Knowledge catalogue
arXiv:2608.04735v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is increasingly treated as an important safety layer for frontier reasoning models. Most monitorability evaluations st
arXiv:2308.04214v2 Announce Type: replace-cross Abstract: In the domain of knowledge representation and reasoning within AI, datalog engines play an ever-increasingly crucial role. The crux of their o
Plus and Pro users can access the updated version of GPT‑5.6 Sol in ChatGPT along with the new slider starting today. This version of GPT-5.6 Sol is for everyday chats, so it will only be available in
arXiv:2608.04794v1 Announce Type: new Abstract: Self-distillation (SD) has emerged as a compute-efficient alternative to reinforcement learning with verifiable rewards: a self-teacher, conditioned on
arXiv:2608.04132v1 Announce Type: new Abstract: High-resolution images and long videos provide vision-language models with rich context for multimodal reasoning and fine-grained perception, but the re
The new GPT-5.6 Sol powers all chats for paid users, including Instant, creating one consistent experience. In our high-stakes factuality evaluation covering finance, medicine and law, the new GPT‑5.6
arXiv:2604.02778v2 Announce Type: replace Abstract: Real-world multimodal knowledge graphs (MMKGs) are dynamic, with new entities, relations, and multimodal knowledge emerging over time. Existing cont
arXiv:2608.03154v1 Announce Type: new Abstract: Biomedical relation extraction (BioRE) extracts structured knowledge from biomedical literature for applications such as knowledge base construction and
arXiv:2608.02791v1 Announce Type: new Abstract: MLLM-based segmentation faces a core segmentation trilemma: high segmentation performance, preserved dialogue ability, and fast inference. Embedding-pre
arXiv:2608.03674v1 Announce Type: new Abstract: Causal diagnostic models must explain how conclusions follow from evidence because diagnoses guide repairs and treatments. Yet serious cases are scarce,
arXiv:2608.03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges. First, purely statistical methods often lack the power to resolve stru
arXiv:2608.03545v1 Announce Type: new Abstract: Test-time reinforcement learning (TTRL) improves the reasoning capabilities of large language models without labeled data by updating the policy with ps
arXiv:2608.03502v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents. Howe
arXiv:2608.03844v1 Announce Type: new Abstract: Memory-augmented LLM agents rely on rich context for long-horizon reasoning and acting, yet their memory modules expose a persistent attack surface for
arXiv:2608.03109v1 Announce Type: new Abstract: Plant root phenotyping is fundamental to understanding below-ground structures, optimizing crop management, and improving agricultural sustainability. T
arXiv:2608.03573v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) exhibit fundamentally different behaviors in enhancing multi-task reasoning for large langu
arXiv:2608.03699v1 Announce Type: new Abstract: Persistent memory helps long-term agents retain knowledge, yet a single update error can repeatedly distort future retrieval and reasoning. Most existin
arXiv:2608.03420v1 Announce Type: new Abstract: Large language models have improved substantially on single-shot reasoning tasks, but their performance in sequential decision-making is less well under
arXiv:2608.03817v1 Announce Type: cross Abstract: Large vision--language models (LVLMs) demonstrate strong multimodal reasoning capabilities but remain prone to hallucination, where model predictions
arXiv:2608.00205v1 Announce Type: new Abstract: Evaluation of faithfulness of text summarization treats a model generated summary as faithful only if every of its sentences is supported by the source
arXiv:2608.00473v1 Announce Type: cross Abstract: Architectural drawings violate the usual assumption behind multi-view reasoning: plans and sections are cuts, while elevations are facade projections,
arXiv:2608.00432v1 Announce Type: new Abstract: Deep research agents are often trained on expensive, environment-grounded tool-use trajectories that require repeated retrieval, document inspection, an
arXiv:2608.01548v1 Announce Type: cross Abstract: Language can be viewed as a formalized subset of thought: a consequence-governed symbolic structure projected from wider situated cognition. Large lan
arXiv:2608.01238v1 Announce Type: new Abstract: Vision Language Models (VLMs) have demonstrated exceptional performance across various tasks. However, they have not yet been thoroughly evaluated on mo
arXiv:2608.01918v1 Announce Type: cross Abstract: Harness design plays a critical role in agent performance by shaping how large language models (LLMs) perceive, reason over, and act within executable
arXiv:2608.01207v1 Announce Type: new Abstract: Test-time scaling lifts large language model reasoning by sampling many candidate solutions and selecting among them, yet the same recipe transfers poor
arXiv:2602.11133v2 Announce Type: replace-cross Abstract: Diffusion language models generate text through iterative refinement, a process that is often computationally inefficient because many tokens
arXiv:2608.00569v1 Announce Type: new Abstract: Deploying billion-parameter Vision-Language-Action (VLA) policies on mobile robots creates a systems conflict: semantic reasoning benefits from cloud GP
arXiv:2608.00106v1 Announce Type: new Abstract: Agentic systems must decide not only what answer to produce, but which reasoning and execution operations should precede it. A controller may answer dir
