Knowledge catalogue
arXiv:2608.19006v1 Announce Type: new Abstract: Hate speech is a real and timely threat that affects a large portion of online users, especially youth and minority groups. While building reliable and
I have a big database of local court decisions with a legal sentence which is like a paragraph summary of the doc. I've been tinkering with FTing for many days now, all results inconclusive never beat
arXiv:2608.18080v1 Announce Type: new Abstract: We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy
arXiv:2608.18681v1 Announce Type: new Abstract: We introduce a failure-aware adversarial retrieval-augmented framework for improving robustness in natural language understanding. Rather than selecting
arXiv:2608.19059v1 Announce Type: cross Abstract: Long-term robot operation in evolving environments requires object-level understanding that persists across repeated revisits. Existing systems either
arXiv:2608.18744v1 Announce Type: new Abstract: Agents improve quickly against a reliable automatic metric and stall without one, and the applications that need them most, report generation among them
arXiv:2608.19000v1 Announce Type: new Abstract: Automating graphic design synthesis from user-provided elements requires both a coherent overall composition and the exact preservation of each asset. E
arXiv:2608.18094v1 Announce Type: cross Abstract: Large pretrained language models have demonstrated remarkable capabilities across diverse languages, yet critically underrepresented low-resource lang
arXiv:2608.18846v1 Announce Type: new Abstract: Last-mile delivery aims to handle dynamically arriving orders with couriers while modeling complex spatial and temporal correlations. Recent learning-ba
arXiv:2608.18078v1 Announce Type: new Abstract: This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and should be requi
I had mistakenly been using a base for H3 prompting from some random tip / example by someone. It worked ok, I thought. But I was getting a bit frustrated because almost every time I was making a long
arXiv:2608.18227v1 Announce Type: new Abstract: Push-T is an iconic benchmark for learning manipulation policies from human demonstrations. The robot must use a single point of contact to push a T-sha
arXiv:2608.18545v1 Announce Type: new Abstract: Multilingual large language models often generalize across languages, and prior work suggests that their internal mechanisms can overlap cross-lingually
arXiv:2608.18779v1 Announce Type: cross Abstract: Semantic-ID mappings are reusable interfaces between item tokenizers and generative recommenders, yet released mappings rarely state whether they are
arXiv:2608.19197v1 Announce Type: cross Abstract: Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environ
arXiv:2608.18222v1 Announce Type: cross Abstract: Recurrent-depth reasoners aim to solve harder problems by iterating their update longer at test time, but additional iterations can improve, preserve,
arXiv:2608.18836v1 Announce Type: new Abstract: Utilities lose a substantial share of treated water to leakage, yet rarely trust artificial-intelligence localizers to dispatch crews: guessing everywhe
arXiv:2608.19083v1 Announce Type: cross Abstract: Readable AI output can leave an evaluability gap: even when the source is shown, an overall-quality judgment may not reflect what an output preserves.
arXiv:2608.18116v1 Announce Type: new Abstract: Vision-language models enable zero-shot classification through natural language prompts, but performance is sensitive to prompt formulation, especially
arXiv:2608.17975v1 Announce Type: cross Abstract: Programmatic representations provide a compelling paradigm for 3D content creation, enabling fine-grained edits, interpretability, and explicit struct
arXiv:2608.17184v1 Announce Type: new Abstract: Job Safety Analysis (JSA) and pre-task planning can benefit from prior incident records, yet historical accident data is often stored as unstructured na
arXiv:2608.17856v1 Announce Type: new Abstract: Tabular prediction is a critical task across numerous applications. The recent success of large language models has sparked various approaches for adapt
arXiv:2608.17684v1 Announce Type: new Abstract: Self-evolving agents turn experience into reusable skills, workflows, or memories, but post-evolution accuracy alone does not show whether learned behav
arXiv:2606.15315v2 Announce Type: replace Abstract: Personalized public transit routing in public transit systems remains challenging due to the difficulty of capturing and integrating diverse user pr
arXiv:2608.17932v1 Announce Type: cross Abstract: Groups routinely complete projects that no single member can plan, execute, or verify alone. We propose a formal model of this phenomenon, Collective
Counterpoint: “If alignment issues are becoming big enough that OpenAI is willing to commit 20% of research inference compute to chain-of-thought monitoring [suggesting] that suggests that alignment i
arXiv:2608.17050v1 Announce Type: cross Abstract: Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to exter
arXiv:2608.17632v1 Announce Type: cross Abstract: Large language models (LLMs) can both expand underspecified queries and encode text as dense representations, suggesting a unified model for query exp
arXiv:2608.17067v1 Announce Type: new Abstract: As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as
arXiv:2608.17950v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable multi-hop reasoning capabilities over long contexts, yet the internal mechanisms enabling these dist
arXiv:2604.00547v2 Announce Type: replace Abstract: Unified Multimodal Large Models (UMLMs) integrate understanding and generation capabilities within a single architecture. While unified architecture
