Knowledge catalogue
arXiv:2509.17449v2 Announce Type: replace Abstract: Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language. Because of t
arXiv:2608.12511v1 Announce Type: cross Abstract: Privacy documents (e.g., privacy policies) are a central mechanism through which digital services disclose data practices and seek user consent. Over
arXiv:2604.03181v2 Announce Type: replace-cross Abstract: Robotic manipulation requires understanding both the 3D spatial structure of the environment and its temporal evolution, yet most existing pol
arXiv:2608.13317v1 Announce Type: new Abstract: Large language model based multi-agent systems usually communicate in text, i.e., using discrete tokens. However, text introduces a discrete bottleneck.
arXiv:2608.13029v1 Announce Type: cross Abstract: The field of bioinformatics struggles with legacy code - old code that is commonly used but may no longer have a maintainer, or may be written in an n
arXiv:2608.12874v1 Announce Type: new Abstract: Plasticity loss has emerged as a critical challenge in continual learning that significantly hinders the acquisition of sequential tasks. While optimizi
arXiv:2608.13482v1 Announce Type: cross Abstract: As language-model-based AI is increasingly deployed in autonomous settings, aligning its goals and values with those of humans becomes critical. Today
arXiv:2608.13212v1 Announce Type: new Abstract: Networked systems, from power grids to traffic networks and cloud clusters, carry loads across nodes with limited capacity. A node whose load exceeds it
arXiv:2608.13179v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) offers a verifier-bounded performance ceiling for training multi-turn tool-use agents, yet its tra
arXiv:2608.13026v1 Announce Type: new Abstract: Outcome-driven reinforcement learning offers a scalable way to post-train vision-language-action (VLA) policies from sparse task-success feedback. In co
that should be fairly obvious even now Looking back with a few year's perspective, it seems pretty likely to me that @hlntnr was correct that ending OpenAI due to firing CEO Sam Altman 'would actually
arXiv:2606.18479v2 Announce Type: replace Abstract: Reject inference methods are widely used to mitigate survival bias in credit scoring, yet their effectiveness remains poorly understood. We systemat
arXiv:2608.12677v1 Announce Type: new Abstract: Detecting infection-related behavioral changes in mosquitoes from video data is challenging because mosquitoes are small, move rapidly and irregularly,
arXiv:2608.13297v1 Announce Type: new Abstract: In evolutionary search, a weak child can be a valuable ancestor that makes high-fitness regions reachable. Immediate-return control is blind to this del
arXiv:2608.12791v1 Announce Type: cross Abstract: What a finite learning device has recorded and what will hold value for it on future tasks are not the same quantity. We develop a typed accounting fo
“This nightmare will only cost you eleventy trillion dollars…” Wild that to hear tech people speculate that the growing AI backlash is some kind of “Chinese op” when, like, “we’re taking your job and
arXiv:2608.13453v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as generalist robotic policies capable of following diverse language instructions and performing a wi
arXiv:2501.14844v3 Announce Type: replace-cross Abstract: Detecting biases in the outputs produced by generative models is essential to reduce the potential risks associated with their application in
arXiv:2505.02779v2 Announce Type: replace Abstract: Retinal image registration, particularly for color fundus images, is a challenging yet essential task with diverse clinical applications. Existing r
arXiv:2602.05379v2 Announce Type: replace-cross Abstract: Effective reinforcement learning (RL) for complex stochastic systems requires leveraging historical data to improve sample efficiency and acce
arXiv:2608.13263v1 Announce Type: new Abstract: Large language model serving faces a critical memory bottleneck: the KV cache grows with sequence length and batch size. PagedAttention uses fixed-size
arXiv:2608.13237v1 Announce Type: cross Abstract: Multi-round retrieval-augmented generation (RAG) must decide when to stop searching as evidence accumulates. Because the deployed policy is determined
arXiv:2608.13410v1 Announce Type: new Abstract: Parliamentary proceedings are a primary record of democratic deliberation, yet their volume and fragmentation make multi-perspective access difficult fo
wow! new OpenAI study shows “no correlation between how often employees use AI and how much money they make. The ROI of the latest tech boom is still very much up in the air.” Let that sink in. This p
arXiv:2512.06547v4 Announce Type: replace-cross Abstract: Decoupled PPO has been a successful reinforcement learning (RL) algorithm to deal with the high data staleness under the asynchronous RL setti
arXiv:2512.02702v3 Announce Type: replace Abstract: The UK Biobank is a large-scale study collecting imaging and non-imaging health data. Robust and accurate inter-subject image registration of these
arXiv:2608.11483v1 Announce Type: new Abstract: Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and s
arXiv:2409.03682v2 Announce Type: replace Abstract: Learning new tasks by leveraging prior experience is a fundamental trait of intelligent systems. While Model-Agnostic Meta-Learning (MAML) is a lead
arXiv:2608.11947v1 Announce Type: cross Abstract: Multiple-choice benchmarks are widely used to evaluate large language models, but MCQ scores conflate knowledge with sensitivity to option order, whic
arXiv:2608.11363v1 Announce Type: cross Abstract: A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction. Yet
arXiv:2608.11392v1 Announce Type: cross Abstract: Long-running agents periodically compact their context, replacing the transcript with a model-generated summary.Recent work shows that dropping a stan
