Knowledge catalogue
arXiv:2607.24512v1 Announce Type: new Abstract: Mathematical models are central to formalizing research problems, yet their documentation often falls short of FAIR principles. Knowledge bases such as
arXiv:2601.17039v2 Announce Type: replace-cross Abstract: Mangroves are critical for climate-change mitigation, requiring reliable monitoring for effective conservation. While deep learning has emerge
arXiv:2607.22566v1 Announce Type: new Abstract: MedLoCoMo is a Medical Long-Context Memory benchmark for patient-specific clinical reasoning over multi-admission medical dialogue. Existing medical QA
arXiv:2505.17492v2 Announce Type: replace Abstract: Project duplication detection is critical for project quality assessment because it helps avoid investment in repeated proposals. Existing methods u
arXiv:2606.18037v2 Announce Type: replace Abstract: Tool-using LLM agents increasingly use the Model Context Protocol (MCP) to answer from heterogeneous evidence sources, including search, APIs, datab
arXiv:2607.14178v2 Announce Type: replace Abstract: Recent advances in Large Language Models have fueled autonomous AI agents capable of tackling complex scientific tasks, yet existing automated resea
arXiv:2607.22926v1 Announce Type: new Abstract: High-impact generative AI makes catastrophic misuse a lifecycle-control problem, not merely a prompt-filtering problem. SAGE is a safety-first, authoriz
arXiv:2607.22554v1 Announce Type: new Abstract: Large language models (LLMs) often achieve strong accuracy on benchmarks, yet it remains unclear how reliably they apply this knowledge when the same qu
arXiv:2607.24588v1 Announce Type: new Abstract: Early warning of extreme weather is essential for mitigating the societal, economic, and environmental risks posed by hazardous weather events. However,
arXiv:2607.24359v1 Announce Type: new Abstract: Real-time long-form digital-human generation relies on causal models to extend audio-visual content while preserving subject appearance and audio-video
arXiv:2607.22574v1 Announce Type: new Abstract: Evidence-based clinical decision making requires specialists to identify, evaluate and synthesize relevant scientific literature. However, PubMed search
Security hyperautomation platform company Torq Ltd. today introduced Torq SOC Brain, a new layer of its artificial intelligence security operations center platform that trains on a customer’s own inve
arXiv:2607.24563v1 Announce Type: new Abstract: Security Operations Centers increasingly rely on automated mapping of Cyber Threat Intelligence reports to MITRE ATT&CK, yet extractor outputs remain fa
arXiv:2607.23702v1 Announce Type: new Abstract: Manipulating objects with hidden internal state, such as a latched microwave, forces a robot to probe before it can act. Yet a robot that has solved an
arXiv:2607.23581v1 Announce Type: new Abstract: Multimodal misinformation verification is challenging because misleading signals may come from different parts of a post and require different forms of
arXiv:2607.23920v1 Announce Type: new Abstract: Emotional image editing requires more than applying affective filters or modifying predefined visual factors: an effective edit must identify what a par
arXiv:2607.23271v1 Announce Type: cross Abstract: Contrastive vision-language models such as CLIP map semantically opposite phrases (e.g., 'a dog' vs. 'not a dog') to nearly identical embeddings, rend
arXiv:2607.22781v1 Announce Type: cross Abstract: High task performance does not show whether a model retains prediction-relevant structural information in its internal representation. Temporal graph
arXiv:2607.24077v1 Announce Type: new Abstract: Optical Character Recognition (OCR) is a key component in the digitization of historical archives. Recently, Vision-Language Models (VLMs) have emerged
arXiv:2607.22376v1 Announce Type: new Abstract: Most endangered languages lack the parallel data required for machine translation, despite the existence of descriptive grammar books. We introduce a pi
I've been working on an open-source workflow automation platform over the past few months, with Ollama as one of the first-class providers rather than treating it as an afterthought. The goal wasn't t
arXiv:2607.21627v1 Announce Type: cross Abstract: End-to-end reinforcement learning can improve the accuracy of compound LLM systems, but it does not constrain how modules divide labor internally. We
Great technical paper from Harvard and MIT. It's on role drift in compound LLM systems. (bookmark it) End-to-end RL improves the accuracy of a multi-module LLM pipeline without constraining how the mo
arXiv:2607.21692v1 Announce Type: cross Abstract: Sparse attention reduces the cost of long contexts by allowing each query to read only selected parts of the input. These selectors are often trained
arXiv:2508.12393v3 Announce Type: replace Abstract: The rapid expansion of medical literature challenges the scalable structuring of domain knowledge. Knowledge Graphs (KGs) offer a solution, yet curr
arXiv:2607.18292v2 Announce Type: replace-cross Abstract: As language models scale, answers start truer but degrade faster: scaling buys capability but erodes reliability. The knowledge-gap account --
arXiv:2607.21927v1 Announce Type: new Abstract: Full self-attention in large language models scales as O(N^2), which limits long-context document analysis to 65,536 tokens and requires costly GPU clus
arXiv:2607.22345v1 Announce Type: new Abstract: Generative social robots (GSRs) powered by large language models offer new possibilities for personalized tutoring in higher education, but also introdu
