand, the yield strength of m
arXiv:2609.00904v1 Announce Type: new Abstract: Whether large language models (LLMs) can control their own internal representations matters for both machine metacognition and AI safety. A recent study
arXiv:2505.22156v4 Announce Type: replace Abstract: Although existing model editing methods perform well in recalling exact edit facts, they often struggle in complex scenarios that require deeper sem
arXiv:2609.00005v1 Announce Type: new Abstract: Financial scams targeting older adults increasingly occur through text and voice channels such as email, SMS, and phone calls, unfolding over multiple c
arXiv:2609.00504v1 Announce Type: cross Abstract: In this work, we study radically uncoupled learning in discounted general-sum Markov games. Assuming ``mathsf{ETH} for mathsf{PPAD}', we show that, fo
arXiv:2511.09454v2 Announce Type: replace-cross Abstract: As algorithms increasingly mediate competitive decision-making, their influence extends beyond individual outcomes to shaping strategic market
arXiv:2609.01383v1 Announce Type: new Abstract: Vision Language Models have demonstrated remarkable proficiency in interpreting static visual artifacts, but modern data analysis is inherently dynamic,
arXiv:2609.00998v1 Announce Type: cross Abstract: Word Sense Disambiguation has advanced rapidly for English and a handful of well-resourced modern languages, but it continues to assume the existence
arXiv:2609.00447v1 Announce Type: new Abstract: Accurate 3D abnormality segmentation in chest CT requires dense spatial supervision, but obtaining expert voxel-level labels is costly. Radiology report
arXiv:2609.00791v1 Announce Type: cross Abstract: In this work, we introduce Instella-MoE, a fully open Mixture-of-Experts (MoE) language model with 16 billion total parameters and 2.8 billion active
arXiv:2609.01339v1 Announce Type: new Abstract: Road traffic noise remains a major environmental challenge, yet most speed management strategies are static and do not respond to short-term variations
arXiv:2609.00181v1 Announce Type: cross Abstract: The number of edge devices in large-scale edge systems is rapidly increasing. Edge devices have limited processing power, memory, and network bandwidt
arXiv:2608.29632v1 Announce Type: cross Abstract: Competitive programming is increasingly being used to evaluate the algorithmic reasoning capabilities of large language models (LLMs). However, existi
arXiv:2609.01375v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved strong multimodal performance, yet ensuring the factual correctness of generated content remains ch
arXiv:2609.00243v1 Announce Type: new Abstract: LLM agents that cache recovery suggestions from API errors can skip re-derivation in later episodes, spending fewer tokens and fewer model calls on cons
arXiv:2609.00311v1 Announce Type: new Abstract: The inverse kinematics of generic 3R robots has been investigated through multiple approaches, mainly algebraic methods involving the solution of certai
arXiv:2603.03229v3 Announce Type: replace Abstract: The shock response spectrum (SRS) is widely used to characterize the response of single-degree-of-freedom (SDOF) systems to transient accelerations.
arXiv:2609.00625v1 Announce Type: cross Abstract: Faithfully capturing diverse real-world objects with fuzzy, anisotropic structures, such as hair, fur, fibers, and textiles, for efficient real-time v
arXiv:2609.00608v1 Announce Type: new Abstract: LLM-based user simulators are increasingly used to evaluate autonomous agents at scale, in place of costly human evaluations. Despite this promise, thes
arXiv:2609.00449v1 Announce Type: cross Abstract: Neuroevolution of Augmenting Topologies (NEAT) and its advanced version, Evolvable-Substrate HyperNEAT (ES-HyperNEAT), have shown great potential in d
arXiv:2609.01361v1 Announce Type: new Abstract: Linear classifiers trained on hidden states of a large language model (LLM), linear probes, can flag factual errors from a single forward pass. Geometri
arXiv:2609.00883v1 Announce Type: new Abstract: Phase retrieval in broadband coherent anti-Stokes Raman spectroscopy (BCARS) is an ill-posed inverse problem. The Raman-like signal is encoded in the im
arXiv:2602.09691v2 Announce Type: replace Abstract: Knowledge distillation (KD) is a technique to compress a larger teacher system into a smaller student. In machine translation, KD is commonly evalua
arXiv:2609.00513v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates large language models (LLMs) hallucinations, yet conventional dense retrieval struggles with the complex
arXiv:2609.00638v1 Announce Type: cross Abstract: Retrieval is the first stage of modern search and advertising systems, selecting a candidate set from a large item universe for downstream ranking and
arXiv:2609.01092v1 Announce Type: new Abstract: Text-guided image fusion has recently emerged as an effective paradigm for integrating multi-modal information while enabling flexible and task-oriented
arXiv:2511.21638v2 Announce Type: replace Abstract: Practical LLM agents often operate over multi-turn conversations where success is determined only after the full interaction ends. Most multi-turn R
arXiv:2609.01168v1 Announce Type: new Abstract: Text-to-image (T2I) models remain vulnerable to jailbreak attacks that elicit Not-Safe-For-Work (NSFW) content, despite increasingly being guarded by he
