What Symmetry Buys a Learned Motion Planner
arXiv:2609.10033v1 Announce Type: new Abstract: Learning-based motion planners pay at training what classical planners pay per query. Trained in world coordinates, they relearn the same motion at ever
Knowledge catalogue
arXiv:2609.10033v1 Announce Type: new Abstract: Learning-based motion planners pay at training what classical planners pay per query. Trained in world coordinates, they relearn the same motion at ever
arXiv:2609.05531v1 Announce Type: cross Abstract: No specification says how a governed autotelic AI agent organization, where agents pursue self-generated goals inside guardrails, should be designed a
arXiv:2609.06941v1 Announce Type: new Abstract: Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundation
arXiv:2609.09696v1 Announce Type: new Abstract: Large language models are increasingly proposed as automated auditors of document quality, yet their reliability as detectors of planted errors is poorl
arXiv:2609.07987v1 Announce Type: new Abstract: LLM-based digital twins promise to reduce repeated human data collection by generating person- specific responses, yet existing evaluations provide litt
arXiv:2609.08736v1 Announce Type: new Abstract: We study certificates of positivity and optimality for learning problems whose objectives and constraints need not be polynomial. We isolate an axiomati
arXiv:2504.00285v2 Announce Type: replace Abstract: Large Language Models (LLMs) are effective at deceiving when prompted to do so. Models that demonstrate better performance on reasoning tasks are al
arXiv:2609.09887v1 Announce Type: new Abstract: LLMs have been used to simulate human decision-making in professional settings, yet their behaviors in common-law jury trials remain unexplored. We stud
arXiv:2609.09854v1 Announce Type: cross Abstract: Low-bit quantization can achieve high recall on some vector representations and fail sharply on others, while average distortion and global rank corre
arXiv:2609.05441v1 Announce Type: new Abstract: Long-term memory for LLM agents is evaluated today by conversational recall benchmarks (LoCoMo, LongMemEval), which measure question answering over dial
arXiv:2609.10261v1 Announce Type: new Abstract: Multi-modal medical image segmentation leverages complementary diagnostic information, yet fusion can underperform single-modality baselines when spatia
arXiv:2609.07741v1 Announce Type: new Abstract: Persistent AI assistants are intended to extend human attention, memory, and coordination across changing digital and physical environments. To be truly
arXiv:2508.15754v2 Announce Type: replace-cross Abstract: Tool use is often assumed to monotonically improve reasoning, where external evidence is expected to help when relevant and be ignored when ir
arXiv:2609.05871v1 Announce Type: cross Abstract: Audio-conditioned language models often underuse acoustic cues such as prosody, emotion, and non-speech sounds, raising the question of whether ASR-su
arXiv:2609.09684v1 Announce Type: new Abstract: Medical question-answering datasets often contain answer labels, whereas high-quality rationales remain scarce, noisy, or costly to validate. This chang
arXiv:2609.09628v1 Announce Type: cross Abstract: Inferring speaker relationships from spoken conversations is an important step towards socially aware speech understanding. However, this task remains
arXiv:2609.10192v1 Announce Type: new Abstract: Political debates are often analyzed through Argument Mining (AM) to investigate the key arguments that drive them. However, political arguments are rar
arXiv:2601.03103v2 Announce Type: replace-cross Abstract: Humor preferences vary widely across individuals and cultures, complicating the evaluation of humor using large language models (LLMs). In thi
arXiv:2609.05677v1 Announce Type: cross Abstract: Lifelong LLM agents increasingly rely on external skill artifacts as one element for preserving and reusing capabilities over time. These skills (usua
arXiv:2510.15047v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) as agents often fail to improve in new environments. We identify and characterize a failure mode we call explorat
arXiv:2609.10355v1 Announce Type: cross Abstract: Video understanding has rapidly evolved toward video large language models (VideoLLMs): systems that couple video representations with pretrained larg
arXiv:2609.09656v1 Announce Type: cross Abstract: We identify a diagonal saturation principle in modal inverse problems: when truncation noise is isotropic, the Bayes-optimal Tikhonov shape is a close
arXiv:2609.05591v1 Announce Type: cross Abstract: Safety evaluations typically focus on individual agents, but interacting agents can spread harmful information and influence the environment in which
arXiv:2609.07299v1 Announce Type: new Abstract: Learned world models typically assume that observations arrive synchronously, an abstraction inherited from simulators that return a complete state vect
arXiv:2609.09163v1 Announce Type: new Abstract: LLMs generalize across a domain only after seeing many real, labeled examples, which most domains lack. We study a way to manufacture it cheaply. When a
arXiv:2609.08162v1 Announce Type: new Abstract: How can vision-language-action (VLA) models adapt to new environments where world dynamics shift? While recent research has combined world modeling and
arXiv:2609.09402v1 Announce Type: cross Abstract: Enterprises managing large X.509 certificate inventories face a prioritization problem: deterministic analysis tools that precisely identify standards
arXiv:2609.09166v1 Announce Type: new Abstract: This paper investigates collaborative speculative decoding (CoSD), a distributed large language model (LLM) inference framework in which an on-device sm
arXiv:2609.09677v1 Announce Type: new Abstract: Incremental-text streaming text-to-speech (TTS) needs online text progress tracking for synchronized highlighting, interruption handling, and dialogue-h
