Knowledge catalogue
arXiv:2604.21229v1 Announce Type: cross Abstract: Large language model assistants are increasingly expected to retain and reason over information accumulated across many sessions. We introduce Engrama
arXiv:2604.20972v1 Announce Type: new Abstract: Content moderation systems are typically evaluated by measuring agreement with human labels. In rule-governed environments this assumption fails: multip
arXiv:2602.19208v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for Large Language Model (LLM) reasoning, yet current methods face
arXiv:2604.21053v1 Announce Type: cross Abstract: Robotic systems operating in human environments must reason about how object interactions evolve over time, which actions are currently being performe
arXiv:2603.07961v3 Announce Type: replace Abstract: Scene Graph Generation (SGG) structures visual scenes as graphs of objects and their relations. While Multimodal Large Language Models (MLLMs) have
arXiv:2601.10003v2 Announce Type: replace Abstract: Constructing Knowledge Graphs (KGs) from unstructured text provides a structured framework for knowledge representation and reasoning, yet current L
arXiv:2503.16416v2 Announce Type: replace Abstract: LLM-based agents represent a paradigm shift in AI, enabling autonomous systems to plan, reason, and use tools while interacting with dynamic environ
arXiv:2604.19839v1 Announce Type: cross Abstract: Vision-language models (VLMs) have shown strong perception and reasoning abilities for instruction-following embodied agents. However, despite these a
arXiv:2604.20244v1 Announce Type: cross Abstract: Knowledge distillation (KD) is a powerful paradigm for compressing large language models (LLMs), whose effectiveness depends on intertwined choices of
arXiv:2510.13928v2 Announce Type: replace-cross Abstract: We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language mode
arXiv:2604.19137v1 Announce Type: new Abstract: Knowledge Graph (KG) can effectively integrate valuable information from massive data, and thus has been rapidly developed and widely used in many field
arXiv:2604.19298v1 Announce Type: cross Abstract: We introduce IndiaFinBench, to our knowledge the first publicly available evaluation benchmark for assessing large language model (LLM) performance on
arXiv:2604.18913v1 Announce Type: new Abstract: Knowledge graphs (KGs) are increasingly integrated with large language models (LLMs) to provide structured, verifiable reasoning. A core operation in th
arXiv:2604.19401v1 Announce Type: cross Abstract: Knowledge Graph Embeddings (KGEs) support a wide range of downstream tasks over Knowledge Graphs (KGs). In practice, KGs evolve as new entities and fa
arXiv:2411.18275v2 Announce Type: replace Abstract: Vision-language models (VLMs) have significantly advanced autonomous driving (AD) by enhancing reasoning capabilities. However, these models remain
arXiv:2604.17609v1 Announce Type: new Abstract: LLM-based agents are assumed to integrate environmental observations into their reasoning: discovering highly relevant but unexpected information should
arXiv:2509.20823v5 Announce Type: replace-cross Abstract: Time series captioning, the task of describing time series in natural language, requires numeric and temporal reasoning, trend interpretation,
arXiv:2604.18245v1 Announce Type: new Abstract: Large language models are increasingly deployed as protocols: structured multi-call procedures that spend additional computation to transform a baseline
arXiv:2512.00592v2 Announce Type: replace Abstract: Autonomous navigation in partially observable environments requires agents to reason beyond immediate sensor input, exploit occlusion, and ensure sa
arXiv:2510.06700v3 Announce Type: replace Abstract: Both humans and large language models (LLMs) exhibit content effects: biases in which the plausibility of the semantic content of a reasoning proble
arXiv:2604.16881v1 Announce Type: new Abstract: Cross-cultural entity translation remains challenging for large language models (LLMs) as literal or phonetic renderings are usually yielded instead of
arXiv:2604.17274v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in various perception and reasoning tasks. Despite this success, ens
arXiv:2604.17224v1 Announce Type: new Abstract: Recursive architectures such as Tiny Recursive Models (TRMs) perform implicit reasoning through iterative latent computation, yet the geometric structur
arXiv:2604.04815v2 Announce Type: replace Abstract: The rapid development of Large Language Models (LLMs) has transformed fake news detection and fact-checking tasks from simple classification to comp
OK, here's a resolution - I managed to get it to think using these settings: 'thinking': { 'type': 'adaptive', 'display': 'summarized' }, 'output_config': { 'effort': 'max' } Without 'display': 'summa
arXiv:2604.18459v1 Announce Type: new Abstract: Visual agents operating in the wild must respond to queries precisely when sufficient evidence first appears in a video stream, a critical capability th
arXiv:2505.21471v2 Announce Type: replace Abstract: With the rapid advancement of post-training techniques for reasoning and information seeking, large language models (LLMs) can incorporate a large q
arXiv:2509.24328v2 Announce Type: replace Abstract: LLMs have low GPU efficiency and high latency due to autoregressive decoding. Speculative decoding (SD) mitigates this using a small draft model to
arXiv:2509.02547v5 Announce Type: replace-cross Abstract: The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large
arXiv:2604.06155v2 Announce Type: replace-cross Abstract: Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (N
arXiv:2603.22368v2 Announce Type: replace Abstract: Visualizations help communicate data insights, but deceptive data representations can distort their interpretation and propagate misinformation. Whi