arXiv:2606.20563v2 Announce Type: replace Abstract: Creating 3D visual illusions, a single 3D mesh that reveals entirely different semantics from various viewing angles, is a fascinating but tough cha
arXiv:2609.00756v1 Announce Type: new Abstract: The Mutual Reinforcement Effect (MRE) asks whether a fine, span-level and a coarse, document-level task help each other when one model handles both. We
arXiv:2408.12845v3 Announce Type: replace-cross Abstract: This paper considers a novel variant of the online fair division problem involving multiple agents in which a learner sequentially observes an
arXiv:2511.04093v2 Announce Type: replace Abstract: Large language models (LLMs) excel at reasoning but struggle with knowledge-intensive questions due to limited context and parametric knowledge. How
arXiv:2609.00082v1 Announce Type: cross Abstract: LLMs acquire vast amounts of knowledge during pre-training, but often lack the specialized knowledge needed to answer questions from niche sources suc
arXiv:2609.00908v1 Announce Type: new Abstract: Action chunking is a standard execution strategy in modern Vision-Language-Action (VLA) frameworks, but fixed execution horizons impose a trade-off betw
arXiv:2609.01532v1 Announce Type: new Abstract: Logit-based knowledge distillation (KD) is used to train smaller language models (LMs) via supervision from stronger teachers, but whether its benefits
arXiv:2609.01048v1 Announce Type: cross Abstract: Across the full Pythia suite (160M-12B, eight checkpoints, four task families), a linear probe can read a target variable from the residual stream as
arXiv:2609.00416v1 Announce Type: new Abstract: Probing studies have established that syntactic information is decodable in early and middle transformer layers, but what happens to that information in
arXiv:2609.00325v1 Announce Type: new Abstract: Multilingual large language models can exhibit unintended code-switching -- unnecessarily alternating between languages during generation. We present a
arXiv:2609.01117v1 Announce Type: new Abstract: Chain-of-thought reasoning unfolds in discrete token space: each step is committed as text, errors propagate, and eliciting good traces presupposes trac
arXiv:2609.00332v1 Announce Type: cross Abstract: Generative models for implied volatility surfaces must produce outputs that satisfy static no-arbitrage constraints. We study these constraints in lat
arXiv:2609.01507v1 Announce Type: cross Abstract: Compressed context is usually carried as human-readable text or as rendered images that must be decoded, even when its consumer is a language model. W
arXiv:2609.01337v1 Announce Type: new Abstract: LLM-based forecasting systems have improved on real-world tasks such as financial markets and sports outcomes, largely through stronger search and tool
arXiv:2609.01430v1 Announce Type: cross Abstract: Decision trees are among the most widely used models in machine learning, largely due to their transparent decision logic, making them well-suited for
arXiv:2609.00518v1 Announce Type: new Abstract: Antibody-specific language models pretrained via masked language modeling (MLM) learn representations that are critical for downstream sequence design a
arXiv:2602.18493v2 Announce Type: replace-cross Abstract: Long-context LLMs and Retrieval-Augmented Generation defer state tracking and evidence consolidation to query time, which is brittle when fact
arXiv:2609.00237v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems tackle complex reasoning by orchestrating how multiple agents are configured and how they collabora
arXiv:2609.00476v1 Announce Type: new Abstract: Graphic designers often blend visual concepts to communicate multiple ideas within a single image, leveraging positive and negative space to create bala
arXiv:2609.01072v1 Announce Type: cross Abstract: Post-hoc calibration corrects reported confidence, yet a multiclass calibrator can also change the associated top-1 prediction. Accuracy captures only
arXiv:2609.00068v1 Announce Type: cross Abstract: Medical AI is moving beyond recognition towards clinical dialogue and longitudinal prediction. Yet a central question remains: how would a patient's s
arXiv:2601.13880v2 Announce Type: replace Abstract: Personalized lifestyle health analysis requires long-horizon, multi-dimensional reasoning over heterogeneous lifestyle signals, and recent advances
arXiv:2609.00282v1 Announce Type: cross Abstract: Motor imagery (MI) electroencephalography (EEG) decoding could support post-stroke rehabilitation, but models developed on healthy cohorts may not tra
arXiv:2609.01060v1 Announce Type: cross Abstract: Compact snapshot hyperspectral cameras provide rich instantaneous spectral measurements for ground-level machine vision, but at lower spatial resoluti
arXiv:2609.00155v1 Announce Type: cross Abstract: Latent language identification is often used to argue that multilingual language models route computation through language-specific states, such as En
arXiv:2609.00222v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as judges for subjective tasks, where annotators disagree and the relevant question is not only how a
arXiv:2609.00192v1 Announce Type: new Abstract: Public trust in Autonomous Vehicles (AVs) may depend not only on technical success but also on the fairness of their decision making. While a recent tre