Knowledge catalogue
arXiv:2610.15908v1 Announce Type: new Abstract: Phase-annotated conversational data is essential for studying the progression of online grooming of minors and for developing early warning tools. Howev
arXiv:2610.19138v1 Announce Type: cross Abstract: How well can a language model turn a large, incomplete and partly contradictory body of documents into a plan when no single correct answer exists? We
arXiv:2610.06170v2 Announce Type: replace Abstract: Biomedical large language model (LLM) evaluation requires auditable assessment of narrow, evolving, source-grounded subspecialty knowledge. Multiple
arXiv:2610.02022v2 Announce Type: replace Abstract: AI-based ideation systems are often evaluated on the novelty of their ideas, and that judgment is increasingly delegated to large language models. S
arXiv:2610.16538v1 Announce Type: cross Abstract: We present PatTransBench, a 12,000-item Chinese-English/Japanese/Korean patent machine translation (PatMT) benchmark with a four-dimensional evaluatio
arXiv:2610.19048v1 Announce Type: new Abstract: Record linkage and demographic inference from names are routine demands in social science. Most approaches to these problems require labeled training da
arXiv:2610.16133v1 Announce Type: cross Abstract: Puzzles offer a natural testbed for Large Language Models (LLMs) reasoning because they pose verifiable goals under partial information and require mu
arXiv:2601.11100v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are reshaping the industrial landscape. However, most practical agents still rely on human-designed scaffolds, mak
arXiv:2610.19674v1 Announce Type: new Abstract: Geometry foundation models estimate camera poses and 3D scene structure from video sequences, but maintaining consistent reconstructions over long horiz
arXiv:2610.14918v1 Announce Type: new Abstract: Evaluating an agent's ability to strictly satisfy diverse and procedurally complex requirements is essential to determining its practical reliability. C
arXiv:2604.05656v2 Announce Type: replace Abstract: Vision-language-action (VLA) models with flow-matching action heads, such as {pi}0 and {pi}0.5, have become a standard recipe for generalist robot m
arXiv:2610.20139v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have emerged as a promising approach to mechanistic interpretability by decomposing language-model activations into sparse, p
arXiv:2610.16661v1 Announce Type: new Abstract: An LLM agent using tools can fail silently. It stops before the task is done, raises no error, and answers fluently. Two kinds of monitor address this,
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery Blog: https://pub.sakana.ai/agentic-scientist/ GitHub: https://github.com/SakanaAI/AI-Scientist-v3 arXiv: htt
arXiv:2610.20083v1 Announce Type: new Abstract: Visual world knowledge is a core capability of multimodal large language models (MLLMs), underpinning knowledge-intensive visual question answering and
arXiv:2610.19611v1 Announce Type: new Abstract: Modern vision-language models can accept multi-image contexts, but aggregate accuracy rarely reveals where a multi-image reasoning pipeline fails. We st
arXiv:2610.16325v1 Announce Type: new Abstract: When a steering intervention changes a language model's behavior, which part of the intervention deserves the credit? We test this question for a target
arXiv:2602.07549v3 Announce Type: replace-cross Abstract: In agentic search, an LLM agent searches the web, reads the pages it finds, and decides what to look for next before returning an answer. But
arXiv:2610.19910v1 Announce Type: new Abstract: Large language model agents increasingly improve by distilling experience into reusable skills, but errors in that experience can persist in learned rul
arXiv:2610.20067v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently encounter conflicts between internal parametric knowledge and external contextual knowledge, yet the mechanisms
arXiv:2610.14334v1 Announce Type: new Abstract: In Model Context Protocol (MCP) systems, LLMs select tools from metadata rather than from their implementations. Names and descriptions summarize what a
arXiv:2610.14915v1 Announce Type: new Abstract: Developing terminal-based agents for long-horizon execution demands synthetic tasks that tightly couple complex instructions with authentic, interactive
arXiv:2610.15403v1 Announce Type: cross Abstract: Crowdsourced disaster imagery provides timely, ground-level observations when conventional disaster mapping is delayed or incomplete, yet its geograph
arXiv:2602.19503v2 Announce Type: replace Abstract: As drone-based object detection technology continues to evolve, the demand is shifting from merely detecting objects to enabling users to accurately
arXiv:2610.14688v1 Announce Type: new Abstract: LLM-augmented data workflows often involve multiple heterogeneous models operating over substantial shared context, such as schemas, tool specifications
arXiv:2610.16324v1 Announce Type: new Abstract: Training-free language-based video anomaly detection (VAD) uses frozen vision--language models (VLMs) and large language models (LLMs) to localize abnor
arXiv:2610.15388v1 Announce Type: new Abstract: Contrastive representation learning (CRL) is a fundamental paradigm for learning transferable representations. Recent work identifies its population tar
arXiv:2610.14589v1 Announce Type: new Abstract: Walking sequences reveal human-interpretable attributes related to body structure, posture, and motion. Gait attribute recognition provides a concrete r
arXiv:2610.14746v1 Announce Type: cross Abstract: World Action Models (WAMs) commonly couple video and action experts through dense, layer-wise Mixture-of-Transformers (MoT) attention, exposing action
arXiv:2606.15320v2 Announce Type: replace Abstract: Multimodal large language models have made rapid progress in video temporal grounding, yet real-world applications routinely require localizing ever
arXiv:2610.18848v1 Announce Type: new Abstract: In-context robot learning enables robots to acquire new behaviors from demonstrations without test-time parameter updates. However, visual and kinematic
arXiv:2510.06640v3 Announce Type: replace-cross Abstract: State Space Models (SSMs) have emerged as efficient, linear-time alternatives to Transformer-Based Models (TBMs) for long-context processing.
