Knowledge catalogue
arXiv:2607.23803v1 Announce Type: new Abstract: Multi-view Multi-object Association and Tracking (MvMoAT) associates objects across camera views and tracks them over time, supporting identity persiste
arXiv:2607.22635v1 Announce Type: new Abstract: Target-oriented dialogue systems have demonstrated strong capabilities in completing user goals through interactive conversations. However, existing stu
arXiv:2607.22705v1 Announce Type: new Abstract: Object-centric learning aims to represent scenes as objects whose properties can be reused in new combinations. Existing evaluations usually score segme
arXiv:2607.23123v1 Announce Type: new Abstract: Existing evaluations of large language models cover knowledge, reasoning, coding, and tool use, but they rarely treat a verifiable deliverable produced
arXiv:2607.22067v1 Announce Type: new Abstract: The integration of large language models (LLMs) into the nuclear power industry requires outputs grounded in domain-specific knowledge. This study evalu
arXiv:2607.22139v1 Announce Type: new Abstract: Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluatio
On FrontierCode 1.1 Extended, our benchmark for real-world engineering tasks that grades mergeability and quality, Kimi K3 scores 58.2% with a 63.6% pass rate. Within Devin, it excels on reproducing b
One of the least explored questions in AI is how to build rich open-ended environments for learning. Benchmarks evaluate intelligence, environments shape it. iLands is interesting because it doesn't j
Really interesting concept -- instead of optimizing against static benchmarks or internally generated reward models, agents are evaluated through real economic interactions. Using an external market a
arXiv:2607.21866v1 Announce Type: new Abstract: Prior classical-ML learning-curve work fits power laws to tree, linear, and kernel models on tabular data, but at small scale: typically one curve, one
training to benchmarks ≠ getting to AGI This suggests that Opus 5 's gain on ARC-AGI-3 was the result of specific training to improve on that eval, and not a generalized increase in abstract reasoning
An interesting thing I'm observing from the blog/system card is that on a good chunk of the reported benchmarks (~20-30% from a skim), Opus 5 max thinking leads to a degradation in performance compare
arXiv:2607.20536v1 Announce Type: new Abstract: Tool-use agents that address day-to-day digital tasks such as ordering groceries must not only operate applications, but also interact with the user, e.
arXiv:2607.20476v1 Announce Type: new Abstract: We present an empirical benchmark evaluating how five large language models assess multisensor physical hazard data. Testing 60 scenarios across three c
arXiv:2604.01925v2 Announce Type: replace-cross Abstract: Large Language Models increasingly suppress biased outputs when demographic identity is stated explicitly, yet may still exhibit implicit bias
arXiv:2607.20468v1 Announce Type: new Abstract: AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed workflows or n
arXiv:2607.21228v1 Announce Type: new Abstract: Structured understanding of satellite video is essential for advancing dynamic geospatial scene analysis from low-level perception to high-level cogniti
arXiv:2607.20874v1 Announce Type: new Abstract: Training deep learning models with freely available web images can reduce their dependence on costly manual annotations. Although webly supervised learn
arXiv:2607.15434v3 Announce Type: replace-cross Abstract: Multi-agent systems routinely place one AI agent in authority over another. When a subordinate refuses a task, the manager chooses the outcome
arXiv:2607.19834v1 Announce Type: new Abstract: With the wide application of large language models (LLMs) in real-world scenarios, the value implication of their outputs is crucial. However, existing
arXiv:2607.19409v1 Announce Type: new Abstract: Recent advances in large language models have accelerated deployment of agentic systems in operational finance. Existing benchmarks emphasize measuring
arXiv:2607.20219v1 Announce Type: new Abstract: Large language models (LLMs) can generate fluent Arabic answers, yet factual errors remain difficult to detect, localize, explain, and verify. Existing
arXiv:2607.19407v1 Announce Type: new Abstract: Formal theorem proving has emerged as a frontier challenge for machine learning, yet the ecosystem is fragmented: proofs remain siloed across incompatib
arXiv:2607.19261v1 Announce Type: new Abstract: Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating multi-scal
arXiv:2607.18448v1 Announce Type: cross Abstract: Pathology report generation from whole-slide images (WSIs) is a rapidly growing multimodal learning problem, yet progress is difficult to measure beca
arXiv:2509.24739v4 Announce Type: replace Abstract: Vision-Language Foundation Models (VLMs), trained on large-scale multimodal datasets, have driven significant advances in Artificial Intelligence (A
We benchmarked Gemini 3.6 Flash and Gemini 3.5 Flash Lite on document understanding. We compared against their prior versions - Gemini 3.5 Flash and Gemini 3.1 Flash Lite. 1️⃣ Gemini 3.6 Flash has rou
arXiv:2511.18685v4 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) show promising results as decision-making engines for embodied agents operating in complex, physical enviro
arXiv:2607.11915v1 Announce Type: cross Abstract: Plasma diagnostic models for tokamak fusion devices are almost universally evaluated on clean, complete sensor data. In practice, fusion diagnostics f
Interesting surprise drop from Thinky! The Inkling model looks pretty solid on benchmarks, and it has some little surprises in its architecture: - Small conv layers in several places - An RMSNorm for