arXiv:2607.22077v1 Announce Type: cross Abstract: Single-pixel sensing encodes a scene as a short sequence of coded measurements, and image-free methods infer the task directly from that sequence. Rem
arXiv:2607.14514v2 Announce Type: replace Abstract: Training-free ObjectNav agents increasingly use vision-language models (VLMs), yet typically discard acquired scene knowledge after each request. We
There is a lot of buzz around running 100B parameter models on cheap local hardware using ternary (1.58-bit) quantization like Microsoft's BitNet architecture. The theoretical hardware shortcuts are i
There are a few reasons why problems from International Mathematical Olympiad function as a good benchmark for LLMs: - The problems are new, not included in the training data of any model - Hard math
arXiv:2607.20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge,
// Agentic Context Management // Great read for the weekend. (bookmark it) Production agents fail less on reasoning and more on what sits in their context. Conversation history, big prompts, huge tool
arXiv:2607.21503v1 Announce Type: new Abstract: Production AI agents' failures are less often due to an inability to reason well and more often because they cannot manage what is in their reasoning co
arXiv:2607.21106v1 Announce Type: new Abstract: Effective memory is crucial for LLM agents, yet constructing it effectively remains challenging. A memory-construction policy decides what information t
arXiv:2607.20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradit
arXiv:2607.21125v1 Announce Type: new Abstract: Image restoration agents have recently emerged as a flexible paradigm for handling diverse and unpredictable degradations in real-world scenarios. Exist
arXiv:2607.20499v1 Announce Type: new Abstract: Large Language Models generate plausible backend code, but a single-pass paradigm provides no guarantee of correctness or runtime reliability. We presen
arXiv:2607.20946v1 Announce Type: new Abstract: Grievance is one of the warning signs analysts look for when assessing threats of violence. It is increasingly measured at scale from online text, most
Built an open-source AI coding agent that was 7%–75% cheaper than a cold 'claude -p' run on 6/6 well-localized tasks across repositories up to ~82k LOC. The biggest difference: Cold agent: 6.83, 207 t
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:2512.10092v2 Announce Type: replace Abstract: Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors or biases
arXiv:2607.20769v1 Announce Type: new Abstract: Learning-enabled decision systems often use offline data or computation to reduce online compute cost. Despite the empirical success of such approaches,
arXiv:2607.20482v1 Announce Type: new Abstract: Recent advances in large language models have enabled web agents to autonomously execute complex tasks. In practice, users frequently provide underspeci
arXiv:2607.21067v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable ability in generating personalized content by leveraging user histories and contextual cues. H
arXiv:2607.21022v1 Announce Type: new Abstract: Improving video captioning quality typically demands retraining large vision-language models, an expensive and often impractical requirement. Existing t
arXiv:2607.20528v1 Announce Type: new Abstract: Online recommendation platforms increasingly use Large Language Models (LLMs) to extract structured features from ad creatives. While deploying a single
arXiv:2607.20833v1 Announce Type: new Abstract: Large language models increasingly rely on long-form reasoning for complex tasks, yet their reasoning traces may drift away from the supplied context wh
arXiv:2607.21495v1 Announce Type: new Abstract: AI agents are increasingly created inside organizations by non-engineering users through low-code, no-code, and conversational development environments.
arXiv:2607.20916v1 Announce Type: new Abstract: Generative AI lets large language models produce scholarly-looking text within seconds, yet fluency does not equal valid explanation. The deepest risk i
arXiv:2607.21400v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions. However, route-level instructions commonly encod
arXiv:2511.12997v2 Announce Type: replace Abstract: Multimodal LLM-powered agents have recently demonstrated impressive capabilities in web navigation, enabling agents to complete complex browsing tas
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:2607.19363v1 Announce Type: new Abstract: Rotary Position Embedding (RoPE) is widely adopted in Transformers to encode positional information, yet standard implementations enforce a uniform freq
AI infrastructure buildouts are moving so fast that plans made just months ago are already obsolete, forcing hardware makers to rewrite how they design, source and ship the systems powering the next g
arXiv:2607.11338v2 Announce Type: replace Abstract: Symbolic expressions can effectively characterize and predict circuit behavior, but deriving them directly from circuit schematics is challenging. T
arXiv:2607.18688v1 Announce Type: new Abstract: Visible-Infrared Person Re-Identification (VI-ReID) operates under a closed-world assumption, where queries and galleries are from heterogeneous modalit
arXiv:2607.16900v2 Announce Type: replace Abstract: Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories. However, collecting such data at scale
arXiv:2607.18344v2 Announce Type: replace-cross Abstract: EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics. However, existing methods often overlook the perc