arXiv:2608.11565v1 Announce Type: cross Abstract: This paper proposes an algorithm for estimating the similarity transformation, namely translation, scale, and rotation, between two images with subpix
anthropic’s best hope is that OpenAI falls apart. which it might. NEW from Ramp AI Index: disappointing adoption of Fable 5 We've heard several reasons from businesses...mainly Fable 5 is just too exp
arXiv:2608.11789v1 Announce Type: new Abstract: Knowledge distillation (KD) trains a compact student by attracting it towards a converged teacher. It is silent about which directions the teacher itsel
arXiv:2608.11431v1 Announce Type: new Abstract: Graph learning presupposes a graph, and tables and relational databases do not come with one. Applying a GNN to them requires deciding which entities be
arXiv:2608.11981v1 Announce Type: new Abstract: Small Language Models (SLMs) have emerged as a more efficient alternative to traditional Large Language Models (LLMs), offering promising potential in r
arXiv:2608.11244v1 Announce Type: new Abstract: Construction standards are critical for building safety and sustainability. Existing standard application workflows rely on keyword-based document retri
arXiv:2608.12078v1 Announce Type: cross Abstract: Learning world models from offline trajectories enables agents to accomplish different tasks through planning. Object-centric (OC) representations, wh
arXiv:2608.11631v1 Announce Type: new Abstract: In open-domain human-computer interaction scenarios, large language models (LLMs) frequently encounter user queries that are ambiguous or incomplete. In
arXiv:2608.12037v1 Announce Type: new Abstract: Modern stochastic predictors can model rich, multi-modal outcome distributions. However, this expressive power comes with challenges in ensuring robust
arXiv:2608.12166v1 Announce Type: cross Abstract: Algorithm registers have been championed as a means of providing transparency on the use of algorithms in public services. Yet potential publics diffe
arXiv:2608.11588v1 Announce Type: new Abstract: Mobile GUI agents remain brittle when deployed to applications absent from source training. We study novel-app generalization under a limited target int
arXiv:2608.12100v1 Announce Type: cross Abstract: In high-stakes applications, reliable confidence estimates are as important as the predictions themselves. Confidence calibration ensures that predict
arXiv:2608.12304v1 Announce Type: new Abstract: Dynamic Master Logic (DML) provides a hierarchical framework for representing system behavior by linking functional objectives to underlying structural
arXiv:2608.11324v1 Announce Type: cross Abstract: This paper proposes a contextual quality-diversity evolutionary reinforcement-learning controller, CQD-ERL, for the supervisory control of a tropical,
arXiv:2608.11656v1 Announce Type: new Abstract: Recent advances in EEG foundation models have demonstrated the potential of large-scale pretraining to enable generalizable neural decoding across subje
arXiv:2608.11590v1 Announce Type: cross Abstract: Human voice generation has made rapid progress in speech generation, singing voice generation, voice cloning, and voice editing. However, most existin
Maxwell Zeff / Wired: Current and former OpenAI employees say pressure to quickly ship products left less time for safety, contributing to incidents like the rogue agent hack — OpenAI's rogue agent ha
arXiv:2608.11495v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) serve as the backbone for high-stakes applications in Machine-Learning-as-a-Service (MLaaS). Still, their black-box deploym
arXiv:2608.11772v1 Announce Type: new Abstract: Self-correction is particularly useful when a failure constrains the next repair. Coding agents benefit from this property because compilers, tests, and
arXiv:2603.29426v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning (MARL) provides a promising solution for cooperative target tracking in networks of autonomous underwater v
arXiv:2608.11909v1 Announce Type: new Abstract: Two accounts recur in explanations of the success of rotary position embeddings (RoPE). Expressivity studies associate periodic position information wit
arXiv:2608.12051v1 Announce Type: new Abstract: Aggregate metrics may not fully reflect performance in insufficiently examined high-risk driving conditions. We propose RISC (Risk-Informed Slice Covera
arXiv:2608.11933v1 Announce Type: new Abstract: Zero-shot anomaly detection (ZSAD) aims to identify anomalies in unseen domains, a setting that is particularly critical for industrial and medical appl
arXiv:2608.11335v1 Announce Type: cross Abstract: Clinical text can narrow down what to segment, but recent text-guided designs emphasize spatial alignment while overlooking frequency content that gov
arXiv:2608.11207v1 Announce Type: new Abstract: When two LLM agents with structurally opposed objectives interact across multiple turns, the absence of a shared goal function produces not competition
arXiv:2608.11641v1 Announce Type: cross Abstract: Energy feasibility under wind uncertainty is a critical safety issue for low-altitude air-ground delivery. In truck-UAV systems, UAVs complete assigne
arXiv:2608.11870v1 Announce Type: new Abstract: In vision-based behavior cloning (BC), conventional image augmentations such as Random Crop and Color Jitter often fall short under substantial visual d
arXiv:2608.11746v1 Announce Type: cross Abstract: Modern systems are increasingly expected to transfer across tasks not specified during training. What data facilitates generalization in these new, un
arXiv:2608.12083v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) achieve strong predictive performance on graph-structured data across domains such as chemistry, biology, and network ana