arXiv:2610.14875v1 Announce Type: new Abstract: While contrastive learning is widely used to learn deterministic representations, it does not inherently provide estimates of uncertainty. Post-hoc unce
arXiv:2610.17208v1 Announce Type: cross Abstract: Cutting a piece of food looks effortless in human hands. For a robot, however, every cut is a high-contact interaction with a deformable, elasto-plast
arXiv:2610.16177v1 Announce Type: new Abstract: Open-vocabulary 3D Gaussian segmentation typically binds semantic features to the per-Gaussian state via per-scene gradient training (1-4 h) or the more
arXiv:2610.14624v1 Announce Type: new Abstract: Interpreting Earth system conditions is central to understanding environmental risks and informing decisions across weather- and climate-sensitive secto
arXiv:2610.16118v1 Announce Type: new Abstract: Hour-long videos, streaming feeds, and movie-length content are pushing vision-language models (VLMs) past their breaking point -- the standard recipe o
arXiv:2610.14609v1 Announce Type: new Abstract: Determining whether an image is AI-generated does not establish whether the situation it depicts is factually supported. We formulate Image Factuality A
arXiv:2610.16679v1 Announce Type: new Abstract: Tool-augmented vision-language agents compose perception and measurement to reason about spatial relationships, but effective execution also requires tr
arXiv:2610.15207v1 Announce Type: new Abstract: Large Language Models (LLMs) have been shown to be computationally expensive and vulnerable to jailbreak attacks, in which adversarial prompts bypass sa
arXiv:2604.02973v2 Announce Type: replace Abstract: Text-driven human motion generation aims to synthesize realistic motion sequences that follow textual descriptions. Despite recent advances, accurat
arXiv:2610.15921v1 Announce Type: new Abstract: With ever increasing personalized large language model (LLM) services, the next question to answer is how to improve the quality of services/users' expe
arXiv:2610.14579v1 Announce Type: cross Abstract: Embedding models underpin modern job-candidate matching: resumes, job descriptions, and queries are encoded as dense vectors, and recommendations come
arXiv:2610.14568v1 Announce Type: new Abstract: In autoregressive video generation, retaining all historical key-value (KV) states increases memory usage and per-step attention costs as videos grow lo
arXiv:2610.14098v1 Announce Type: new Abstract: Facade articulation at building scale is usually resolved manually or through parametric systems that encode design intent as explicit rules. This paper
arXiv:2610.17742v1 Announce Type: cross Abstract: Dimensionality reduction can preserve the average performance of text embeddings even when a substantial fraction of their dimensions is removed. Aggr
arXiv:2610.17117v1 Announce Type: cross Abstract: Existing interpretability tools do not test whether a representation keeps the same content when only the phrasing changes. We treat each paraphrase p
arXiv:2610.18369v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated promising capabilities for remote sensing reasoning, yet they frequently generate visually un
arXiv:2610.18238v1 Announce Type: new Abstract: Vision-language models (VLMs) have been widely adopted for document parsing and optical character recognition (OCR), as they can accurately extract page
arXiv:2610.14123v1 Announce Type: cross Abstract: AI-assisted scientific discovery increasingly seeks to move beyond generating plausible ideas toward reproducible, constraint-aware workflows that can
arXiv:2603.29602v2 Announce Type: replace-cross Abstract: Existing multi-turn image editing paradigms are often confined to isolated single-step execution. Due to a lack of context-awareness and close
arXiv:2610.16502v1 Announce Type: new Abstract: Discovering and disambiguating entities from multimodal and heterogeneous data is a key challenge in discovery science, where relevant knowledge is ofte
arXiv:2610.18684v1 Announce Type: cross Abstract: Personal-agent memory has largely focused on learning what users know, prefer, and want. However, knowing the user does not necessarily mean knowing w
arXiv:2610.17937v1 Announce Type: new Abstract: Table reasoning with language models typically assumes access to clean textual tables. In practice, tables are often serialized into text, which can cor
arXiv:2610.16895v1 Announce Type: cross Abstract: LLMs are used as high-level controllers for embodied agents, but it remains unclear whether they support collaborative reasoning, such as anticipating
arXiv:2610.17656v1 Announce Type: new Abstract: Reliable video reasoning requires models to produce correct answers and expose supporting visual evidence. Open-o3-Video enables verifiable reasoning by
arXiv:2507.05291v2 Announce Type: replace Abstract: We propose a physics-informed machine learning framework called P-DivGNN to reconstruct local stress fields at the micro-scale, in the context of mu
arXiv:2605.24273v2 Announce Type: replace Abstract: Automated detection and masking of individual methane plumes from satellite imagery is important for operational emission attribution and quantifica
arXiv:2610.17409v1 Announce Type: new Abstract: We argue that cooperative multi-agent reinforcement learning (MARL) should treat test-time coordination as part of the problem definition rather than a
arXiv:2610.18481v1 Announce Type: new Abstract: Protein sequence, structure, and functional annotations each capture complementary aspects of protein biology, yet existing tokenizers encode each modal