The best base model for training isn't about someone else's benchmarks. Excited to offer @thinkymachines' first open-weights model: Inkling. 975B MoE, multimodal, with Apache 2.0. A great foundation f
Thinky with a ~1T param, 41B active, apache-2 model Benchmarks are a clear step up from Nemotron Ultra (55B active), new best American model, and omni input. A bit behind GLM 5.2 on agentic benchies,
Trending AND fastest-growing in the same month? Benchmarks change daily, but only @tryramp has the database of real receipts to track this. We can confirm: demand for open-weight inference and trainin
arXiv:2607.08758v1 Announce Type: new Abstract: Scientific ideas rarely start from a blank page. They inherit mechanisms, repair known limitations, and recombine pieces of earlier work, much like biol
arXiv:2607.08203v1 Announce Type: new Abstract: Progress in colonoscopy polyp segmentation is routinely reported through leaderboard comparisons on a small set of public benchmarks. We argue that this
arXiv:2607.07844v1 Announce Type: cross Abstract: While closed-loop motion planners trained on large-scale, object-level datasets, e.g., nuPlan, demonstrate strong in-distribution (ID) performance, th
arXiv:2607.07343v1 Announce Type: cross Abstract: Online truckload bid acceptance is a closed-loop stochastic decision problem in which a carrier or broker must, in real time, accept or reject a tende
Julie Bort / TechCrunch: Ollama, which helps developers run open-weight AI models locally, raised a 65M Series B led by Theory Venture, following a 15M Series A led by Benchmark — The popular open sou
@OpenAI released GPT 5.6 today and we ran a day 0 benchmark in ParseBench to test improvements in document understanding. The new family of models continues to excel at reading text and tables, but co
Cognition: Cognition releases SWE-1.7, trained from Kimi K2.7 and available at 1,000 tokens/second, claiming it nears GPT-5.5 and Opus 4.8 on benchmarks at a lower cost — Today, we're launching SWE-1.
arXiv:2607.05571v1 Announce Type: new Abstract: Large language models are increasingly explored as AI tutors, yet deploying them in K-12 settings raises concerns around privacy, cost, and reliance on
arXiv:2607.06468v1 Announce Type: new Abstract: We introduce EgoPolice, a carefully curated dataset of real, egocentric police-civilian interactions, sourced from publicly available body-worn camera v
arXiv:2607.05545v1 Announce Type: cross Abstract: LLM conformity is often used to describe cases where a model changes a correct answer toward a peer or group response. We show that most of this appar
Not another demo or benchmark. @ShoucongChen is a senior member of our technical staff. A real project, scoped at 1-month. Delivered in 4 days with GLM5.2 Fast. The best devs deserve >400 t/sec. Take
OpenAI: OpenAI says it found widespread task issues in SWE-Bench Pro, estimates ~30% of tasks are broken, and retracts its earlier recommendation to adopt the benchmark — Through a detailed audit, we
arXiv:2607.06196v1 Announce Type: new Abstract: Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional law
arXiv:2607.06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and
We audited SWE-Bench Pro, one of the most widely used AI coding benchmarks, and found it no longer reliably measures frontier coding capability. We find 30% of SWE-Bench Pro tasks to be broken, and ar
We tuned the harness for @NVIDIAAI Nemotron 3 Ultra. Benchmark-leading performance. 10x lower inference costs. ✅ An aggregate score of 0.86 at a cost of 4.48 ✅ The closest-performing model: 43.48 http
arXiv:2607.06118v1 Announce Type: new Abstract: As web agents increasingly demonstrate capabilities in automated task execution, the development of robust evaluation frameworks for assessing their nav
arXiv:2607.02586v1 Announce Type: new Abstract: Governance frameworks ask AI providers and auditors for documented evaluation evidence, and perturbation-based construct-validity audits are a common fo
arXiv:2607.04072v1 Announce Type: cross Abstract: Large language models can generate plausible quantum code, but it is unclear whether they can reliably target the specific software development kit (S
arXiv:2603.28660v2 Announce Type: replace Abstract: Automated semantic understanding of dense terrestrial laser scanning (TLS) point clouds is a prerequisite for Scan-to-BIM, digital twin maintenance,
arXiv:2607.02825v1 Announce Type: cross Abstract: We release extsc{JavaVulBench}, a benchmark dataset and evaluation harness for Java vulnerability detection. The dataset contains sim30{,}600 Java met
MAI-1 has no independent benchmarks yet, but the ones they released suggest it is worse than Sonnet 4.6. Not sure it is going to be great as a Copilot at Excel and Outlook, especially as there are alr
arXiv:2607.02934v1 Announce Type: cross Abstract: Materials phase diagrams are a core knowledge representation in materials science, encoding temperature,composition, phase stability, and phase transf
arXiv:2607.03644v1 Announce Type: cross Abstract: Decades of orbital missions have produced multi-modal remote sensing data for the Moon, spanning optical imagery, spectroscopy, thermal emission, rada
arXiv:2607.04234v1 Announce Type: cross Abstract: Deformable object manipulation poses challenges beyond task completion: successful execution must also maintain safe physical interaction, holding the
arXiv:2606.19073v2 Announce Type: replace Abstract: Current image editing methods excel at static attributes but fail at complex Human-Object Interactions (HOI), a critical challenge unaddressed by ex
arXiv:2607.04401v1 Announce Type: new Abstract: How robust and generalisable are pathology foundation models and have their scaling limites been reached? We benchmarked twelve pathology foundation mod