arXiv:2506.00079v2 Announce Type: replace-cross Abstract: The rapid integration of Large Language Models (LLMs) in high-stakes decision-making -- such as allocating scarce resources like donor organs
Ethan Mollick shared context about OpenAI's o1-preview model launch, likely discussing the capabilities and implications of this new reasoning-focused AI system that represents a shift toward models d
arXiv:2511.00739v3 Announce Type: replace Abstract: Agentic AI serving converts monolithic LLM-based inference to autonomous problem-solvers that can plan, call tools, perform reasoning, and adapt on
arXiv:2601.21262v3 Announce Type: replace Abstract: Although Multimodal Large Language Models (MLLMs) have shown remarkable potential in Visual Document Retrieval (VDR) through generating high-quality
arXiv:2604.13151v1 Announce Type: new Abstract: Language Model (LM) agents are increasingly used in complex open-ended decision-making tasks, from AI coding to physical AI. A core requirement in these
arXiv:2604.14388v1 Announce Type: new Abstract: Humans routinely infer taste, smell, texture, and even sound from food images a phenomenon well studied in cognitive science. However, prior vision lang
arXiv:2604.14262v1 Announce Type: new Abstract: GUI grounding models report over 85% accuracy on standard benchmarks, yet drop 27-56 percentage points when instructions require spatial reasoning rathe
arXiv:2604.15149v1 Announce Type: new Abstract: As reinforcement Learning with Verifiable Rewards (RLVR) has become the dominant paradigm for scaling reasoning capabilities in LLMs, a new failure mode
arXiv:2604.13699v1 Announce Type: cross Abstract: Large language models (LLMs) have enabled agentic AI systems for scientific discovery, but most approaches remain limited to textbased reasoning witho
arXiv:2509.06921v2 Announce Type: replace-cross Abstract: Cybersecurity demands both rapid pattern recognition and deliberative reasoning, yet purely neural or purely symbolic approaches each address
arXiv:2604.14399v1 Announce Type: new Abstract: Autonomous on-orbit servicing demands embodied agents that perceive through visual sensors, reason about 3D spatial situations, and execute multi-phase
arXiv:2512.18436v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown impressive capability to understand and develop code. However, their capability to rigorously reason a
arXiv:2510.26491v2 Announce Type: replace Abstract: Data selection is a critical aspect of Reinforcement Learning with Verifiable Rewards (RLVR) for enhancing the reasoning capabilities of large langu
GPT-Rosalind, our Life Sciences model series, is optimized for scientific workflows, with stronger performance in protein and chemical reasoning, genomics analysis, biochemistry knowledge, and scienti
arXiv:2604.13710v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) exhibit strong reasoning and world knowledge, yet adapting them for retrieval remains challenging. Existing app
arXiv:2604.13660v1 Announce Type: new Abstract: In Deepfake Detection (DFD) tasks, researchers proposed two types of MLLM-based methods: complementary combination with small DFD detectors, or static f
arXiv:2604.12147v1 Announce Type: cross Abstract: Agents aspire to eliminate the need for task-specific prompt crafting through autonomous reason-act-observe loops. Still, they are commonly instructed
arXiv:2604.12955v1 Announce Type: new Abstract: There is growing interest in leveraging large language models (LLMs) for text-to-model translation and optimization tasks. This paper aims to advance th
arXiv:2604.12160v1 Announce Type: new Abstract: Reasoning post-training with reinforcement learning from verifiable rewards (RLVR) is typically studied in centralized settings, yet many realistic appl
arXiv:2604.10985v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have rapidly advanced by leveraging powerful pre-trained Large Language Models (LLMs) as core reasoning backbones. As new
arXiv:2604.10114v1 Announce Type: cross Abstract: The generation of high-fidelity synthetic data is a cornerstone of modern machine learning, yet Large Language Models (LLMs) frequently suffer from ha
arXiv:2507.03336v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly tasked with invoking enterprise APIs, yet they routinely falter when near-duplicate tools vie for the
arXiv:2604.09687v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) excel on many multimodal reasoning benchmarks, but these evaluations often do not require an exhaustive readout of the i
arXiv:2510.07794v2 Announce Type: replace-cross Abstract: Agentic RAG is a powerful technique for incorporating external information that LLMs lack, enabling better problem solving and question answer
arXiv:2604.10539v1 Announce Type: cross Abstract: Key-Value (KV) cache plays a crucial role in accelerating inference in large language models (LLMs) by storing intermediate attention states and avoid
arXiv:2604.09841v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly adapted through domain-specific fine-tuning, yet it remains unclear whether this improves reasoning bey
arXiv:2604.11307v1 Announce Type: new Abstract: Leveraging Multi-modal Large Language Models (MLLMs) to accelerate frontier scientific research is promising, yet how to rigorously evaluate such system
arXiv:2604.10760v1 Announce Type: cross Abstract: Artificial agents can be made to 'help' for many reasons, including explicit social reward, hard-coded prosocial bonuses, or direct access to another
arXiv:2604.09021v1 Announce Type: cross Abstract: Auditory large language models (ALLMs) have demonstrated strong general capabilities in audio understanding and reasoning tasks. However, their reliab