Knowledge catalogue
arXiv:2606.26379v1 Announce Type: new Abstract: Visual prompt tuning has emerged as a parameter-efficient fine-tuning approach for adapting large-scale Vision Transformers (ViTs) to downstream tasks.
arXiv:2606.26315v1 Announce Type: new Abstract: Disturbance-robust UAV position control is easy to demonstrate in benign simulations but much harder to make fast in approach, well behaved near the tar
arXiv:2606.26857v1 Announce Type: new Abstract: The interpretation phase of life cycle assessment often lacks structured mechanisms for translating quantified improvement opportunities addressing envi
arXiv:2601.12062v2 Announce Type: replace Abstract: The core of video-based visible-infrared person re-identification (VVI-ReID) lies in learning sequence-level modal-invariant representations across
arXiv:2602.18131v2 Announce Type: replace Abstract: Temporal Predictive Coding provides a layer-local, parallelisable mechanism for learning in recurrent systems, making it an attractive candidate for
arXiv:2606.26700v1 Announce Type: cross Abstract: Motion feasibility prediction plays a central role in robotics, particularly in task and motion planning and manipulation. A major bottleneck for this
arXiv:2606.26902v1 Announce Type: new Abstract: Multi-task model merging aims to consolidate several task-specific experts into a unified model, yet static merging consistently suffers from parameter
arXiv:2508.08005v4 Announce Type: replace-cross Abstract: The Maximum Clique Problem (MCP) is an NP-hard problem with wide-ranging applications in fields such as bioinformatics, network science, and s
Semafor: Letter: the US lifts its block on Mythos 5, allowing Anthropic to release it to more than 100 US institutions; sources: talks about Fable 5 are ongoing — THE SCOOP — The US government Friday
arXiv:2606.26158v1 Announce Type: new Abstract: When a benchmark's accuracy saturates, it is often retired and replaced with a more challenging version. We show that this approach privileges accuracy
arXiv:2509.09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-qual
arXiv:2606.26183v1 Announce Type: cross Abstract: Building a generalist robot that can leverage prior knowledge for continuous task adaptation remains a significant challenge. Previous works alleviate
arXiv:2606.26849v1 Announce Type: new Abstract: General Salient Object Detection (SOD) aims to identify and segment visually interesting objects from uni-modality or multi-modality scenes, recently ad
arXiv:2606.26740v1 Announce Type: new Abstract: Streaming video editing has made rapid progress, yet practical deployment is still limited by two core issues: maintaining stable backgrounds and non-ed
arXiv:2606.27237v1 Announce Type: new Abstract: Language models (LMs) capture large amounts of factual knowledge applicable to a wide range of tasks, motivating the view of their parameters as a knowl
arXiv:2606.26964v1 Announce Type: new Abstract: As embodied AI and world models increasingly operate in dynamic 3D environments, visual perception must move beyond passively interpreting given observa
arXiv:2606.26107v1 Announce Type: cross Abstract: Sign language communication systems, that integrate emotional expression remain underexplored, particularly for low-resource languages. This pilot stu
arXiv:2606.26711v1 Announce Type: new Abstract: Transforming foundation segmentation models from human-prompted tools into auto-promptable annotators is critical for scalable medical data annotation.
arXiv:2606.26498v1 Announce Type: cross Abstract: This paper addresses model-free continuous-time mean-field control in a setting where the population dynamics evolve continuously according to an unkn
arXiv:2606.26806v1 Announce Type: new Abstract: Long-running language agents need more than memory access. Retrieval systems can fetch past facts at query time, but they do not decide which experience
arXiv:2606.18640v2 Announce Type: replace Abstract: Glucose forecasting algorithms are an important aspect of glycemic control management in type 1 diabetes. So far, the research community has develop
arXiv:2606.26458v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) over knowledge graphs has emerged as a promising approach for grounding large language models, yet existing benchma
arXiv:2606.26712v1 Announce Type: cross Abstract: Skin lesion segmentation is a key task in computer-aided dermatological diagnosis, where accuracy directly impacts downstream analysis and disease cla
This post discusses apparent contradictions in claims about Mythos' cybersecurity capabilities, suggesting tension between assertions that it excels at cyber defense versus reports that it failed to d
arXiv:2606.26265v1 Announce Type: new Abstract: Robot autonomous navigation that accounts for surrounding human activities is crucial for ensuring both safety and natural human-robot interaction in re
arXiv:2606.26671v1 Announce Type: new Abstract: Post-training alignment determines the reasoning and human preference following capabilities of large language models, yet most existing works withhold
arXiv:2606.26185v1 Announce Type: new Abstract: LLM-as-judge ('grader') components are now standard in evaluation harnesses, including safety evaluations where a pass/fail verdict may gate downstream
arXiv:2606.26493v1 Announce Type: new Abstract: Diffusion language models offer a promising alternative to autoregressive models due to their potential for parallel and iterative generation. However,
New paper on giving LLM agents experience that improves the weights and stays readable at the same time. Agent-experience methods split into two camps. Externalized natural-language rules stay interpr
arXiv:2606.27047v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong performance across a wide range of tasks, but ensuring their reliability in highly technical dom