arXiv:2609.08672v1 Announce Type: cross Abstract: Streaming automatic speech recognition (ASR) for real-time voice agents and full-duplex dialogue must provide accurate partial transcripts with low co
arXiv:2609.09388v1 Announce Type: new Abstract: Brain-age prediction models are commonly evaluated by predictive accuracy, yet accurate predictions alone do not establish that a model relies on reprod
arXiv:2609.05701v1 Announce Type: cross Abstract: One of the biggest risks faced by Software Defined Networks (SDN) is the Distributed Denial of Service (DDoS) attack in which a compromised controller
arXiv:2609.07784v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for everyday assistance, yet existing benchmarks only partially reflect the requests users naturally
arXiv:2609.06842v1 Announce Type: cross Abstract: When non-expert users ask LLMs for assistance, their queries can often have misconceptions (e.g., 'How do I parse XML with regex?'). In such cases, of
arXiv:2609.10153v1 Announce Type: new Abstract: Arabic morphology remains challenging for large language models, since fluent generation does not guarantee accurate morphosyntactic control. Existing A
arXiv:2609.06851v1 Announce Type: cross Abstract: Broad misalignment has been produced by finetuning on narrow data, harmful or benign, and in context only by demonstrations of the undesirable behavio
arXiv:2609.07675v1 Announce Type: cross Abstract: Transaction-local controls answer whether one financial request may proceed, but market behavior can be distributed across messages, agents, assets, a
arXiv:2609.10051v1 Announce Type: cross Abstract: Voice-cloning fraud increasingly relies on surgical injection: a genuine conversation in which only one or two sentences are replaced by synthetic spe
arXiv:2609.08247v1 Announce Type: new Abstract: Wallet reputation scores decide who receives an airdrop, who can borrow, and who enters an allowlist across decentralised finance. They almost always be
arXiv:2609.06535v1 Announce Type: new Abstract: We introduce 3DHarnessBench, a benchmark that evaluates the agentic ability of frontier vision-language models (VLMs) to recover 3D geometry as Blender
arXiv:2609.08224v1 Announce Type: new Abstract: Intermediate representations are key to bridging the modality gap between generalizable manipulation policies and large-scale pretrained vision-language
arXiv:2609.08211v1 Announce Type: cross Abstract: Real-world Multi-Objective Reinforcement Learning (MORL) often suffers from sparse rewards, reward conflicts, and late-stage reward tug-of-war, causin
arXiv:2609.08013v1 Announce Type: new Abstract: Human pose estimation and keypoint-based action recognition models are increasingly deployed as components of video understanding pipelines, yet their v
arXiv:2609.05985v1 Announce Type: new Abstract: Robots that follow open-ended language instructions need to connect semantic intent to visual scene understanding, geometric feasibility, object states,
arXiv:2609.05877v1 Announce Type: new Abstract: Selecting compact training sets for machine-learned interatomic potentials requires deciding whether to preserve structural diversity or target configur
arXiv:2609.08826v1 Announce Type: cross Abstract: When an external reference set (an anchor) is used to decompose an LLM-judge panel's error into a quality signal and a shared common-mode error, stand
arXiv:2609.06507v1 Announce Type: new Abstract: Accurate vehicle license plate detection is essential for applications such as intelligent transportation systems, toll collection, parking management,
arXiv:2609.06276v1 Announce Type: new Abstract: This study evaluates pneumonia classification in chest X-rays using VGG19, MobileNetV2, ResNet50, and a custom CNN, and explores Generative Adversarial
arXiv:2609.08770v1 Announce Type: new Abstract: Input devices for robotic microsurgery are frequently described as preserving the surgeon's trained technique, but the claim is rarely measured. We comp
arXiv:2609.05919v1 Announce Type: new Abstract: We propose a graph dictionary learning (GDL) framework where each graph is represented as a zero-mean Gaussian distribution derived from its filtered La
arXiv:2609.09066v1 Announce Type: new Abstract: Maritime target tracking over large distances often requires multi-agent teams without centralized coordination, and intermittent communication. Each ag
arXiv:2609.05895v1 Announce Type: new Abstract: We study a finite-horizon online resource allocation problem with initial resource capacities proportional to the horizon. In each period, a request typ
arXiv:2609.09126v1 Announce Type: cross Abstract: Amari's contributions to information geometry and machine learning are well known. Here, we revisit Amari's work on Bayesian duality which has not rec
arXiv:2609.08070v1 Announce Type: cross Abstract: The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timing
arXiv:2609.09010v1 Announce Type: new Abstract: Three-dimensional femoral reconstruction from radiographs supports surgical planning, implant sizing, and post-operative follow-up, but remains ill-pose
arXiv:2609.08078v1 Announce Type: new Abstract: Food waste in the restaurant sector poses a substantial challenge to environmental sustainability and economic efficiency. This paper presents an explor
arXiv:2609.08307v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as backends for intelligent web services, but serving them across the edge continuum requires balan
arXiv:2609.08338v1 Announce Type: new Abstract: Object detection and tracking are fundamental components of perception systems for autonomous driving. Achieving robust performance under adverse condit
arXiv:2609.05748v1 Announce Type: new Abstract: Alternative property recommendations play a critical role in vacation rental marketplaces, helping users discover relevant options when viewing a specif
arXiv:2609.07930v1 Announce Type: new Abstract: Deep learning models have been increasingly applied to Time Series Forecasting (TSF) in recent years. Transformer-based and MLP-based models have both b