Knowledge catalogue
arXiv:2609.38955v1 Announce Type: cross Abstract: Inverse Reinforcement Learning (IRL) aims to recover a reward function that explains expert demonstrations. Existing IRL methods typically rely on a b
arXiv:2609.39870v1 Announce Type: new Abstract: World-action models (WAMs) augment robot policies with action-conditioned environment dynamics, yet existing approaches largely rely on future observati
arXiv:2609.39888v1 Announce Type: new Abstract: Neural trajectory predictors can reach low prediction error while violating dynamics, actuator limits, or state constraints, especially when controls ar
arXiv:2609.39765v1 Announce Type: new Abstract: Agent memory faces heterogeneous access needs: a single-hop question may require one piece of evidence, whereas a multi-hop question must combine eviden
arXiv:2609.38900v1 Announce Type: new Abstract: Streaming video understanding requires models to process unbounded visual streams while preserving rich visual semantics across vast temporal horizons,
arXiv:2609.39147v1 Announce Type: new Abstract: Current image-to-video models achieve visual realism and physical plausibility, but reasoning about mental states remains unexplored. Actions are driven
arXiv:2406.13547v2 Announce Type: replace Abstract: ModSecurity is widely recognized as the standard open-source Web Application Firewall (WAF), maintained by the OWASP Foundation. It detects maliciou
arXiv:2609.39324v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have recently incorporated world models to provide richer dynamic supervision beyond sparse action labels. However
arXiv:2609.39076v1 Announce Type: new Abstract: A pool of language models can collaborate and improve collectively by learning from one another's responses. These interactions depend on the instructio
arXiv:2609.40362v1 Announce Type: new Abstract: We present Multimodal Flow, a fully continuous generative model of language and vision. Most unified multimodal models either model both language and qu
arXiv:2609.39915v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires embodied agents to generate actions based on instructions and observations. General-purpose multimodal agents
arXiv:2609.38587v1 Announce Type: new Abstract: Electroencephalography (EEG) is recorded continuously over hours, with relevant dynamics spanning timescales from milliseconds to hours. Most EEG founda
arXiv:2609.39673v1 Announce Type: new Abstract: Can a pretrained graph model replace training and tuning a separate predictor for each dataset? Answering this requires evaluating prediction quality al
arXiv:2609.39884v1 Announce Type: cross Abstract: Role prompting elicits specialized behavior from large language models through an expert identity, offering a lightweight way to guide reasoning on de
arXiv:2609.38978v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have become a dominant architecture for video generation, but their efficiency is limited by the quadratic complexity of f
arXiv:2609.38490v1 Announce Type: cross Abstract: Large language model (LLM) agents that reason over clinical records must track changes in a patient's state while preserving the history needed to und
arXiv:2609.40287v1 Announce Type: new Abstract: Physics-constrained generative models aim to generate physical fields that match a target distribution and satisfy prescribed constraints. However, enfo
arXiv:2609.38427v1 Announce Type: cross Abstract: Major venues now publish detailed rules about how authors, reviewers, and area chairs may use AI, and those rules differ by role, by task, and by what
arXiv:2609.39848v1 Announce Type: new Abstract: Foundation models are increasingly adopted across a wide range of applications, often serving as core blocks within AI systems. Yet different foundation
arXiv:2609.39637v1 Announce Type: new Abstract: A central question in deploying teams of mobile robots for persistent monitoring is how task performance scales with fleet size, and whether this scalin
arXiv:2609.38890v1 Announce Type: new Abstract: Outcome-based reinforcement learning (RL) post-trains vision--language--action policies using terminal success signals, but assigns the same trajectory-
arXiv:2609.38632v1 Announce Type: new Abstract: Recent probabilistic weather forecasters train stochastic predictors with the continuous ranked probability score (CRPS) to generate each ensemble membe
arXiv:2609.40051v1 Announce Type: cross Abstract: Estimating causal effects from observational data is central to science and policy, but the effects are not identified when confounders are unmeasured
I put an old RTX 3090 to work as a local coding agent with Qwen3.8-27B, llama.cpp, and OpenCode. Here are the setup and results, including what failed. This is a summary of my own blog post, linked be
arXiv:2609.39229v1 Announce Type: cross Abstract: Automatic evaluation of faithfulness increasingly relies on a large language model acting as a judge, yet the most reliable judges are proprietary fro
arXiv:2609.38869v1 Announce Type: new Abstract: In finance, interpreting machine learning predictions is essential, yet the numerical outputs of explainable AI can be difficult for non-experts to unde
arXiv:2609.40158v1 Announce Type: new Abstract: We focus on legible robot motion generation in social navigation settings. Legibility in human-robot interaction (HRI) is often described as the propert
arXiv:2609.39644v1 Announce Type: new Abstract: Ribosome profiling (Ribo-seq) measures ribosome distributions along mRNAs, but observed occupancy profiles also contain experiment-specific distortions
arXiv:2609.39243v1 Announce Type: new Abstract: Causal interventions such as activation patching and distributed alignment search (DAS) are the main tool for making mechanistic claims about neural net
arXiv:2310.11379v2 Announce Type: replace-cross Abstract: Voice-based interfaces rely on a wake-up word mechanism to initiate communication with devices. However, achieving a robust, energy-efficient,