arXiv:2608.01964v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interde
For robotaxis and other autonomous vehicles (AVs), the hardest problems aren’t the everyday scenarios. They’re the rare, complex situations that are difficult to anticipate and train for. Handling the
arXiv:2602.06423v2 Announce Type: replace Abstract: Humor is a commonly used and intricate human language in daily life. Humor generation, especially in multi-modal scenarios, is a challenging task fo
arXiv:2601.02457v2 Announce Type: replace Abstract: Current foundation models for 3D shapes excel at global tasks (retrieval, classification) but transfer poorly to local part-level reasoning. Recent
arXiv:2608.00902v1 Announce Type: new Abstract: LLM agents accumulate long trajectories of reasoning steps, tool calls, and environment feedback, making the KV cache a major inference bottleneck. KV c
arXiv:2512.06716v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used as the core of agentic systems due to their strong reasoning, planning, and tool-use capabi
arXiv:2608.00042v1 Announce Type: new Abstract: Domain adaptation of small language models (SLMs) has emerged as a practical strategy for deploying capable NLP systems in resource-constrained, high-st
arXiv:2607.28991v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in multimodal understanding and generation. However, when textual inp
arXiv:2607.29082v1 Announce Type: new Abstract: Child malnutrition remains a major public health challenge in low- and middle-income countries, particularly in South Asia, where early identification o
arXiv:2607.28636v1 Announce Type: new Abstract: LLMs increasingly serve as automated judges, but their judgments remain vulnerable to cognitive biases. Existing mitigations mostly rely on prompt-drive
arXiv:2607.29009v1 Announce Type: new Abstract: Multi-robot systems, particularly heterogeneous robot swarms, can improve the efficiency of complex task execution through parallel collaboration and co
arXiv:2603.11872v3 Announce Type: replace-cross Abstract: Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI sy
arXiv:2505.21736v2 Announce Type: replace Abstract: Translation equivariance is a central reason convolutional neural networks have been successful in computer vision. Other symmetries, such as rotati
Hey guys! After a LOT of good feedback on our previous models like Supra-50M-Instruct and -Reasoning, many community likes, follows and upvotes we saw many community requests asking for new models. We
arXiv:2607.28908v1 Announce Type: new Abstract: Reflection, the ability to revisit and revise prior reasoning, is central to how humans improve their answers. Large language models (LLMs) are increasi
arXiv:2603.21846v2 Announce Type: replace Abstract: Explainable AI is increasingly important to scientific discovery. However, existing methods largely ignore that explanation quality is not universal
arXiv:2601.22546v2 Announce Type: replace-cross Abstract: The recent advancements in Large Language Models (LLMs) have attracted interest in exploring their in-context learning abilities and chain-of-
Spent way too much time with V4-Flash-0731 this weekend and wanted to share my vibes as briefly as possible. I sent it through a bit of real-work and some of my personal benchmarks. My quick thoughts
Don’t think I have seen more misconceptions around one model (I count 8) since the fantasies people had before GPT-5 was released. That turned out to be a letdown; so will Astra, for people who are dr
DeepSeek V4 Flash got me thinking... We keep seeing smaller models get way better. A model at a certain parameter count today can be much smarter than a model of the same size from a year or two ago.
**Kimi K3 is Moonshot AI’s 2.8‑trillion‑parameter open‑weight language model—the largest ever released—designed for frontier tasks such as long‑horizon coding and deep reasoning.** Its architecture us
arXiv:2607.28037v1 Announce Type: new Abstract: As LLM-based agents are deployed in complex, multi-step workflows, a critical evaluation gap has emerged: most existing benchmarks judge only final outc
deepseek-ai/DeepSeek-V4-Flash-0731 The latest release in DeepSeek's V4 family, 'with substantially enhanced agentic capabilities'. It's 304 billion parameters - 167GB on Hugging Face - but it appears
arXiv:2607.27420v1 Announce Type: cross Abstract: Humanity's Last Exam (HLE) is widely used to evaluate frontier language models. HLE organizes its questions into eight subject-domain categories, whos
arXiv:2607.27263v1 Announce Type: new Abstract: Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation
arXiv:2607.27654v1 Announce Type: new Abstract: Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of do
arXiv:2607.28135v1 Announce Type: new Abstract: Machine learning for combinatorial optimization typically relies on neural constructors trained via reinforcement learning on large offline datasets for
arXiv:2607.28434v1 Announce Type: cross Abstract: What do a language model's hidden states say about the organization of a single text? From one forward pass, without training, we score every token po
arXiv:2603.16140v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven recent capability advances of large language models across various domains. Recent
arXiv:2607.28312v1 Announce Type: new Abstract: Streaming video understanding requires models to continuously retain useful visual evidence before future questions are known. Existing approaches prima
arXiv:2607.27902v1 Announce Type: new Abstract: Scene text spotting requires high-precision alignment between textual recognition and spatial localization. While visual-token grounding has emerged as