arXiv:2601.13300v2 Announce Type: replace Abstract: Benchmarking large language models (LLMs) is critical for understanding their capabilities, limitations, and robustness. In addition to interface ar
Amrith Ramkumar / Wall Street Journal: OpenAI hopes to make GPT-5.6 generally available in the coming weeks and says “this kind of government access process” should not become the long-term default —
OpenAI Group PBC today introduced GPT-5.6, a new series of large language models that it says can outperform Claude Mythos 5 across certain coding tasks. The most advanced algorithm in the lineup is k
Axios: OpenAI releases three versions of GPT-5.6, called Sol, Terra, and Luna, as a limited preview to ~20 companies, with participants disclosed to the US government — OpenAI is rolling out GPT-5.6 F
OpenAI: OpenAI says GPT-5.6 Sol and Terra were capable of identifying vulnerabilities but were unable to execute autonomous, end-to-end attacks against hardened targets — GPT-5.6 is a new family of th
Less than 24 hours after news broke that OpenAI would stagger its next model release at the request of the Trump administration, that model, GPT-5.6, is here. On Friday, the company unveiled the limit
arXiv:2606.26350v1 Announce Type: new Abstract: Although large language model agents are increasingly applied to quantitative-finance workflows, their evaluation remains fragmented across isolated tas
arXiv:2606.27154v1 Announce Type: new Abstract: Root cause analysis (RCA) poses a holistic test of LLM agentic capabilities, such as long-context understanding, multi-step reasoning, and tool use. How
arXiv:2406.10090v3 Announce Type: replace Abstract: Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization. However, having
arXiv:2606.26317v1 Announce Type: cross Abstract: Accurate fault diagnosis of rolling element bearings in rotating machinery is considered essential for ensuring industrial safety and enabling predict
arXiv:2606.27068v1 Announce Type: cross Abstract: Open-source game theory studies agents whose behavior may depend on one another's decision procedures, but most existing models use discrete or symbol
arXiv:2511.10657v2 Announce Type: replace-cross Abstract: We study self-supervised patent representation learning with contrastive objectives. A standard baseline constructs positives by encoding the
arXiv:2606.26552v1 Announce Type: cross Abstract: The rapid advancement of generative models presents a significant challenge to existing deepfake detection methods, particularly given the widespread
arXiv:2606.26666v1 Announce Type: new Abstract: Autoregressive large language model (LLM) serving is increasingly limited by key-value (KV) cache movement rather than dense matrix multiplication. Mode
arXiv:2606.26551v1 Announce Type: new Abstract: While instruction-based image editing, enabled by multi-modal generative models, has advanced significantly, existing benchmarks lack a comprehensive ev
arXiv:2606.26930v1 Announce Type: new Abstract: Reinforcement Learning like Group Relative Policy Optimization (GRPO) has significantly advanced text-to-image post-training. However, current methods o
arXiv:2606.20632v2 Announce Type: replace-cross Abstract: Multi-LLM systems use multiple language models to deliberate, judge each other's outputs, or coordinate as agents. Their value depends on the
arXiv:2606.26449v1 Announce Type: cross Abstract: Retrieval-augmented systems routinely present citations alongside generated answers, yet a citation does not confirm that the corresponding source mea
We're beginning a limited preview of the GPT‑5.6 series: Sol, our flagship model; Terra, a balanced model for everyday work; and Luna, a fast and affordable model. Terra has competitive performance to
arXiv:2606.26907v1 Announce Type: new Abstract: While text-to-image (T2I) models have achieved remarkable progress, they struggle with real-world requests that are often underspecified, implicit, or d
arXiv:2606.18786v2 Announce Type: replace Abstract: Robot soccer is a challenging testbed for multi-agent reinforcement learning because it combines partial observability, cooperative and adversarial
arXiv:2502.09241v2 Announce Type: replace Abstract: This paper presents a novel approach to real-time safety monitoring in human-robot collaborative manufacturing environments through a wrist-mounted
arXiv:2606.26794v1 Announce Type: cross Abstract: CLIP and its variants are widely adopted visual backbones in multimodal systems, but their pretraining remains dominated by descriptive image-text ali
arXiv:2606.26968v1 Announce Type: new Abstract: Speech-capable models are increasingly deployed in real-world applications across languages. Yet their safety and fairness beyond English settings and u
arXiv:2606.26161v1 Announce Type: new Abstract: Linear directions in activation space have been identified for both refusal and persona traits in instruction-tuned chat models, but the two have been s
arXiv:2510.09976v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models such as OpenVLA, Octo, and pi_0 have shown strong generalization by leveraging large-scale demonstrations, yet t
arXiv:2606.27369v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) for training LLMs typically rely on ground-truth answers to assign rewards, limiting their applica
arXiv:2602.18446v2 Announce Type: replace-cross Abstract: Users increasingly rely on Large Language Models (LLMs) for Deep Research, using them to synthesize diverse sources into structured reports th
arXiv:2606.14954v3 Announce Type: replace-cross Abstract: We develop a general framework for analyzing representation costs of parametric data-fitting methods through their parameter-space regularizer
arXiv:2606.26783v1 Announce Type: cross Abstract: Fang et al. (2025) introduced a null-space constrained projection, named AlphaEdit, for locate-then-edit knowledge editing methods, theoretically guar