arXiv:2609.38491v1 Announce Type: new Abstract: Clinical research in psychiatry increasingly relies on large scale collection of spoken language data to identify acoustic and linguistic biomarkers. Ye
arXiv:2609.38956v1 Announce Type: new Abstract: Routing signals of modern vision transformers -- expert gates, attention-residual weights and halting scores -- often improve probes that predict whethe
arXiv:2609.38832v1 Announce Type: new Abstract: Scaling attention parameters can improve language model quality, but retaining full token histories makes additional heads costly at long contexts. Furt
arXiv:2609.39785v1 Announce Type: new Abstract: The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised m
arXiv:2609.38757v1 Announce Type: new Abstract: Large language models are increasingly participating in complex real-world tasks in the form of algorithm-design agents, designing and refining algorith
arXiv:2609.38997v1 Announce Type: new Abstract: Reasoning models often continue generating after their answers have settled. Settle learns when to stop from answer stability in completed traces. It tr
arXiv:2609.39082v1 Announce Type: new Abstract: As new evidence arrives, a sequence model must update what it remembers and how memory influences predictions. While Transformers incur computation and
arXiv:2609.38638v1 Announce Type: cross Abstract: Pickup trucks account for 14% of new light-duty vehicles produced in the United States, yet are among the least aerodynamic. Their open cargo bed adds
arXiv:2609.39382v1 Announce Type: new Abstract: Textual skills provide reusable guidance for large language model agents, but existing approaches often rely on manually curated skill banks or reinforc
arXiv:2609.38987v1 Announce Type: new Abstract: Preference distillation typically treats a teacher response as preferred and the student's own response as rejected. This assumes that self-generated fa
arXiv:2609.39514v1 Announce Type: new Abstract: Vision-language-action (VLA) models bridge multimodal understanding and robotic control, advancing the dominant paradigm for embodied intelligence. Howe
arXiv:2609.40245v1 Announce Type: cross Abstract: Robot navigation in dynamic, human-centered environments requires socially-compliant decisions grounded in robust scene understanding. Recent Vision-L
arXiv:2609.38881v1 Announce Type: new Abstract: Real-time strategy (RTS) games require agents to coordinate economic development, production and construction, base defense, unit organization, and atta
arXiv:2609.38612v1 Announce Type: new Abstract: As natural language drives more applications, language models increasingly run inside programs as decision components: the program sends them the curren
arXiv:2609.38630v1 Announce Type: new Abstract: Privacy redaction must remove personal information while preserving relationships expressed in text. We develop a multilingual named-entity tagger with
arXiv:2609.39926v1 Announce Type: new Abstract: Achieving cross-sensor generalization and arbitrary-scale reconstruction with a single model remains challenging in hyperspectral super-resolution (HSR)
arXiv:2609.39019v1 Announce Type: new Abstract: Multi-view learning seeks to learn more comprehensive representations by exploiting the complementarity and consistency across diverse modalities or vie
arXiv:2609.38434v1 Announce Type: cross Abstract: Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge,
arXiv:2609.38658v1 Announce Type: cross Abstract: TTS systems with autoregressive semantic modeling have demonstrated strong zero-shot voice cloning performance and rich expressive variation, but thei
arXiv:2609.39969v1 Announce Type: cross Abstract: Physical LiDAR attacks are often evaluated using fixed primitives and manually selected parameters, despite their strong dependence on surrounding tra
arXiv:2609.38637v1 Announce Type: cross Abstract: Visual object tracking typically assumes that the initial template and subsequent search frames share the same sensing modality. In practice, sensor a
arXiv:2609.39055v1 Announce Type: new Abstract: How can we personalize a shared expert library from a user's pairwise choices? Prior work can realize different reward trade-offs by merging reward-spec
Today we’re introducing Extract v2.5 - a series of frontier agents tuned for document extraction. The agents (cost-effective, agentic, agentic plus) are tuned for value accuracy and grounding. Our ext
arXiv:2609.39611v1 Announce Type: new Abstract: Agile multi-UAV flight requires accurate and low-latency onboard estimation of the kinematic states of neighboring UAVs for collision avoidance, motion
arXiv:2609.39489v1 Announce Type: new Abstract: Sample-level reliability heterogeneity is common in deep time series learning. Standard training pipelines apply a uniform regularization setting to all
arXiv:2609.39162v1 Announce Type: cross Abstract: Speaker diarization systems based on speaker embeddings and neural diarization exploit complementary forms of speaker information, but their intermedi
arXiv:2609.39157v1 Announce Type: new Abstract: Video object removal presents a uniquely difficult editing challenge. Because a removal prompt specifies only what to erase rather than what to generate
arXiv:2609.40330v1 Announce Type: new Abstract: Automating the search for effective harnesses is an important step toward enabling agents to recursively self-improve. Existing harness optimizations ty
arXiv:2609.39132v1 Announce Type: new Abstract: We study few-step video generation, i.e., distilling a multi-step video generator, which typically requires tens of sampling steps, incurring substantia
arXiv:2609.38895v1 Announce Type: cross Abstract: Approximate machine unlearning seeks to remove the influence of a forget set from a trained model without full retraining. Existing gradient-based met