arXiv:2608.17567v1 Announce Type: cross Abstract: Pretrained molecular language models are increasingly used as molecular encoders for learning structure-property relationships. However, their practic
arXiv:2603.02876v2 Announce Type: replace Abstract: Large Language Model personas, explicit profiles specifying a user's attributes, preferences, and behavioural tendencies, are increasingly used to s
arXiv:2608.17247v1 Announce Type: new Abstract: Personalized agents must decide whether retrieved user memory should be used, ignored, updated, or queried before it affects a current task. We use this
It's felt like harness month on Twitter. We're seeing much faster and cheaper gains on a bunch of benchmarks by focusing on harness improvement rather than model improvement. Allowing the harness to b
arXiv:2601.22170v2 Announce Type: replace-cross Abstract: Large language models (LLMs) process and predict sequences containing text to answer questions, and address tasks including document summariza
arXiv:2608.17386v1 Announce Type: new Abstract: Foundation-model policies for robotic manipulation are advancing rapidly on task success, but rigorous evaluation of whether they succeed safely is stil
arXiv:2608.18009v1 Announce Type: new Abstract: Answering questions accurately and efficiently in embodied scenarios presents significant challenges due to limited computational and memory resources f
arXiv:2608.16921v2 Announce Type: cross Abstract: Effective cybersecurity operations require timely and accurate analysis of large-scale heterogeneous security information; however, analysts increasin
arXiv:2608.17687v1 Announce Type: new Abstract: Despite their widespread use, Large Language Models (LLMs) remain limited by a fundamental problem: the generation of plausible but false content, known
arXiv:2608.17402v1 Announce Type: new Abstract: Vision encoders are a critical component of vision-language models, and scaling their capacity effectively improves performance. However, dense scaling
arXiv:2506.01297v5 Announce Type: replace Abstract: Representation learning of geospatial locations remains a core challenge in achieving general geospatial intelligence, with increasingly diverging p
arXiv:2608.17823v1 Announce Type: cross Abstract: Powered two-wheeler riders face critical safety challenges in low- and middle-income countries, yet limited studies exist on how cognitive stressors s
NVIDIA’s Cosmos 3 Edge is a 4‑billion‑parameter omni‑model (with a 2‑billion‑parameter Nemotron reasoner) that can run on an NVIDIA Jetson Thor, providing on‑device policy inference for robot manipula
arXiv:2608.17947v1 Announce Type: new Abstract: Large language models can generate executable programs, which makes it possible to search directly over procedural content generators rather than indivi
arXiv:2603.08913v2 Announce Type: replace Abstract: Genomic language models (GLMs) have emerged as powerful tools for learning representations of DNA sequences, enabling advances in variant prediction
arXiv:2608.17574v1 Announce Type: new Abstract: How cautious should an agent be while it is still learning its environment? We propose RATTL (Risk-Adversarial Total-Reward Learning), which ties cautio
arXiv:2608.16900v1 Announce Type: cross Abstract: Language-model agents increasingly produce quantum-science results; we ask whether the same agentic paradigm can also scrutinize them in an auditable,
arXiv:2510.01832v2 Announce Type: replace Abstract: Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complic
arXiv:2608.16934v1 Announce Type: cross Abstract: RTL code generation is a critical stage in hardware design, and the emergence of agentic systems offers new opportunities to automate this process. To
arXiv:2608.17170v1 Announce Type: new Abstract: Algorithm selection for constraint satisfaction problems requires extracting features that capture problem structure. Manually designing feature extract
arXiv:2505.22533v2 Announce Type: replace-cross Abstract: In this paper, we introduce a novel quantum generative model for synthesizing tabular data. Synthetic data is valuable in scenarios where real
arXiv:2608.17180v1 Announce Type: cross Abstract: Contextual Reinforcement Learning (CRL) seeks to generalize classical RL by maximizing task coverage across a context space of related tasks. While pr
arXiv:2608.17218v1 Announce Type: new Abstract: Works of literature are complicated; they balance plot, suspense, surprise, and artistic expression. Summaries of literature prioritize plot, and theref
Today we release my favorite episode of Training Data yet: the great Rich Sutton. @RichardSSutton wrote the textbook, wrote The Bitter Lesson (and many other on-point essays like 'Self-Verification, T
arXiv:2608.17128v1 Announce Type: new Abstract: A personal AI system needs a model of the user's goals, constraints, and ongoing commitments to plan and act on their behalf, and the quality of that mo
arXiv:2608.17153v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has significantly enhanced the performance of large language models (LLMs), yet these systems remain vulnerable to
Very interesting new work from Microsoft. (bookmark it) This work is related to this emerging theme of leveraging harnesses for model post-training. Modern agents run inside a harness that owns tools,
arXiv:2608.17055v1 Announce Type: cross Abstract: Large Language Models (LLMs) are machine learning (ML) models that have an increasingly large carbon footprint through their development and use. Effo
arXiv:2608.17490v1 Announce Type: new Abstract: Foundation-model hubs turn multi-view fusion into a selection problem: from a large heterogeneous encoder pool, which views should be fused, and how man